diff --git a/_quarto.yml b/_quarto.yml index 001c50f7..c588ba0b 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -8,7 +8,9 @@ project: - "*.qmd" - "contents/*.qmd" - "contents/*/*.qmd" - - "contents/labs/*/*/*.qmd" + - "contents/*/*/*.qmd" + - "contents/*/*/*/*.qmd" + - "contents/*/*/*/*/*.qmd" # contents/labs////*.qmd title-prefix: "" @@ -47,6 +49,8 @@ book: cover-image: cover-image-transparent.png cover-image-alt: "Cover image." + bread-crumbs: true + sidebar: collapse-level: 2 border: true @@ -119,10 +123,25 @@ book: chapters: - references.qmd - text: "---" - - part: contents/labs/labs.qmd + # LABS + - part: LABS + chapters: + - contents/labs/labs.qmd + # nicla vision + - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd + chapters: + - contents/labs/arduino/nicla_vision/setup/setup.qmd + - contents/labs/arduino/nicla_vision/kws/kws.qmd + - contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd + - contents/labs/arduino/nicla_vision/object_detection/object_detection.qmd + - contents/labs/arduino/nicla_vision/motion_classification/motion_classification.qmd + # xiao sense + - part: contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd chapters: - - file: contents/labs/arduino/nicla_vision/nicla_vision.qmd - - contents/labs/seeed/xiao_esp32s3/xiao_esp32S3.qmd + - contents/labs/seeed/xiao_esp32s3/setup/setup.qmd + - contents/labs/seeed/xiao_esp32s3/kws/kws.qmd + - contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd + - contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd - text: "---" appendices: @@ -162,14 +181,6 @@ bibliography: - contents/training/training.bib - contents/workflow/workflow.bib - contents/conclusion/conclusion.bib - # labs - - contents/labs/arduino/nicla_vision/niclav_sys/niclav_sys.bib - - contents/labs/arduino/nicla_vision/kws_feature_eng/kws_feature_eng.bib - - contents/labs/arduino/nicla_vision/dsp_spectral_features_block/dsp_spectral_features_block.bib - - contents/labs/arduino/nicla_vision/kws_nicla/kws_nicla.bib - - contents/labs/arduino/nicla_vision/object_detection_fomo/object_detection_fomo.bib - - contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.bib - - contents/labs/arduino/nicla_vision/image_classification/image_classification.bib comments: giscus: diff --git a/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd b/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd index 73c1d975..6cc27c7e 100644 --- a/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd +++ b/contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd @@ -2,7 +2,7 @@ bibliography: image_classification.bib --- -# CV on Nicla Vision {.unnumbered} +# Image Classification {.unnumbered} ![*DALL·E 3 Prompt: Cartoon in a 1950s style featuring a compact electronic device with a camera module placed on a wooden table. The screen displays blue robots on one side and green periquitos on the other. LED lights on the device indicate classifications, while characters in retro clothing observe with interest.*](images/jpg/img_class_ini.jpg){fig-align="center" width="6.5in"} diff --git a/contents/kws_nicla/images/jpg/KWS_PROJ_INF_BLK.jpg b/contents/labs/arduino/nicla_vision/kws/images/jpg/KWS_PROJ_INF_BLK.jpg similarity index 100% rename from contents/kws_nicla/images/jpg/KWS_PROJ_INF_BLK.jpg rename to contents/labs/arduino/nicla_vision/kws/images/jpg/KWS_PROJ_INF_BLK.jpg diff --git a/contents/kws_nicla/images/jpg/KWS_PROJ_TRAIN_BLK.jpg b/contents/labs/arduino/nicla_vision/kws/images/jpg/KWS_PROJ_TRAIN_BLK.jpg similarity index 100% rename from contents/kws_nicla/images/jpg/KWS_PROJ_TRAIN_BLK.jpg rename to contents/labs/arduino/nicla_vision/kws/images/jpg/KWS_PROJ_TRAIN_BLK.jpg diff --git a/contents/kws_nicla/images/jpg/MFCC.jpg b/contents/labs/arduino/nicla_vision/kws/images/jpg/MFCC.jpg similarity index 100% rename from contents/kws_nicla/images/jpg/MFCC.jpg rename to 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--git a/contents/labs/arduino/nicla_vision/kws_feature_eng/kws_feature_eng.qmd b/contents/labs/arduino/nicla_vision/kws_feature_eng/kws_feature_eng.qmd deleted file mode 100644 index e27bd15b..00000000 --- a/contents/labs/arduino/nicla_vision/kws_feature_eng/kws_feature_eng.qmd +++ /dev/null @@ -1,149 +0,0 @@ ---- -bibliography: kws_feature_eng.bib ---- - -# Audio Feature Engineering {.unnumbered} - -![*DALL·E 3 Prompt: 1950s style cartoon scene set in an audio research room. Two scientists, one holding a magnifying glass and the other taking notes, examine large charts pinned to the wall. These charts depict FFT graphs and time curves related to audio data analysis. The room has a retro ambiance, with wooden tables, vintage lamps, and classic audio analysis tools.*](images/jpg/kws_under_the_hood_ini.jpg){fig-align="center" width="6.5in"} - -## Introduction - -In this hands-on tutorial, the emphasis is on the critical role that feature engineering plays in optimizing the performance of machine learning models applied to audio classification tasks, such as speech recognition. It is essential to be aware that the performance of any machine learning model relies heavily on the quality of features used, and we will deal with "under-the-hood" mechanics of feature extraction, mainly focusing on Mel-frequency Cepstral Coefficients (MFCCs), a cornerstone in the field of audio signal processing. - -Machine learning models, especially traditional algorithms, don't understand audio waves. They understand numbers arranged in some meaningful way, i.e., features. These features encapsulate the characteristics of the audio signal, making it easier for models to distinguish between different sounds. - -> This tutorial will deal with generating features specifically for audio classification. This can be particularly interesting for applying machine learning to a variety of audio data, whether for speech recognition, music categorization, insect classification based on wingbeat sounds, or other sound analysis tasks - -## The KWS - -The most common TinyML application is Keyword Spotting (KWS), a subset of the broader field of speech recognition. While general speech recognition aims to transcribe all spoken words into text, Keyword Spotting focuses on detecting specific "keywords" or "wake words" in a continuous audio stream. The system is trained to recognize these keywords as predefined phrases or words, such as *yes* or *no*. In short, KWS is a specialized form of speech recognition with its own set of challenges and requirements. - -Here a typical KWS Process using MFCC Feature Converter: - -![](images/jpg/kws_diagram.jpg){fig-align="center" width="7.29in"} - -#### Applications of KWS: - -- **Voice Assistants:** In devices like Amazon's Alexa or Google Home, KWS is used to detect the wake word ("Alexa" or "Hey Google") to activate the device. -- **Voice-Activated Controls:** In automotive or industrial settings, KWS can be used to initiate specific commands like "Start engine" or "Turn off lights." -- **Security Systems:** Voice-activated security systems may use KWS to authenticate users based on a spoken passphrase. -- **Telecommunication Services:** Customer service lines may use KWS to route calls based on spoken keywords. - -#### Differences from General Speech Recognition: - -- **Computational Efficiency:** KWS is usually designed to be less computationally intensive than full speech recognition, as it only needs to recognize a small set of phrases. -- **Real-time Processing:** KWS often operates in real-time and is optimized for low-latency detection of keywords. -- **Resource Constraints:** KWS models are often designed to be lightweight, so they can run on devices with limited computational resources, like microcontrollers or mobile phones. -- **Focused Task:** While general speech recognition models are trained to handle a broad range of vocabulary and accents, KWS models are fine-tuned to recognize specific keywords, often in noisy environments accurately. - -## Introduction to Audio Signals - -Understanding the basic properties of audio signals is crucial for effective feature extraction and, ultimately, for successfully applying machine learning algorithms in audio classification tasks. Audio signals are complex waveforms that capture fluctuations in air pressure over time. These signals can be characterized by several fundamental attributes: sampling rate, frequency, and amplitude. - -- **Frequency and Amplitude:** [Frequency](https://en.wikipedia.org/wiki/Audio_frequency) refers to the number of oscillations a waveform undergoes per unit time and is also measured in Hz. In the context of audio signals, different frequencies correspond to different pitches. [Amplitude](https://en.wikipedia.org/wiki/Amplitude), on the other hand, measures the magnitude of the oscillations and correlates with the loudness of the sound. Both frequency and amplitude are essential features that capture audio signals' tonal and rhythmic qualities. - -- **Sampling Rate:** The [sampling rate](https://en.wikipedia.org/wiki/Sampling_(signal_processing)), often denoted in Hertz (Hz), defines the number of samples taken per second when digitizing an analog signal. A higher sampling rate allows for a more accurate digital representation of the signal but also demands more computational resources for processing. Typical sampling rates include 44.1 kHz for CD-quality audio and 16 kHz or 8 kHz for speech recognition tasks. Understanding the trade-offs in selecting an appropriate sampling rate is essential for balancing accuracy and computational efficiency. In general, with TinyML projects, we work with 16KHz. Altough music tones can be heard at frequencies up to 20 kHz, voice maxes out at 8 kHz. Traditional telephone systems use an 8 kHz sampling frequency. - -> For an accurate representation of the signal, the sampling rate must be at least twice the highest frequency present in the signal. - -- **Time Domain vs. Frequency Domain:** Audio signals can be analyzed in the time and frequency domains. In the time domain, a signal is represented as a waveform where the amplitude is plotted against time. This representation helps to observe temporal features like onset and duration but the signal's tonal characteristics are not well evidenced. Conversely, a frequency domain representation provides a view of the signal's constituent frequencies and their respective amplitudes, typically obtained via a Fourier Transform. This is invaluable for tasks that require understanding the signal's spectral content, such as identifying musical notes or speech phonemes (our case). - -The image below shows the words `YES` and `NO` with typical representations in the Time (Raw Audio) and Frequency domains: - -![](images/jpg/time_vs_freq.jpg){fig-align="center" width="6.5in"} - -### Why Not Raw Audio? - -While using raw audio data directly for machine learning tasks may seem tempting, this approach presents several challenges that make it less suitable for building robust and efficient models. - -Using raw audio data for Keyword Spotting (KWS), for example, on TinyML devices poses challenges due to its high dimensionality (using a 16 kHz sampling rate), computational complexity for capturing temporal features, susceptibility to noise, and lack of semantically meaningful features, making feature extraction techniques like MFCCs a more practical choice for resource-constrained applications. - -Here are some additional details of the critical issues associated with using raw audio: - -- **High Dimensionality:** Audio signals, especially those sampled at high rates, result in large amounts of data. For example, a 1-second audio clip sampled at 16 kHz will have 16,000 individual data points. High-dimensional data increases computational complexity, leading to longer training times and higher computational costs, making it impractical for resource-constrained environments. Furthermore, the wide dynamic range of audio signals requires a significant amount of bits per sample, while conveying little useful information. - -- **Temporal Dependencies:** Raw audio signals have temporal structures that simple machine learning models may find hard to capture. While recurrent neural networks like [LSTMs](https://annals-csis.org/Volume_18/drp/pdf/185.pdf) can model such dependencies, they are computationally intensive and tricky to train on tiny devices. - -- **Noise and Variability:** Raw audio signals often contain background noise and other non-essential elements affecting model performance. Additionally, the same sound can have different characteristics based on various factors such as distance from the microphone, the orientation of the sound source, and acoustic properties of the environment, adding to the complexity of the data. - -- **Lack of Semantic Meaning:** Raw audio doesn't inherently contain semantically meaningful features for classification tasks. Features like pitch, tempo, and spectral characteristics, which can be crucial for speech recognition, are not directly accessible from raw waveform data. - -- **Signal Redundancy:** Audio signals often contain redundant information, with certain portions of the signal contributing little to no value to the task at hand. This redundancy can make learning inefficient and potentially lead to overfitting. - -For these reasons, feature extraction techniques such as Mel-frequency Cepstral Coefficients (MFCCs), Mel-Frequency Energies (MFEs), and simple Spectograms are commonly used to transform raw audio data into a more manageable and informative format. These features capture the essential characteristics of the audio signal while reducing dimensionality and noise, facilitating more effective machine learning. - -## Introduction to MFCCs - -### What are MFCCs? - -[Mel-frequency Cepstral Coefficients (MFCCs)](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum) are a set of features derived from the spectral content of an audio signal. They are based on human auditory perceptions and are commonly used to capture the phonetic characteristics of an audio signal. The MFCCs are computed through a multi-step process that includes pre-emphasis, framing, windowing, applying the Fast Fourier Transform (FFT) to convert the signal to the frequency domain, and finally, applying the Discrete Cosine Transform (DCT). The result is a compact representation of the original audio signal's spectral characteristics. - -The image below shows the words `YES` and `NO` in their MFCC representation: - -![](images/jpg/yes_no_mfcc.jpg){fig-align="center" width="6.5in"} - -> This [video](https://youtu.be/SJo7vPgRlBQ?si=KSgzmDg8DtSVqzXp) explains the Mel Frequency Cepstral Coefficients (MFCC) and how to compute them. - -### Why are MFCCs important? - -MFCCs are crucial for several reasons, particularly in the context of Keyword Spotting (KWS) and TinyML: - -- **Dimensionality Reduction:** MFCCs capture essential spectral characteristics of the audio signal while significantly reducing the dimensionality of the data, making it ideal for resource-constrained TinyML applications. -- **Robustness:** MFCCs are less susceptible to noise and variations in pitch and amplitude, providing a more stable and robust feature set for audio classification tasks. -- **Human Auditory System Modeling:** The Mel scale in MFCCs approximates the human ear's response to different frequencies, making them practical for speech recognition where human-like perception is desired. -- **Computational Efficiency:** The process of calculating MFCCs is computationally efficient, making it well-suited for real-time applications on hardware with limited computational resources. - -In summary, MFCCs offer a balance of information richness and computational efficiency, making them popular for audio classification tasks, particularly in constrained environments like TinyML. - -### Computing MFCCs - -The computation of Mel-frequency Cepstral Coefficients (MFCCs) involves several key steps. Let's walk through these, which are particularly important for Keyword Spotting (KWS) tasks on TinyML devices. - -- **Pre-emphasis:** The first step is pre-emphasis, which is applied to accentuate the high-frequency components of the audio signal and balance the frequency spectrum. This is achieved by applying a filter that amplifies the difference between consecutive samples. The formula for pre-emphasis is: y(t) = x(t) - $\alpha$ x(t-1) , where $\alpha$ is the pre-emphasis factor, typically around 0.97. - -- **Framing:** Audio signals are divided into short frames (the *frame length*), usually 20 to 40 milliseconds. This is based on the assumption that frequencies in a signal are stationary over a short period. Framing helps in analyzing the signal in such small time slots. The *frame stride* (or step) will displace one frame and the adjacent. Those steps could be sequential or overlapped. - -- **Windowing:** Each frame is then windowed to minimize the discontinuities at the frame boundaries. A commonly used window function is the Hamming window. Windowing prepares the signal for a Fourier transform by minimizing the edge effects. The image below shows three frames (10, 20, and 30) and the time samples after windowing (note that the frame length and frame stride are 20 ms): - -![](images/jpg/frame_wind.jpg){fig-align="center" width="6.5in"} - -- **Fast Fourier Transform (FFT)** The Fast Fourier Transform (FFT) is applied to each windowed frame to convert it from the time domain to the frequency domain. The FFT gives us a complex-valued representation that includes both magnitude and phase information. However, for MFCCs, only the magnitude is used to calculate the Power Spectrum. The power spectrum is the square of the magnitude spectrum and measures the energy present at each frequency component. - -> The power spectrum $P(f)$ of a signal $x(t)$ is defined as $P(f) = |X(f)|^2$, where $X(f)$ is the Fourier Transform of $x(t)$. By squaring the magnitude of the Fourier Transform, we emphasize *stronger* frequencies over *weaker* ones, thereby capturing more relevant spectral characteristics of the audio signal. This is important in applications like audio classification, speech recognition, and Keyword Spotting (KWS), where the focus is on identifying distinct frequency patterns that characterize different classes of audio or phonemes in speech. - -![](images/jpg/frame_to_fft.jpg){fig-align="center" width="6.5in"} - -- **Mel Filter Banks:** The frequency domain is then mapped to the [Mel scale](https://en.wikipedia.org/wiki/Mel_scale), which approximates the human ear's response to different frequencies. The idea is to extract more features (more filter banks) in the lower frequencies and less in the high frequencies. Thus, it performs well on sounds distinguished by the human ear. Typically, 20 to 40 triangular filters extract the Mel-frequency energies. These energies are then log-transformed to convert multiplicative factors into additive ones, making them more suitable for further processing. - -![](images/jpg/melbank-1_00.hires.jpg){fig-align="center" width="6.5in"} - -- **Discrete Cosine Transform (DCT):** The last step is to apply the [Discrete Cosine Transform (DCT)](https://en.wikipedia.org/wiki/Discrete_cosine_transform) to the log Mel energies. The DCT helps to decorrelate the energies, effectively compressing the data and retaining only the most discriminative features. Usually, the first 12-13 DCT coefficients are retained, forming the final MFCC feature vector. - -![](images/jpg/mfcc_final.jpg){fig-align="center" width="6.5in"} - -## Hands-On using Python - -Let's apply what we discussed while working on an actual audio sample. Open the notebook on Google CoLab and extract the MLCC features on your audio samples: [\[Open In Colab\]](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/KWS/Audio_Data_Analysis.ipynb) - -## Conclusion - -### **What** Feature Extraction technique **should we use?** - -Mel-frequency Cepstral Coefficients (MFCCs), Mel-Frequency Energies (MFEs), or Spectrogram are techniques for representing audio data, which are often helpful in different contexts. - -In general, MFCCs are more focused on capturing the envelope of the power spectrum, which makes them less sensitive to fine-grained spectral details but more robust to noise. This is often desirable for speech-related tasks. On the other hand, spectrograms or MFEs preserve more detailed frequency information, which can be advantageous in tasks that require discrimination based on fine-grained spectral content. - -#### MFCCs are particularly strong for: - -1. **Speech Recognition:** MFCCs are excellent for identifying phonetic content in speech signals. -2. **Speaker Identification:** They can be used to distinguish between different speakers based on voice characteristics. -3. **Emotion Recognition:** MFCCs can capture the nuanced variations in speech indicative of emotional states. -4. **Keyword Spotting:** Especially in TinyML, where low computational complexity and small feature size are crucial. - -#### Spectrograms or MFEs are often more suitable for: - -1. **Music Analysis:** Spectrograms can capture harmonic and timbral structures in music, which is essential for tasks like genre classification, instrument recognition, or music transcription. -2. **Environmental Sound Classification:** In recognizing non-speech, environmental sounds (e.g., rain, wind, traffic), the full spectrogram can provide more discriminative features. -3. **Birdsong Identification:** The intricate details of bird calls are often better captured using spectrograms. -4. **Bioacoustic Signal Processing:** In applications like dolphin or bat call analysis, the fine-grained frequency information in a spectrogram can be essential. -5. **Audio Quality Assurance:** Spectrograms are often used in professional audio analysis to identify unwanted noises, clicks, or other artifacts. diff --git a/contents/labs/arduino/nicla_vision/kws_nicla/images/jpg/KWS_PROJ_INF_BLK.jpg b/contents/labs/arduino/nicla_vision/kws_nicla/images/jpg/KWS_PROJ_INF_BLK.jpg deleted file mode 100644 index 78ced62b..00000000 Binary files a/contents/labs/arduino/nicla_vision/kws_nicla/images/jpg/KWS_PROJ_INF_BLK.jpg and /dev/null differ diff --git a/contents/labs/arduino/nicla_vision/kws_nicla/images/jpg/KWS_PROJ_TRAIN_BLK.jpg 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Two Afro-American female scientists are at the center. One holds a magnifying glass, closely examining ancient circuitry, while the other takes notes. On their wooden table, there are multiple boards with sensors, notably featuring a microphone. Behind these boards, a computer with a large, rounded back displays the Arduino IDE. The IDE showcases code for LED pin assignments and machine learning inference for voice command detection. A distinct window in the IDE, the Serial Monitor, reveals outputs indicating the spoken commands 'yes' and 'no'. The room ambiance is nostalgic with vintage lamps, classic audio analysis tools, and charts depicting FFT graphs and time-domain curves.*](images/jpg/nicla-kws.jpg){fig-align="center" width="6.5in"} - -## Introduction - -Having already explored the Nicla Vision board in the *Image Classification* and *Object Detection* applications, we are now shifting our focus to voice-activated applications with a project on Keyword Spotting (KWS). - -As introduced in the *Feature Engineering for Audio Classification* Hands-On tutorial, Keyword Spotting (KWS) is integrated into many voice recognition systems, enabling devices to respond to specific words or phrases. While this technology underpins popular devices like Google Assistant or Amazon Alexa, it's equally applicable and feasible on smaller, low-power devices. This tutorial will guide you through implementing a KWS system using TinyML on the Nicla Vision development board equipped with a digital microphone. - -Our model will be designed to recognize keywords that can trigger device wake-up or specific actions, bringing them to life with voice-activated commands. - -## How does a voice assistant work? - -As said, *voice assistants* on the market, like Google Home or Amazon Echo-Dot, only react to humans when they are "waked up" by particular keywords such as " Hey Google" on the first one and "Alexa" on the second. - -![](images/png/hey_google.png){fig-align="center" width="6.5in"} - -In other words, recognizing voice commands is based on a multi-stage model or Cascade Detection. - -![](images/jpg/pa_block.jpg){fig-align="center" width="6.5in"} - -**Stage 1:** A small microprocessor inside the Echo Dot or Google Home continuously listens, waiting for the keyword to be spotted, using a TinyML model at the edge (KWS application). - -**Stage 2:** Only when triggered by the KWS application on Stage 1 is the data sent to the cloud and processed on a larger model. - -The video below shows an example of a Google Assistant being programmed on a Raspberry Pi (Stage 2), with an Arduino Nano 33 BLE as the TinyML device (Stage 1). - -{{< video https://youtu.be/e_OPgcnsyvM width="480" height="270" center >}} - -> To explore the above Google Assistant project, please see the tutorial: [Building an Intelligent Voice Assistant From Scratch](https://www.hackster.io/mjrobot/building-an-intelligent-voice-assistant-from-scratch-2199c3). - -In this KWS project, we will focus on Stage 1 (KWS or Keyword Spotting), where we will use the Nicla Vision, which has a digital microphone that will be used to spot the keyword. - -## The KWS Hands-On Project - -The diagram below gives an idea of how the final KWS application should work (during inference): - -![](images/jpg/KWS_PROJ_INF_BLK.jpg){fig-align="center" width="6.5in"} - -Our KWS application will recognize four classes of sound: - -- **YES** (Keyword 1) -- **NO** (Keyword 2) -- **NOISE** (no words spoken; only background noise is present) -- **UNKNOW** (a mix of different words than YES and NO) - -> For real-world projects, it is always advisable to include other sounds besides the keywords, such as "Noise" (or Background) and "Unknown." - -### The Machine Learning workflow - -The main component of the KWS application is its model. So, we must train such a model with our specific keywords, noise, and other words (the "unknown"): - -![](images/jpg/KWS_PROJ_TRAIN_BLK.jpg){fig-align="center" width="6.5in"} - -## Dataset - -The critical component of any Machine Learning Workflow is the **dataset**. Once we have decided on specific keywords, in our case (*YES* and NO), we can take advantage of the dataset developed by Pete Warden, ["Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)." This dataset has 35 keywords (with +1,000 samples each), such as yes, no, stop, and go. In words such as *yes* and *no,* we can get 1,500 samples. - -You can download a small portion of the dataset from Edge Studio ([Keyword spotting pre-built dataset](https://docs.edgeimpulse.com/docs/pre-built-datasets/keyword-spotting)), which includes samples from the four classes we will use in this project: yes, no, noise, and background. For this, follow the steps below: - -- Download the [keywords dataset.](https://cdn.edgeimpulse.com/datasets/keywords2.zip) -- Unzip the file to a location of your choice. - -### Uploading the dataset to the Edge Impulse Studio - -Initiate a new project at Edge Impulse Studio (EIS) and select the `Upload Existing Data` tool in the `Data Acquisition` section. Choose the files to be uploaded: - -![](images/jpg/files.jpg){fig-align="center" width="6.5in"} - -Define the Label, select `Automatically split between train and test,` and `Upload data` to the EIS. Repeat for all classes. - -![](images/jpg/upload.jpg){fig-align="center" width="6.5in"} - -The dataset will now appear in the `Data acquisition` section. Note that the approximately 6,000 samples (1,500 for each class) are split into Train (4,800) and Test (1,200) sets. - -![](images/jpg/dataset.jpg){fig-align="center" width="6.5in"} - -### Capturing additional Audio Data - -Although we have a lot of data from Pete's dataset, collecting some words spoken by us is advised. When working with accelerometers, creating a dataset with data captured by the same type of sensor is essential. In the case of *sound*, this is optional because what we will classify is, in reality, *audio* data. - -> The key difference between sound and audio is the type of energy. Sound is mechanical perturbation (longitudinal sound waves) that propagate through a medium, causing variations of pressure in it. Audio is an electrical (analog or digital) signal representing sound. - -When we pronounce a keyword, the sound waves should be converted to audio data. The conversion should be done by sampling the signal generated by the microphone at a 16KHz frequency with 16-bit per sample amplitude. - -So, any device that can generate audio data with this basic specification (16KHz/16bits) will work fine. As a *device*, we can use the NiclaV, a computer, or even your mobile phone. - -![](images/jpg/audio_capt.jpg){fig-align="center" width="6.5in"} - -#### Using the NiclaV and the Edge Impulse Studio - -As we learned in the chapter *Setup Nicla Vision*, EIS officially supports the Nicla Vision, which simplifies the capture of the data from its sensors, including the microphone. So, please create a new project on EIS and connect the Nicla to it, following these steps: - -- Download the last updated [EIS Firmware](https://cdn.edgeimpulse.com/firmware/arduino-nicla-vision.zip) and unzip it. - -- Open the zip file on your computer and select the uploader corresponding to your OS: - -![](images/png/image17.png){fig-align="center" width="4.416666666666667in"} - -- Put the NiclaV in Boot Mode by pressing the reset button twice. - -- Upload the binary *arduino-nicla-vision.bin* to your board by running the batch code corresponding to your OS. - -Go to your project on EIS, and on the `Data Acquisition tab`, select `WebUSB`. A window will pop up; choose the option that shows that the `Nicla is paired` and press `[Connect]`. - -You can choose which sensor data to pick in the `Collect Data` section on the `Data Acquisition` tab. Select: `Built-in microphone`, define your `label` (for example, *yes*), the sampling `Frequency`\[16000Hz\], and the `Sample length (in milliseconds)`, for example \[10s\]. `Start sampling`. - -![](images/jpg/ei_data_collection.jpg){fig-align="center" width="6.5in"} - -Data on Pete's dataset have a length of 1s, but the recorded samples are 10s long and must be split into 1s samples. Click on `three dots` after the sample name and select `Split sample`. - -A window will pop up with the Split tool. - -![](images/jpg/split.jpg){fig-align="center" width="6.5in"} - -Once inside the tool, split the data into 1-second (1000 ms) records. If necessary, add or remove segments. This procedure should be repeated for all new samples. - -#### Using a smartphone and the EI Studio - -You can also use your PC or smartphone to capture audio data, using a sampling frequency of 16KHz and a bit depth of 16. - -Go to `Devices`, scan the `QR Code` using your phone, and click on the link. A data Collection app will appear in your browser. Select `Collecting Audio`, and define your `Label`, data capture `Length,` and `Category`. - -![](images/jpg/phone.jpg){fig-align="center" width="6.5in"} - -Repeat the same procedure used with the NiclaV. - -> Note that any app, such as [Audacity](https://www.audacityteam.org/), can be used for audio recording, provided you use 16KHz/16-bit depth samples. - -## Creating Impulse (Pre-Process / Model definition) - -*An* **impulse** *takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.* - -### Impulse Design - -![](images/jpg/impulse.jpg){fig-align="center" width="6.5in"} - -First, we will take the data points with a 1-second window, augmenting the data and sliding that window in 500ms intervals. Note that the option zero-pad data is set. It is essential to fill with 'zeros' samples smaller than 1 second (in some cases, some samples can result smaller than the 1000 ms window on the split tool to avoid noise and spikes). - -Each 1-second audio sample should be pre-processed and converted to an image (for example, 13 x 49 x 1). As discussed in the *Feature Engineering for Audio Classification* Hands-On tutorial, we will use `Audio (MFCC)`, which extracts features from audio signals using [Mel Frequency Cepstral Coefficients](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), which are well suited for the human voice, our case here. - -Next, we select the `Classification` block to build our model from scratch using a Convolution Neural Network (CNN). - -> Alternatively, you can use the `Transfer Learning (Keyword Spotting)` block, which fine-tunes a pre-trained keyword spotting model on your data. This approach has good performance with relatively small keyword datasets. - -### Pre-Processing (MFCC) - -The following step is to create the features to be trained in the next phase: - -We could keep the default parameter values, but we will use the DSP `Autotune parameters` option. - -![](images/jpg/ei_MFCC.jpg){fig-align="center" width="6.5in"} - -We will take the `Raw features` (our 1-second, 16KHz sampled audio data) and use the MFCC processing block to calculate the `Processed features`. For every 16,000 raw features (16,000 x 1 second), we will get 637 processed features (13 x 49). - -![](images/jpg/MFCC.jpg){fig-align="center" width="6.5in"} - -The result shows that we only used a small amount of memory to pre-process data (16KB) and a latency of 34ms, which is excellent. For example, on an Arduino Nano (Cortex-M4f \@ 64MHz), the same pre-process will take around 480ms. The parameters chosen, such as the `FFT length` \[512\], will significantly impact the latency. - -Now, let's `Save parameters` and move to the `Generated features` tab, where the actual features will be generated. Using [UMAP](https://umap-learn.readthedocs.io/en/latest/), a dimension reduction technique, the `Feature explorer` shows how the features are distributed on a two-dimensional plot. - -![](images/jpg/feat_expl.jpg){fig-align="center" width="5.9in"} - -The result seems OK, with a visually clear separation between *yes* features (in red) and *no* features (in blue). The *unknown* features seem nearer to the *no space* than the *yes*. This suggests that the keyword *no* has more propensity to false positives. - -### Going under the hood - -To understand better how the raw sound is preprocessed, look at the *Feature Engineering for Audio Classification* chapter. You can play with the MFCC features generation by downloading this [notebook](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/KWS/KWS_MFCC_Analysis.ipynb) from GitHub or [\[Opening it In Colab\]](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/KWS/KWS_MFCC_Analysis.ipynb) - -## Model Design and Training - -We will use a simple Convolution Neural Network (CNN) model, tested with 1D and 2D convolutions. The basic architecture has two blocks of Convolution + MaxPooling (\[8\] and \[16\] filters, respectively) and a Dropout of \[0.25\] for the 1D and \[0.5\] for the 2D. For the last layer, after Flattening, we have \[4\] neurons, one for each class: - -![](images/jpg/models_1d-2d.jpg){fig-align="center" width="6.5in"} - -As hyper-parameters, we will have a `Learning Rate` of \[0.005\] and a model trained by \[100\] epochs. We will also include a data augmentation method based on [SpecAugment](https://arxiv.org/abs/1904.08779). We trained the 1D and the 2D models with the same hyperparameters. The 1D architecture had a better overall result (90.5% accuracy when compared with 88% of the 2D, so we will use the 1D. - -![](images/jpg/train_result.jpg){fig-align="center" width="6.5in"} - -> Using 1D convolutions is more efficient because it requires fewer parameters than 2D convolutions, making them more suitable for resource-constrained environments. - -It is also interesting to pay attention to the 1D Confusion Matrix. The F1 Score for `yes` is 95%, and for `no`, 91%. That was expected by what we saw with the Feature Explorer (`no` and `unknown` at close distance). In trying to improve the result, you can inspect closely the results of the samples with an error. - -![](images/jpg/train_errors.jpg){fig-align="center" width="6.5in"} - -Listen to the samples that went wrong. For example, for `yes`, most of the mistakes were related to a yes pronounced as "yeh". You can acquire additional samples and then retrain your model. - -### Going under the hood - -If you want to understand what is happening "under the hood," you can download the pre-processed dataset (`MFCC training data`) from the `Dashboard` tab and run this [Jupyter Notebook](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/KWS/KWS_CNN_training.ipynb), playing with the code or [\[Opening it In Colab\]](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/KWS/KWS_CNN_training.ipynb). For example, you can analyze the accuracy by each epoch: - -![](images/jpg/train_graphs.jpg){fig-align="center" width="6.5in"} - -## Testing - -Testing the model with the data reserved for training (Test Data), we got an accuracy of approximately 76%. - -![](images/jpg/test.jpg){fig-align="center" width="6.5in"} - -Inspecting the F1 score, we can see that for YES, we got 0.90, an excellent result since we expect to use this keyword as the primary "trigger" for our KWS project. The worst result (0.70) is for UNKNOWN, which is OK. - -For NO, we got 0.72, which was expected, but to improve this result, we can move the samples that were not correctly classified to the training dataset and then repeat the training process. - -### Live Classification - -We can proceed to the project's next step but also consider that it is possible to perform `Live Classification` using the NiclaV or a smartphone to capture live samples, testing the trained model before deployment on our device. - -## Deploy and Inference - -The EIS will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. Go to the `Deployment` section, select `Arduino Library`, and at the bottom, choose `Quantized (Int8)` and press `Build`. - -![](images/jpg/deploy.jpg){fig-align="center" width="5.29in"} - -When the `Build` button is selected, a zip file will be created and downloaded to your computer. On your Arduino IDE, go to the `Sketch` tab, select the option `Add .ZIP Library`, and Choose the .zip file downloaded by EIS: - -![](images/jpg/install_zip.jpg){fig-align="center" width="6.5in"} - -Now, it is time for a real test. We will make inferences while completely disconnected from the EIS. Let's use the NiclaV code example created when we deployed the Arduino Library. - -In your Arduino IDE, go to the `File/Examples` tab, look for your project, and select `nicla-vision/nicla-vision_microphone` (or `nicla-vision_microphone_continuous`) - -![](images/jpg/code_ide.jpg){fig-align="center" width="6.5in"} - -Press the reset button twice to put the NiclaV in boot mode, upload the sketch to your board, and test some real inferences: - -![](images/jpg/yes_no.jpg){fig-align="center" width="6.5in"} - -## Post-processing - -Now that we know the model is working since it detects our keywords, let's modify the code to see the result with the NiclaV completely offline (disconnected from the PC and powered by a battery, a power bank, or an independent 5V power supply). - -The idea is that whenever the keyword YES is detected, the Green LED will light; if a NO is heard, the Red LED will light, if it is a UNKNOW, the Blue LED will light; and in the presence of noise (No Keyword), the LEDs will be OFF. - -We should modify one of the code examples. Let's do it now with the `nicla-vision_microphone_continuous`. - -Start with initializing the LEDs: - -``` cpp -... -void setup() -{ - // Once you finish debugging your code, you can comment or delete the Serial part of the code - Serial.begin(115200); - while (!Serial); - Serial.println("Inferencing - Nicla Vision KWS with LEDs"); - - // Pins for the built-in RGB LEDs on the Arduino NiclaV - pinMode(LEDR, OUTPUT); - pinMode(LEDG, OUTPUT); - pinMode(LEDB, OUTPUT); - - // Ensure the LEDs are OFF by default. - // Note: The RGB LEDs on the Arduino Nicla Vision - // are ON when the pin is LOW, OFF when HIGH. - digitalWrite(LEDR, HIGH); - digitalWrite(LEDG, HIGH); - digitalWrite(LEDB, HIGH); -... -} -``` - -Create two functions, `turn_off_leds()` function , to turn off all RGB LEDs - -``` cpp -** - * @brief turn_off_leds function - turn-off all RGB LEDs - */ -void turn_off_leds(){ - digitalWrite(LEDR, HIGH); - digitalWrite(LEDG, HIGH); - digitalWrite(LEDB, HIGH); -} -``` - -Another `turn_on_led()` function is used to turn on the RGB LEDs according to the most probable result of the classifier. - -``` cpp -/** - * @brief turn_on_leds function used to turn on the RGB LEDs - * @param[in] pred_index - * no: [0] ==> Red ON - * noise: [1] ==> ALL OFF - * unknown: [2] ==> Blue ON - * Yes: [3] ==> Green ON - */ -void turn_on_leds(int pred_index) { - switch (pred_index) - { - case 0: - turn_off_leds(); - digitalWrite(LEDR, LOW); - break; - - case 1: - turn_off_leds(); - break; - - case 2: - turn_off_leds(); - digitalWrite(LEDB, LOW); - break; - - case 3: - turn_off_leds(); - digitalWrite(LEDG, LOW); - break; - } -} -``` - -And change the `// print the predictions` portion of the code on `loop()`: - -``` cpp -... - - if (++print_results >= (EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW)) { - // print the predictions - ei_printf("Predictions "); - ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)", - result.timing.dsp, result.timing.classification, result.timing.anomaly); - ei_printf(": \n"); - - int pred_index = 0; // Initialize pred_index - float pred_value = 0; // Initialize pred_value - - for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) { - if (result.classification[ix].value > pred_value){ - pred_index = ix; - pred_value = result.classification[ix].value; - } - // ei_printf(" %s: ", result.classification[ix].label); - // ei_printf_float(result.classification[ix].value); - // ei_printf("\n"); - } - ei_printf(" PREDICTION: ==> %s with probability %.2f\n", - result.classification[pred_index].label, pred_value); - turn_on_leds (pred_index); - - -#if EI_CLASSIFIER_HAS_ANOMALY == 1 - ei_printf(" anomaly score: "); - ei_printf_float(result.anomaly); - ei_printf("\n"); -#endif - - print_results = 0; - } -} - -... -``` - -You can find the complete code on the [project's GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/KWS/nicla_vision_microphone_continuous_LED). - -Upload the sketch to your board and test some real inferences. The idea is that the Green LED will be ON whenever the keyword YES is detected, the Red will lit for a NO, and any other word will turn on the Blue LED. All the LEDs should be off if silence or background noise is present. Remember that the same procedure can "trigger" an external device to perform a desired action instead of turning on an LED, as we saw in the introduction. - -{{< video https://youtu.be/25Rd76OTXLY width="480" height="270" center >}} - -## Conclusion - -> You will find the notebooks and codes used in this hands-on tutorial on the [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/KWS) repository. - -Before we finish, consider that Sound Classification is more than just voice. For example, you can develop TinyML projects around sound in several areas, such as: - -- **Security** (Broken Glass detection, Gunshot) -- **Industry** (Anomaly Detection) -- **Medical** (Snore, Cough, Pulmonary diseases) -- **Nature** (Beehive control, insect sound, pouching mitigation) diff --git a/contents/labs/arduino/nicla_vision/motion_classify_ad/images/jpg/Parameters_definition.jpg b/contents/labs/arduino/nicla_vision/motion_classification/images/jpg/Parameters_definition.jpg similarity index 100% rename from contents/labs/arduino/nicla_vision/motion_classify_ad/images/jpg/Parameters_definition.jpg rename to contents/labs/arduino/nicla_vision/motion_classification/images/jpg/Parameters_definition.jpg diff --git a/contents/labs/arduino/nicla_vision/motion_classify_ad/images/jpg/anom_det_train.jpg b/contents/labs/arduino/nicla_vision/motion_classification/images/jpg/anom_det_train.jpg similarity index 100% rename from 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motion_classify_ad.bib +bibliography: motion_classification.bib --- # Motion Classification and Anomaly Detection {.unnumbered} diff --git a/contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.qmd b/contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.qmd deleted file mode 100644 index 01fd760c..00000000 --- a/contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.qmd +++ /dev/null @@ -1,412 +0,0 @@ ---- -bibliography: motion_classify_ad.bib ---- - -# Motion Classification and Anomaly Detection {.unnumbered} - -![*DALL·E 3 Prompt: 1950s style cartoon illustration depicting a movement research room. In the center of the room, there's a simulated container used for transporting goods on trucks, boats, and forklifts. The container is detailed with rivets and markings typical of industrial cargo boxes. Around the container, the room is filled with vintage equipment, including an oscilloscope, various sensor arrays, and large paper rolls of recorded data. The walls are adorned with educational posters about transportation safety and logistics. The overall ambiance of the room is nostalgic and scientific, with a hint of industrial flair.*](images/jpg/movement_anomaly_ini.jpg){fig-align="center"} - - -## Introduction - -Transportation is the backbone of global commerce. Millions of containers are transported daily via various means, such as ships, trucks, and trains, to destinations worldwide. Ensuring these containers' safe and efficient transit is a monumental task that requires leveraging modern technology, and TinyML is undoubtedly one of them. - -In this hands-on tutorial, we will work to solve real-world problems related to transportation. We will develop a Motion Classification and Anomaly Detection system using the Arduino Nicla Vision board, the Arduino IDE, and the Edge Impulse Studio. This project will help us understand how containers experience different forces and motions during various phases of transportation, such as terrestrial and maritime transit, vertical movement via forklifts, and stationary periods in warehouses. - -::: callout-tip -## Learning Objectives - -- Setting up the Arduino Nicla Vision Board -- Data Collection and Preprocessing -- Building the Motion Classification Model -- Implementing Anomaly Detection -- Real-world Testing and Analysis -::: - -By the end of this tutorial, you'll have a working prototype that can classify different types of motion and detect anomalies during the transportation of containers. This knowledge can be a stepping stone to more advanced projects in the burgeoning field of TinyML involving vibration. - -## IMU Installation and testing - -For this project, we will use an accelerometer. As discussed in the Hands-On Tutorial, *Setup Nicla Vision*, the Nicla Vision Board has an onboard **6-axis IMU:** 3D gyroscope and 3D accelerometer, the [LSM6DSOX](https://www.st.com/resource/en/datasheet/lsm6dsox.pdf). Let's verify if the [LSM6DSOX IMU library](https://github.com/arduino-libraries/Arduino_LSM6DSOX) is installed. If not, install it. - -![](images/jpg/imu_ide.jpg){fig-align="center" width="6.5in"} - -Next, go to `Examples > Arduino_LSM6DSOX > SimpleAccelerometer` and run the accelerometer test. You can check if it works by opening the IDE Serial Monitor or Plotter. The values are in g (earth gravity), with a default range of +/- 4g: - -![](images/jpg/imu_test.jpg){fig-align="center" width="6.5in"} - -### Defining the Sampling frequency: - -Choosing an appropriate sampling frequency is crucial for capturing the motion characteristics you're interested in studying. The Nyquist-Shannon sampling theorem states that the sampling rate should be at least twice the highest frequency component in the signal to reconstruct it properly. In the context of motion classification and anomaly detection for transportation, the choice of sampling frequency would depend on several factors: - -1. **Nature of the Motion:** Different types of transportation (terrestrial, maritime, etc.) may involve different ranges of motion frequencies. Faster movements may require higher sampling frequencies. - -2. **Hardware Limitations:** The Arduino Nicla Vision board and any associated sensors may have limitations on how fast they can sample data. - -3. **Computational Resources:** Higher sampling rates will generate more data, which might be computationally intensive, especially critical in a TinyML environment. - -4. **Battery Life:** A higher sampling rate will consume more power. If the system is battery-operated, this is an important consideration. - -5. **Data Storage:** More frequent sampling will require more storage space, another crucial consideration for embedded systems with limited memory. - -In many human activity recognition tasks, **sampling rates of around 50 Hz to 100 Hz** are commonly used. Given that we are simulating transportation scenarios, which are generally not high-frequency events, a sampling rate in that range (50-100 Hz) might be a reasonable starting point. - -Let's define a sketch that will allow us to capture our data with a defined sampling frequency (for example, 50Hz): - -``` cpp -/* - * Based on Edge Impulse Data Forwarder Example (Arduino) - - https://docs.edgeimpulse.com/docs/cli-data-forwarder - * Developed by M.Rovai @11May23 - */ - -/* Include ----------------------------------------------------------------- */ -#include - -/* Constant defines -------------------------------------------------------- */ -#define CONVERT_G_TO_MS2 9.80665f -#define FREQUENCY_HZ 50 -#define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) - -static unsigned long last_interval_ms = 0; -float x, y, z; - -void setup() { - Serial.begin(9600); - while (!Serial); - - if (!IMU.begin()) { - Serial.println("Failed to initialize IMU!"); - while (1); - } -} - -void loop() { - if (millis() > last_interval_ms + INTERVAL_MS) { - last_interval_ms = millis(); - - if (IMU.accelerationAvailable()) { - // Read raw acceleration measurements from the device - IMU.readAcceleration(x, y, z); - - // converting to m/s2 - float ax_m_s2 = x * CONVERT_G_TO_MS2; - float ay_m_s2 = y * CONVERT_G_TO_MS2; - float az_m_s2 = z * CONVERT_G_TO_MS2; - - Serial.print(ax_m_s2); - Serial.print("\t"); - Serial.print(ay_m_s2); - Serial.print("\t"); - Serial.println(az_m_s2); - } - } -} -``` - -Uploading the sketch and inspecting the Serial Monitor, we can see that we are capturing 50 samples per second. - -![](images/jpg/sampling.jpg){fig-align="center" width="6.5in"} - -> Note that with the Nicla board resting on a table (with the camera facing down), the z-axis measures around 9.8m/s$^2$, the expected earth acceleration. - -## The Case Study: Simulated Container Transportation - -We will simulate container (or better package) transportation through different scenarios to make this tutorial more relatable and practical. Using the built-in accelerometer of the Arduino Nicla Vision board, we'll capture motion data by manually simulating the conditions of: - -1. **Terrestrial** Transportation (by road or train) -2. **Maritime**-associated Transportation -3. Vertical Movement via Fork-**Lift** -4. Stationary **(Idle**) period in a Warehouse - -![](images/jpg/classes.jpg){fig-align="center" width="6.5in"} - -From the above images, we can define for our simulation that primarily horizontal movements (x or y axis) should be associated with the "Terrestrial class," Vertical movements (z-axis) with the "Lift Class," no activity with the "Idle class," and movement on all three axes to [Maritime class.](https://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.htm) - -![](images/jpg/classes_mov_def.jpg){fig-align="center" width="6.5in"} - -## Data Collection - -For data collection, we can have several options. In a real case, we can have our device, for example, connected directly to one container, and the data collected on a file (for example .CSV) and stored on an SD card (Via SPI connection) or an offline repo in your computer. Data can also be sent remotely to a nearby repository, such as a mobile phone, using Bluetooth (as done in this project: [Sensor DataLogger](https://www.hackster.io/mjrobot/sensor-datalogger-50e44d)). Once your dataset is collected and stored as a .CSV file, it can be uploaded to the Studio using the [CSV Wizard tool](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/csv-wizard). - -> In this [video](https://youtu.be/2KBPq_826WM), you can learn alternative ways to send data to the Edge Impulse Studio. - -### Connecting the device to Edge Impulse - -We will connect the Nicla directly to the Edge Impulse Studio, which will also be used for data pre-processing, model training, testing, and deployment. For that, you have two options: - -1. Download the latest firmware and connect it directly to the `Data Collection` section. -2. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) tool to capture sensor data from the sensor and send it to the Studio. - -Option 1 is more straightforward, as we saw in the *Setup Nicla Vision* hands-on, but option 2 will give you more flexibility regarding capturing your data, such as sampling frequency definition. Let's do it with the last one. - -Please create a new project on the Edge Impulse Studio (EIS) and connect the Nicla to it, following these steps: - -1. Install the [Edge Impulse CLI](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation) and the [Node.js](https://nodejs.org/en/) into your computer. -2. Upload a sketch for data capture (the one discussed previously in this tutorial). -3. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) to capture data from the Nicla's accelerometer and send it to the Studio, as shown in this diagram: - -![](images/jpg/data-forw.jpg){fig-align="center" width="5.25in"} - -Start the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) on your terminal, entering (if it is the first time) the following command: - -``` -$ edge-impulse-data-forwarder --clean -``` - -Next, enter your EI credentials and choose your project, variables (for example, *accX,* *accY*, and *accZ*), and device name (for example, *NiclaV*: - -![](images/jpg/term.jpg){fig-align="center" width="6.5in"} - -Go to the `Devices` section on your EI Project and verify if the device is connected (the dot should be green): - -![](images/jpg/device.jpg){fig-align="center" width="6.5in"} - -> You can clone the project developed for this hands-on: [NICLA Vision Movement Classification](https://studio.edgeimpulse.com/public/302078/latest). - -### Data Collection - -On the `Data Acquisition` section, you should see that your board `[NiclaV]` is connected. The sensor is available: `[sensor with 3 axes (accX, accY, accZ)]` with a sampling frequency of `[50Hz]`. The Studio suggests a sample length of `[10000]` ms (10s). The last thing left is defining the sample label. Let's start with`[terrestrial]`: - -![](images/jpg/collect_data.jpg){fig-align="center" width="5.64in"} - -**Terrestrial** (palettes in a Truck or Train), moving horizontally. Press `[Start Sample]`and move your device horizontally, keeping one direction over your table. After 10 s, your data will be uploaded to the studio. Here is how the sample was collected: - -![](images/jpg/terrestrial_result.jpg){fig-align="center" width="6.5in"} - -As expected, the movement was captured mainly in the Y-axis (green). In the blue, we see the Z axis, around -10 m/s$^2$ (the Nicla has the camera facing up). - -As discussed before, we should capture data from all four Transportation Classes. So, imagine that you have a container with a built-in accelerometer facing the following situations: - -**Maritime** (pallets in boats into an angry ocean). The movement is captured on all three axes: - -![](images/jpg/maritime_result.jpg){fig-align="center" width="6.5in"} - -**Lift** (Palettes being handled vertically by a Forklift). Movement captured only in the Z-axis: - -![](images/jpg/lift_result.jpg){fig-align="center" width="6.5in"} - -**Idle** (Paletts in a warehouse). No movement detected by the accelerometer: - -![](images/jpg/idle_result.jpg){fig-align="center" width="6.5in"} - -You can capture, for example, 2 minutes (twelve samples of 10 seconds) for each of the four classes (a total of 8 minutes of data). Using the `three dots` menu after each one of the samples, select 2 of them, reserving them for the Test set. Alternatively, you can use the automatic `Train/Test Split tool` on the `Danger Zone` of `Dashboard` tab. Below, you can see the resulting dataset: - -![](images/jpg/dataset.jpg){fig-align="center" width="6.5in"} - -Once you have captured your dataset, you can explore it in more detail using the [Data Explorer](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/data-explorer), a visual tool to find outliers or mislabeled data (helping to correct them). The data explorer first tries to extract meaningful features from your data (by applying signal processing and neural network embeddings) and then uses a dimensionality reduction algorithm such as [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) or [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) to map these features to a 2D space. This gives you a one-look overview of your complete dataset. - -![](images/jpg/data_explorer.jpg){fig-align="center" width="6.5in"} - -In our case, the dataset seems OK (good separation). But the PCA shows we can have issues between maritime (green) and lift (orange). This is expected, once on a boat, sometimes the movement can be only "vertical". - -## Impulse Design - -The next step is the definition of our Impulse, which takes the raw data and uses signal processing to extract features, passing them as the input tensor of a *learning block* to classify new data. Go to `Impulse Design` and `Create Impulse`. The Studio will suggest the basic design. Let's also add a second *Learning Block* for `Anomaly Detection`. - -![](images/jpg/impulse.jpg){fig-align="center" width="6.5in"} - -This second model uses a K-means model. If we imagine that we could have our known classes as clusters, any sample that could not fit on that could be an outlier, an anomaly such as a container rolling out of a ship on the ocean or falling from a Forklift. - -![](images/jpg/anomaly_detect.jpg){fig-align="center" width="6.5in"} - -The sampling frequency should be automatically captured, if not, enter it: `[50]`Hz. The Studio suggests a *Window Size* of 2 seconds (`[2000]` ms) with a *sliding window* of `[20]`ms. What we are defining in this step is that we will pre-process the captured data (Time-Seres data), creating a tabular dataset features) that will be the input for a Neural Networks Classifier (DNN) and an Anomaly Detection model (K-Means), as shown below: - -![](images/jpg/impulse-block.jpg){fig-align="center" width="6.5in"} - -Let's dig into those steps and parameters to understand better what we are doing here. - -### Data Pre-Processing Overview - -Data pre-processing is extracting features from the dataset captured with the accelerometer, which involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. - -Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can clean and standardize the data, making it more suitable for feature extraction. In our case, we should divide the data into smaller segments or **windows**. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window increase**) choice depend on the application and the frequency of the events of interest. As a thumb rule, we should try to capture a couple of "cycles of data". - -> With a sampling rate (SR) of 50Hz and a window size of 2 seconds, we will get 100 samples per axis, or 300 in total (3 axis x 2 seconds x 50 samples). We will slide this window every 200ms, creating a larger dataset where each instance has 300 raw features. - -![](images/jpg/pre-process.jpg){fig-align="center" width="6.5in"} - -Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include: - -- **Time-domain** features describe the data's statistical properties within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate. -- **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the Fast Fourier Transform (FFT). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy. -- **Time-frequency** domain features combine the time and frequency domain information, such as the Short-Time Fourier Transform (STFT) or the Discrete Wavelet Transform (DWT). They can provide a more detailed understanding of how the signal's frequency content changes over time. - -In many cases, the number of extracted features can be large, which may lead to overfitting or increased computational complexity. Feature selection techniques, such as mutual information, correlation-based methods, or principal component analysis (PCA), can help identify the most relevant features for a given application and reduce the dimensionality of the dataset. The Studio can help with such feature importance calculations. - -### EI Studio Spectral Features - -Data preprocessing is a challenging area for embedded machine learning, still, Edge Impulse helps overcome this with its digital signal processing (DSP) preprocessing step and, more specifically, the [Spectral Features Block](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features). - -On the Studio, the collected raw dataset will be the input of a Spectral Analysis block, which is excellent for analyzing repetitive motion, such as data from accelerometers. This block will perform a DSP (Digital Signal Processing), extracting features such as [FFT](https://en.wikipedia.org/wiki/Fast_Fourier_transform) or [Wavelets](https://en.wikipedia.org/wiki/Digital_signal_processing#Wavelet). - -For our project, once the time signal is continuous, we should use FFT with, for example, a length of `[32]`. - -The per axis/channel **Time Domain Statistical features** are: - -- [RMS](https://en.wikipedia.org/wiki/Root_mean_square): 1 feature -- [Skewness](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FSkewness): 1 feature -- [Kurtosis](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FKurtosis): 1 feature - -The per axis/channel **Frequency Domain Spectral features** are: - -- [Spectral Power](https://en.wikipedia.org/wiki/Spectral_density): 16 features (FFT Length/2) -- Skewness: 1 feature -- Kurtosis: 1 feature - -So, for an FFT length of 32 points, the resulting output of the Spectral Analysis Block will be 21 features per axis (a total of 63 features). - -> You can learn more about how each feature is calculated by downloading the notebook [Edge Impulse - Spectral Features Block Analysis](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb) [TinyML under the hood: Spectral Analysis](https://www.hackster.io/mjrobot/tinyml-under-the-hood-spectral-analysis-94676c) or [opening it directly on Google CoLab](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb). - -### Generating features - -Once we understand what the pre-processing does, it is time to finish the job. So, let's take the raw data (time-series type) and convert it to tabular data. For that, go to the `Spectral Features` section on the `Parameters` tab, define the main parameters as discussed in the previous section (`[FFT]` with `[32]` points), and select`[Save Parameters]`: - -![](images/jpg/Parameters_definition.jpg){fig-align="center" width="6.5in"} - -At the top menu, select the `Generate Features` option and the `Generate Features` button. Each 2-second window data will be converted into one data point of 63 features. - -> The Feature Explorer will show those data in 2D using [UMAP.](https://umap-learn.readthedocs.io/en/latest/) Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE but is also applicable for general non-linear dimension reduction. - -The visualization makes it possible to verify that after the feature generation, the classes present keep their excellent separation, which indicates that the classifier should work well. Optionally, you can analyze how important each one of the features is for one class compared with others. - -![](images/jpg/feature_generation.jpg){fig-align="center" width="6.5in"} - -## Models Training - -Our classifier will be a Dense Neural Network (DNN) that will have 63 neurons on its input layer, two hidden layers with 20 and 10 neurons, and an output layer with four neurons (one per each class), as shown here: - -![](images/jpg/model.jpg){fig-align="center" width="6.5in"} - -As hyperparameters, we will use a Learning Rate of `[0.005]`, a Batch size of `[32]`, and `[20]`% of data for validation for `[30]` epochs. After training, we can see that the accuracy is 98.5%. The cost of memory and latency is meager. - -![](images/jpg/train.jpg){fig-align="center" width="6.5in"} - -For Anomaly Detection, we will choose the suggested features that are precisely the most important ones in the Feature Extraction, plus the accZ RMS. The number of clusters will be `[32]`, as suggested by the Studio: - -![](images/jpg/anom_det_train.jpg){fig-align="center" width="6.5in"} - -## Testing - -We can verify how our model will behave with unknown data using 20% of the data left behind during the data capture phase. The result was almost 95%, which is good. You can always work to improve the results, for example, to understand what went wrong with one of the wrong results. If it is a unique situation, you can add it to the training dataset and then repeat it. - -The default minimum threshold for a considered uncertain result is `[0.6]` for classification and `[0.3]` for anomaly. Once we have four classes (their output sum should be 1.0), you can also set up a lower threshold for a class to be considered valid (for example, 0.4). You can `Set confidence thresholds` on the `three dots` menu, besides the `Classy all` button. - -![](images/jpg/model_testing.jpg){fig-align="center" width="6.5in"} - -You can also perform Live Classification with your device (which should still be connected to the Studio). - -> Be aware that here, you will capture real data with your device and upload it to the Studio, where an inference will be taken using the trained model (But the **model is NOT in your device**). - -## Deploy - -It is time to deploy the preprocessing block and the trained model to the Nicla. The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option `Arduino Library`, and at the bottom, you can choose `Quantized (Int8)` or `Unoptimized (float32)` and `[Build]`. A Zip file will be created and downloaded to your computer. - -![](images/jpg/deploy.jpg){fig-align="center" width="6.5in"} - -On your Arduino IDE, go to the `Sketch` tab, select `Add.ZIP Library`, and Choose the.zip file downloaded by the Studio. A message will appear in the IDE Terminal: `Library installed`. - -### Inference - -Now, it is time for a real test. We will make inferences wholly disconnected from the Studio. Let's change one of the code examples created when you deploy the Arduino Library. - -In your Arduino IDE, go to the `File/Examples` tab and look for your project, and on examples, select `Nicla_vision_fusion`: - -![](images/jpg/inference.jpg){fig-align="center" width="6.5in"} - -Note that the code created by Edge Impulse considers a *sensor fusion* approach where the IMU (Accelerometer and Gyroscope) and the ToF are used. At the beginning of the code, you have the libraries related to our project, IMU and ToF: - -``` cpp -/* Includes ---------------------------------------------------------------- */ -#include -#include //IMU -#include "VL53L1X.h" // ToF -``` - -> You can keep the code this way for testing because the trained model will use only features pre-processed from the accelerometer. But consider that you will write your code only with the needed libraries for a real project. - -And that is it! - -You can now upload the code to your device and proceed with the inferences. Press the Nicla `[RESET]` button twice to put it on boot mode (disconnect from the Studio if it is still connected), and upload the sketch to your board. - -Now you should try different movements with your board (similar to those done during data capture), observing the inference result of each class on the Serial Monitor: - -- **Idle and lift classes:** - -![](images/jpg/inference_1.jpg){fig-align="center" width="6.5in"} - -- **maritime and terrestrial:** - -![](images/jpg/inference_2.jpg){fig-align="center" width="6.5in"} - -Note that in all situations above, the value of the `anomaly score` was smaller than 0.0. Try a new movement that was not part of the original dataset, for example, "rolling" the Nicla, facing the camera upside-down, as a container falling from a boat or even a boat accident: - -- **anomaly detection:** - -![](images/jpg/anomaly-boat.jpg){fig-align="center" width="6.5in"} - -In this case, the anomaly is much bigger, over 1.00 - -### Post-processing - -Now that we know the model is working since it detects the movements, we suggest that you modify the code to see the result with the NiclaV completely offline (disconnected from the PC and powered by a battery, a power bank, or an independent 5V power supply). - -The idea is to do the same as with the KWS project: if one specific movement is detected, a specific LED could be lit. For example, if *terrestrial* is detected, the Green LED will light; if *maritime*, the Red LED will light, if it is a *lift,* the Blue LED will light; and if no movement is detected *(idle*), the LEDs will be OFF. You can also add a condition when an anomaly is detected, in this case, for example, a white color can be used (all e LEDs light simultaneously). - -## Conclusion - -> The notebooks and codes used in this hands-on tutorial will be found on the [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Motion_Classification) repository. - -Before we finish, consider that Movement Classification and Object Detection can be utilized in many applications across various domains. Here are some of the potential applications: - -### Case Applications - -#### Industrial and Manufacturing - -- **Predictive Maintenance:** Detecting anomalies in machinery motion to predict failures before they occur. -- **Quality Control:** Monitoring the motion of assembly lines or robotic arms for precision assessment and deviation detection from the standard motion pattern. -- **Warehouse Logistics:** Managing and tracking the movement of goods with automated systems that classify different types of motion and detect anomalies in handling. - -#### Healthcare - -- **Patient Monitoring:** Detecting falls or abnormal movements in the elderly or those with mobility issues. -- **Rehabilitation:** Monitoring the progress of patients recovering from injuries by classifying motion patterns during physical therapy sessions. -- **Activity Recognition:** Classifying types of physical activity for fitness applications or patient monitoring. - -#### Consumer Electronics - -- **Gesture Control:** Interpreting specific motions to control devices, such as turning on lights with a hand wave. -- **Gaming:** Enhancing gaming experiences with motion-controlled inputs. - -#### Transportation and Logistics - -- **Vehicle Telematics:** Monitoring vehicle motion for unusual behavior such as hard braking, sharp turns, or accidents. -- **Cargo Monitoring:** Ensuring the integrity of goods during transport by detecting unusual movements that could indicate tampering or mishandling. - -#### Smart Cities and Infrastructure - -- **Structural Health Monitoring:** Detecting vibrations or movements within structures that could indicate potential failures or maintenance needs. -- **Traffic Management:** Analyzing the flow of pedestrians or vehicles to improve urban mobility and safety. - -#### Security and Surveillance - -- **Intruder Detection:** Detecting motion patterns typical of unauthorized access or other security breaches. -- **Wildlife Monitoring:** Detecting poachers or abnormal animal movements in protected areas. - -#### Agriculture - -- **Equipment Monitoring:** Tracking the performance and usage of agricultural machinery. -- **Animal Behavior Analysis:** Monitoring livestock movements to detect behaviors indicating health issues or stress. - -#### Environmental Monitoring - -- **Seismic Activity:** Detecting irregular motion patterns that precede earthquakes or other geologically relevant events. -- **Oceanography:** Studying wave patterns or marine movements for research and safety purposes. - -### Nicla 3D case - -For real applications, as some described before, we can add a case to our device, and Eoin Jordan, from Edge Impulse, developed a great wearable and machine health case for the Nicla range of boards. It works with a 10mm magnet, 2M screws, and a 16mm strap for human and machine health use case scenarios. Here is the link: [Arduino Nicla Voice and Vision Wearable Case](https://www.thingiverse.com/thing:5923305). - -![](images/jpg/case.jpg){fig-align="center" width="6.5in"} - -The applications for motion classification and anomaly detection are extensive, and the Arduino Nicla Vision is well-suited for scenarios where low power consumption and edge processing are advantageous. Its small form factor and efficiency in processing make it an ideal choice for deploying portable and remote applications where real-time processing is crucial and connectivity may be limited. diff --git a/contents/labs/arduino/nicla_vision/nicla_vision.qmd b/contents/labs/arduino/nicla_vision/nicla_vision.qmd index 5a1315c4..ebb11a10 100644 --- a/contents/labs/arduino/nicla_vision/nicla_vision.qmd +++ b/contents/labs/arduino/nicla_vision/nicla_vision.qmd @@ -13,16 +13,12 @@ These labs provide a unique opportunity to gain practical experience with machin ## Setup -- [Setup Nicla Vision](./niclav_sys/niclav_sys.qmd) - -{{< include ./niclav_sys/niclav_sys.qmd >}} - +- [Setup Nicla Vision](./setup/setup.qmd) ## Exercises | **Modality** | **Task** | **Description** | **Link** | |--------------|--------------|-----------------|----------| | Vision | Image Classification | Learn to classify images | [Link](./image_classification/image_classification.qmd) | -| Vision | Object Detection | Implement object detection | [Link](./object_detection_fomo/object_detection_fomo.qmd) | -| Sound | Audio Feature Engineering | Explore audio features and preprocessing | [Link](./kws_feature_eng/kws_feature_eng.qmd) | -| IMU | Motion Classification and Anomaly Detection | Classify motion data and detect anomalies | [Link](./motion_classify_ad/motion_classify_ad.qmd) | \ No newline at end of file +| Vision | Object Detection | Implement object detection | [Link](./object_detection/object_detection.qmd) | +| IMU | Motion Classification and Anomaly Detection | Classify motion data and detect anomalies | [Link](./motion_classification/motion_classification.qmd) | \ No newline at end of file diff --git a/contents/labs/arduino/nicla_vision/niclav_sys/niclav_sys.qmd b/contents/labs/arduino/nicla_vision/niclav_sys/niclav_sys.qmd deleted file mode 100644 index 65c2e5b9..00000000 --- a/contents/labs/arduino/nicla_vision/niclav_sys/niclav_sys.qmd +++ /dev/null @@ -1,307 +0,0 @@ -# Setup Nicla Vision {.unnumbered} - -![*DALL·E 3 Prompt: Illustration reminiscent of a 1950s cartoon where the Arduino NICLA VISION board, equipped with a variety of sensors including a camera, is the focal point on an old-fashioned desk. In the background, a computer screen with rounded edges displays the Arduino IDE. The code seen is related to LED configurations and machine learning voice command detection. Outputs on the Serial Monitor explicitly display the words 'yes' and 'no'.*](images/jpg/nicla_sys_ini.jpg){fig-align="center" width="6.5in"} - -## Introduction - -The [Arduino Nicla Vision](https://docs.arduino.cc/hardware/nicla-vision) (sometimes called *NiclaV*) is a development board that includes two processors that can run tasks in parallel. It is part of a family of development boards with the same form factor but designed for specific tasks, such as the [Nicla Sense ME](https://www.bosch-sensortec.com/software-tools/tools/arduino-nicla-sense-me/) and the [Nicla Voice](https://store-usa.arduino.cc/products/nicla-voice?_gl=1*l3abc6*_ga*MTQ3NzE4Mjk4Mi4xNjQwMDIwOTk5*_ga_NEXN8H46L5*MTY5NjM0Mzk1My4xMDIuMS4xNjk2MzQ0MjQ1LjAuMC4w). The *Niclas* can efficiently run processes created with TensorFlow Lite. For example, one of the cores of the NiclaV runs a computer vision algorithm on the fly (inference), while the other executes low-level operations like controlling a motor and communicating or acting as a user interface. The onboard wireless module allows the management of WiFi and Bluetooth Low Energy (BLE) connectivity simultaneously. - -![](images/jpg/image29.jpg){fig-align="center" width="6.5in"} - -## Hardware - -### Two Parallel Cores - -The central processor is the dual-core [STM32H747,](https://content.arduino.cc/assets/Arduino-Portenta-H7_Datasheet_stm32h747xi.pdf?_gl=1*6quciu*_ga*MTQ3NzE4Mjk4Mi4xNjQwMDIwOTk5*_ga_NEXN8H46L5*MTY0NzQ0NTg1My4xMS4xLjE2NDc0NDYzMzkuMA..) including a Cortex M7 at 480 MHz and a Cortex M4 at 240 MHz. The two cores communicate via a Remote Procedure Call mechanism that seamlessly allows calling functions on the other processor. Both processors share all the on-chip peripherals and can run: - -- Arduino sketches on top of the Arm Mbed OS - -- Native Mbed applications - -- MicroPython / JavaScript via an interpreter - -- TensorFlow Lite - -![](images/jpg/image22.jpg){fig-align="center" width="6.5in"} - -### Memory - -Memory is crucial for embedded machine learning projects. The NiclaV board can host up to 16 MB of QSPI Flash for storage. However, it is essential to consider that the MCU SRAM is the one to be used with machine learning inferences; the STM32H747 is only 1MB, shared by both processors. This MCU also has incorporated 2MB of FLASH, mainly for code storage. - -### Sensors - -- **Camera:** A GC2145 2 MP Color CMOS Camera. - -- **Microphone:** The `MP34DT05` is an ultra-compact, low-power, omnidirectional, digital MEMS microphone built with a capacitive sensing element and the IC interface. - -- **6-Axis IMU:** 3D gyroscope and 3D accelerometer data from the `LSM6DSOX` 6-axis IMU. - -- **Time of Flight Sensor:** The `VL53L1CBV0FY` Time-of-Flight sensor adds accurate and low power-ranging capabilities to the Nicla Vision. The invisible near-infrared VCSEL laser (including the analog driver) is encapsulated with receiving optics in an all-in-one small module below the camera. - -## Arduino IDE Installation - -Start connecting the board (*microUSB*) to your computer: - -![](images/jpg/image14.jpg){fig-align="center" width="6.5in"} - -Install the Mbed OS core for Nicla boards in the Arduino IDE. Having the IDE open, navigate to `Tools > Board > Board Manager`, look for Arduino Nicla Vision on the search window, and install the board. - -![](images/jpg/image2.jpg){fig-align="center" width="6.5in"} - -Next, go to `Tools > Board > Arduino Mbed OS Nicla Boards` and select `Arduino Nicla Vision`. Having your board connected to the USB, you should see the Nicla on Port and select it. - -> Open the Blink sketch on Examples/Basic and run it using the IDE Upload button. You should see the Built-in LED (green RGB) blinking, which means the Nicla board is correctly installed and functional! - -### Testing the Microphone - -On Arduino IDE, go to `Examples > PDM > PDMSerialPlotter`, open and run the sketch. Open the Plotter and see the audio representation from the microphone: - -![](images/png/image9.png){fig-align="center" width="6.5in"} - -> Vary the frequency of the sound you generate and confirm that the mic is working correctly. - -### Testing the IMU - -Before testing the IMU, it will be necessary to install the LSM6DSOX library. For that, go to Library Manager and look for LSM6DSOX. Install the library provided by Arduino: - -![](images/jpg/image19.jpg){fig-align="center" width="6.5in"} - -Next, go to `Examples > Arduino_LSM6DSOX > SimpleAccelerometer` and run the accelerometer test (you can also run Gyro and board temperature): - -![](images/png/image28.png){fig-align="center" width="6.5in"} - -### Testing the ToF (Time of Flight) Sensor - -As we did with IMU, it is necessary to install the VL53L1X ToF library. For that, go to Library Manager and look for VL53L1X. Install the library provided by Pololu: - -![](images/jpg/image15.jpg){fig-align="center" width="6.5in"} - -Next, run the sketch [proximity_detection.ino](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Arduino-IDE/proximity_detection/proximity_detection.ino): - -![](images/png/image12.png){fig-align="center" width="6.5in"} - -On the Serial Monitor, you will see the distance from the camera to an object in front of it (max of 4m). - -![](images/jpg/image13.jpg){fig-align="center" width="6.5in"} - -### Testing the Camera - -We can also test the camera using, for example, the code provided on `Examples > Camera > CameraCaptureRawBytes`. We cannot see the image directly, but it is possible to get the raw image data generated by the camera. - -Anyway, the best test with the camera is to see a live image. For that, we will use another IDE, the OpenMV. - -## Installing the OpenMV IDE - -OpenMV IDE is the premier integrated development environment with OpenMV Cameras like the one on the Nicla Vision. It features a powerful text editor, debug terminal, and frame buffer viewer with a histogram display. We will use MicroPython to program the camera. - -Go to the [OpenMV IDE page](https://openmv.io/pages/download), download the correct version for your Operating System, and follow the instructions for its installation on your computer. - -![](images/png/image21.png){fig-align="center" width="6.5in"} - -The IDE should open, defaulting to the helloworld_1.py code on its Code Area. If not, you can open it from `Files > Examples > HelloWord > helloword.py` - -![](images/png/image7.png){fig-align="center" width="6.5in"} - -Any messages sent through a serial connection (using print() or error messages) will be displayed on the **Serial Terminal** during run time. The image captured by a camera will be displayed in the **Camera Viewer** Area (or Frame Buffer) and in the Histogram area, immediately below the Camera Viewer. - -> Before connecting the Nicla to the OpenMV IDE, ensure you have the latest bootloader version. Go to your Arduino IDE, select the Nicla board, and open the sketch on `Examples > STM_32H747_System STM32H747_manageBootloader`. Upload the code to your board. The Serial Monitor will guide you. - -After updating the bootloader, put the Nicla Vision in bootloader mode by double-pressing the reset button on the board. The built-in green LED will start fading in and out. Now return to the OpenMV IDE and click on the connect icon (Left ToolBar): - -![](images/jpg/image23.jpg){fig-align="center" width="4.010416666666667in"} - -A pop-up will tell you that a board in DFU mode was detected and ask how you would like to proceed. First, select `Install the latest release firmware (vX.Y.Z)`. This action will install the latest OpenMV firmware on the Nicla Vision. - -![](images/png/image10.png){fig-align="center" width="6.5in"} - -You can leave the option `Erase internal file system` unselected and click `[OK]`. - -Nicla's green LED will start flashing while the OpenMV firmware is uploaded to the board, and a terminal window will then open, showing the flashing progress. - -![](images/png/image5.png){fig-align="center" width="4.854166666666667in"} - -Wait until the green LED stops flashing and fading. When the process ends, you will see a message saying, "DFU firmware update complete!". Press `[OK]`. - -![](images/png/image1.png){fig-align="center" width="3.875in"} - -A green play button appears when the Nicla Vison connects to the Tool Bar. - -![](images/jpg/image18.jpg){fig-align="center" width="4.791666666666667in"} - -Also, note that a drive named "NO NAME" will appear on your computer.: - -![](images/png/image3.png){fig-align="center" width="6.447916666666667in"} - -Every time you press the `[RESET]` button on the board, it automatically executes the *main.py* script stored on it. You can load the [main.py](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/main.py) code on the IDE (`File > Open File...`). - -![](images/png/image16.png){fig-align="center" width="4.239583333333333in"} - -> This code is the "Blink" code, confirming that the HW is OK. - -For testing the camera, let's run *helloword_1.py*. For that, select the script on `File > Examples > HelloWorld > helloword.py`, - -When clicking the green play button, the MicroPython script (*hellowolrd.py*) on the Code Area will be uploaded and run on the Nicla Vision. On-Camera Viewer, you will start to see the video streaming. The Serial Monitor will show us the FPS (Frames per second), which should be around 14fps. - -![](images/png/image6.png){fig-align="center" width="6.5in"} - -Here is the [helloworld.py](http://helloworld.py/) script: - -``` python -# Hello World Example 2 -# -# Welcome to the OpenMV IDE! Click on the green run arrow button below to run the script! - -import sensor, image, time - -sensor.reset() # Reset and initialize the sensor. -sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) -sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) -sensor.skip_frames(time = 2000) # Wait for settings take effect. -clock = time.clock() # Create a clock object to track the FPS. - -while(True): - clock.tick() # Update the FPS clock. - img = sensor.snapshot() # Take a picture and return the image. - print(clock.fps()) -``` - -In [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision), you can find the Python scripts used here. - -The code can be split into two parts: - -- **Setup:** Where the libraries are imported, initialized and the variables are defined and initiated. - -- **Loop:** (while loop) part of the code that runs continually. The image (*img* variable) is captured (one frame). Each of those frames can be used for inference in Machine Learning Applications. - -To interrupt the program execution, press the red `[X]` button. - -> Note: OpenMV Cam runs about half as fast when connected to the IDE. The FPS should increase once disconnected. - -In the [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Micropython), You can find other Python scripts. Try to test the onboard sensors. - -## Connecting the Nicla Vision to Edge Impulse Studio - -We will need the Edge Impulse Studio later in other exercises. [Edge Impulse](https://www.edgeimpulse.com/) is a leading development platform for machine learning on edge devices. - -Edge Impulse officially supports the Nicla Vision. So, for starting, please create a new project on the Studio and connect the Nicla to it. For that, follow the steps: - -- Download the most updated [EI Firmware](https://cdn.edgeimpulse.com/firmware/arduino-nicla-vision.zip) and unzip it. - -- Open the zip file on your computer and select the uploader corresponding to your OS: - -![](images/png/image17.png){fig-align="center" width="4.416666666666667in"} - -- Put the Nicla-Vision on Boot Mode, pressing the reset button twice. - -- Execute the specific batch code for your OS for uploading the binary *arduino-nicla-vision.bin* to your board. - -Go to your project on the Studio, and on the `Data Acquisition tab`, select `WebUSB` (1). A window will pop up; choose the option that shows that the `Nicla is paired` (2) and press `[Connect]` (3). - -![](images/png/image27.png){fig-align="center" width="6.5in"} - -In the *Collect Data* section on the `Data Acquisition` tab, you can choose which sensor data to pick. - -![](images/png/image25.png){fig-align="center" width="6.5in"} - -For example. `IMU data`: - -![](images/png/image8.png){fig-align="center" width="6.5in"} - -Or Image (`Camera`): - -![](images/png/image4.png){fig-align="center" width="6.5in"} - -And so on. You can also test an external sensor connected to the `ADC` (Nicla pin 0) and the other onboard sensors, such as the microphone and the ToF. - -## Expanding the Nicla Vision Board (optional) - -A last item to be explored is that sometimes, during prototyping, it is essential to experiment with external sensors and devices, and an excellent expansion to the Nicla is the [Arduino MKR Connector Carrier (Grove compatible)](https://store-usa.arduino.cc/products/arduino-mkr-connector-carrier-grove-compatible). - -The shield has 14 Grove connectors: five single analog inputs (A0-A5), one double analog input (A5/A6), five single digital I/Os (D0-D4), one double digital I/O (D5/D6), one I2C (TWI), and one UART (Serial). All connectors are 5V compatible. - -> Note that all 17 Nicla Vision pins will be connected to the Shield Groves, but some Grove connections remain disconnected. - -![](images/jpg/image20.jpg){fig-align="center" width="6.5in"} - -This shield is MKR compatible and can be used with the Nicla Vision and Portenta. - -![](images/jpg/image26.jpg){fig-align="center" width="4.34375in"} - -For example, suppose that on a TinyML project, you want to send inference results using a LoRaWAN device and add information about local luminosity. Often, with offline operations, a local low-power display such as an OLED is advised. This setup can be seen here: - -![](images/jpg/image11.jpg){fig-align="center" width="6.5in"} - -The [Grove Light Sensor](https://wiki.seeedstudio.com/Grove-Light_Sensor/) would be connected to one of the single Analog pins (A0/PC4), the [LoRaWAN device](https://wiki.seeedstudio.com/Grove_LoRa_E5_New_Version/) to the UART, and the [OLED](https://arduino.cl/producto/display-oled-grove/) to the I2C connector. - -The Nicla Pins 3 (Tx) and 4 (Rx) are connected with the Serial Shield connector. The UART communication is used with the LoRaWan device. Here is a simple code to use the UART: - -``` python -# UART Test - By: marcelo_rovai - Sat Sep 23 2023 - -import time -from pyb import UART -from pyb import LED - -redLED = LED(1) # built-in red LED - -# Init UART object. -# Nicla Vision's UART (TX/RX pins) is on "LP1" -uart = UART("LP1", 9600) - -while(True): - uart.write("Hello World!\r\n") - redLED.toggle() - time.sleep_ms(1000) -``` - -To verify that the UART is working, you should, for example, connect another device as the Arduino UNO, displaying "Hello Word" on the Serial Monitor. Here is the [code](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Arduino-IDE/teste_uart_UNO/teste_uart_UNO.ino). - -![](images/jpg/image24.jpg){fig-align="center" width="2.8125in"} - -Below is the *Hello World code* to be used with the I2C OLED. The MicroPython SSD1306 OLED driver (ssd1306.py), created by Adafruit, should also be uploaded to the Nicla (the ssd1306.py script can be found in [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/ssd1306.py)). - -``` python -# Nicla_OLED_Hello_World - By: marcelo_rovai - Sat Sep 30 2023 - -#Save on device: MicroPython SSD1306 OLED driver, I2C and SPI interfaces created by Adafruit -import ssd1306 - -from machine import I2C -i2c = I2C(1) - -oled_width = 128 -oled_height = 64 -oled = ssd1306.SSD1306_I2C(oled_width, oled_height, i2c) - -oled.text('Hello, World', 10, 10) -oled.show() -``` - -Finally, here is a simple script to read the ADC value on pin "PC4" (Nicla pin A0): - -``` python - -# Light Sensor (A0) - By: marcelo_rovai - Wed Oct 4 2023 - -import pyb -from time import sleep - -adc = pyb.ADC(pyb.Pin("PC4")) # create an analog object from a pin -val = adc.read() # read an analog value - -while (True): - - val = adc.read() - print ("Light={}".format (val)) - sleep (1) -``` - -The ADC can be used for other sensor variables, such as [Temperature](https://wiki.seeedstudio.com/Grove-Temperature_Sensor_V1.2/). - -> Note that the above scripts ([[downloaded from Github]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Micropython)) introduce only how to connect external devices with the Nicla Vision board using MicroPython. - -## Conclusion - -The Arduino Nicla Vision is an excellent *tiny device* for industrial and professional uses! However, it is powerful, trustworthy, low power, and has suitable sensors for the most common embedded machine learning applications such as vision, movement, sensor fusion, and sound. - -> On the [GitHub repository,](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main) you will find the last version of all the codes used or commented on in this hands-on exercise. diff --git a/contents/labs/arduino/nicla_vision/object_detection_fomo/images/jpg/cv_obj_detect.jpg b/contents/labs/arduino/nicla_vision/object_detection/images/jpg/cv_obj_detect.jpg similarity index 100% rename from contents/labs/arduino/nicla_vision/object_detection_fomo/images/jpg/cv_obj_detect.jpg rename to contents/labs/arduino/nicla_vision/object_detection/images/jpg/cv_obj_detect.jpg diff --git a/contents/labs/arduino/nicla_vision/object_detection_fomo/images/jpg/data_folder.jpg b/contents/labs/arduino/nicla_vision/object_detection/images/jpg/data_folder.jpg similarity index 100% rename from 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a/contents/niclav_sys/niclav_sys.qmd +++ b/contents/labs/arduino/nicla_vision/setup/setup.qmd @@ -1,5 +1,5 @@ --- -bibliography: niclav_sys.bib +bibliography: setup.bib --- # Setup Nicla Vision {.unnumbered} diff --git a/contents/labs/labs.qmd b/contents/labs/labs.qmd index ec2fc362..21a74203 100644 --- a/contents/labs/labs.qmd +++ b/contents/labs/labs.qmd @@ -1,10 +1,24 @@ -# LABS {.unnumbered} +# Overview {.unnumbered} -The following labs offer a unique chance to gain hands-on experience with machine learning (ML) systems by deploying TinyML models onto real embedded devices. Instead of working with large models that need data center-scale resources, you'll interact directly with both hardware and software. These exercises cover different modalities, giving you exposure to a variety of applications. This approach helps you understand the real-world challenges and opportunities in embedded AI. +The following labs offer a unique chance to gain hands-on experience with machine learning (ML) systems by deploying TinyML models onto real embedded devices. Instead of working with large models that need data center-scale resources, you'll interact directly with both hardware and software. These exercises cover different sensor modalities, giving you exposure to a variety of applications. This approach helps you understand the real-world challenges and opportunities in deploying AI on real systems. -## Hardware Kits +## Supported Devices -| Board Name | Lab Exercises | Board Image | -|---------------------|---------------------------------------------------------|---------------------| -| [Nicla Vision](https://store.arduino.cc/products/nicla-vision) | [Link](./arduino/nicla_vision/nicla_vision.qmd) | ![Nicla Vision](./arduino/nicla_vision/images/jpg/nicla_vision.jpeg){height=1in} | -| [XIAO ESP32S3](https://www.seeedstudio.com/XIAO-ESP32S3-p-5627.html) | [Link](./seeed/xiao_esp32s3/xiao_esp32S3.qmd) | ![XIAO ESP32S3](./seeed/xiao_esp32s3/images/jpg/xiao_esp32s3_decked.jpeg){height=1in} | \ No newline at end of file +| Device/Board | Installaion & Setup | Keyword Spotting (KWS) | Image Classification | Object Detection | Motion Detection | +| --------------------------------- | ------------------------------- | --------------------------------------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------- | +| [Nicla Vision](./arduino/nicla_vision/nicla_vision.qmd) | [Link](./arduino/nicla_vision/setup/setup.qmd) | [Link](./arduino/nicla_vision/kws/kws.qmd) | [Link](./arduino/nicla_vision/image_classification/image_classification.qmd) | [Link](./arduino/nicla_vision/object_detection/object_detection.qmd) | [Link](./arduino/nicla_vision/motion_classification/motion_classification.qmd) | +| [XIAO ESP32S3](./seeed/xiao_esp32s3/xiao_esp32s3.qmd) | [Link](./seeed/xiao_esp32s3/setup/setup.qmd) | [Link](./seeed/xiao_esp32s3/kws/kws.qmd) | [Link](./seeed/xiao_esp32s3/image_classification/image_classification.qmd) | Coming soon. | [Link](./seeed/xiao_esp32s3/motion_classification/motion_classification.qmd) | + +## Lab Structure + +Each lab follows a similar structure: + +#. Introduction to the application and its real-world significance +#. Step-by-step instructions to set up the hardware and software environment +#. Detailed guidance on deploying the pre-trained TinyML model +#. Exercises to modify and experiment with the model and its parameters +#. Discussion on the results and potential improvements + +## Troubleshooting and Support + +If you encounter any issues during the labs, please refer to the troubleshooting guides and FAQs provided with each lab. 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #ffffff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box 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.value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return 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00000000..d9fd98f0 --- /dev/null +++ b/contents/labs/labs_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/contents/labs/labs_files/libs/quarto-html/tippy.css b/contents/labs/labs_files/libs/quarto-html/tippy.css new file mode 100644 index 00000000..e6ae635c --- /dev/null +++ b/contents/labs/labs_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.bib similarity index 100% rename from contents/labs/arduino/nicla_vision/kws_feature_eng/kws_feature_eng.bib rename to contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.bib diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd new file mode 100644 index 00000000..ea244bbc --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd @@ -0,0 +1,327 @@ +# Image Classification {.unnumbered} + +![](https://hackster.imgix.net/uploads/attachments/1587471/_nOXij20mq1.blob?auto=compress%2Cformat&w=900&h=675&fit=min) + +## Introduction + +More and more, we are facing an artificial intelligence (AI) revolution where, as stated by Gartner, **Edge AI** has a very high impact potential, and **it is for now**! + +![](https://hackster.imgix.net/uploads/attachments/1587506/image_EZKT6sirt5.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +At the forefront of the Emerging Technologies Radar is the universal language of Edge Computer Vision. When we delve into Machine Learning (ML) applied to vision, the first concept that greets us is Image Classification, a kind of ML' Hello World ' that is both simple and profound! + +The Seeed Studio XIAO ESP32S3 Sense is a powerful tool that combines camera and SD card support. With its embedded ML computing power and photography capability, it is an excellent starting point for exploring TinyML vision AI. + +## A TinyML Image Classification Project - Fruits versus Veggies + +![](./images/png/imgs_classif/vegetables.png) + +The whole idea of our project will be to train a model and proceed with inference on the XIAO ESP32S3 Sense. For training, we should find some data **(in fact, tons of data!**). + +*But first of all, we need a goal! What do we want to classify?* + +With TinyML, a set of techniques associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory). We will differentiate **apples** from **bananas** and **potatoes** (you can try other categories)**.** + +So, let's find a specific dataset that includes images from those categories. Kaggle is a good start: + +https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition + +This dataset contains images of the following food items: + +- **Fruits** - *banana, apple*, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango. +- **Vegetables** - cucumber, carrot, capsicum, onion, *potato,* lemon, tomato, radish, beetroot, cabbage, lettuce, spinach, soybean, cauliflower, bell pepper, chili pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant. + +Each category is split into the **train** (100 images), **test** (10 images), and **validation** (10 images). + +- Download the dataset from the Kaggle website and put it on your computer. + +> Optionally, you can add some fresh photos of bananas, apples, and potatoes from your home kitchen, using, for example, the codes discussed in the setup lab. + +## Training the model with Edge Impulse Studio + +We will use the Edge Impulse Studio to train our model. As you may know, [Edge Impulse](https://www.edgeimpulse.com/) is a leading development platform for machine learning on edge devices. + +Enter your account credentials (or create a free account) at Edge Impulse. Next, create a new project: + +![](https://hackster.imgix.net/uploads/attachments/1587543/image_MDgkE355g3.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +### Data Acquisition + +Next, on the `UPLOAD DATA` section, upload from your computer the files from chosen categories: + +![](https://hackster.imgix.net/uploads/attachments/1587488/image_brdDCN6bc5.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +It would be best if you now had your training dataset split into three classes of data: + +![](https://hackster.imgix.net/uploads/attachments/1587489/image_QyxusuY3DM.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> You can upload extra data for further model testing or split the training data. I will leave it as it is to use the most data possible. + +### Impulse Design + +> An impulse takes raw data (in this case, images), extracts features (resize pictures), and then uses a learning block to classify new data. + +Classifying images is the most common use of deep learning, but a lot of data should be used to accomplish this task. We have around 90 images for each category. Is this number enough? Not at all! We will need thousands of images to "teach or model" to differentiate an apple from a banana. But, we can solve this issue by re-training a previously trained model with thousands of images. We call this technique "Transfer Learning" (TL). + +![](https://hackster.imgix.net/uploads/attachments/1587490/tl_fuVIsKd7YV.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +With TL, we can fine-tune a pre-trained image classification model on our data, performing well even with relatively small image datasets (our case). + +So, starting from the raw images, we will resize them (96x96) pixels and feed them to our Transfer Learning block: + +![](https://hackster.imgix.net/uploads/attachments/1587491/image_QhTt0Av8u3.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +#### Pre-processing (Feature Generation) + +Besides resizing the images, we can change them to Grayscale or keep the actual RGB color depth. Let's start selecting `Grayscale`. Doing that, each one of our data samples will have dimension 9, 216 features (96x96x1). Keeping RGB, this dimension would be three times bigger. Working with Grayscale helps to reduce the amount of final memory needed for inference. + +![](https://hackster.imgix.net/uploads/attachments/1587492/image_eqGdUoXrMb.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +Remember to `[Save parameters]`. This will generate the features to be used in training. + +#### Model Design + +**Transfer Learning** + +In 2007, Google introduced [MobileNetV1,](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html) a family of general-purpose computer vision neural networks designed with mobile devices in mind to support classification, detection, and more. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of various use cases. + +Although the base MobileNet architecture is already tiny and has low latency, many times, a specific use case or application may require the model to be smaller and faster. MobileNet introduces a straightforward parameter α (alpha) called width multiplier to construct these smaller, less computationally expensive models. The role of the width multiplier α is to thin a network uniformly at each layer. + +Edge Impulse Studio has **MobileNet V1 (96x96 images)** and **V2 (96x96 and 160x160 images)** available, with several different **α** values (from 0.05 to 1.0). For example, you will get the highest accuracy with V2, 160x160 images, and α=1.0. Of course, there is a trade-off. The higher the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be needed to run the model, implying more latency. + +The smaller footprint will be obtained at another extreme with **MobileNet V1** and α=0.10 (around 53.2K RAM and 101K ROM). + +For this first pass, we will use **MobileNet V1** and α=0.10. + +### Training + +**Data Augmentation** + +Another necessary technique to use with deep learning is **data augmentation**. Data augmentation is a method that can help improve the accuracy of machine learning models, creating additional artificial data. A data augmentation system makes small, random changes to your training data during the training process (such as flipping, cropping, or rotating the images). + +Under the rood, here you can see how Edge Impulse implements a data Augmentation policy on your data: + +```cpp +# Implements the data augmentation policy +def augment_image(image, label): + # Flips the image randomly + image = tf.image.random_flip_left_right(image) + + # Increase the image size, then randomly crop it down to + # the original dimensions + resize_factor = random.uniform(1, 1.2) + new_height = math.floor(resize_factor * INPUT_SHAPE[0]) + new_width = math.floor(resize_factor * INPUT_SHAPE[1]) + image = tf.image.resize_with_crop_or_pad(image, new_height, new_width) + image = tf.image.random_crop(image, size=INPUT_SHAPE) + + # Vary the brightness of the image + image = tf.image.random_brightness(image, max_delta=0.2) + + return image, label +``` + +Exposure to these variations during training can help prevent your model from taking shortcuts by "memorizing" superficial clues in your training data, meaning it may better reflect the deep underlying patterns in your dataset. + +The final layer of our model will have 16 neurons with a 10% dropout for overfitting prevention. Here is the Training output: + +![](./images/png/imgs_classif/train.png) + +The result could be better. The model reached around 77% accuracy, but the amount of RAM expected to be used during the inference is relatively tiny (about 60 KBytes), which is very good. + +### Deployment + +The trained model will be deployed as a .zip Arduino library: + +![](./images/png/imgs_classif/depl.png) + +Open your Arduino IDE, and under **Sketch,** go to **Include Library** and **add.ZIP Library.** Please select the file you download from Edge Impulse Studio, and that's it! + +![](./images/png/imgs_classif/arduino_zip.png) + +Under the **Examples** tab on Arduino IDE, you should find a sketch code under your project name. + +![](./images/png/imgs_classif/sketch.png) + +Open the Static Buffer example: + +![](./images/png/imgs_classif/static_buffer.png) + +You can see that the first line of code is exactly the calling of a library with all the necessary stuff for running inference on your device. + +```cpp +#include +``` + +Of course, this is a generic code (a "template") that only gets one sample of raw data (stored on the variable: features = {} and runs the classifier, doing the inference. The result is shown on the Serial Monitor. + +We should get the sample (image) from the camera and pre-process it (resizing to 96x96, converting to grayscale, and flatting it). This will be the input tensor of our model. The output tensor will be a vector with three values (labels), showing the probabilities of each one of the classes. + +![](./images/png/imgs_classif/deploy_block.png) + +Returning to your project (Tab Image), copy one of the Raw Data Sample: + +![](./images/png/imgs_classif/get_test_data.png) + +9, 216 features will be copied to the clipboard. This is the input tensor (a flattened image of 96x96x1), in this case, bananas. Past this Input tensor on`features[] = {0xb2d77b, 0xb5d687, 0xd8e8c0, 0xeaecba, 0xc2cf67, ...}` + +![](./images/png/imgs_classif/features.png) + +Edge Impulse included the [library ESP NN](https://github.com/espressif/esp-nn) in its SDK, which contains optimized NN (Neural Network) functions for various Espressif chips, including the ESP32S3 (running at Arduino IDE). + +When running the inference, you should get the highest score for "banana." + +![](./images/png/imgs_classif/inference1.png) + +Great news! Our device handles an inference, discovering that the input image is a banana. Also, note that the inference time was around 317ms, resulting in a maximum of 3 fps if you tried to classify images from a video. + +Now, we should incorporate the camera and classify images in real time. + +Go to the Arduino IDE Examples and download from your project the sketch `esp32_camera`: + +![](https://hackster.imgix.net/uploads/attachments/1587604/image_hjX5k8gTl8.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +You should change lines 32 to 75, which define the camera model and pins, using the data related to our model. Copy and paste the below lines, replacing the lines 32-75: + +```cpp +#define PWDN_GPIO_NUM -1 +#define RESET_GPIO_NUM -1 +#define XCLK_GPIO_NUM 10 +#define SIOD_GPIO_NUM 40 +#define SIOC_GPIO_NUM 39 +#define Y9_GPIO_NUM 48 +#define Y8_GPIO_NUM 11 +#define Y7_GPIO_NUM 12 +#define Y6_GPIO_NUM 14 +#define Y5_GPIO_NUM 16 +#define Y4_GPIO_NUM 18 +#define Y3_GPIO_NUM 17 +#define Y2_GPIO_NUM 15 +#define VSYNC_GPIO_NUM 38 +#define HREF_GPIO_NUM 47 +#define PCLK_GPIO_NUM 13 +``` + +Here you can see the resulting code: + +![](./images/png/imgs_classif/camera_set.png) + +The modified sketch can be downloaded from GitHub: [xiao_esp32s3_camera](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_camera). + +> Note that you can optionally keep the pins as a .h file as we did in the Setup Lab. + +Upload the code to your XIAO ESP32S3 Sense, and you should be OK to start classifying your fruits and vegetables! You can check the result on Serial Monitor. + +## Testing the Model (Inference) + +![](./images/png/imgs_classif/inf_banana.jpg) + +Getting a photo with the camera, the classification result will appear on the Serial Monitor: + +![](./images/png/imgs_classif/inf_banana.png) + +Other tests: + +![](./images/png/imgs_classif/inferencia2_apple.png) + +![](./images/png/imgs_classif/inferencia3.png) + +## Testing with a Bigger Model + +Now, let's go to the other side of the model size. Let's select a MobilinetV2 96x96 0.35, having as input RGB images. + +![](./images/png/imgs_classif/train_2.png) + +Even with a bigger model, the accuracy could be better, and the amount of memory necessary to run the model increases five times, with latency increasing seven times. + +> Note that the performance here is estimated with a smaller device, the ESP-EYE. The actual inference with the ESP32S3 should be better. + +To improve our model, we will need to train more images. + +Even though our model did not improve accuracy, let's test whether the XIAO can handle such a bigger model. We will do a simple inference test with the Static Buffer sketch. + +Let's redeploy the model. If the EON Compiler is enabled when you generate the library, the total memory needed for inference should be reduced, but it does not influence accuracy. + +> ⚠️ **Attention** - The Xiao ESP32S3 with PSRAM enable has enought memory to run the inference, even in such bigger model. Keep the EON Compiler **NOT ENABLED**. + +![](./images/png/imgs_classif/deploy_2.png) + +Doing an inference with MobilinetV2 96x96 0.35, having as input RGB images, the latency was 219ms, which is great for such a bigger model. + +![](./images/png/imgs_classif/inf_2.png) + +For the test, we can train the model again, using the smallest version of MobileNet V2, with an alpha of 0.05. Interesting that the result in accuracy was higher. + +![](https://hackster.imgix.net/uploads/attachments/1591705/image_lwYLKM696A.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +> Note that the estimated latency for an Arduino Portenta (ou Nicla), running with a clock of 480MHz is 45ms. + +Deploying the model, we got an inference of only 135ms, remembering that the XIAO runs with half of the clock used by the Portenta/Nicla (240MHz): + +![](https://hackster.imgix.net/uploads/attachments/1591706/image_dAfOl9Tguz.png?auto=compress%2Cformat&w=1280&h=960&fit=max) + +## Running inference on the SenseCraft-Web-Toolkit + +One significant limitation of viewing inference on Arduino IDE is that we can not see what the camera focuses on. A good alternative is the **SenseCraft-Web-Toolkit**, a visual model deployment tool provided by [SSCMA ](https://sensecraftma.seeed.cc/)(Seeed SenseCraft Model Assistant). This tool allows you to deploy models to various platforms easily through simple operations. The tool offers a user-friendly interface and does not require any coding. + +Follow the following steps to start the SenseCraft-Web-Toolkit: + +1. Open the [SenseCraft-Web-Toolkit website.](https://seeed-studio.github.io/SenseCraft-Web-Toolkit/#/setup/process) +2. Connect the XIAO to your computer: + +- Having the XIAO connected, select it as below: + +![](./images/jpeg/imgs_classif/senseCraft-1.jpg) + +- Select the device/Port and press `[Connect]`: + + ![](./images/jpeg/imgs_classif/senseCraft-2.jpg) + +> You can try several Computer Vision models previously uploaded by Seeed Studio. Try them and have fun! + +In our case, we will use the blue button at the bottom of the page: `[Upload Custom AI Model]`. + +But first, we must download from Edge Impulse Studio our **quantized .tflite** model. + +3. Go to your project at Edge Impulse Studio, or clone this one: + +- [XIAO-ESP32S3-CAM-Fruits-vs-Veggies-v1-ESP-NN](https://studio.edgeimpulse.com/public/228516/live) + +4. On the `Dashboard`, download the model ("block output"): `Transfer learning model - TensorFlow Lite (int8 quantized).` + +![](./images/jpeg/imgs_classif/senseCraft-4.jpg) + +5. On SenseCraft-Web-Toolkit, use the blue button at the bottom of the page: `[Upload Custom AI Model]`. A window will pop up. Enter the Model file that you downloaded to your computer from Edge Impulse Studio, choose a Model Name, and enter with labels (ID: Object): + +![](./images/jpeg/imgs_classif/senseCraft-3.jpg) + +> Note that you should use the labels trained on EI Studio, entering them in alphabetic order (in our case: apple, banana, potato). + +After a few seconds (or minutes), the model will be uploaded to your device, and the camera image will appear in real-time on the Preview Sector: + +![](./images/jpeg/imgs_classif/senseCraft-apple.jpg) + +The Classification result will be at the top of the image. You can also select the Confidence of your inference cursor `Confidence`. + +Clicking on the top button (Device Log), you can open a Serial Monitor to follow the inference, the same that we have done with the Arduino IDE: + +![](./images/jpeg/imgs_classif/senseCraft-apple-2.jpg) + +On Device Log, you will get information as: + +![](./images/jpeg/imgs_classif//senseCraft-log.jpg) + +- Preprocess time (image capture and Crop): 4ms; +- Inference time (model latency): 106ms, +- Postprocess time (display of the image and inclusion of data): 0ms. +- Output tensor (classes), for example: [[89,0]]; where 0 is Apple (and 1is banana and 2 is potato) + +Here are other screenshots: + +![](./images/jpeg/imgs_classif//inference.jpg) + +## Conclusion + +The XIAO ESP32S3 Sense is very flexible, inexpensive, and easy to program. The project proves the potential of TinyML. Memory is not an issue; the device can handle many post-processing tasks, including communication. + +You will find the last version of the codes on the GitHub repository: [XIAO-ESP32S3-Sense.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/images/arduino_zip.png b/contents/labs/seeed/xiao_esp32s3/image_classification/images/arduino_zip.png new file mode 100644 index 00000000..fe33f499 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/image_classification/images/arduino_zip.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/images/camera_set.png b/contents/labs/seeed/xiao_esp32s3/image_classification/images/camera_set.png new file mode 100644 index 00000000..c94e1675 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/image_classification/images/camera_set.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/image_classification/images/depl.png 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+# Keyword Spotting (KWS) {.unnumbered} + +![*Image by Marcelo Rovai*](./images/png/kws/kws_ini.png) + +## Introduction + +Keyword Spotting (KWS) is integral to many voice recognition systems, enabling devices to respond to specific words or phrases. While this technology underpins popular devices like Google Assistant or Amazon Alexa, it's equally applicable and achievable on smaller, low-power devices. This lab will guide you through implementing a KWS system using TinyML on the XIAO ESP32S3 microcontroller board. + +The XIAO ESP32S3, equipped with Espressif's ESP32-S3 chip, is a compact and potent microcontroller offering a dual-core Xtensa LX7 processor, integrated Wi-Fi, and Bluetooth. Its balance of computational power, energy efficiency, and versatile connectivity make it a fantastic platform for TinyML applications. Also, with its expansion board, we will have access to the "sense" part of the device, which has a 1600x1200 OV2640 camera, an SD card slot, and a **digital microphone**. The integrated microphone and the SD card will be essential in this project. + +We will utilize the [Edge Impulse Studio](https://www.edgeimpulse.com/), a powerful, user-friendly platform that simplifies creating and deploying machine learning models onto edge devices. We'll train a KWS model step-by-step, optimizing and deploying it onto the XIAO ESP32S3 Sense. + +Our model will be designed to recognize keywords that can trigger device wake-up or specific actions (in the case of "YES"), bringing your projects to life with voice-activated commands. + +Leveraging our experience with TensorFlow Lite for Microcontrollers (the engine "under the hood" on the EI Studio), we'll create a KWS system capable of real-time machine learning on the device. + +As we progress through the lab, we'll break down each process stage - from data collection and preparation to model training and deployment - to provide a comprehensive understanding of implementing a KWS system on a microcontroller. + +### How does a voice assistant work? + +Keyword Spotting (KWS) is critical to many voice assistants, enabling devices to respond to specific words or phrases. To start, it is essential to realize that Voice Assistants on the market, like Google Home or Amazon Echo-Dot, only react to humans when they are “waked up” by particular keywords such as “ Hey Google” on the first one and “Alexa” on the second. + +![img](https://hackster.imgix.net/uploads/attachments/1594299/1_3n44ykL_GNR5jQSwrUSKWA.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +In other words, recognizing voice commands is based on a multi-stage model or Cascade Detection. + +![img](https://hackster.imgix.net/uploads/attachments/1594300/image_Zd5vTdG9RB.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Stage 1:** A smaller microprocessor inside the Echo Dot or Google Home **continuously** listens to the sound, waiting for the keyword to be spotted. For such detection, a TinyML model at the edge is used (KWS application). + +**Stage 2:** Only when triggered by the KWS application on Stage 1 is the data sent to the cloud and processed on a larger model. + +The video below shows an example where I emulate a Google Assistant on a Raspberry Pi (Stage 2), having an Arduino Nano 33 BLE as the tinyML device (Stage 1). + + + +> If you want to go deeper on the full project, please see my tutorial: [Building an Intelligent Voice Assistant From Scratch](https://www.hackster.io/mjrobot/building-an-intelligent-voice-assistant-from-scratch-2199c3). + +In this lab, we will focus on Stage 1 (KWS or Keyword Spotting), where we will use the XIAO ESP2S3 Sense, which has a digital microphone for spotting the keyword. + +### The KWS Project + +The below diagram will give an idea of how the final KWS application should work (during inference): + +![image.png](https://hackster.imgix.net/uploads/attachments/1594331/image_buEZet7Pje.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Our KWS application will recognize four classes of sound: + +- **YES** (Keyword 1) +- **NO** (Keyword 2) +- **NOISE** (no keywords spoken, only background noise is present) +- **UNKNOW** (a mix of different words than YES and NO) + +> Optionally for real-world projects, it is always advised to include different words than keywords, such as "Noise" (or Background) and "Unknow." + +### The Machine Learning workflow + +The main component of the KWS application is its model. So, we must train such a model with our specific keywords, noise, and other words (the "unknown"): + +![img](https://hackster.imgix.net/uploads/attachments/1594302/image_VjDpbeenv9.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Dataset + +The critical component of Machine Learning Workflow is the **dataset**. Once we have decided on specific keywords (*YES* and NO), we can take advantage of the dataset developed by Pete Warden, ["Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)." This dataset has 35 keywords (with +1,000 samples each), such as yes, no, stop, and go. In other words, we can get 1,500 samples of *yes* and *no*. + +You can download a small portion of the dataset from Edge Studio ([Keyword spotting pre-built dataset](https://docs.edgeimpulse.com/docs/pre-built-datasets/keyword-spotting)), which includes samples from the four classes we will use in this project: yes, no, noise, and background. For this, follow the steps below: + +- Download the [keywords dataset.](https://cdn.edgeimpulse.com/datasets/keywords2.zip) +- Unzip the file in a location of your choice. + +Although we have a lot of data from Pete's dataset, collecting some words spoken by us is advised. When working with accelerometers, creating a dataset with data captured by the same type of sensor was essential. In the case of *sound*, it is different because what we will classify is, in reality, *audio* data. + +> The key difference between sound and audio is their form of energy. Sound is mechanical wave energy (longitudinal sound waves) that propagate through a medium causing variations in pressure within the medium. Audio is made of electrical energy (analog or digital signals) that represent sound electrically. + +The sound waves should be converted to audio data when we speak a keyword. The conversion should be done by sampling the signal generated by the microphone in 16KHz with a 16-bit depth. + +So, any device that can generate audio data with this basic specification (16Khz/16bits) will work fine. As a device, we can use the proper XIAO ESP32S3 Sense, a computer, or even your mobile phone. + +![sound-audio.png](https://hackster.imgix.net/uploads/attachments/1594337/sound-audio_lOADMI6ern.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Capturing online Audio Data with Edge Impulse and a smartphone** + +In the lab Motion Classification and Anomaly Detection, we connect our device directly to Edge Impulse Studio for data capturing (having a sampling frequency of 50Hz to 100Hz). For such low frequency, we could use the EI CLI function *Data Forwarder,* but according to Jan Jongboom, Edge Impulse CTO, *audio (*16KHz) *goes too fast for the data forwarder to be captured.* So, once we have the digital data captured by the microphone, we can turn *it into a WAV file* to be sent to the Studio via Data Uploader (same as we will do with Pete's dataset)*.* + +> If we want to collect audio data directly on the Studio, we can use any smartphone connected online with it. We will not explore this option here, but you can easily follow EI [documentation](https://docs.edgeimpulse.com/docs/development-platforms/using-your-mobile-phone). + +### Capturing (offline) Audio Data with the XIAO ESP32S3 Sense + +The built-in microphone is the [MSM261D3526H1CPM](https://files.seeedstudio.com/wiki/XIAO-BLE/mic-MSM261D3526H1CPM-ENG.pdf), a PDM digital output MEMS microphone with Multi-modes. Internally, it is connected to the ESP32S3 via an I2S bus using pins IO41 (Clock) and IO41 (Data). + +![Pasted Graphic 62.png](https://hackster.imgix.net/uploads/attachments/1594599/pasted_graphic_62_RRD6zoEXwv.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**What is I2S?** + +I2S, or Inter-IC Sound, is a standard protocol for transmitting digital audio from one device to another. It was initially developed by Philips Semiconductor (now NXP Semiconductors). It is commonly used in audio devices such as digital signal processors, digital audio processors, and, more recently, microcontrollers with digital audio capabilities (our case here). + +The I2S protocol consists of at least three lines: + +![image.png](https://hackster.imgix.net/uploads/attachments/1594628/image_8CRJmXD9Fr.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**1. Bit (or Serial) clock line (BCLK or CLK)**: This line toggles to indicate the start of a new bit of data (pin IO42). + +**2. Word select line (WS)**: This line toggles to indicate the start of a new word (left channel or right channel). The Word select clock (WS) frequency defines the sample rate. In our case, L/R on the microphone is set to ground, meaning that we will use only the left channel (mono). + +**3. Data line (SD)**: This line carries the audio data (pin IO41) + +In an I2S data stream, the data is sent as a sequence of frames, each containing a left-channel word and a right-channel word. This makes I2S particularly suited for transmitting stereo audio data. However, it can also be used for mono or multichannel audio with additional data lines. + +Let's start understanding how to capture raw data using the microphone. Go to the [GitHub project ](https://github.com/Mjrovai/XIAO-ESP32S3-Sense)and download the sketch: [XIAOEsp2s3_Mic_Test](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/XiaoEsp32s3_Mic_Test): + +``` +/* + XIAO ESP32S3 Simple Mic Test +*/ + +#include + +void setup() { + Serial.begin(115200); + while (!Serial) { + } + + // start I2S at 16 kHz with 16-bits per sample + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, 16000, 16)) { + Serial.println("Failed to initialize I2S!"); + while (1); // do nothing + } +} + +void loop() { + // read a sample + int sample = I2S.read(); + + if (sample && sample != -1 && sample != 1) { + Serial.println(sample); + } +} +``` + +This code is a simple microphone test for the XIAO ESP32S3 using the I2S (Inter-IC Sound) interface. It sets up the I2S interface to capture audio data at a sample rate of 16 kHz with 16 bits per sample and then continuously reads samples from the microphone and prints them to the serial monitor. + +Let's dig into the code's main parts: + +- Include the I2S library: This library provides functions to configure and use the [I2S interface](https://espressif-docs.readthedocs-hosted.com/projects/arduino-esp32/en/latest/api/i2s.html), which is a standard for connecting digital audio devices. +- I2S.setAllPins(-1, 42, 41, -1, -1): This sets up the I2S pins. The parameters are (-1, 42, 41, -1, -1), where the second parameter (42) is the PIN for the I2S clock (CLK), and the third parameter (41) is the PIN for the I2S data (DATA) line. The other parameters are set to -1, meaning those pins are not used. +- I2S.begin(PDM_MONO_MODE, 16000, 16): This initializes the I2S interface in Pulse Density Modulation (PDM) mono mode, with a sample rate of 16 kHz and 16 bits per sample. If the initialization fails, an error message is printed, and the program halts. +- int sample = I2S.read(): This reads an audio sample from the I2S interface. + +If the sample is valid, it is printed on the Serial Monitor and Plotter. + +Below is a test "whispering" in two different tones. + +![plotter.png](https://hackster.imgix.net/uploads/attachments/1594603/plotter_zIdxqUxqkY.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +### Save recorded sound samples (dataset) as .wav audio files to a microSD card. + +Let's use the onboard SD Card reader to save .wav audio files; we must habilitate the XIAO PSRAM first. + +> ESP32-S3 has only a few hundred kilobytes of internal RAM on the MCU chip. It can be insufficient for some purposes so that ESP32-S3 can use up to 16 MB of external PSRAM (Psuedostatic RAM) connected in parallel with the SPI flash chip. The external memory is incorporated in the memory map and, with certain restrictions, is usable in the same way as internal data RAM. + +For a start, Insert the SD Card on the XIAO as shown in the photo below (the SD Card should be formatted to FAT32). + +![image.png](https://hackster.imgix.net/uploads/attachments/1594791/image_qIPJ5vK4IA.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Turn the PSRAM function of the ESP-32 chip on (Arduino IDE): Tools>PSRAM: "OPI PSRAM”>OPI PSRAM + +![image.png](https://hackster.imgix.net/uploads/attachments/1594639/image_Zo8usTd0A2.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +- Download the sketch [Wav_Record_dataset](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Wav_Record_dataset),[ ](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Wav_Record_dataset)which you can find on the project's GitHub. + +This code records audio using the I2S interface of the Seeed XIAO ESP32S3 Sense board, saves the recording as a.wav file on an SD card, and allows for control of the recording process through commands sent from the serial monitor. The name of the audio file is customizable (it should be the class labels to be used with the training), and multiple recordings can be made, each saved in a new file. The code also includes functionality to increase the volume of the recordings. + +Let's break down the most essential parts of it: + +``` +#include +#include "FS.h" +#include "SD.h" +#include "SPI.h" +``` + +Those are the necessary libraries for the program. I2S.h allows for audio input, FS.h provides file system handling capabilities, SD.h enables the program to interact with an SD card, and SPI.h handles the SPI communication with the SD card. + +``` +#define RECORD_TIME 10 +#define SAMPLE_RATE 16000U +#define SAMPLE_BITS 16 +#define WAV_HEADER_SIZE 44 +#define VOLUME_GAIN 2 +``` + +Here, various constants are defined for the program. + +- **RECORD_TIME** specifies the length of the audio recording in seconds. +- **SAMPLE_RATE** and **SAMPLE_BITS** define the audio quality of the recording. +- **WAV_HEADER_SIZE** specifies the size of the .wav file header. +- **VOLUME_GAIN** is used to increase the volume of the recording. + +``` +int fileNumber = 1; +String baseFileName; +bool isRecording = false; +``` + +These variables keep track of the current file number (to create unique file names), the base file name, and whether the system is currently recording. + +``` +void setup() { + Serial.begin(115200); + while (!Serial); + + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, SAMPLE_RATE, SAMPLE_BITS)) { + Serial.println("Failed to initialize I2S!"); + while (1); + } + + if(!SD.begin(21)){ + Serial.println("Failed to mount SD Card!"); + while (1); + } + Serial.printf("Enter with the label name\n"); +} +``` + +The setup function initializes the serial communication, I2S interface for audio input, and SD card interface. If the I2S did not initialize or the SD card fails to mount, it will print an error message and halt execution. + +``` +void loop() { + if (Serial.available() > 0) { + String command = Serial.readStringUntil('\n'); + command.trim(); + if (command == "rec") { + isRecording = true; + } else { + baseFileName = command; + fileNumber = 1; //reset file number each time a new basefile name is set + Serial.printf("Send rec for starting recording label \n"); + } + } + if (isRecording && baseFileName != "") { + String fileName = "/" + baseFileName + "." + String(fileNumber) + ".wav"; + fileNumber++; + record_wav(fileName); + delay(1000); // delay to avoid recording multiple files at once + isRecording = false; + } +} +``` + +In the main loop, the program waits for a command from the serial monitor. If the command is rec, the program starts recording. Otherwise, the command is assumed to be the base name for the .wav files. If it's currently recording and a base file name is set, it records the audio and saves it as a.wav file. The file names are generated by appending the file number to the base file name. + +``` +void record_wav(String fileName) +{ + ... + + File file = SD.open(fileName.c_str(), FILE_WRITE); + ... + rec_buffer = (uint8_t *)ps_malloc(record_size); + ... + + esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, rec_buffer, record_size, &sample_size, portMAX_DELAY); + ... +} +``` + +This function records audio and saves it as a.wav file with the given name. It starts by initializing the sample_size and record_size variables. record_size is calculated based on the sample rate, size, and desired recording time. Let's dig into the essential sections; + +``` +File file = SD.open(fileName.c_str(), FILE_WRITE); +// Write the header to the WAV file +uint8_t wav_header[WAV_HEADER_SIZE]; +generate_wav_header(wav_header, record_size, SAMPLE_RATE); +file.write(wav_header, WAV_HEADER_SIZE); +``` + +This section of the code opens the file on the SD card for writing and then generates the .wav file header using the generate_wav_header function. It then writes the header to the file. + +``` +// PSRAM malloc for recording +rec_buffer = (uint8_t *)ps_malloc(record_size); +if (rec_buffer == NULL) { + Serial.printf("malloc failed!\n"); + while(1) ; +} +Serial.printf("Buffer: %d bytes\n", ESP.getPsramSize() - ESP.getFreePsram()); +``` + +The ps_malloc function allocates memory in the PSRAM for the recording. If the allocation fails (i.e., rec_buffer is NULL), it prints an error message and halts execution. + +``` +// Start recording +esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, rec_buffer, record_size, &sample_size, portMAX_DELAY); +if (sample_size == 0) { + Serial.printf("Record Failed!\n"); +} else { + Serial.printf("Record %d bytes\n", sample_size); + } +``` + +The i2s_read function reads audio data from the microphone into rec_buffer. It prints an error message if no data is read (sample_size is 0). + +``` +// Increase volume +for (uint32_t i = 0; i < sample_size; i += SAMPLE_BITS/8) { + (*(uint16_t *)(rec_buffer+i)) <<= VOLUME_GAIN; +} +``` + +This section of the code increases the recording volume by shifting the sample values by VOLUME_GAIN. + +``` +// Write data to the WAV file +Serial.printf("Writing to the file ...\n"); +if (file.write(rec_buffer, record_size) != record_size) + Serial.printf("Write file Failed!\n"); + +free(rec_buffer); +file.close(); +Serial.printf("Recording complete: \n"); +Serial.printf("Send rec for a new sample or enter a new label\n\n"); +``` + +Finally, the audio data is written to the .wav file. If the write operation fails, it prints an error message. After writing, the memory allocated for rec_buffer is freed, and the file is closed. The function finishes by printing a completion message and prompting the user to send a new command. + +``` +void generate_wav_header(uint8_t *wav_header, uint32_t wav_size, uint32_t sample_rate) +{ + ... + memcpy(wav_header, set_wav_header, sizeof(set_wav_header)); +} +``` + +The generate_wav_header function creates a.wav file header based on the parameters (wav_size and sample_rate). It generates an array of bytes according to the .wav file format, which includes fields for the file size, audio format, number of channels, sample rate, byte rate, block alignment, bits per sample, and data size. The generated header is then copied into the wav_header array passed to the function. + +Now, upload the code to the XIAO and get samples from the keywords (yes and no). You can also capture noise and other words. + +The Serial monitor will prompt you to receive the label to be recorded. + +![Pasted Graphic.png](https://hackster.imgix.net/uploads/attachments/1594657/pasted_graphic_x87Mi3IFkT.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Send the label (for example, yes). The program will wait for another command: rec + +![Pasted Graphic 2.png](https://hackster.imgix.net/uploads/attachments/1594659/pasted_graphic_2_ONWtwJmxr6.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +And the program will start recording new samples every time a command rec is sent. The files will be saved as yes.1.wav, yes.2.wav, yes.3.wav, etc., until a new label (for example, no) is sent. In this case, you should send the command rec for each new sample, which will be saved as no.1.wav, no.2.wav, no.3.wav, etc. + +![Pasted Graphic 4.png](https://hackster.imgix.net/uploads/attachments/1594661/pasted_graphic_4_8cwca5pRTa.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Ultimately, we will get the saved files on the SD card. + +![image.png](https://hackster.imgix.net/uploads/attachments/1594663/image_Cos4bNiaDF.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The files are ready to be uploaded to Edge Impulse Studio + +### Capturing (offline) Audio Data Apps + +Alternatively, you can also use your PC or smartphone to capture audio data with a sampling frequency 16KHz and a bit depth of 16 Bits. A good app for that is [*Voice Recorder Pro*](https://www.bejbej.ca/app/voicerecordpro) [(](https://www.bejbej.ca/app/voicerecordpro)IOS). You should save your records as .wav files and send them to your computer. + +![image.png](https://hackster.imgix.net/uploads/attachments/1594808/image_pNmXUg1ux5.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Note that any app, such as [Audacity](https://www.audacityteam.org/), can be used for audio recording or even your computer[.](https://www.audacityteam.org/) + +## Training model with Edge Impulse Studio + +### Uploading the Data + +When the raw dataset is defined and collected (Pete's dataset + recorded keywords), we should initiate a new project at Edge Impulse Studio: + +![Pasted Graphic 44.png](https://hackster.imgix.net/uploads/attachments/1594809/pasted_graphic_44_AxzJtW0fRQ.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Once the project is created, select the Upload Existing Data tool in the Data Acquisition section. Choose the files to be uploaded: + +![Pasted Graphic 48.png](https://hackster.imgix.net/uploads/attachments/1594810/pasted_graphic_48_JAwBsZY3lh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +And upload them to the Studio (You can automatically split data in train/test). Repete to all classes and all raw data. + +![Pasted Graphic 46.png](https://hackster.imgix.net/uploads/attachments/1594813/pasted_graphic_46_Zyg8bVdDuG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The samples will now appear in the Data acquisition section. + +![Pasted Graphic 49.png](https://hackster.imgix.net/uploads/attachments/1594834/pasted_graphic_49_OaHcAmQTRg.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +All data on Pete's dataset have a 1s length, but the samples recorded in the previous section have 10s and must be split into 1s samples to be compatible. + +Click on three dots after the sample name and select Split sample. + +![image.png](https://hackster.imgix.net/uploads/attachments/1594836/image_gE0k6Mevup.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Once inside the tool, split the data into 1-second records. If necessary, add or remove segments: + +![image.png](https://hackster.imgix.net/uploads/attachments/1594852/image_4Ii4Ng4m2f.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This procedure should be repeated for all samples. + +> Note: For longer audio files (minutes), first, split into 10-second segments and after that, use the tool again to get the final 1-second splits. + +Suppose we do not split data automatically in train/test during upload. In that case, we can do it manually (using the three dots menu, moving samples individually) or using Perform Train / Test Split on Dashboard - Danger Zone. + +> We can optionally check all datasets using the tab Data Explorer. + +### Creating Impulse (Pre-Process / Model definition) + +*An* **impulse** *takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.* + +![Pasted Graphic 51.png](https://hackster.imgix.net/uploads/attachments/1594912/pasted_graphic_51_BoV3CAx2lS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +First, we will take the data points with a 1-second window, augmenting the data, sliding that window each 500ms. Note that the option zero-pad data is set. It is essential to fill with zeros samples smaller than 1 second (in some cases, I reduced the 1000 ms window on the split tool to avoid noises and spikes). + +Each 1-second audio sample should be pre-processed and converted to an image (for example, 13 x 49 x 1). We will use MFCC, which extracts features from audio signals using [Mel Frequency Cepstral Coefficients](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), which are great for the human voice. + +![image.png](https://hackster.imgix.net/uploads/attachments/1595150/image_uk5EiFvTHh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Next, we select KERAS for classification and build our model from scratch by doing Image Classification using Convolution Neural Network). + +### Pre-Processing (MFCC) + +The next step is to create the images to be trained in the next phase: + +We can keep the default parameter values or take advantage of the DSP Autotuneparameters option, which we will do. + +![image.png](https://hackster.imgix.net/uploads/attachments/1595153/image_qLl1o4Ruj5.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The result will not spend much memory to pre-process data (only 16KB). Still, the estimated processing time is high, 675 ms for an Espressif ESP-EYE (the closest reference available), with a 240KHz clock (same as our device), but with a smaller CPU ( XTensa LX6, versus the LX7 on the ESP32S). The real inference time should be smaller. + +Suppose we need to reduce the inference time later. In that case, we should return to the pre-processing stage and, for example, reduce the FFT length to 256, change the Number of coefficients, or another parameter. + +For now, let's keep the parameters defined by the Autotuning tool. Save parameters and generate the features. + +![Pasted Graphic 54.png](https://hackster.imgix.net/uploads/attachments/1595159/pasted_graphic_54_ejdOEShDDa.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> If you want to go further with converting temporal serial data into images using FFT, Spectrogram, etc., you can play with this CoLab: [IESTI01_Audio_Raw_Data_Analisys.ipynb.](https://colab.research.google.com/github/Mjrovai/UNIFEI-IESTI01-TinyML-2022.1/blob/main/00_Curse_Folder/2_Applications_Deploy/Class_24/IESTI01_Audio_Raw_Data_Analisys.ipynb) + +### Model Design and Training + +We will use a Convolution Neural Network (CNN) model. The basic architecture is defined with two blocks of Conv1D + MaxPooling (with 8 and 16 neurons, respectively) and a 0.25 Dropout. And on the last layer, after Flattening four neurons, one for each class: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595163/image_tLZhhkaWgS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +As hyper-parameters, we will have a Learning Rate of 0.005 and a model that will be trained by 100 epochs. We will also include data augmentation, as some noise. The result seems OK: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595165/image_iJtkzDOJ11.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +If you want to understand what is happening "under the hood, " you can download the dataset and run a Jupyter Notebook playing with the code. For example, you can analyze the accuracy by each epoch: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595193/image_wi6KMb5EcS.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This CoLab Notebook can explain how you can go further: [KWS Classifier Project - Looking “Under the hood](https://colab.research.google.com/github/Mjrovai/XIAO-ESP32S3-Sense/blob/main/KWS Training/xiao_esp32s3_keyword_spotting_project_nn_classifier.ipynb).” + +## Testing + +Testing the model with the data put apart before training (Test Data), we got an accuracy of approximately 87%. + +![Pasted Graphic 58.png](https://hackster.imgix.net/uploads/attachments/1595225/pasted_graphic_58_TmPGA8iljK.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Inspecting the F1 score, we can see that for YES. We got 0.95, an excellent result once we used this keyword to "trigger" our postprocessing stage (turn on the built-in LED). Even for NO, we got 0.90. The worst result is for unknown, what is OK. + +We can proceed with the project, but it is possible to perform Live Classification using a smartphone before deployment on our device. Go to the Live Classification section and click on Connect a Development board: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595226/image_7MfzDDxs1C.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Point your phone to the barcode and select the link. + +![image.png](https://hackster.imgix.net/uploads/attachments/1595229/image_dGusVuQ6HI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Your phone will be connected to the Studio. Select the option Classification on the app, and when it is running, start testing your keywords, confirming that the model is working with live and real data: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595228/image_jVLeBB4tbk.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Deploy and Inference + +The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option Arduino Library, and at the bottom, choose Quantized (Int8) and press the button Build. + +![Pasted Graphic 59.png](https://hackster.imgix.net/uploads/attachments/1595230/pasted_graphic_59_SdCzZ80grw.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now it is time for a real test. We will make inferences wholly disconnected from the Studio. Let's change one of the ESP32 code examples created when you deploy the Arduino Library. + +In your Arduino IDE, go to the File/Examples tab look for your project, and select esp32/esp32_microphone: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595434/image_o2IC7U796n.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +This code was created for the ESP-EYE built-in microphone, which should be adapted for our device. + +Start changing the libraries to handle the I2S bus: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595435/image_APjcWclO6P.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +#include +#define SAMPLE_RATE 16000U +#define SAMPLE_BITS 16 +``` + +Initialize the IS2 microphone at setup(), including the lines: + +``` +void setup() +{ +... + I2S.setAllPins(-1, 42, 41, -1, -1); + if (!I2S.begin(PDM_MONO_MODE, SAMPLE_RATE, SAMPLE_BITS)) { + Serial.println("Failed to initialize I2S!"); + while (1) ; +... +} +``` + +On the static void capture_samples(void* arg) function, replace the line 153 that reads data from I2S mic: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595437/image_lQtCch3Ptw.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +/* read data at once from i2s */ +esp_i2s::i2s_read(esp_i2s::I2S_NUM_0, (void*)sampleBuffer, i2s_bytes_to_read, &bytes_read, 100); +``` + +On function static bool microphone_inference_start(uint32_t n_samples), we should comment or delete lines 198 to 200, where the microphone initialization function is called. This is unnecessary because the I2S microphone was already initialized during the setup(). + +![image.png](https://hackster.imgix.net/uploads/attachments/1595444/image_8G6p7WF9ga.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Finally, on static void microphone_inference_end(void) function, replace line 243: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595438/image_jjY4COA0DE.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +By: + +``` +static void microphone_inference_end(void) +{ + free(sampleBuffer); + ei_free(inference.buffer); +} +``` + +You can find the complete code on the [project's GitHub](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_microphone). Upload the sketch to your board and test some real inferences: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595484/image_iPcCPucH2k.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Postprocessing + +Now that we know the model is working by detecting our keywords, let's modify the code to see the internal LED going on every time a YES is detected. + +You should initialize the LED: + +``` +#define LED_BUILT_IN 21 +... +void setup() +{ +... + pinMode(LED_BUILT_IN, OUTPUT); // Set the pin as output + digitalWrite(LED_BUILT_IN, HIGH); //Turn off +... +} +``` + +And change the // print the predictions portion of the previous code (on loop(): + +``` +int pred_index = 0; // Initialize pred_index +float pred_value = 0; // Initialize pred_value + +// print the predictions +ei_printf("Predictions "); +ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)", + result.timing.dsp, result.timing.classification, result.timing.anomaly); +ei_printf(": \n"); +for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) { + ei_printf(" %s: ", result.classification[ix].label); + ei_printf_float(result.classification[ix].value); + ei_printf("\n"); + + if (result.classification[ix].value > pred_value){ + pred_index = ix; + pred_value = result.classification[ix].value; + } +} + +// show the inference result on LED +if (pred_index == 3){ + digitalWrite(LED_BUILT_IN, LOW); //Turn on +} +else{ + digitalWrite(LED_BUILT_IN, HIGH); //Turn off +} +``` + +You can find the complete code on the [project's GitHub.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/xiao_esp32s3_microphone_led) Upload the sketch to your board and test some real inferences: + +![image.png](https://hackster.imgix.net/uploads/attachments/1595542/image_UTzc7GrWWp.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +The idea is that the LED will be ON whenever the keyword YES is detected. In the same way, instead of turning on an LED, this could be a "trigger" for an external device, as we saw in the introduction. + + + +## Conclusion + +The Seeed XIAO ESP32S3 Sense is a *giant tiny device*! However, it is powerful, trustworthy, not expensive, low power, and has suitable sensors to be used on the most common embedded machine learning applications such as vision and sound. Even though Edge Impulse does not officially support XIAO ESP32S3 Sense (yet!), we realized that using the Studio for training and deployment is straightforward. + +> On my [GitHub repository](https://github.com/Mjrovai/XIAO-ESP32S3-Sense), you will find the last version all the codes used on this project and the previous ones of the XIAO ESP32S3 series. + +Before we finish, consider that Sound Classification is more than just voice. For example, you can develop TinyML projects around sound in several areas, such as: + +- **Security** (Broken Glass detection) +- **Industry** (Anomaly Detection) +- **Medical** (Snore, Toss, Pulmonary diseases) +- **Nature** (Beehive control, insect sound) \ No newline at end of file diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/anomaly-inference.png b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/anomaly-inference.png new file mode 100644 index 00000000..f6408777 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/anomaly-inference.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/idle-inference.png b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/idle-inference.png new file mode 100644 index 00000000..3bb3b90a Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/idle-inference.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.avif b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.avif new file mode 100644 index 00000000..aa398ddb Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.avif differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.png b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.png new file mode 100644 index 00000000..aa398ddb Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/lift-inference.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/maritime-inference.png b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/maritime-inference.png new file mode 100644 index 00000000..7ed4a5a3 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/maritime-inference.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/images/terrestrial-inference.png b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/terrestrial-inference.png new file mode 100644 index 00000000..b397bafe Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/motion_classification/images/terrestrial-inference.png differ diff --git a/contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.bib b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.bib similarity index 100% rename from contents/labs/arduino/nicla_vision/motion_classify_ad/motion_classify_ad.bib rename to contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.bib diff --git a/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd new file mode 100644 index 00000000..1ac69dba --- /dev/null +++ b/contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd @@ -0,0 +1,484 @@ +# Motion Classification and Anomaly Detection {.unnumbered} + +![*DALL·E prompt - 1950s style cartoon illustration set in a vintage audio lab. Scientists, dressed in classic attire with white lab coats, are intently analyzing audio data on large chalkboards. The boards display intricate FFT (Fast Fourier Transform) graphs and time-domain curves. Antique audio equipment is scattered around, but the data representations are clear and detailed, indicating their focus on audio analysis.*](./images/jpeg/motion_class_ad/ini.jpg){fig-align="center" width="6.5in"} + +## Introduction + +The XIAO ESP32S3 Sense, with its built-in camera and mic, is a versatile device. But what if you need to add another type of sensor, such as an IMU? No problem! One of the standout features of the XIAO ESP32S3 is its multiple pins that can be used as an I2C bus (SDA/SCL pins), making it a suitable platform for sensor integration. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590599/image_GstFLMyDUy.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Installing the IMU + +When selecting your IMU, the market offers a wide range of devices, each with unique features and capabilities. You could choose, for example, the ADXL362 (3-axis), MAX21100 (6-axis), MPU6050 (6-axis), LIS3DHTR (3-axis), or the LCM20600Seeed Grove— (6-axis), which is part of the IMU 9DOF (lcm20600+AK09918). This variety allows you to tailor your choice to your project's specific needs. + +For this project, we will use an IMU, the MPU6050 (or 6500), a low-cost (less than 2.00 USD) 6-axis Accelerometer/Gyroscope unit. + +> At the end of the lab, we will also comment on using the LCM20600. + +The [MPU-6500](https://invensense.tdk.com/download-pdf/mpu-6500-datasheet/) is a 6-axis Motion Tracking device that combines a 3-axis gyroscope, 3-axis accelerometer, and a Digital Motion ProcessorTM (DMP) in a small 3x3x0.9mm package. It also features a 4096-byte FIFO that can lower the traffic on the serial bus interface and reduce power consumption by allowing the system processor to burst read sensor data and then go into a low-power mode. + +With its dedicated I2C sensor bus, the MPU-6500 directly accepts inputs from external I2C devices. MPU-6500, with its 6-axis integration, on-chip DMP, and run-time calibration firmware, enables manufacturers to eliminate the costly and complex selection, qualification, and system-level integration of discrete devices, guaranteeing optimal motion performance for consumers. MPU-6500 is also designed to interface with multiple non-inertial digital sensors, such as pressure sensors, on its auxiliary I2C port. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590608/image_ZFuJgZIdRi.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Usually, the libraries available are for MPU6050, but they work for both devices. + +**Connecting the HW** + +Connect the IMU to the XIAO according to the below diagram: + +- MPU6050 **SCL** --> XIAO **D5** +- MPU6050 **SDA** --> XIAO **D4** +- MPU6050 **VCC** --> XIAO **3.3V** +- MPU6050 **GND** --> XIAO **GND** + +![Drawing.png](https://hackster.imgix.net/uploads/attachments/1590645/drawing_Vp4G8xChAB.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +**Install the Library** + +Go to Arduino Library Manager and type MPU6050. Install the latest version. + +![Pasted Graphic 16.png](https://hackster.imgix.net/uploads/attachments/1590642/pasted_graphic_16_CH1rHB6s2M.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Download the sketch [MPU6050_Acc_Data_Acquisition.in](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/MPU6050_Acc_Data_Acquisition): + +``` +/* + * Based on I2C device class (I2Cdev) Arduino sketch for MPU6050 class by Jeff Rowberg + * and Edge Impulse Data Forwarder Exampe (Arduino) - https://docs.edgeimpulse.com/docs/cli-data-forwarder + * + * Developed by M.Rovai @11May23 + */ + +#include "I2Cdev.h" +#include "MPU6050.h" +#include "Wire.h" + +#define FREQUENCY_HZ 50 +#define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) +#define ACC_RANGE 1 // 0: -/+2G; 1: +/-4G + +// convert factor g to m/s2 ==> [-32768, +32767] ==> [-2g, +2g] +#define CONVERT_G_TO_MS2 (9.81/(16384.0/(1.+ACC_RANGE))) + +static unsigned long last_interval_ms = 0; + +MPU6050 imu; +int16_t ax, ay, az; + +void setup() { + + Serial.begin(115200); + + + // initialize device + Serial.println("Initializing I2C devices..."); + Wire.begin(); + imu.initialize(); + delay(10); + +// // verify connection +// if (imu.testConnection()) { +// Serial.println("IMU connected"); +// } +// else { +// Serial.println("IMU Error"); +// } + delay(300); + + //Set MCU 6050 OffSet Calibration + imu.setXAccelOffset(-4732); + imu.setYAccelOffset(4703); + imu.setZAccelOffset(8867); + imu.setXGyroOffset(61); + imu.setYGyroOffset(-73); + imu.setZGyroOffset(35); + + /* Set full-scale accelerometer range. + * 0 = +/- 2g + * 1 = +/- 4g + * 2 = +/- 8g + * 3 = +/- 16g + */ + imu.setFullScaleAccelRange(ACC_RANGE); +} + +void loop() { + + if (millis() > last_interval_ms + INTERVAL_MS) { + last_interval_ms = millis(); + + // read raw accel/gyro measurements from device + imu.getAcceleration(&ax, &ay, &az); + + // converting to m/s2 + float ax_m_s2 = ax * CONVERT_G_TO_MS2; + float ay_m_s2 = ay * CONVERT_G_TO_MS2; + float az_m_s2 = az * CONVERT_G_TO_MS2; + + Serial.print(ax_m_s2); + Serial.print("\t"); + Serial.print(ay_m_s2); + Serial.print("\t"); + Serial.println(az_m_s2); + } +} +``` + +**Some comments about the code:** + +Note that the values generated by the accelerometer and gyroscope have a range: [-32768, +32767], so for example, if the default accelerometer range is used, the range in Gs should be: [-2g, +2g]. So, "1G" means 16384. + +For conversion to m/s2, for example, you can define the following: + +``` +#define CONVERT_G_TO_MS2 (9.81/16384.0) +``` + +In the code, I left an option (ACC_RANGE) to be set to 0 (+/-2G) or 1 (+/- 4G). We will use +/-4G; that should be enough for us. In this case. + +We will capture the accelerometer data on a frequency of 50Hz, and the acceleration data will be sent to the Serial Port as meters per squared second (m/s2). + +When you ran the code with the IMU resting over your table, the accelerometer data shown on the Serial Monitor should be around 0.00, 0.00, and 9.81. If the values are a lot different, you should calibrate the IMU. + +The MCU6050 can be calibrated using the sketch: [mcu6050-calibration.ino](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/mcu6050-calibration). + +Run the code. The following will be displayed on the Serial Monitor: + +![Pasted Graphic 19.png](https://hackster.imgix.net/uploads/attachments/1590654/pasted_graphic_19_FhU4qX0dLU.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Send any character (in the above example, "x"), and the calibration should start. + +> Note that A message MPU6050 connection failed. Ignore this message. For some reason, imu.testConnection() is not returning a correct result. + +In the end, you will receive the offset values to be used on all your sketches: + +![Pasted Graphic 20.png](https://hackster.imgix.net/uploads/attachments/1590656/pasted_graphic_20_Tui5mRNqOL.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Take the values and use them on the setup: + +``` +//Set MCU 6050 OffSet Calibration +imu.setXAccelOffset(-4732); +imu.setYAccelOffset(4703); +imu.setZAccelOffset(8867); +imu.setXGyroOffset(61); +imu.setYGyroOffset(-73); +imu.setZGyroOffset(35); +``` + +Now, run the sketch [MPU6050_Acc_Data_Acquisition.in:](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/MPU6050_Acc_Data_Acquisition) + +Once you run the above sketch, open the Serial Monitor: + +![Pasted Graphic 21.png](https://hackster.imgix.net/uploads/attachments/1590659/pasted_graphic_21_DTRap3UbE7.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Or check the Plotter: + +![Pasted Graphic 23.png](https://hackster.imgix.net/uploads/attachments/1590660/pasted_graphic_23_hM0BpXdmeI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Move your device in the three axes. You should see the variation on Plotter: + +![Pasted Graphic 22.png](https://hackster.imgix.net/uploads/attachments/1590661/pasted_graphic_22_qOS34YmKic.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## The TinyML Motion Classification Project + +For our lab, we will simulate mechanical stresses in transport. Our problem will be to classify four classes of movement: + +- **Maritime** (pallets in boats) +- **Terrestrial** (palettes in a Truck or Train) +- **Lift** (Palettes being handled by Fork-Lift) +- **Idle** (Palettes in Storage houses) + +So, to start, we should collect data. Then, accelerometers will provide the data on the palette (or container). + +![img](https://hackster.imgix.net/uploads/attachments/1590536/data1_sg5MS6KfkM.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +From the above images, we can see that primarily horizontal movements should be associated with the "Terrestrial class, " Vertical movements with the "Lift Class, " no activity with the "Idle class, " and movement on all three axes to [Maritime class.](https://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.htm) + +## Connecting the device to Edge Impulse + +For data collection, we should first connect our device to the Edge Impulse Studio, which will also be used for data pre-processing, model training, testing, and deployment. + +> Follow the instructions [here ](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation)to install the [Node.js ](https://nodejs.org/en/)and Edge Impulse CLI on your computer. + +Once the XIAO ESP32S3 is not a fully supported development board by Edge Impulse, we should, for example, use the [CLI Data Forwarder t](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder)o capture data from our sensor and send it to the Studio, as shown in this diagram: + +![img](https://hackster.imgix.net/uploads/attachments/1590537/image_PHK0GELEYh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> You can alternately capture your data "offline," store them on an SD card or send them to your computer via Bluetooth or Wi-Fi. In this [video](https://youtu.be/2KBPq_826WM), you can learn alternative ways to send data to the Edge Impulse Studio. + +Connect your device to the serial port and run the previous code to capture IMU (Accelerometer) data, "printing them" on the serial. This will allow the Edge Impulse Studio to "capture" them. + +Go to the Edge Impulse page and create a project. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590663/image_xUyC0uWhnG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> The maximum length for an Arduino library name is **63 characters**. Note that the Studio will name the final library using your project name and include “_inference” to it. The name I chose initially did not work when I tried to deploy the Arduino library because it resulted in 64 characters. So, I need to change it by taking out the “anomaly detection” part. + +Start the [CLI Data Forwarder ](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder)on your terminal, entering (if it is the first time) the following command: + +$ edge-impulse-data-forwarder --clean + +``` +$ edge-impulse-data-forwarder --clean +``` + +Next, enter your EI credentials and choose your project, variables, and device names: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590664/image_qkRsm7A981.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Go to your EI Project and verify if the device is connected (the dot should be green): + +![image.png](https://hackster.imgix.net/uploads/attachments/1590667/image_a5J303wHbE.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Data Collection + +As discussed before, we should capture data from all four Transportation Classes. Imagine that you have a container with a built-in accelerometer: + +![boat.jpg](https://hackster.imgix.net/uploads/attachments/1591091/boat_aOqDzqArqs.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now imagine your container is on a boat, facing an angry ocean, on a truck, etc.: + +- **Maritime** (pallets in boats) + - Move the XIAO in all directions, simulating an undulatory boat movement. + +- **Terrestrial** (palettes in a Truck or Train) + - Move the XIAO over a horizontal line. + +- **Lift** (Palettes being handled by + - Move the XIAO over a vertical line. + +- **Idle** (Palettes in Storage houses) + - Leave the XIAO over the table. + +![idle.jpg](https://hackster.imgix.net/uploads/attachments/1590677/idle_OiZWwciVVh.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +Below is one sample (raw data) of 10 seconds: + +![img](https://hackster.imgix.net/uploads/attachments/1590541/image_E3mFL7tvSh.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +You can capture, for example, 2 minutes (twelve samples of 10 seconds each) for the four classes. Using the "3 dots" after each one of the samples, select 2, moving them for the Test set (or use the automatic Train/Test Split tool on the Danger Zone of Dashboard tab). Below, you can see the result datasets: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590679/image_WB3eKzzN6R.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Data Pre-Processing + +The raw data type captured by the accelerometer is a "time series" and should be converted to "tabular data". We can do this conversion using a sliding window over the sample data. For example, in the below figure, + +![image.png](https://hackster.imgix.net/uploads/attachments/1590693/image_KQNIPcxqXV.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +We can see 10 seconds of accelerometer data captured with a sample rate (SR) of 50Hz. A 2-second window will capture 300 data points (3 axis x 2 seconds x 50 samples). We will slide this window each 200ms, creating a larger dataset where each instance has 300 raw features. + +> You should use the best SR for your case, considering Nyquist's theorem, which states that a periodic signal must be sampled at more than twice the signal's highest frequency component. + +Data preprocessing is a challenging area for embedded machine learning. Still, Edge Impulse helps overcome this with its digital signal processing (DSP) preprocessing step and, more specifically, the Spectral Features. + +On the Studio, this dataset will be the input of a Spectral Analysis block, which is excellent for analyzing repetitive motion, such as data from accelerometers. This block will perform a DSP (Digital Signal Processing), extracting features such as "FFT" or "Wavelets". In the most common case, FFT, the **Time Domain Statistical features** per axis/channel are: + +- RMS +- Skewness +- Kurtosis + +And the **Frequency Domain Spectral features** per axis/channel are: + +- Spectral Power +- Skewness +- Kurtosis + +So, for example, for an FFT length of 32 points, the Spectral Analysis Block's resulting output will be 21 features per axis (a total of 63 features). + +Those 63 features will be the Input Tensor of a Neural Network Classifier and the Anomaly Detection model (K-Means). + +> You can learn more by digging into the lab *DSP - Spectral Features* + +## Model Design + +Our classifier will be a Dense Neural Network (DNN) that will have 63 neurons on its input layer, two hidden layers with 20 and 10 neurons, and an output layer with four neurons (one per each class), as shown here: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590702/image_ojSbkXrKse.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Impulse Design + +An impulse takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data. + +We also take advantage of a second model, the K-means, that can be used for Anomaly Detection. If we imagine that we could have our known classes as clusters, any sample that could not fit on that could be an outlier, an anomaly (for example, a container rolling out of a ship on the ocean). + +![img](https://hackster.imgix.net/uploads/attachments/1590547/image_pFnNVK4Wjc.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +> Imagine our XIAO rolling or moving upside-down, on a movement complement different from the one trained + +![img](https://hackster.imgix.net/uploads/attachments/1590548/image_iW1ygppsHi.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Below is our final Impulse design: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590696/image_W8xMffuTwP.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Generating features + +At this point in our project, we have defined the pre-processing method and the model designed. Now, it is time to have the job done. First, let's take the raw data (time-series type) and convert it to tabular data. Go to the Spectral Features tab and select Save Parameters: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590697/image_bsHjHtleGs.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +At the top menu, select the Generate Features option and the Generate Features button. Each 2-second window data will be converted into one data point of 63 features. + +> The Feature Explorer will show those data in 2D using [UMAP.](https://umap-learn.readthedocs.io/en/latest/) Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE but also for general non-linear dimension reduction. + +The visualization allows one to verify that the classes present an excellent separation, which indicates that the classifier should work well. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590706/image_fyynJu1laN.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Optionally, you can analyze the relative importance of each feature for one class compared with other classes. + +## Training + +Our model has four layers, as shown below: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590707/image_0M4u1e4dJI.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +As hyperparameters, we will use a Learning Rate of 0.005 and 20% of data for validation for 30 epochs. After training, we can see that the accuracy is 97%. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590709/image_cCscB5HMw9.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +For anomaly detection, we should choose the suggested features that are precisely the most important in feature extraction. The number of clusters will be 32, as suggested by the Studio: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590710/image_8IOqOw1yoX.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Testing + +Using 20% of the data left behind during the data capture phase, we can verify how our model will behave with unknown data; if not 100% (what is expected), the result was not that good (8%), mainly due to the terrestrial class. Once we have four classes (which output should add 1.0), we can set up a lower threshold for a class to be considered valid (for example, 0.4): + +![image.png](https://hackster.imgix.net/uploads/attachments/1590714/image_ecSV5fIlPu.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Now, the Test accuracy will go up to 97%. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590715/image_TnLYYt60Vc.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +You should also use your device (which is still connected to the Studio) and perform some Live Classification. + +> Be aware that here you will capture real data with your device and upload it to the Studio, where an inference will be taken using the trained model (But the model is NOT in your device). + +## Deploy + +Now it is time for magic˜! The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option Arduino Library, and at the bottom, choose Quantized (Int8) and Build. A Zip file will be created and downloaded to your computer. + +![image.png](https://hackster.imgix.net/uploads/attachments/1590716/image_d5jrYgBErG.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +On your Arduino IDE, go to the Sketch tab, select the option Add.ZIP Library, and Choose the.zip file downloaded by the Studio: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590717/image_6w7t1NYsBV.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +## Inference + +Now, it is time for a real test. We will make inferences that are wholly disconnected from the Studio. Let's change one of the code examples created when you deploy the Arduino Library. + +In your Arduino IDE, go to the File/Examples tab and look for your project, and on examples, select nano_ble_sense_accelerometer: + +![image.png](https://hackster.imgix.net/uploads/attachments/1590718/image_M3k3wqDRto.png?auto=compress%2Cformat&w=740&h=555&fit=max) + +Of course, this is not your board, but we can have the code working with only a few changes. + +For example, at the beginning of the code, you have the library related to Arduino Sense IMU: + +``` +/* Includes --------------------------------------------------------------- */ +#include +#include +``` + +Change the "includes" portion with the code related to the IMU: + +``` +#include +#include "I2Cdev.h" +#include "MPU6050.h" +#include "Wire.h" +``` + +Change the Constant Defines + +``` +/* Constant defines ------------------------------------------------------- */ +MPU6050 imu; +int16_t ax, ay, az; + +#define ACC_RANGE 1 // 0: -/+2G; 1: +/-4G +#define CONVERT_G_TO_MS2 (9.81/(16384/(1.+ACC_RANGE))) +#define MAX_ACCEPTED_RANGE (2*9.81)+(2*9.81)*ACC_RANGE +``` + +On the setup function, initiate the IMU set the off-set values and range: + +``` +// initialize device +Serial.println("Initializing I2C devices..."); +Wire.begin(); +imu.initialize(); +delay(10); + +//Set MCU 6050 OffSet Calibration +imu.setXAccelOffset(-4732); +imu.setYAccelOffset(4703); +imu.setZAccelOffset(8867); +imu.setXGyroOffset(61); +imu.setYGyroOffset(-73); +imu.setZGyroOffset(35); + +imu.setFullScaleAccelRange(ACC_RANGE); +``` + +At the loop function, the buffers buffer[ix], buffer[ix + 1], and buffer[ix + 2] will receive the 3-axis data captured by the accelerometer. On the original code, you have the line: + +``` +IMU.readAcceleration(buffer[ix], buffer[ix + 1], buffer[ix + 2]); +``` + +Change it with this block of code: + +``` +imu.getAcceleration(&ax, &ay, &az); +buffer[ix + 0] = ax; +buffer[ix + 1] = ay; +buffer[ix + 2] = az; +``` + +You should change the order of the following two blocks of code. First, you make the conversion to raw data to "Meters per squared second (ms2)", followed by the test regarding the maximum acceptance range (that here is in ms2, but on Arduino, was in Gs): + +``` +buffer[ix + 0] *= CONVERT_G_TO_MS2; +buffer[ix + 1] *= CONVERT_G_TO_MS2; +buffer[ix + 2] *= CONVERT_G_TO_MS2; + +for (int i = 0; i < 3; i++) { + if (fabs(buffer[ix + i]) > MAX_ACCEPTED_RANGE) { + buffer[ix + i] = ei_get_sign(buffer[ix + i]) * MAX_ACCEPTED_RANGE; + } +} +``` + +And that is it! You can now upload the code to your device and proceed with the inferences. The complete code is available on the [project's GitHub](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU). + +Now you should try your movements, seeing the result of the inference of each class on the images: + +![](./images/png/motion_class_ad/idle-inference.png) + +![](./images/png/motion_class_ad/terrestrial-inference.png) + +![](./images/png/motion_class_ad/lift-inference.png) + +![](./images/png/motion_class_ad/maritime-inference.png) + +And of course some "anomaly", for example, puting the XIAO upside-down. The anomaly score will be over 1: + +![](./images/png/motion_class_ad/anomaly-inference.png) + +## Conclusion. + +Regarding the IMU, this project used the low-cost MPU6050 but could also use other IMUs, for example, the LCM20600 (6-axis), which is part of the [Seeed Grove - IMU 9DOF (lcm20600+AK09918)](https://wiki.seeedstudio.com/Grove-IMU_9DOF-lcm20600+AK09918/). You can took advantage of this senso, witch has integrated a Grove connector, which can be helpful in the case you use the [XIAO with an extension board](https://wiki.seeedstudio.com/Seeeduino-XIAO-Expansion-Board/), as shown below: + +![Grove-ICM2060-small.jpg](https://hackster.imgix.net/uploads/attachments/1591025/grove-icm2060-small_plZuu0oQ5W.jpg?auto=compress%2Cformat&w=740&h=555&fit=max) + +You can follow the instructions [here](https://wiki.seeedstudio.com/Grove-IMU_9DOF-lcm20600+AK09918/#specification) to connect the IMU with the MCU. Only note that for using the Grove ICM20600 Accelerometer, it is essential to update the files **I2Cdev.cpp** and **I2Cdev.h** that you will download from the [library provided by Seeed Studio](https://github.com/Seeed-Studio/Seeed_ICM20600_AK09918). For that, replace both files from this [link](https://github.com/jrowberg/i2cdevlib/tree/master/Arduino/I2Cdev). You can find on the GitHub project a sketch for testing the IMU: [accelerometer_test.ino](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU/accelerometer_test). + +> On the projet's GitHub repository, you will find the last version of all codes and other docs: [XIAO-ESP32S3 - IMU](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/IMU). \ No newline at end of file diff --git a/contents/labs/seeed/xiao_esp32s3/setup/images/app.png b/contents/labs/seeed/xiao_esp32s3/setup/images/app.png new file mode 100644 index 00000000..9ff5ac3c Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/setup/images/app.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/setup/images/app2.png b/contents/labs/seeed/xiao_esp32s3/setup/images/app2.png new file mode 100644 index 00000000..130a9e21 Binary files /dev/null and b/contents/labs/seeed/xiao_esp32s3/setup/images/app2.png differ diff --git a/contents/labs/seeed/xiao_esp32s3/setup/images/blink.png 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The board is placed on a classic lab table with various sensors, including a microphone. Behind the board, a vintage computer screen displays the Arduino IDE in muted colors, with code focusing on LED pin setups and machine learning inference for voice commands. The Serial Monitor on the IDE showcases outputs detecting voice commands like 'yes' and 'no'. The scene merges the retro charm of mid-century labs with modern electronics.*](./images/jpeg/imags-setup/xiao_setup.jpg){fig-align="center" width="6.5in"} + +## Introduction + +The [XIAO ESP32S3 Sense](https://www.seeedstudio.com/XIAO-ESP32S3-Sense-p-5639.html) is Seeed Studio's affordable development board, which integrates a camera sensor, digital microphone, and SD card support. Combining embedded ML computing power and photography capability, this development board is a great tool to start with TinyML (intelligent voice and vision AI). + +![](./images/png/imags-setup/xiao.png) + +**XIAO ESP32S3 Sense Main Features** + +- **Powerful MCU Board**: Incorporate the ESP32S3 32-bit, dual-core, Xtensa processor chip operating up to 240 MHz, mounted multiple development ports, Arduino / MicroPython supported +- **Advanced Functionality**: Detachable OV2640 camera sensor for 1600 * 1200 resolution, compatible with OV5640 camera sensor, integrating an additional digital microphone +- **Elaborate Power Design**: Lithium battery charge management capability offers four power consumption models, which allows for deep sleep mode with power consumption as low as 14μA +- **Great Memory for more Possibilities**: Offer 8MB PSRAM and 8MB FLASH, supporting SD card slot for external 32GB FAT memory +- **Outstanding RF performance**: Support 2.4GHz Wi-Fi and BLE dual wireless communication, support 100m+ remote communication when connected with U.FL antenna +- **Thumb-sized Compact Design**: 21 x 17.5mm, adopting the classic form factor of XIAO, suitable for space-limited projects like wearable devices + +![](./images/png/imags-setup/xiao_pins.png) + + +Below is the general board pinout: + +![](./images/png/imags-setup/xiao_esp32c3_sense_pin-out.png) + +> For more details, please refer to the Seeed Studio WiKi page:
+> https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/ + +## Installing the XIAO ESP32S3 Sense on Arduino IDE + +On Arduino IDE, navigate to **File > Preferences**, and fill in the URL: + +[*https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json*](https://raw.githubusercontent.com/espressif/arduino-esp32/gh-pages/package_esp32_dev_index.json) + +on the field ==> **Additional Boards Manager URLs** + +![](./images/png/imags-setup/board_manag.png) + +Next, open boards manager. Go to **Tools** > **Board** > **Boards Manager...** and enter with *esp32.* Select and install the most updated and stable package (avoid *alpha* versions) : + +![](./images/png/imags-setup/board_manag2.png) + +> ⚠️ **Attention** +> +> Alpha versions (for example, 3.x-alpha) do not work correctly with the XIAO and Edge Impulse. Use the last stable version (for example, 2.0.11) instead. + +On **Tools**, select the Board (**XIAO ESP32S3**): + +![](./images/png/imags-setup/tools_board.png) + +Last but not least, choose the **Port** where the ESP32S3 is connected. + +That is it! The device should be OK. Let's do some tests. + +## Testing the board with BLINK + +The XIAO ESP32S3 Sense has a built-in LED that is connected to GPIO21. So, you can run the blink sketch as it is (using the `LED_BUILTIN` Arduino constant) or by changing the Blink sketch accordingly: + +```cpp +#define LED_BUILT_IN 21 + +void setup() { + pinMode(LED_BUILT_IN, OUTPUT); // Set the pin as output +} + +// Remember that the pin work with inverted logic +// LOW to Turn on and HIGH to turn off +void loop() { + digitalWrite(LED_BUILT_IN, LOW); //Turn on + delay (1000); //Wait 1 sec + digitalWrite(LED_BUILT_IN, HIGH); //Turn off + delay (1000); //Wait 1 sec +} +``` + +> Note that the pins work with inverted logic: LOW to Turn on and HIGH to turn off. + +![](./images/png/imags-setup/blink.png) + +## Connecting Sense module (Expansion Board) + +When purchased, the expansion board is separated from the main board, but installing the expansion board is very simple. You need to align the connector on the expansion board with the B2B connector on the XIAO ESP32S3, press it hard, and when you hear a "click," the installation is complete. + +As commented in the introduction, the expansion board, or the "sense" part of the device, has a 1600x1200 OV2640 camera, an SD card slot, and a digital microphone. + +## Microphone Test + +Let's start with sound detection. Go to the [GitHub project](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) and download the sketch: [XIAOEsp2s3_Mic_Test](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/XiaoEsp32s3_Mic_Test) and run it on the Arduino IDE: + +![](./images/png/imags-setup/sound_test.png) + +When producing sound, you can verify it on the Serial Plotter. + +**Save recorded sound (.wav audio files) to a microSD card.** + +Now, the onboard SD Card reader can save .wav audio files. To do that, we need to habilitate the XIAO PSRAM. + +> ESP32-S3 has only a few hundred kilobytes of internal RAM on the MCU chip. This can be insufficient for some purposes, so up to 16 MB of external PSRAM (pseudo-static RAM) can be connected with the SPI flash chip. The external memory is incorporated in the memory map and, with certain restrictions, is usable in the same way as internal data RAM. + +For a start, Insert the SD Card on the XIAO as shown in the photo below (the SD Card should be formatted to **FAT32**). + +![](./images/png/imags-setup/sdcard.png) + +- Download the sketch [Wav_Record](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Mic_Test/Wav_Record), which you can find on GitHub. +- To execute the code (Wav Record), it is necessary to use the PSRAM function of the ESP-32 chip, so turn it on before uploading.: Tools\>PSRAM: "OPI PSRAM"\>OPI PSRAM + +![](./images/png/imags-setup/psram.png) + +- Run the code `Wav_Record.ino` +- This program is executed only once after the user **turns on the serial monitor. It records for 20 seconds and saves the recording file to a microSD card as "arduino_rec.wav." +- When the "." is output every 1 second in the serial monitor, the program execution is finished, and you can play the recorded sound file with the help of a card reader. + +![](./images/png/imags-setup/rec.png) + +The sound quality is excellent! + +> The explanation of how the code works is beyond the scope of this tutorial, but you can find an excellent description on the [wiki](https://wiki.seeedstudio.com/xiao_esp32s3_sense_mic#save-recorded-sound-to-microsd-card) page. + +## Testing the Camera + +To test the camera, you should download the folder [take_photos_command](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/take_photos_command) from GitHub. The folder contains the sketch (.ino) and two .h files with camera details. + +- Run the code: `take_photos_command.ino`. Open the Serial Monitor and send the command `capture` to capture and save the image on the SD Card: + +> Verify that `[Both NL & CR]` are selected on Serial Monitor. + +![](./images/png/imags-setup/pic_capture.png) + +Here is an example of a taken photo: + +![](./images/png/imags-setup/image_test.png) + +## Testing WiFi + +One of the XIAO ESP32S3's differentiators is its WiFi capability. So, let's test its radio by scanning the Wi-Fi networks around it. You can do this by running one of the code examples on the board. + +Go to Arduino IDE Examples and look for **WiFI ==\> WiFIScan** + +You should see the Wi-Fi networks (SSIDs and RSSIs) within your device's range on the serial monitor. Here is what I got in the lab: + +![](./images/png/imags-setup/wifi.png) + +**Simple WiFi Server (Turning LED ON/OFF)** + +Let's test the device's capability to behave as a WiFi Server. We will host a simple page on the device that sends commands to turn the XIAO built-in LED ON and OFF. + +Like before, go to GitHub to download the folder using the sketch [SimpleWiFiServer](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/SimpleWiFiServer). + +Before running the sketch, you should enter your network credentials: + +```cpp +const char* ssid = "Your credentials here"; +const char* password = "Your credentials here"; +``` + +You can monitor how your server is working with the Serial Monitor. + +![](./images/png/imags-setup/wifi-2.png) + +Take the IP address and enter it on your browser: + +![](./images/png/imags-setup/app.png) + +You will see a page with links that can turn the built-in LED of your XIAO ON and OFF. + +**Streaming video to Web** + +Now that you know that you can send commands from the webpage to your device, let's do the reverse. Let's take the image captured by the camera and stream it to a webpage: + +Download from GitHub the [folder](https://github.com/Mjrovai/XIAO-ESP32S3-Sense/tree/main/Streeming_Video) that contains the code: XIAO-ESP32S3-Streeming_Video.ino. + +> Remember that the folder contains the.ino file and a couple of .h files necessary to handle the camera. + +Enter your credentials and run the sketch. On the Serial monitor, you can find the page address to enter in your browser: + +![](./images/png/imags-setup/wifi3.png) + +Open the page on your browser (wait a few seconds to start the streaming). That's it. + +![](./images/png/imags-setup/image_web.png) + +Streamlining what your camera is "seen" can be important when you position it to capture a dataset for an ML project (for example, using the code "take_phots_commands.ino". + +Of course, we can do both things simultaneously: show what the camera sees on the page and send a command to capture and save the image on the SD card. For that, you can use the code Camera_HTTP_Server_STA, which can be downloaded from GitHub. + +![](./images/png/imags-setup/app2.png) + +The program will do the following tasks: + +- Set the camera to JPEG output mode. +- Create a web page (for example ==\> http://192.168.4.119//). The correct address will be displayed on the Serial Monitor. +- If server.on ("/capture", HTTP_GET, serverCapture), the program takes a photo and sends it to the Web. +- It is possible to rotate the image on webPage using the button \[ROTATE\] +- The command \[CAPTURE\] only will preview the image on the webpage, showing its size on the Serial Monitor +- The `[SAVE]` command will save an image on the SD Card and show the image on the browser. +- Saved images will follow a sequential naming (image1.jpg, image2.jpg. + +![](./images/png/imags-setup/terminal.png) + +> This program can capture an image dataset with an image classification project. + +Inspect the code; it will be easier to understand how the camera works. This code was developed based on the great Rui Santos Tutorial [ESP32-CAM Take Photo and Display in Web Server](https://randomnerdtutorials.com/esp32-cam-take-photo-display-web-server/), which I invite all of you to visit. + +**Using the CameraWebServer** + +In the Arduino IDE, go to `File > Examples > ESP32 > Camera`, and select `CameraWebServer` + +You also should comment on all cameras' models, except the XIAO model pins: + +`#define CAMERA_MODEL_XIAO_ESP32S3 // Has PSRAM` + +Do not forget the `Tools` to enable the PSRAM. + +Enter your wifi credentials and upload the code to the device: + +![](./images/jpeg/imags-setup/webCap1.jpg) + +If the code is executed correctly, you should see the address on the Serial Monitor: + +![](./images/jpeg/imags-setup/serial_monitor.png) + +Copy the address on your browser and wait for the page to be uploaded. Select the camera resolution (for example, QVGA) and select `[START STREAM]`. Wait for a few seconds/minutes, depending on your connection. Using the `[Save]` button, you can save an image to your computer download area. + +![](./images/jpeg/imags-setup/img_cap.jpg) + +That's it! You can save the images directly on your computer for use on projects. + +## Conclusion + +The XIAO ESP32S3 Sense is flexible, inexpensive, and easy to program. With 8 MB of RAM, memory is not an issue, and the device can handle many post-processing tasks, including communication. + +You will find the last version of the codes on the GitHub repository: [XIAO-ESP32S3-Sense.](https://github.com/Mjrovai/XIAO-ESP32S3-Sense) diff --git a/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd b/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd index 61a18d5b..6bbddd1d 100644 --- a/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd +++ b/contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd @@ -1,3 +1,26 @@ # XIAO ESP32S3 {.unnumbered} -Coming soon. \ No newline at end of file +These labs provide a unique opportunity to gain practical experience with machine learning (ML) systems. Unlike working with large models requiring data center-scale resources, these exercises allow you to directly interact with hardware and software using TinyML. This hands-on approach gives you a tangible understanding of the challenges and opportunities in embedded AI. + +![](./images/jpeg/xiao_esp32s3_decked.jpeg){height=3in} + +## Pre-requisites + +- **XIAO ESP32S3 Sense Board**: Ensure you have the XIAO ESP32S3 Sense Board. +- **USB-C Cable**: This is for connecting the board to your computer. +- **Network**: With internet access for downloading necessary software. +- **SD Card and an SD card Adapter**: This saves audio and images (optional). + +## Setup + +- [Setup XIAO ESP32S3](./xiao_esp32s3_setup.qmd) + +## Exercises + +| **Modality** | **Task** | **Description** | **Link** | +| ------------ | ------------------------------------------- | ----------------------------------------- | --------------------------------------- | +| Vision | Image Classification | Learn to classify images | [Link](./xiao_image_classification.qmd) | +| Vision | Object Detection | Implement object detection | [Link](./xiao_object_detection.qmd) | +| Sound | KeyWording Spotting (KWS) | Explore voice recognition systems | [Link](./xiao_kws.qmd) | +| IMU | Motion Classification and Anomaly Detection | Classify motion data and detect anomalies | [Link](./xiao_object_detection.qmd) | + diff --git a/contents/labs/arduino/nicla_vision/object_detection_fomo/object_detection_fomo.bib b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.bib similarity index 100% rename from contents/labs/arduino/nicla_vision/object_detection_fomo/object_detection_fomo.bib rename to contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.bib diff --git a/contents/labs/arduino/nicla_vision/dsp_spectral_features_block/dsp_spectral_features_block.qmd b/contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd similarity index 100% rename from contents/labs/arduino/nicla_vision/dsp_spectral_features_block/dsp_spectral_features_block.qmd rename to 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was created with betterbib v5.0.11.} - diff --git a/contents/object_detection_fomo/object_detection_fomo.qmd b/contents/object_detection_fomo/object_detection_fomo.qmd deleted file mode 100644 index 85c9927c..00000000 --- a/contents/object_detection_fomo/object_detection_fomo.qmd +++ /dev/null @@ -1,313 +0,0 @@ ---- -bibliography: object_detection_fomo.bib ---- - -# Object Detection {.unnumbered} - -![*DALL·E 3 Prompt: Cartoon in the style of the 1940s or 1950s showcasing a spacious industrial warehouse interior. A conveyor belt is prominently featured, carrying a mixture of toy wheels and boxes. The wheels are distinguishable with their bright yellow centers and black tires. The boxes are white cubes painted with alternating black and white patterns. At the end of the moving conveyor stands a retro-styled robot, equipped with tools and sensors, diligently classifying and counting the arriving wheels and boxes. The overall aesthetic is reminiscent of mid-century animation with bold lines and a classic color palette.*](images/jpg/obj_det_ini.jpg){fig-align="center" width="6.5in"} - -## Introduction - -This is a continuation of **CV on Nicla Vision**, now exploring **Object Detection** on microcontrollers. - -![](images/jpg/cv_obj_detect.jpg){fig-align="center" width="6.5in"} - -### Object Detection versus Image Classification - -The main task with Image Classification models is to produce a list of the most probable object categories present on an image, for example, to identify a tabby cat just after his dinner: - -![](images/png/img_1.png){fig-align="center"} - -But what happens when the cat jumps near the wine glass? The model still only recognizes the predominant category on the image, the tabby cat: - -![](images/png/img_2.png){fig-align="center"} - -And what happens if there is not a dominant category on the image? - -![](images/png/img_3.png){fig-align="center"} - -The model identifies the above image completely wrong as an "ashcan," possibly due to the color tonalities. - -> The model used in all previous examples is the *MobileNet*, trained with a large dataset, the *ImageNet*. - -To solve this issue, we need another type of model, where not only **multiple categories** (or labels) can be found but also **where** the objects are located on a given image. - -As we can imagine, such models are much more complicated and bigger, for example, the **MobileNetV2 SSD FPN-Lite 320x320, trained with the COCO dataset.** This pre-trained object detection model is designed to locate up to 10 objects within an image, outputting a bounding box for each object detected. The below image is the result of such a model running on a Raspberry Pi: - -![](images/png/img_4.png){fig-align="center" width="6.5in"} - -Those models used for Object detection (such as the MobileNet SSD or YOLO) usually have several MB in size, which is OK for use with Raspberry Pi but unsuitable for use with embedded devices, where the RAM usually is lower than 1M Bytes. - -### An innovative solution for Object Detection: FOMO - -[Edge Impulse launched in 2022, **FOMO** (Faster Objects, More Objects)](https://docs.edgeimpulse.com/docs/edge-impulse-studio/learning-blocks/object-detection/fomo-object-detection-for-constrained-devices), a novel solution to perform object detection on embedded devices, not only on the Nicla Vision (Cortex M7) but also on Cortex M4F CPUs (Arduino Nano33 and OpenMV M4 series) as well the Espressif ESP32 devices (ESP-CAM and XIAO ESP32S3 Sense). - -In this Hands-On exercise, we will explore using FOMO with Object Detection, not entering many details about the model itself. To understand more about how the model works, you can go into the [official FOMO announcement](https://www.edgeimpulse.com/blog/announcing-fomo-faster-objects-more-objects) by Edge Impulse, where Louis Moreau and Mat Kelcey explain in detail how it works. - -## The Object Detection Project Goal - -All Machine Learning projects need to start with a detailed goal. Let's assume we are in an industrial facility and must sort and count **wheels** and special **boxes**. - -![](images/jpg/proj_goal.jpg){fig-align="center" width="6.5in"} - -In other words, we should perform a multi-label classification, where each image can have three classes: - -- Background (No objects) - -- Box - -- Wheel - -Here are some not labeled image samples that we should use to detect the objects (wheels and boxes): - -![](images/jpg/samples.jpg){fig-align="center" width="6.5in"} - -We are interested in which object is in the image, its location (centroid), and how many we can find on it. The object's size is not detected with FOMO, as with MobileNet SSD or YOLO, where the Bounding Box is one of the model outputs. - -We will develop the project using the Nicla Vision for image capture and model inference. The ML project will be developed using the Edge Impulse Studio. But before starting the object detection project in the Studio, let's create a *raw dataset* (not labeled) with images that contain the objects to be detected. - -## Data Collection - -We can use the Edge Impulse Studio, the OpenMV IDE, your phone, or other devices for the image capture. Here, we will use again the OpenMV IDE for our purpose. - -### Collecting Dataset with OpenMV IDE - -First, create in your computer a folder where your data will be saved, for example, "data." Next, on the OpenMV IDE, go to Tools \> Dataset Editor and select New Dataset to start the dataset collection: - -![](images/jpg/data_folder.jpg){fig-align="center" width="6.5in"} - -Edge impulse suggests that the objects should be of similar size and not overlapping for better performance. This is OK in an industrial facility, where the camera should be fixed, keeping the same distance from the objects to be detected. Despite that, we will also try with mixed sizes and positions to see the result. - -> We will not create separate folders for our images because each contains multiple labels. - -Connect the Nicla Vision to the OpenMV IDE and run the `dataset_capture_script.py`. Clicking on the Capture Image button will start capturing images: - -![](images/jpg/img_5.jpg){fig-align="center" width="6.5in"} - -We suggest around 50 images mixing the objects and varying the number of each appearing on the scene. Try to capture different angles, backgrounds, and light conditions. - -> The stored images use a QVGA frame size 320x240 and RGB565 (color pixel format). - -After capturing your dataset, close the Dataset Editor Tool on the `Tools > Dataset Editor`. - -## Edge Impulse Studio - -### Setup the project - -Go to [Edge Impulse Studio,](https://www.edgeimpulse.com/) enter your credentials at **Login** (or create an account), and start a new project. - -![](images/png/img_6.png){fig-align="center" width="6.5in"} - -> Here, you can clone the project developed for this hands-on: [NICLA_Vision_Object_Detection](https://studio.edgeimpulse.com/public/292737/latest). - -On your Project Dashboard, go down and on **Project info** and select **Bounding boxes (object detection)** and Nicla Vision as your Target Device: - -![](images/png/img_7.png){fig-align="center" width="6.5in"} - -### Uploading the unlabeled data - -On Studio, go to the `Data acquisition` tab, and on the `UPLOAD DATA` section, upload from your computer files captured. - -![](images/png/img_8.png){fig-align="center" width="6.5in"} - -> You can leave for the Studio to split your data automatically between Train and Test or do it manually. - -![](images/png/img_9.png){fig-align="center" width="6.5in"} - -All the not labeled images (51) were uploaded but they still need to be labeled appropriately before using them as a dataset in the project. The Studio has a tool for that purpose, which you can find in the link `Labeling queue (51)`. - -There are two ways you can use to perform AI-assisted labeling on the Edge Impulse Studio (free version): - -- Using yolov5 -- Tracking objects between frames - -> Edge Impulse launched an [auto-labeling feature](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/auto-labeler) for Enterprise customers, easing labeling tasks in object detection projects. - -Ordinary objects can quickly be identified and labeled using an existing library of pre-trained object detection models from YOLOv5 (trained with the COCO dataset). But since, in our case, the objects are not part of COCO datasets, we should select the option of `tracking objects`. With this option, once you draw bounding boxes and label the images in one frame, the objects will be tracked automatically from frame to frame, *partially* labeling the new ones (not all are correctly labeled). - -> You can use the [EI uploader](https://docs.edgeimpulse.com/docs/tools/edge-impulse-cli/cli-uploader#bounding-boxes) to import your data if you already have a labeled dataset containing bounding boxes. - -### Labeling the Dataset - -Starting with the first image of your unlabeled data, use your mouse to drag a box around an object to add a label. Then click **Save labels** to advance to the next item. - -![](images/png/img_10.png){fig-align="center" width="6.5in"} - -Continue with this process until the queue is empty. At the end, all images should have the objects labeled as those samples below: - -![](images/jpg/img_11.jpg){fig-align="center" width="6.5in"} - -Next, review the labeled samples on the `Data acquisition` tab. If one of the labels was wrong, you can edit it using the *`three dots`* menu after the sample name: - -![](images/png/img_12.png){fig-align="center" width="6.5in"} - -You will be guided to replace the wrong label, correcting the dataset. - -![](images/jpg/img_13.jpg){fig-align="center" width="6.5in"} - -## The Impulse Design - -In this phase, you should define how to: - -- **Pre-processing** consists of resizing the individual images from `320 x 240` to `96 x 96` and squashing them (squared form, without cropping). Afterwards, the images are converted from RGB to Grayscale. - -- **Design a Model,** in this case, "Object Detection." - -![](images/png/img_14.png){fig-align="center" width="6.5in"} - -### Preprocessing all dataset - -In this section, select **Color depth** as `Grayscale`, which is suitable for use with FOMO models and Save `parameters`. - -![](images/png/img_15.png){fig-align="center" width="6.5in"} - -The Studio moves automatically to the next section, `Generate features`, where all samples will be pre-processed, resulting in a dataset with individual 96x96x1 images or 9,216 features. - -![](images/png/img_16.png){fig-align="center" width="6.5in"} - -The feature explorer shows that all samples evidence a good separation after the feature generation. - -> One of the samples (46) apparently is in the wrong space, but clicking on it can confirm that the labeling is correct. - -## Model Design, Training, and Test - -We will use FOMO, an object detection model based on MobileNetV2 (alpha 0.35) designed to coarsely segment an image into a grid of **background** vs **objects of interest** (here, *boxes* and *wheels*). - -FOMO is an innovative machine learning model for object detection, which can use up to 30 times less energy and memory than traditional models like Mobilenet SSD and YOLOv5. FOMO can operate on microcontrollers with less than 200 KB of RAM. The main reason this is possible is that while other models calculate the object's size by drawing a square around it (bounding box), FOMO ignores the size of the image, providing only the information about where the object is located in the image, by means of its centroid coordinates. - -**How FOMO works?** - -FOMO takes the image in grayscale and divides it into blocks of pixels using a factor of 8. For the input of 96x96, the grid would be 12x12 (96/8=12). Next, FOMO will run a classifier through each pixel block to calculate the probability that there is a box or a wheel in each of them and, subsequently, determine the regions which have the highest probability of containing the object (If a pixel block has no objects, it will be classified as *background*). From the overlap of the final region, the FOMO provides the coordinates (related to the image dimensions) of the centroid of this region. - -![](images/png/img_17.png){fig-align="center" width="6.5in"} - -For training, we should select a pre-trained model. Let's use the **`FOMO (Faster Objects, More Objects) MobileNetV2 0.35`\`.** This model uses around 250KB RAM and 80KB of ROM (Flash), which suits well with our board since it has 1MB of RAM and ROM. - -![](images/png/img_18.png){fig-align="center" width="6.5in"} - -Regarding the training hyper-parameters, the model will be trained with: - -- Epochs: 60, -- Batch size: 32 -- Learning Rate: 0.001. - -For validation during training, 20% of the dataset (*validation_dataset*) will be spared. For the remaining 80% (*train_dataset*), we will apply Data Augmentation, which will randomly flip, change the size and brightness of the image, and crop them, artificially increasing the number of samples on the dataset for training. - -As a result, the model ends with practically 1.00 in the F1 score, with a similar result when using the Test data. - -> Note that FOMO automatically added a 3rd label background to the two previously defined (*box* and *wheel*). - -![](images/png/img_19.png){fig-align="center" width="6.5in"} - -> In object detection tasks, accuracy is generally not the primary [evaluation metric](https://learnopencv.com/mean-average-precision-map-object-detection-model-evaluation-metric/). Object detection involves classifying objects and providing bounding boxes around them, making it a more complex problem than simple classification. The issue is that we do not have the bounding box, only the centroids. In short, using accuracy as a metric could be misleading and may not provide a complete understanding of how well the model is performing. Because of that, we will use the F1 score. - -### Test model with "Live Classification" - -Since Edge Impulse officially supports the Nicla Vision, let's connect it to the Studio. For that, follow the steps: - -- Download the [last EI Firmware](https://cdn.edgeimpulse.com/firmware/arduino-nicla-vision.zip) and unzip it. - -- Open the zip file on your computer and select the uploader related to your OS: - -![](images/png/image17.png){fig-align="center"} - -- Put the Nicla-Vision on Boot Mode, pressing the reset button twice. - -- Execute the specific batch code for your OS for uploading the binary (`arduino-nicla-vision.bin`) to your board. - -Go to `Live classification` section at EI Studio, and using *webUSB,* connect your Nicla Vision: - -![](images/png/img_20.png){fig-align="center" width="6.5in"} - -Once connected, you can use the Nicla to capture actual images to be tested by the trained model on Edge Impulse Studio. - -![](images/png/img_21.png){fig-align="center" width="6.5in"} - -One thing to be noted is that the model can produce false positives and negatives. This can be minimized by defining a proper `Confidence Threshold` (use the `Three dots` menu for the set-up). Try with 0.8 or more. - -## Deploying the Model - -Select OpenMV Firmware on the Deploy Tab and press \[Build\]. - -![](images/png/img_22.png){fig-align="center" width="6.5in"} - -When you try to connect the Nicla with the OpenMV IDE again, it will try to update its FW. Choose the option `Load a specific firmware` instead. - -![](images/png/img_24.png){fig-align="center"} - -You will find a ZIP file on your computer from the Studio. Open it: - -![](images/png/img_23.png){fig-align="center" width="6.5in"} - -Load the .bin file to your board: - -![](images/png/img_25.png){fig-align="center" width="6.5in"} - -After the download is finished, a pop-up message will be displayed. `Press OK`, and open the script **ei_object_detection.py** downloaded from the Studio. - -Before running the script, let's change a few lines. Note that you can leave the window definition as 240 x 240 and the camera capturing images as QVGA/RGB. The captured image will be pre-processed by the FW deployed from Edge Impulse - -``` python -# Edge Impulse - OpenMV Object Detection Example - -import sensor, image, time, os, tf, math, uos, gc - -sensor.reset() # Reset and initialize the sensor. -sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE) -sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240) -sensor.set_windowing((240, 240)) # Set 240x240 window. -sensor.skip_frames(time=2000) # Let the camera adjust. - -net = None -labels = None -``` - -Redefine the minimum confidence, for example, to 0.8 to minimize false positives and negatives. - -``` python -min_confidence = 0.8 -``` - -Change if necessary, the color of the circles that will be used to display the detected object's centroid for a better contrast. - -``` python -try: - # Load built in model - labels, net = tf.load_builtin_model('trained') -except Exception as e: - raise Exception(e) - -colors = [ # Add more colors if you are detecting more than 7 types of classes at once. - (255, 255, 0), # background: yellow (not used) - ( 0, 255, 0), # cube: green - (255, 0, 0), # wheel: red - ( 0, 0, 255), # not used - (255, 0, 255), # not used - ( 0, 255, 255), # not used - (255, 255, 255), # not used -] -``` - -Keep the remaining code as it is and press the `green Play button` to run the code: - -![](images/png/img_26.png){fig-align="center" width="6.5in"} - -On the camera view, we can see the objects with their centroids marked with 12 pixel-fixed circles (each circle has a distinct color, depending on its class). On the Serial Terminal, the model shows the labels detected and their position on the image window (240X240). - -> Be ware that the coordinate origin is in the upper left corner. - -![](images/jpg/img_27.jpg){fig-align="center" width="624"} - -Note that the frames per second rate is around 8 fps (similar to what we got with the Image Classification project). This happens because FOMO is cleverly built over a CNN model, not with an object detection model like the SSD MobileNet. For example, when running a MobileNetV2 SSD FPN-Lite 320x320 model on a Raspberry Pi 4, the latency is around 5 times higher (around 1.5 fps) - -Here is a short video showing the inference results: {{< video https://youtu.be/JbpoqRp3BbM width="480" height="270" center >}} - -## Conclusion - -FOMO is a significant leap in the image processing space, as Louis Moreau and Mat Kelcey put it during its launch in 2022: - -> FOMO is a ground-breaking algorithm that brings real-time object detection, tracking, and counting to microcontrollers for the first time. - -Multiple possibilities exist for exploring object detection (and, more precisely, counting them) on embedded devices, for example, to explore the Nicla doing sensor fusion (camera + microphone) and object detection. This can be very useful on projects involving bees, for example. - -![](images/jpg/img_28.jpg){fig-align="center" width="624"}