CHROM Python API¶
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bob.rppg.chrom.extract_utils.compute_mean_rgb(image, mask=None)[source]¶ computes the mean R, G and B of an image.
Note that a mask could be provided to tell which pixels should be taken into account when computing the mean.
- Parameters
image (numpy.ndarray) – The image to process
mask (numpy.ndarray) – Mask of the size of the image, telling which pixels should be considered
- Returns
mean_r (float) – The mean red value
mean_g (float) – The mean green value
mean_b (float) – The mean blue value
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bob.rppg.chrom.extract_utils.compute_gray_diff(previous, current)[source]¶ computes the difference in intensity between two images.
- Parameters
previous (numpy.ndarray) – The previous frame.
current (numpy.ndarray) – The current frame.
- Returns
The sum of the absolute difference in pixel intensity between two frames
- Return type
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bob.rppg.chrom.extract_utils.select_stable_frames(diff, n)[source]¶ selects a stable subset of consecutive frames
The selection is made by considering the grayscale difference between frames. The subset is chosen as the one for which the sum of difference is minimized
- Parameters
diff (numpy.ndarray) – The sum of absolute pixel intensity differences between consecutive frames, across the whole sequence.
n (int) – The number of consecutive frames you want to select.
- Returns
index – The frame index at which the stable segment begins.
- Return type
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bob.rppg.chrom.extract_utils.project_chrominance(r, g, b)[source]¶ Projects rgb values onto the x and y chrominance space
See equation (9) of [dehaan-tbe-2013].