ret, mask = cv2.threshold(mask, 1, 255, cv2.THRESH_BINARY)
# Denoise the mask
mask = SegmentationNetwork.denoise(mask)
# Invert the mask, since Emma painted the particles black rather than white in the mask layer,
# whereas the neural network uses classification label 0 for "not particle" and 1 for "particle"
mask = 255 - mask
# Open the file containing the RGB channel data
channels = mergetiff.rasterFromFile(channelsFile)
if channels.shape[2] > 3:
channels = channels[:, :, 0:3]
elif channels.shape[2] < 3:
Logger.error('could not extract RGB channel data!')
# Merge the RGB channels with the modified mask
shape = (channels.shape[0], channels.shape[1], 4)
merged = np.zeros(shape, dtype=channels.dtype)
merged[:, :, 0:3] = channels
merged[:, :, 3] = mask
# Write the output file (convert RGB to BGR for OpenCV)
if os.path.exists(outfile) == True:
Logger.warning('overwriting existing file {}'.format(outfile))
cv2.imwrite(outfile, merged[..., [2, 1, 0, 3]])
# Progress output
timer.end()
Logger.success('preprocessing complete ({}).'.format(timer.report()))
