Source code for bob.rppg.chrom.extract_utils

#!/usr/bin/env python
# encoding: utf-8

import numpy
import bob.ip.base

[docs]def compute_mean_rgb(image, mask=None): """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 """ assert len(image.shape) == 3, "This is meant to work with color images (3 channels)" mean_r = numpy.mean(image[0, mask]) mean_g = numpy.mean(image[1, mask]) mean_b = numpy.mean(image[2, mask]) return mean_r, mean_g, mean_b
[docs]def compute_gray_diff(previous, current): """computes the difference in intensity between two images. Parameters ---------- previous: numpy.ndarray The previous frame. current: numpy.ndarray The current frame. Returns ------- float: The sum of the absolute difference in pixel intensity between two frames """ from bob.ip.color import rgb_to_gray prevg = rgb_to_gray(previous) currg = rgb_to_gray(current) return numpy.sum(numpy.absolute(prevg - currg))
[docs]def select_stable_frames(diff, n): """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: int The frame index at which the stable segment begins. """ current_min = float("inf") current_index = 0 for i in range(0, diff.shape[0]-n, 1): current_sum = sum(diff[i: i+n]) if current_sum < current_min: current_index = i current_min = current_sum return current_index
[docs]def 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 """ x = (3.0 * r) - (2.0 * g) y = (1.5 * r) + g - (1.5 * b) return x, y