Using the amazing Matterport's Mask_RCNN implementation and following Priya's example, I trained an algorithm that highlights areas where there is damage to a car (i.e. dents, scratches, etc.). You can run the step-by-step notebook in Google Colab or use the following:
Usage: import the module (see Jupyter notebooks for examples), or run from
the command line as such:
# Train a new model starting from pre-trained COCO weights
python3 custom.py train --dataset=/path/to/dataset --weights=coco
# Resume training a model that you had trained earlier
python3 custom.py train --dataset=/path/to/dataset --weights=last
# Train a new model starting from ImageNet weights
python3 custom.py train --dataset=/path/to/dataset --weights=imagenet
# Apply color splash to an image
python3 custom.py splash --weights=/path/to/weights/file.h5 --image=<URL or path to file>
# Apply color splash to video using the last weights you trained
python3 custom.py splash --weights=last --video=<URL or path to file>
"""
This script supports Python 2.7 and 3.7, although if you run into problems with TensorFlow and Python 3.7, it might be easier to just run everything from Google Colaboratory notebook.
$ pip install -r requirements.txt
This step is done only first time. We download the 'coco' weights and start training from that point with our custom images (transfer learning). It will download the weights automatically if it can't find them. Training takes around 4 mins per epoch with 10 epochs.
$ python3 custom.py train --dataset='dataset' --weights=coco # it will download coco weights if you don't have them
Weights of a pre trained model is already present in the logs folder. Run the index.py file
Send any car image file using postman as form-data as a POST request at http://localhost:5005/api
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