vermaarn/icatcher

iCatcher uses neural networks to classify discrete eye gaze directions for infant populations

iCatcher

A CNN that classifies discrete eye gaze direction ("Left", "Right", "Away") from low-res in-the-wild infant videos (per-frame classification). Based on "Automatic, Real-Time Coding of Looking-While-Listening Children Videos Using Neural Networks" presented in ICIS 2020, and in BUCLD 46.

Preprint of icatcher available here.

Preprint of icatcher+, which this repository uses (partially) available here.

Step 1: Clone this repository to get a copy of the code to run locally.

git clone https://github.com/yoterel/iCatcher.git

Step 2: Navigate to the iCatcher directory, then create a virtual environment.

Using conda

We recommend installing Miniconda for this, but you can also Install Anaconda if needed, then create an environment using the environment.yml file in this repository:

conda env create -n env -f environment.yml

Activate the environment

conda activate env

Step 3: Download the latest network model & weights file:

Download all required model & weight files here.

Extract the files from this step to the models directory (the models directory needs to directly contain the extracted files).

This zip contains:

• The original iCatcher model (tf model) trained on the Princeton look-while-listening dataset (model.h5).

• A proposed improvement of iCatcher (torch model) trained on the lookit dataset (icatcher+.pt).

  Note1: the improvments were proposed by these guys.

  Note2: this model does not include the face selection mechanism described by the original authors (yet).

• Face extraction model files from opencv-dnn (config.prototxt & face_model.caffemodel)

Step 4:

To run icatcher with the webcam (id for default webcam is usually 0):

python icatcher.py --source_type webcam my_webcam_id --show_output

To run icatcher with a video file:

python icatcher.py --source_type file /path/to/my/video.mp4 --show_output

You can save a labeled video by adding:

--output_video_path /path/to/output_folder

If you want to output annotations to a file, use:

--output_annotation /path/to/output_annotation_folder

By default, this will save a file in the format described here describing the output of the automated coding. Other formats will be added upon request.

An example video file can be found here.

For all command line options, use:

python icatcher.py --help

Training:

If you want to retrain the model from scratch / finetune it, use train.py.

Note: this script expects a dataset orginized in a particular way. To create such dataset follow these steps:

• Gather raw video files into some folder
• Gather label files into some other folder (these can be in any format you choose, but a parser is required - see below)
• Use "create_dataset_from_videos" in dataset.py script to automatically extract faces from each frame into a output folder (with subfolders away, left and right). Notice this requires creating your own parser - see parsers.py for examples.
• Use "create_custom_dataset" in dataset.py script to further process the dataset into the final form (we recommend using default values unless architectural changes are made to the network). The final dataset structure will be a folder containing the subfolders {train, validation, holdout} each with their own subfolders {away, left, right}, consisting of 5-tuples of non-consecutive frames from the original videos in the appropriate class.
• Finally, use train.py to train the network.

For more detailed information, see function documentation in code.

Contributions

Feel free to contribute code by submitting a pull request.