The code is based(mostly all NN code) on the ultralytics/yolov3 and is freely available for redistribution under the GPL-3.0 license. The original repository is licensed under GPL-3.0, hence the license is GPL-3.0.

The neural network to detect human face touch in an image or video stream using Yolo V3. The idea comes from Keras creator Francois Chollet tweet.

Demo

Here is the network prediction on a clip of Slavoj Zizek

Source: https://www.youtube.com/embed/YvJocp4Ovzg

Network output on the first ten second clip of the original video - https://www.youtube.com/embed/n44WsmRiAvY

Prediction on set of 17 images

Installation (Mac)

• conda env create -f environment.yml. This is for Mac OSX.
• conda activate facetouch.

Installation (Non-Mac)

• Create a virtual environment and install requirements using conda and pip.
• conda install cython numpy
• pip install -r requirements.txt.

If you issue with installation, all you need is the following libraries, pytorch, torchvision, matplotlib, pycocotools, numpy, opencv-python, opencv-python-headless.

TODO: Add the dockerfile

Inference(after installation)

• Download the pretrained weights, gdown https://drive.google.com/uc?id=16lYS4bcIdM2HdmyJBVDOvt3Trx6N3W2R.
• Remove the webcam lid or sticker(if any), python detect.py --cfg cfg/yolov3-1cls.cfg --weights best_dataset_v5.pt --source 0.
• Note, while rendering the UI (QT) for webcam or audio, the depending on OS, you may encounter errors. Now you can leave the webcam on in the background and hear the sound "Don't touch the face" while you bring the hand close to your face.
• To run on set of images or videos, run the command, python detect.py --cfg cfg/yolov3-1cls.cfg --weights best_dataset_v5.pt --source test_images. The output is stored in output directory.

Training

• The documentation for transfer learning is available in the original repo wiki.
• Download the dataset using gdown gdown https://drive.google.com/uc?id=1ziZ0Bw40KE4pfxezyjq7FMzOntDNNzuu.
• Unzip the dataset, unzip dataset_v5.zip.
• Download the pretrained weights from ultralytics, gdown https://drive.google.com/uc?id=16lYS4bcIdM2HdmyJBVDOvt3Trx6N3W2R.
• Train the network, python train.py --weights yolov3-spp.weights --data dataset_v5/coco1.data --cfg cfg/yolov3-1cls.cfg.
• The train will run 300 epochs and store the results in the weights directory. The best.pt is the weights is used for later inference.

Dataset Creation

• To download the images for training, you can run the file face_touch_download.py like python face_touch_download.py --keyword "chin leaning" --limit 500 --dest chin_leaning. The above command opens the firefox web browser, sends the keyword "chin leaning" to google, downloads first 500 images to the directory chin_leaning. You need to have Firefox Selenium plugin.

• To annotate the downloaded images, you can use LabelImg. Install pip install labelimg. You can annotate the images like labelimg <images> coco1.names. The annotation will be stored in the same directory. Do it for all images directories.

• Now there are images and annotation. Create the dataset using following command, $python dataset.py --directories face_touch --directories chin_leaning --name dataset_v5. This will create dataset_v5 directory and zip file with labels and images directories along with train and valid split.

$ls -lat dataset_v5
total 128
drwxr-xr-x  59 user  staff   1888 Mar 15 22:02 ..
-rw-r--r--   1 user  staff     91 Mar 15 17:24 coco1.data
drwxr-xr-x   9 user  staff    288 Mar 15 17:24 .
-rw-r--r--   1 user  staff     10 Mar 15 17:24 coco1.names
-rw-r--r--   1 user  staff   4819 Mar 15 17:24 test.txt
-rw-r--r--   1 user  staff   7051 Mar 15 17:24 val.txt
-rw-r--r--   1 user  staff  36893 Mar 15 17:24 train.txt
drwxr-xr-x   5 user  staff    160 Mar 15 17:24 labels
drwxr-xr-x   5 user  staff    160 Mar 15 17:24 images
$ls -lat dataset_v5/images/
total 0
drwxr-xr-x    9 user  staff    288 Mar 15 17:24 ..
drwxr-xr-x  106 user  staff   3392 Mar 15 17:24 test
drwxr-xr-x    5 user  staff    160 Mar 15 17:24 .
drwxr-xr-x  158 user  staff   5056 Mar 15 17:24 val
drwxr-xr-x  782 user  staff  25024 Mar 15 17:24 train

• The data used for training can be found in Google Drive in the name dataset_v5.zip - https://drive.google.com/drive/u/0/folders/17-rLAQ9GLda7M5mvDiitINqSLBUj_wbm