Full Tutorial posted - https://www.tomshardware.com/how-to/raspberry-pi-face-mask-detector
Full Tutorial posted - https://www.tomshardware.com/how-to/raspberry-pi-face-mask-detector
Full Tutorial posted - https://www.tomshardware.com/how-to/raspberry-pi-face-mask-detector
Full Tutorial posted - https://www.tomshardware.com/how-to/raspberry-pi-face-mask-detector
Hello Mrs Carolinedunn,
Thank you for providing an amazing resource on face mask detection.
but I am getting the following error when I am calling the train_mask_detector. The folders have been arranged in the same order as the one provided in the Github repository.
Pls provide me with the solution to rectify the error
Thanking you,
raise ValueError('Empty training data.')
ValueError: Empty training data.
I've followed this project of yours. Facing error in the end.
Traceback (most recent call last):
File "detect_mask_picam.py", line 95, in
maskNet = load_model(args["model"])
File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/saving/save.py", line 146, in load_model
return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 166, in load_model_from_hdf5
model_config = json.loads(model_config.decode('utf-8'))
AttributeError: 'str' object has no attribute 'decode'
I am getting this error on executing python3 detect_mask_webcam.py
OSError: SavedModel file does not exist at: /home/pi/Documents/face_mask_detection/mask_detector.model/{saved_model.pbtxt|saved_model.pb}
Although there is a mask_detector.model
in the directory referred above
Thank you for the demo. It was fun to setup.
As I was studying the files, I found the pre-trained model for face detection, but could not figure out where it came from. Looking for that file on the net, I found several places where it and the associated deploy.prototxt were added to various github projects and even a reference to OpenCV. But I could not find the source anywhere.
If you have some time, I would love to learn where this came from and how you learned about this model.
Thanks, Al
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