# infer the total number of classes along with the spatial
	# dimensions of the mask image via the shape of the output array
	(numClasses, height, width) = output.shape[1:4]

	# our output class ID map will be num_classes x height x width in
	# size, so we take the argmax to find the class label with the
	# largest probability for each and every (x, y)-coordinate in the
	# image
	classMap = np.argmax(output[0], axis=0)

	# given the class ID map, we can map each of the class IDs to its
	# corresponding color
	mask = COLORS[classMap]

	# resize the mask such that its dimensions match the original size
	# of the input frame
	mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]),
		interpolation=cv2.INTER_NEAREST)

	# perform a weighted combination of the input frame with the mask
	# to form an output visualization
	output = ((0.3 * frame) + (0.7 * mask)).astype("uint8")