81 lines
1.7 KiB
Matlab
81 lines
1.7 KiB
Matlab
%hr = heartRateEstimation(imgDirectory, fps, windowDuration, windowShift)
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%%%%%%%%%%%%%%%%%
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% hr = heartRateEstimation(imgDirectory, fps, windowDuration, windowShift)
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%
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% Task:
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%
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% Inputs:
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% -imgDirectory
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% -fps
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% -windowDuration
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% -windowShift
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%
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% Output:
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% -hr:
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%
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% Author: Guillaume Gibert
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% Date: 06/02/2023
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%
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% Note: images were extracted with ffmpeg -i "video.mkv" ../img/gg_%04d.png
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%%%%%%%%%%%%%%%%%
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%temp var
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imgDirectory = 'img';
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fps = 60;
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windowDuration = 30;
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windowShift = 1;
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roi = [225 750 825 1100];
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% global
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redChannel = [];
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greenChannel = [];
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blueChannel = [];
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%list the images
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listImg = dir([imgDirectory '/*.png']);
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for l_img=1:windowDuration*fps
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% load the current image
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image_original = imread([imgDirectory '/' listImg(l_img).name]);
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%image(image_original);
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% crop the current image around the face
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image_face = image_original(roi(1):roi(2), roi(3):roi(4), :);
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%image(image_face);
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% spatial average for r, g, b channels
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r_mean = mean(mean(image_face(:,:,1)));
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g_mean = mean(mean(image_face(:,:,2)));
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b_mean = mean(mean(image_face(:,:,3)));
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% store the current "average" pixel in global arrays
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redChannel = [redChannel r_mean];
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greenChannel = [greenChannel g_mean];
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blueChannel = [blueChannel b_mean];
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end
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% estimate temporal average and standard deviation
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greenChannel_avg = mean(greenChannel);
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greenChannel_std = std(greenChannel);
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% normalize your data
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% greenChannel_normalized(i) = (greenChannel(i) - greenChannel_avg)/greenChannel_std
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% fft(greenChannel_normalized)
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% power spectrum (https://www.mathworks.com/help/matlab/ref/fft.html)
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% find the peak in the range ([0.75 4] Hz)
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% max -> value, index
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% convert the index from Hz to bpm
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% determine heart rate
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