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feature_extractor

README.md

Please use the MediaPipe YouTube8M feature extractor which extracts both RGB and audio features instead.

YouTube8M Feature Extractor (DEPRECATED)

This directory contains binary and library code that can extract YouTube8M features from images and videos. The code requires the Inception TensorFlow model (tutorial) and our PCA matrix, as outlined in Section 3.3 of our paper. The first time you use our code, it will automatically download the inception model (75 Megabytes, tensorflow GraphDef proto, download link) and the PCA matrix (25 Megabytes, Numpy arrays, download link).

Usage

There are two ways to use this code:

  1. Binary extract_tfrecords_main.py processes a CSV file of videos (and their labels) and outputs tfrecord file. Files created with this binary match the schema of YouTube-8M dataset files, and are therefore are compatible with our training starter code. You can also use the file for inference using your models that are pre-trained on YouTube-8M.
  2. Library feature_extractor.py which can extract features from images.

Using the Binary to create tfrecords from videos

You can use binary extract_tfrecords_main.py to create tfrecord files. However, this binary assumes that you have OpenCV properly installed (see end of subsection). Assume that you have two videos /path/to/vid1 and /path/to/vid2, respectively, with multi-integer labels of (52, 3, 10) and (7, 67). To create tfrecord containing features and labels for those videos, you must first create a CSV file (e.g. on /path/to/vid_dataset.csv) with contents:

/path/to/vid1,52;3;10
/path/to/vid2,7;67

Note that the CSV is comma-separated but the label-field is semi-colon separated to allow for multiple labels per video.

Then, you can create the tfrecord by calling the binary:

python extract_tfrecords_main.py --input_videos_csv /path/to/vid_dataset.csv \
    --output_tfrecords_file /path/to/output.tfrecord

Now, you can use the output file for training and/or inference using our starter code.

extract_tfrecords_main.py requires OpenCV python bindings to be installed and linked with ffmpeg. In other words, running this command should print True:

python -c 'import cv2; print cv2.VideoCapture().open("/path/to/some/video.mp4")'

Using the library to extract features from images

To extract our features from an image file cropped_panda.jpg, you can use this python code:

from PIL import Image
import numpy

# Instantiate extractor. Slow if called first time on your machine, as it
# needs to download 100 MB.
extractor = YouTube8MFeatureExtractor()

image_file = os.path.join(extractor._model_dir, 'cropped_panda.jpg')

im = numpy.array(Image.open(image_file))
features = extractor.extract_rgb_frame_features(im)

The constructor extractor = YouTube8MFeatureExtractor() will create a directory ~/yt8m/, if it does not exist, and will download and untar the two model files (inception and PCA matrix). If you prefer, you can point our extractor to another directory as:

extractor = YouTube8MFeatureExtractor(model_dir="/path/to/yt8m_files")

You can also pre-populate your custom "/path/to/yt8m_files" by manually downloading (e.g. using wget) the URLs and un-tarring them, for example:

mkdir -p /path/to/yt8m_files
cd /path/to/yt8m_files

wget http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
wget http://data.yt8m.org/yt8m_pca.tgz

tar zxvf inception-2015-12-05.tgz
tar zxvf yt8m_pca.tgz