The repository for group 16 for the course Machine Learning. Detecting traffic signs on pictures
For shape matching, you must install OpenCV. I used this to get it working:
Gohlke maintains Windows binaries for many Python packages, including OpenCV 3.0 with Python 3.x bindings! See here:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv
To install, just download the 64-bit or 32-bit .whl file appropriate for your system, then run pip install [filename]. Then the instruction import cv2 should work in your Python 3.x interpreter.
For calculating the zernike moments, we used a library called Mahotas. You can download a build of it from the website:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#mahotas
To install, just download the 64-bit or 32-bit .whl file appropriate for your system, then run pip install [filename]. Then the instruction import cv2 should work in your Python 3.x interpreter.
Lasagne is a lightweight library to build and train neural networks in Theano. How it can be installed is explained in their docs:
http://lasagne.readthedocs.org/en/latest/user/installation.html
All variables can be declared in main.py and feature extractors can be selected.
tsr.make_submission(train_images_path=train_images_dir, test_images_path=test_images_dir, output_file_path="test.xlsx", feature_extractors=feature_extractors, size=64)
print(tsr.local_test(train_images_path=train_images_dir, feature_extractors=feature_extractors, k=2, nr_data_augments=1, size=64))