Traffic light detection for autonomous driving
For detailed explanations visit medium.com/@kenan.r.alkiek and docs/
- cv2
- numpy
- sklearn
- skimage
- matplotlib
- yaml
- Customize settings in config.yaml (see config options for more information)
- Run train.py
- Customize settings in config.yaml (see config options for more information)
- Run run.py
- descriptor - str: hog, lbp, or haar
- classifier - str: svm (more options to be added)
- outfile - str: File location to save the trained model to
- positive_image_directory - str: Where the positive images are stored
- negative_image_directory - str: Where the negative images are stored
- window_size - tuple (int, int): Size of the training windows
- descriptor - str: hog, lbp, or haar
- classifier_location - str: Location of the saved classifier (the output of running train.py)
- detector - str: spotlight, color
- heatmap_memory - int: Number of frames to retain before removing the first frame from memory
- heatmap_threshold - int: Number of overlapping detections in the heatmap before accepting a detection
- window_size - tuple (int, int): Size of the boxes to classify and draw on
- image_directory - str: Image directory to run the classifier on
- block_size - tuple (int, int): Number of cells in each block
- cell_size - tuple (int, int): Size (in pixels) of a cell
- orientations - int: Number of orientation bins
- points - int: Number of circularly symmetric neighbour set points
- radius - float: Radius of circle (spatial resolution of the operator)
- selected_feature_file - str: File containing the coordinates and types of the Haar features you want to use. More information in the docs
- max_size - int: How large a spotlight can grow before being rejected
- kernel - int: Size of the kernel to apply during top-hat morphology
- threshold - int: Threshold value applied after top-hat morphology