Cone detector trained using the Tensorflow Object Detection API
Requirements • Usage • Model • License
Install the following packages with pip, preferably inside a virtual environment.
pip install opencv-python
pip install tensorflowRun the program with the following command:
python cone_detector.pyKeep in mind that the app takes square crops for each frame in the video before performing the detection. This is because the model used has been trained using a pre-trained model from Tensorflow detection model zoo (ssd_mobilenet_v1_coco), whose input volume was a square image; and it performs much better after taking square crops.
However, if the video that you use has a different resolution you might want to change the size of the crops using the variables CROP_WIDTH, CROP_SIZE and CROP_STEP_X. If you don't want to take crops (the detection it is much faster but less accurate), uncomment the following lines:
# CROP_WIDTH = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# CROP_HEIGHT = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
...
# crops = np.array([crops[0]])
# crops_coordinates = [crops_coordinates[0]]The model was trained using arround 200 images (70% train and 30% for validation) extracted from a longer version of the sample video, so it might perform worse on different images. If you want to finetune this model, you can find the .ckpt files in the model folder.
This project is licensed under the MIT License - see the LICENSE.md file for details.