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Merge branch 'develop'

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Loic Delattre 2023-02-18 15:45:41 +01:00
parent fd1ac62d5d 97f9cb2463
commit 0ab5bd0691
7 changed files with 104 additions and 54 deletions
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66
FFT_heart_rate.m Normal file
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@ -0,0 +1,66 @@
%%%%%%%%%%%%%%%%%%%%%
% Script task: Normalize RGB data and plot FFT using the power spectra
%
% Input : RGB_data.csv -> average RGB values of each image
%
% Output : Fast Fourier Transform of X(t): a graph representing the Single-Sided Amplitude Spectrum of X(t)
%
% Author: Loic Delattre and Maryne DEY (maryne.dey@ecam.fr, loic.delattre@ecam.fr)
% Date: 07/02/2023
%%%%%%%%%%%%%%%%%%%%%
clear all
close all
clc
%To be able to extract from external format (excel)
pkg load io
%Normalization of the data
data = frames_RGBs ()';
standard_deviation = std(data);
mean_value = mean(data);
for i = 1:size(data,1)
normalized_data_G(i,1) = (data(i,2)-mean_value(2))/standard_deviation(2); %%2 and not 1 because green is the 2nd color
endfor
%Input characteristics
img_num = length(data);
Fs = img_num/32; % Sampling frequency = X images in 32 seconds
T = 1/Fs; % Sampling period
L = img_num; % Length of signal = 32 seconds
t = (0:L-1)*T; % Time vector
X = normalized_data_G;
%Plot of the RGB data in the time domain
plot(t(1:L),X(1:L))
title("Signal")
xlabel("t (milliseconds)")
ylabel("X(t)")
%Finding power spectra P2 and P1
Y = fft(X);
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
f = Fs*(0:(L/2))/L;
% Finding boudaries
start = find(f==0.75);
stop = find(f==4);
P1_graph = P1(start:stop);
f_graph = f(start:stop);
%Graph of FFT in function of the frequency
plot(f_graph,P1_graph)
title("Single-Sided Amplitude Spectrum of X(t)")
xlabel("f (Hz)")
ylabel("|P1(f)|")
%Extracting the heart rate
[heart_rate_Hz, index] = max(P1_graph);
corresponding_frequency = f_graph(index);
heart_rate_bpm = corresponding_frequency*60;
fprintf("Your heart rate is about %d bpm.\n", heart_rate_bpm)

2
RGB_traces.m
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@ -10,7 +10,7 @@
% Output: % Output:
% -RGB_avg: a 1x3 matrix with the RGB average values, format -> [R, G, B] % -RGB_avg: a 1x3 matrix with the RGB average values, format -> [R, G, B]
% %
% author: Loic Delattre (loic.delattre@ecam.fr) % author: Maryne Dey and Loic Delattre (maryne.dey@ecam.fr, loic.delattre@ecam.fr)
% date: 06/02/2023 % date: 06/02/2023
%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%

2
frames_RGBs.m
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@ -29,7 +29,7 @@ function RGB_data = frames_RGBs ()
endwhile endwhile
catch catch
disp('scanned all frames') disp('scanned all frames')
j = 10001 j = 10001;
end_try_catch end_try_catch
endwhile endwhile

45
get_frames.py
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@ -3,14 +3,34 @@ import os.path
import os import os
from PIL import Image from PIL import Image
# Opens the Video file ###############
# def get_frames(input1)
# ex. get_frames(video.mov)
#
# Task: Extracting frames from a video
#
# Inputs:
# - input1: a video
#
# Output:
# -RGB_avg: a directory with all the frames of the video
#
# author: Loic Delattre and Maryne Dey (loic.delattre@ecam.fr, maryne.dey@ecam.fr)
# date: 06/02/2023
###############
video = 'my_face.mov' video = 'my_face.mov'
path_to_script = os.path.dirname(os.path.abspath(__file__))
if os.path.exists(path_to_script + r"\frames") == False:
os.mkdir(path_to_script + r"\frames")
def get_frames(vid): def get_frames(vid):
path_to_script = os.path.dirname(os.path.abspath(__file__))
if os.path.exists(path_to_script + r"\frames") == False:
os.mkdir(path_to_script + r"\frames")
# Opens the Video file
cap= cv2.VideoCapture(vid) cap= cv2.VideoCapture(vid)
i=0 i=0
while cap.isOpened(): while cap.isOpened():
ret, img = cap.read() ret, img = cap.read()
@ -21,15 +41,15 @@ def get_frames(vid):
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5) faces = face_cascade.detectMultiScale(gray, 1.3, 5) #detect the face in the frame
for (x,y,w,h) in faces: for (x,y,w,h) in faces:
# To draw a rectangle in a face # To draw a rectangle in a face for testing purposes
cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2) cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
roi_gray = gray[y:y+h, x:x+w] roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w] roi_color = img[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray) eyes = eye_cascade.detectMultiScale(roi_gray) #detect the eyes in the frames taking the face as reference
list_ex = [] list_ex = []
list_ey = [] list_ey = []
list_ew = [] list_ew = []
@ -37,7 +57,7 @@ def get_frames(vid):
#To draw a rectangle in eyes #To draw a rectangle in eyes
for (ex,ey,ew,eh) in eyes: for (ex,ey,ew,eh) in eyes:
if ew >= 80 and eh >= 80: if ew >= 82 and eh >= 82:
#cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2) #cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2)
list_ex.append(ex) list_ex.append(ex)
list_ey.append(ey) list_ey.append(ey)
@ -46,10 +66,10 @@ def get_frames(vid):
#rectangle on forhead #rectangle on forhead
try: try:
fx = min(list_ex) + list_ew[list_ex.index(min(list_ex))] fx = min(list_ex) + list_ew[list_ex.index(min(list_ex))] #deducing ROI from the eyes rectangle coordinates
fy = max(list_ey) fy = max(list_ey)
x1 = x + fx x1 = x + fx
x2 = x + fx + 100 x2 = x + fx + 100 #extra values in x and y in the parameters are adjustements made after manual testing
y1 = y + fy - 150 y1 = y + fy - 150
y2 = y + fy - 20 y2 = y + fy - 20
@ -59,8 +79,9 @@ def get_frames(vid):
i+=1 i+=1
except: except:
print('error on min value of ex') print('error on min value of ex')
#extra values in x and y in the parameters are adjustements made after manual testing #cv2.rectangle(roi_color, (fx,fy-150),(fx+100,fy-20),(0,127,255),2) #for testing purposes
#cv2.rectangle(roi_color, (fx,fy-150),(fx+100,fy-20),(0,127,255),2) #cv2.imshow('img',roi_color)
#cv2.waitKey(0)
cap.release() cap.release()
cv2.destroyAllWindows() cv2.destroyAllWindows()

2
matrix_avg.m
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@ -10,7 +10,7 @@
% Output: % Output:
% -RGB_avg: a 1x3 matrix with the RGB average values % -RGB_avg: a 1x3 matrix with the RGB average values
% %
% author: Loic Delattre (loic.delattre@ecam.fr) % author: Maryne Dey and Loic Delattre (maryne.dey@ecam.fr, loic.delattre@ecam.fr)
% date: 06/02/2023 % date: 06/02/2023
%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%

38
normalization.m
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@ -1,38 +0,0 @@
%%%%%%%%%%%%%%%%%%%%%
% Task: Creating FFT of average R,G,B over a period of 30 seconds
%
% Input:
% - CSV file containing the RGB data for each pixels of the region of interest
%
% Output:
% - Fast Fourier Transform of the colors over time
%
% author: Delattre Loïc, Dey Maryne (loic.delattre@ecam.fr, maryne.dey@ecam.fr)
% date: 06/02/2023
%%%%%%%%%%%%%%%%%%%%%
pkg load io %% to be able to extract from external format (excel)
data = csvread('RGB_database/RGB_data.csv')
standard_deviation = std(data)
mean_value = mean(data)
for i = 1:size(data,1)
normalized_data_G(i,1) = (data(i,2)-mean_value(2))/standard_deviation(2); %%2 and not 1 because green is the 2nd color
endfor
fft_green = fft(normalized_data_G)
plot([1:size(fft_green,1)],fft_green)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%For all the colors
%%for j = 1:3
%% for i = 1:size(data,1)
%% normalized_data(i,j) = (data(i,j)-mean_value(j))/standard_deviation(j);
%% endfor
%%endfor
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

3
test_file.m
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@ -4,7 +4,8 @@ clc
threshold = 1e-6; threshold = 1e-6;
RGB_data = frames_RGBs (); RGB_data = frames_RGBs ()';
length(RGB_data)
%TEST 1 %TEST 1
%Average of all the items inside of a matrix %Average of all the items inside of a matrix