CHROM Python API

bob.rppg.chrom.extract_utils.compute_mean_rgb(image, mask=None) → mean_r, mean_g, mean_b

This function 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 (3d numpy array):

The image to process

mask (2d boolen numpy array):

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

bob.rppg.chrom.extract_utils.compute_gray_diff(previous, current) → diff

This function computes the difference in intensity between two images .

Parameters

previous (3d numpy array):

The previous frame.

current (3d numpy array):

The current frame.

Returns

diff (float):

The sum of the absolute difference in pixel intensity between two frames

bob.rppg.chrom.extract_utils.select_stable_frames(diff, n) → index

This functions 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 (1d numpy array):

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 (int):

The frame index at which the stable segment begins.

bob.rppg.chrom.extract_utils.project_chrominance(r, g, b)

Projects rgb values onto the x and y chrominance space

See equation (9) of [dehaan-tbe-2013].

Parameters

r (float):

The red value

g (float):

The green value

b (float):

The blue value

Returns

x (float):

The x value

y (float):

The y value