This repository is fork of https://github.com/mrnugget/opencv-haar-classifier-training . The details are on this blog. Please visit this blog or the original repository for details. The official OpenCV documentation is pretty good too https://docs.opencv.org/3.4.3/d7/d8b/tutorial_py_face_detection.html

Put negative images into the folder ./negative_images/ and positive images into ./positive_images/. And run

make 

To use the existing images, you need to install git-lfs and run

git lfs pull
make 

to pull the default images and start the process.

It will create a LBP classifier and save it to ./classifier_lpb/cascade.xml. To create a HAAR classifier (It will at least 100 times more time to train), run make haar and it will save the classifier to ./classifier_haar/cascade.xml.

If you have your recording in TIFF file, you can use test_cascade.py file e.g.,

python ./test_cascade.py --tiff trial_008.tif --cascade ./trained_classifiers/mouse_eye.xml

And it will locate the trained pattern.

https://youtu.be/7dAdn_viR4c