Webcam capture and imgshow low FPS
System information (version) - OpenCV => 2.4.9.1 - Operating System / Platform => Linux Ubuntu 64 Bit - Compiler => CMake 3.5.1
Detailed description
I use imgshow to prompt the webcam capture and add a rectangle and the subject found with FisherFace algorithm AT&T orl_face photo base. The problem is the program use 80% of CPU (i3) and the FPS of camera are very slow.
EDIT : I tweak the detectMultiScale parameters (flag,minSize,maxSize). It's the maxSize which change the FPS of camera. But, if I change it, there is no prediction (even if I change the minNeighbors parameter).
Steps to reproduce
#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
Mat m = imread(path, 1);
if (m.empty())
{
cerr << path << " could not be read." << endl;
continue;
}
Mat m2;
cvtColor(m,m2,CV_BGR2GRAY);
images.push_back(m2);
labels.push_back(atoi(classlabel.c_str()));
}
}
cout << endl << "Read finish";
}
int main(int argc, const char *argv[]) {
if (argc != 4) {
cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
exit(1);
}
string fn_haar = string(argv[1]);
string fn_csv = string(argv[2]);
int deviceId = atoi(argv[3]);
vector<Mat> images;
vector<int> labels;
try {
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
int im_width = images[0].cols;
int im_height = images[0].rows;
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
VideoCapture cap(deviceId);
if(!cap.isOpened()) {
cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
return -1;
}
Mat frame;
for(;;) {
cap >> frame;
Mat original = frame.clone();
Mat gray;
if(original.empty()){
cout << "An empty matrice has been detected" << endl;
break;
}
else if(original.channels()>1){
cout << "Matrice has been converted";
cvtColor(original, gray, CV_BGR2GRAY);
}
else gray = original;
vector< Rect_<int> > faces;
haar_cascade.detectMultiScale(gray, faces, 1.1, 3);
for(int i = 0; i < faces.size(); i++) {
Rect face_i = faces[i];
Mat face = gray(face_i);
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
int prediction = model->predict(face_resized);
rectangle(original, face_i, CV_RGB(0, 255,0), 1);
string box_text = format("Prediction = %d", prediction);
int pos_x = std::max(face_i.tl().x - 10 ...
if you do some profiling, you'll likely find, that most time is spend in the cascade detection so try: