Supermarket detection

A project combines object detection and faces recognition. Object detection uses the Tensorflow Object Detection API.

Project directory structure

├── build_dataset.py # script for build dataset using fiftyone
├── facenet # directory for scripts and files for object detection adapted from (https://github.com/R4j4n/Face-recognition-Using-Facenet-On-Tensorflow-2.X)
├── object_detect.py # script for loading a model and dectection
├── init.sh # script for install required packages 
├── notebooks # diretory for jupyter notebook
├── requirements.txt
├── supermarket_detection # python module diretory
├── tensorflow_model_garden # tensorflow model garden diretory, created by init.sh
└── workspace # workspace folder for training object detection models
    ├── data # dataset directory
    ├── exported_models # directory for exported models
    ├── models # directory for training models
    ├── pre_trained_models # directory for downloaded pre-trained model models
    ├── download_model.sh
    ├── select_model.sh
    ├── train_model.sh
    ├── evaluate_model.sh
    ├── export_model.sh
    ├── update_pipeline_config.py
    └── sync_workspace_with_drive.sh

Train a object detection model

Note: You can run the jupyter notebook notebooks/training_on_cloud.ipynb to prepare training on Google colab. After you open vscode in colab, the follow steps are the same.

0. install protobuf(for Windows system)

The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be downloaded and compiled.

For Windows user, you need manual to install Protobufs first, pleas follow the following guide:

Head to the protoc releases page
Download the latest protoc-*-*.zip release (e.g. protoc-3.12.3-win64.zip for 64-bit Windows)
Extract the contents of the downloaded protoc-*-*.zip in a directory <PATH_TO_PB> of your choice (e.g. C:\Program Files\Google Protobuf)
Add <PATH_TO_PB>\bin to your Path environment variable (see Environment Setup)

Install git bash shell for running bash script

Install Git for Windows
Open anaconda prompt and run conda init bash
Open bash prompt
- Navigate to anaconda installation folder /etc/profile.d
- touch ~/.bashrc
- echo ". '${PWD}'/conda.sh" >> ~/.bashrc
- echo '. ${HOME}/.bash_profile' >> ~/.bashrc

1. init

./init.sh
cd workspace # after initiation the follow steps are all run in the workspace folder

2. prepare dataset

Create a folder for your dataset and put the tfrecord dataset files and label map file into the data folder. The file name should be as follow:

├── data
│   ├── custom01
│   │   ├── label_map.pbtxt
│   │   ├── test.tfrecord
│   │   ├── train.tfrecord
│   │   └── valid.tfrecord
│   └── oi01
│       ├── label_map.pbtxt
│       ├── train.tfrecord
│       └── valid.tfrecord

If you train on colab, you may want to sync the dataset from Google Drive. Check Sync data with Google Drive

Create a .env in the workspace folder and set DATASET_DIR to the dataset you want. For example:

DATASET_DIR=data/custom01

3. download a pre-train model

Go to tf2_detection_zoo, copy the model file link you want to down then run ./download_model.sh {model_link}. for example:

./download_model.sh http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz
# pretrain model in pre_trained_models/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8
# copy and update the pipeline.config to models/ssd_mobilenet_v2_fpnlite_320x320/pipeline.config

This will download and extract the pre-train model into the pre_trained_models folder and copy the pipeline.config file into the coresponding models folder.

The script will update the num_classes, fine_tune_checkpoint and some of other configurations related to dataset. You can manually update more configurations. Check this article for possible configurationss for model improvement TensorFlow Object Detection API: Best Practices to Training, Evaluation & Deployment - neptune.ai

4. select a model for training

./select_model.sh

This will create or update the .env file in the workspace folder.

5. train the selected model

./train_model.sh [--restart]

If you want the previously trained checkpoints, you can add --restart argument, it will delete all the checkpoints before training.

6. evaluate the model on the validation set

./evaluate_model.sh

You can run this while training.

7. export a trained model

./export_model.sh

This will export the trained model into exported_models folder.

8. test models on test dataset

open /config/default.yml and change the pipeline_config_path and checkpoint_dir to point to your model
run /object_detect.py

Usage:
python object_detect.py [options]

General options:
-f, --config <path>         path to config file
-i, --inputpath <path>      path to test images folder
-o, --outputpath <path>     path to output folder
-e, --export_images         export images with bounding box to output folder
-c, --camera                use camera instead of images from directory

Sync data with Google Drive

You can use the sync_workspace_with_drive.sh in the workspace folder to synchronize data from or to Google Drive.

Note: In colab, you need to mount the google drive into the colab runtime machine. In local, you need to use Google Back and Sync Application to mount the google drive into local drive.

Example usage: Suppose there is a supermarket_detection_workspace in Google Drive. After mounting Google Drive into the colab machine, the corresponding folder is /content/drive/MyDrive/supermarket_detection_workspace. In the workspace folder, run the following command to sync data.

# ./sync_workspace_with_drive.sh [folder] [--form] [--to] [--no-delete]
./sync_workspace_with_drive.sh --from # sync the whole workspace folder from google drive
./sync_workspace_with_drive.sh data --from # sync the only the data folder from google drive
./sync_workspace_with_drive.sh data --from --no-delete 
# by default the script will detele not exist files in target folder, add --no-delete if you don't want to delete

# after training a model in colab, sync the train result to google drive
./sync_workspace_with_drive.sh models/ssd_mobilenet_v2_fpnlite_320x320 --to

In local, suppose /Users/username/SMU/supermarket_detection_workspace is the folder sync by Backup and Sync. Add the follow line into .env.

DRIVER_DIR_PATH=/Users/username/SMU/supermarket_detection_workspace

Copy train result from google drive

./sync_workspace_with_drive.sh models/ssd_mobilenet_v2_fpnlite_320x320 --from

Training on TPU

You booster the training by using TPU in Colab. To do this you need to put the dataset and pre-trained model in Google Cloud Storage and update the relative file path in the pipeline.config.

You should set GS_PATH_PREFIX and DATASET_DIR in the workspace .env file. For example:

GS_PATH_PREFIX=gs://kk17_ml_data/supermarket_detection
DATASET_DIR=gs://kk17_ml_data/supermarket_detection/data/custom01

After that you can run the training and evaluation script with --tpu option. For example:

./train_model.sh --tpu
./evaluation_model.sh --tpu
./tensorboard_model.sh --tpu

Build a dataset for object detection

Use fiftyone to get the require class from open image v6 dataset and build a TFOjbectDetectionDataset. Check notebooks: building_dataset.ipynb

Train a face detection model

The base for the scripts and models can be obtained in R4j4n/Face-recognition-Using-Facenet-On-Tensorflow-2.X, but the codes have been adapted for this project.

Get the adapted pre-trained weights in Google Drive, and paste in /facenet folder

1. prepare face images

Download the face images and put in subfolders in the /facenet/Faces directory, with each person's name as the subfolder name

2. run training code

python ./facenet/train_v2.py

This will update /facenet/encodings/encodings.pkl and /facenet/facenet_keras_weights.h5 according to the new face dataset.

3. test model

run ./face_detect.py to test the model. Omit the inputpath argument to use camera instead.

Usage:
python object_detect.py [options]

General options:
-i, --inputpath <path>      path to test video input file, will use camera if not set
-o, --outputpath <path>     path to test video output file, will not export video if not set
-d, --detection_interval    detect face every specified number of second(s) (default=0.5s)
-s, --show                  display the labeled video as it is being processed
-m, --multi_thread          use multithread processing to speed up

Reference

Object detection

Training Custom Object Detector — TensorFlow 2 Object Detection API tutorial documentation
Training and serving a realtime mobile object detector in 30 minutes with Cloud TPUs — The TensorFlow Blog

Face Recognition

R4j4n/Face-recognition-Using-Facenet-On-Tensorflow-2.X

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README.md

README.md

Supermarket detection

Project directory structure

Train a object detection model

0. install protobuf(for Windows system)

Install git bash shell for running bash script

1. init

2. prepare dataset

3. download a pre-train model

4. select a model for training

5. train the selected model

6. evaluate the model on the validation set

7. export a trained model

8. test models on test dataset

Sync data with Google Drive

Training on TPU

Build a dataset for object detection

Train a face detection model

1. prepare face images

2. run training code

3. test model

Reference

Object detection

Face Recognition

Files

README.md

Latest commit

History

README.md

File metadata and controls

Supermarket detection

Project directory structure

Train a object detection model

0. install protobuf(for Windows system)

Install git bash shell for running bash script

1. init

2. prepare dataset

3. download a pre-train model

4. select a model for training

5. train the selected model

6. evaluate the model on the validation set

7. export a trained model

8. test models on test dataset

Sync data with Google Drive

Training on TPU

Build a dataset for object detection

Train a face detection model

1. prepare face images

2. run training code

3. test model

Reference

Object detection

Face Recognition