Merge branch 'master' of https://gitarero.ecam.fr/nicolas.traglia/SignalLab1
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4dab61c451
336
ppg.cpp
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ppg.cpp
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//C++
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//C++
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#include <iostream>
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//include those opencv2 files in our program
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#include <iostream>
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#include "opencv2/opencv.hpp"
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//include those opencv2 files in our program
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#include "opencv2/videoio.hpp"
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#include "opencv2/opencv.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/videoio.hpp"
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#include "opencv2/highgui.hpp"
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int FPS=10; //FPS variable. FPS is the framerate of your video, aka your recording device's
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int DISCARD_DURATION=5;
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int FPS=10; //FPS variable. FPS is the framerate of your video, aka your recording device's
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bool isDiscardData=true;
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int DISCARD_DURATION=5;
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int countDiscard=0;
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bool isDiscardData=true;
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int countDiscard=0;
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bool isBufferFull = false; //buffer variables to initialise before main();
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int sampleIdBuffer = 0;
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bool isBufferFull = false; //buffer variables to initialise before main();
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int BUFFER_DURATION= 15;
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int sampleIdBuffer = 0;
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int BUFFER_DURATION= 15;
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//Display normalised signal
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template <typename T>
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//Display normalised signal
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cv::Mat plotGraph(std::vector<T>& vals, int YRange[2])
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template <typename T>
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{
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cv::Mat plotGraph(std::vector<T>& vals, int YRange[2])
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auto it = minmax_element(vals.begin(), vals.end());
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{
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float scale = 1./ceil(*it.second - *it.first);
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auto it = minmax_element(vals.begin(), vals.end());
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float bias = *it.first;
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float scale = 1./ceil(*it.second - *it.first);
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int rows = YRange[1] - YRange[0] + 1;
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float bias = *it.first;
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cv::Mat image = 255*cv::Mat::ones( rows, vals.size(), CV_8UC3 );
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int rows = YRange[1] - YRange[0] + 1;
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image.setTo(255);
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cv::Mat image = 255*cv::Mat::ones( rows, vals.size(), CV_8UC3 );
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for (int i = 0; i < (int)vals.size()-1; i++)
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image.setTo(255);
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{
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for (int i = 0; i < (int)vals.size()-1; i++)
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cv::line(image, cv::Point(i, rows - 1 - (vals[i] -
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{
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bias)*scale*YRange[1]), cv::Point(i+1, rows - 1 - (vals[i+1] -
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cv::line(image, cv::Point(i, rows - 1 - (vals[i] -
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bias)*scale*YRange[1]), cv::Scalar(255, 0, 0), 1);
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bias)*scale*YRange[1]), cv::Point(i+1, rows - 1 - (vals[i+1] -
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}
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bias)*scale*YRange[1]), cv::Scalar(255, 0, 0), 1);
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return image;
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}
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}
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return image;
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}
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int main(){
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//Print "PPG algorithm" to terminal
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int main(){
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//Note to self: std::endl; returns to line in terminal; use it everytime when done printing something.
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//Print "PPG algorithm" to terminal
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std::cout << "PPG algorithm"<< std::endl;
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//Note to self: std::endl; returns to line in terminal; use it everytime when done printing something.
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std::cout << "PPG algorithm"<< std::endl;
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cv::VideoCapture cap;
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cap.open(0);
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cv::VideoCapture cap;
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if (!cap.isOpened())
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cap.open(0);
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{
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if (!cap.isOpened())
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//Check if we can access the camera
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{
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std::cerr<<"[ERROR] unable to open camera!"<<std::endl;
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//Check if we can access the camera
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return -2;
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std::cerr<<"[ERROR] unable to open camera!"<<std::endl;
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}
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return -2;
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}
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cv::CascadeClassifier faceDetector;
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if(!faceDetector.load("./haarcascade_frontalface_alt.xml"))//Testing to see if cascade_frontalface.xml is available (necessary for the program to work)
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cv::CascadeClassifier faceDetector;
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{
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if(!faceDetector.load("./haarcascade_frontalface_alt.xml"))//Testing to see if cascade_frontalface.xml is available (necessary for the program to work)
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std::cerr<<"[ERROR] Unable to load face cascade"<<std::endl;
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{
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return -1;
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std::cerr<<"[ERROR] Unable to load face cascade"<<std::endl;
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};
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return -1;
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};
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while (true)
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{
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while (true)
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//Create a matrix to store the image from the cam
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{
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cv::Mat frame;
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//Create a matrix to store the image from the cam
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//Wait for a new frame from camera and store it into "frame"
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cv::Mat frame;
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cap.read(frame);
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//Wait for a new frame from camera and store it into "frame"
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cap.read(frame);
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//Check if we succeeded
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if (frame.empty()) //If the camera records a blank frame, returns an error.
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//Check if we succeeded
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{
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if (frame.empty()) //If the camera records a blank frame, returns an error.
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std::cerr<<"[ERROR] Blank frame grabbed"<<std::endl;
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{
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break;
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std::cerr<<"[ERROR] Blank frame grabbed"<<std::endl;
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}
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break;
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}
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if(isDiscardData) //This function delays the beginning of the analysis of the data to avoid processing frames taken during white balancing.
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{
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if(isDiscardData) //This function delays the beginning of the analysis of the data to avoid processing frames taken during white balancing.
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countDiscard++;
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{
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if (countDiscard == DISCARD_DURATION*FPS)
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countDiscard++;
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{
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if (countDiscard == DISCARD_DURATION*FPS)
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isDiscardData=false;
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{
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}
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isDiscardData=false;
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}
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}
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else
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}
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{
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else
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cv::Mat frame_gray;
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{
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cv::cvtColor(frame, frame_gray, cv::COLOR_BGR2GRAY);
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cv::Mat frame_gray;
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//cv::imshow("Gray", frame_gray); //Shows frame in greyscale
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cv::cvtColor(frame, frame_gray, cv::COLOR_BGR2GRAY);
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std::vector<cv::Rect> faceRectangles;
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//cv::imshow("Gray", frame_gray); //Shows frame in greyscale
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faceDetector.detectMultiScale(frame_gray, faceRectangles, 1.1, 3, 0, cv::Size(20,20)); //Detects face
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std::vector<cv::Rect> faceRectangles;
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faceDetector.detectMultiScale(frame_gray, faceRectangles, 1.1, 3, 0, cv::Size(20,20)); //Detects face
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if (faceRectangles.size() > 0)
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cv::rectangle(frame, faceRectangles[0], cv::Scalar(0,0,255),1,1,0);
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if (faceRectangles.size() > 0)
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cv::rectangle(frame, faceRectangles[0], cv::Scalar(0,0,255),1,1,0);
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cv::Rect foreheadROI; //create a forehead ROI equal to the face ROI slightly moved upward.
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if (faceRectangles.size() > 0)
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cv::Rect foreheadROI; //create a forehead ROI equal to the face ROI slightly moved upward.
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{
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if (faceRectangles.size() > 0)
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foreheadROI = faceRectangles[0];
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{
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foreheadROI.height *= 0.3;
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foreheadROI = faceRectangles[0];
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foreheadROI.height *= 0.3;
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cv::Mat frame_forehead = frame(foreheadROI);
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cv::Scalar avg_forehead = mean(frame_forehead); //calculates mean of object frame_forehead
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cv::Mat frame_forehead = frame(foreheadROI);
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cv::Scalar avg_forehead = mean(frame_forehead); //calculates mean of object frame_forehead
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//Buffer of average value for the green channel over the forehead ROI
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cv::Mat greenSignal(1, FPS*BUFFER_DURATION, CV_64F);
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//Buffer of average value for the green channel over the forehead ROI
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if (!isBufferFull)
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cv::Mat greenSignal(1, FPS*BUFFER_DURATION, CV_64F);
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{
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if (!isBufferFull)
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std::cout << "sampleIdBuffer= " << sampleIdBuffer << " / " << FPS*BUFFER_DURATION << std::endl;
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{
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greenSignal.at<double>(0, sampleIdBuffer) = avg_forehead[1] ;
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std::cout << "sampleIdBuffer= " << sampleIdBuffer << " / " << FPS*BUFFER_DURATION << std::endl;
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sampleIdBuffer++;
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greenSignal.at<double>(0, sampleIdBuffer) = avg_forehead[1] ;
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if (sampleIdBuffer == FPS*BUFFER_DURATION)
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sampleIdBuffer++;
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{
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if (sampleIdBuffer == FPS*BUFFER_DURATION)
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isBufferFull = true;
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{
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std::cout<<"greenSignal= "<<greenSignal<<std::endl;
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isBufferFull = true;
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}
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std::cout<<"greenSignal= "<<greenSignal<<std::endl;
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}
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}
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else
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}
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{
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else
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{
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//Normalisation of our signal
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std::vector<double> greenSignalNormalized;
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//Normalisation of our signal
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cv::Scalar mean, stddev;
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std::vector<double> greenSignalNormalized;
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cv::meanStdDev(greenSignal, mean, stddev);
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cv::Scalar mean, stddev;
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for (int l_sample=0; l_sample < FPS*BUFFER_DURATION; l_sample++)
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cv::meanStdDev(greenSignal, mean, stddev);
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{
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for (int l_sample=0; l_sample < FPS*BUFFER_DURATION; l_sample++)
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greenSignalNormalized.push_back((greenSignal.at<double>(0, l_sample)-mean[0])/stddev[0]);
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{
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}
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greenSignalNormalized.push_back((greenSignal.at<double>(0, l_sample)-mean[0])/stddev[0]);
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//This is used in the main function to display the signal
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}
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int range[2] = {0, (int)(FPS*BUFFER_DURATION)};
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//This is used in the main function to display the signal
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cv::imshow("green", plotGraph(greenSignalNormalized, range));
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int range[2] = {0, (int)(FPS*BUFFER_DURATION)};
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cv::imshow("green", plotGraph(greenSignalNormalized, range));
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cv::Mat greenFFT; //Fast Fourier Transform
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std::vector<double> greenFFTModule;
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cv::Mat greenFFT; //Fast Fourier Transform
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cv::dft(greenSignalNormalized,greenFFT,cv::DFT_ROWS|cv::DFT_COMPLEX_OUTPUT);
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std::vector<double> greenFFTModule;
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cv::Mat planes[] = {cv::Mat::zeros(greenSignalNormalized.size(),1, CV_64F),
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cv::dft(greenSignalNormalized,greenFFT,cv::DFT_ROWS|cv::DFT_COMPLEX_OUTPUT);
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cv::Mat::zeros(greenSignalNormalized.size(),1, CV_64F)};
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cv::Mat planes[] = {cv::Mat::zeros(greenSignalNormalized.size(),1, CV_64F),
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cv::split(greenFFT, planes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
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cv::Mat::zeros(greenSignalNormalized.size(),1, CV_64F)};
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greenFFTModule.clear();
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cv::split(greenFFT, planes); // planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
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for (int l=0; l < planes[1].cols; l++)
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greenFFTModule.clear();
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{
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for (int l=0; l < planes[1].cols; l++)
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double moduleFFT = pow(planes[1].at<double>(0,l),2) +
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{
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pow(planes[0].at<double>(0,l),2);
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double moduleFFT = pow(planes[1].at<double>(0,l),2) +
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greenFFTModule.push_back(sqrt(moduleFFT));
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pow(planes[0].at<double>(0,l),2);
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}
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greenFFTModule.push_back(sqrt(moduleFFT));
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// display green FFT
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}
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cv::imshow("FFT module green", plotGraph(greenFFTModule, range));
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// display green FFT
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cv::imshow("FFT module green", plotGraph(greenFFTModule, range));
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float maxValue=-1;
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int indexValue=0;
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float maxValue=-1;
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for(auto i=0.5*(BUFFER_DURATION);i<(4*BUFFER_DURATION);i++)
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int indexValue=0;
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{
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for(auto i=0.5*(BUFFER_DURATION);i<(4*BUFFER_DURATION);i++)
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if(greenFFTModule[i]>maxValue)
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{
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{
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if(greenFFTModule[i]>maxValue)
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maxValue=greenFFTModule[i];
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{
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indexValue=i;
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maxValue=greenFFTModule[i];
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}
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indexValue=i;
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}
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}
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float HRBPM=(indexValue*60.0)/(BUFFER_DURATION);
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}
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std::cout<<HRBPM << std::endl;
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float HRBPM=(indexValue*60.0)/(BUFFER_DURATION);
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}
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std::cout<<HRBPM << std::endl;
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}
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}
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}
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}
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}
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cv::imshow("Color", frame); //shows the colored frame
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if (cv::waitKey(1000.0/FPS)>=0) //Stops after 1000/FPS frames
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cv::imshow("Color", frame); //shows the colored frame
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{
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if (cv::waitKey(1000.0/FPS)>=0) //Stops after 1000/FPS frames
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break;
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{
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}
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break;
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}
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}
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}
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}
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}
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