Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

193 lines
5.6 KiB

#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID)
#include <opencv2/core/core.hpp>
#include <opencv2/core/internal.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include "opencv2/contrib/detection_based_tracker.hpp"
#include <vector>
#include <iostream>
#include <stdio.h>
#define DEBUGLOGS 1
#ifdef ANDROID
#include <android/log.h>
#define LOG_TAG "DETECTIONBASEDTRACKER__TEST_APPLICAT"
#define LOGD0(...) ((void)__android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__))
#define LOGI0(...) ((void)__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__))
#define LOGW0(...) ((void)__android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__))
#define LOGE0(...) ((void)__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__))
#else
#include <stdio.h>
#define LOGD0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGI0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGW0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#define LOGE0(_str, ...) do{printf(_str , ## __VA_ARGS__); printf("\n");fflush(stdout);} while(0)
#endif
#if DEBUGLOGS
#define LOGD(_str, ...) LOGD0(_str , ## __VA_ARGS__)
#define LOGI(_str, ...) LOGI0(_str , ## __VA_ARGS__)
#define LOGW(_str, ...) LOGW0(_str , ## __VA_ARGS__)
#define LOGE(_str, ...) LOGE0(_str , ## __VA_ARGS__)
#else
#define LOGD(...) do{} while(0)
#define LOGI(...) do{} while(0)
#define LOGW(...) do{} while(0)
#define LOGE(...) do{} while(0)
#endif
using namespace cv;
using namespace std;
#define ORIGINAL 0
#define SHOULD_USE_EXTERNAL_BUFFERS 1
static void usage()
{
LOGE0("usage: filepattern outfilepattern cascadefile");
LOGE0("\t where ");
LOGE0("\t filepattern --- pattern for the paths to the source images");
LOGE0("\t (e.g.\"./Videos/FACESJPG2/Faces2_%%08d.jpg\" ");
LOGE0("\t outfilepattern --- pattern for the paths for images which will be generated");
LOGE0("\t (e.g.\"./resFaces2_%%08d.jpg\" ");
LOGE0("\t cascadefile --- path to the cascade file");
LOGE0("\t (e.g.\"opencv/data/lbpcascades/lbpcascade_frontalface.xml\" ");
}
class CascadeDetectorAdapter: public DetectionBasedTracker::IDetector
{
public:
CascadeDetectorAdapter(cv::Ptr<cv::CascadeClassifier> detector):
Detector(detector)
{
CV_Assert(!detector.empty());
}
void detect(const cv::Mat &Image, std::vector<cv::Rect> &objects)
{
Detector->detectMultiScale(Image, objects, 1.1, 3, 0, minObjSize, maxObjSize);
}
virtual ~CascadeDetectorAdapter()
{}
private:
CascadeDetectorAdapter();
cv::Ptr<cv::CascadeClassifier> Detector;
};
static int test_FaceDetector(int argc, char *argv[])
{
if (argc < 4)
{
usage();
return -1;
}
const char* filepattern=argv[1];
const char* outfilepattern=argv[2];
const char* cascadefile=argv[3];
LOGD0("filepattern='%s'", filepattern);
LOGD0("outfilepattern='%s'", outfilepattern);
LOGD0("cascadefile='%s'", cascadefile);
vector<Mat> images;
{
char filename[256];
for(int n=1; ; n++)
{
snprintf(filename, sizeof(filename), filepattern, n);
LOGD("filename='%s'", filename);
Mat m0;
m0=imread(filename);
if (m0.empty())
{
LOGI0("Cannot read the file --- break");
break;
}
images.push_back(m0);
}
LOGD("read %d images", (int)images.size());
}
std::string cascadeFrontalfilename=cascadefile;
cv::Ptr<cv::CascadeClassifier> cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> MainDetector = new CascadeDetectorAdapter(cascade);
cascade = new cv::CascadeClassifier(cascadeFrontalfilename);
cv::Ptr<DetectionBasedTracker::IDetector> TrackingDetector = new CascadeDetectorAdapter(cascade);
DetectionBasedTracker::Parameters params;
DetectionBasedTracker fd(MainDetector, TrackingDetector, params);
fd.run();
Mat gray;
Mat m;
int64 tprev=getTickCount();
double freq=getTickFrequency();
int num_images=images.size();
for(int n=1; n <= num_images; n++)
{
int64 tcur=getTickCount();
int64 dt=tcur-tprev;
tprev=tcur;
double t_ms=((double)dt)/freq * 1000.0;
LOGD("\n\nSTEP n=%d from prev step %f ms\n", n, t_ms);
m=images[n-1];
CV_Assert(! m.empty());
cvtColor(m, gray, CV_BGR2GRAY);
fd.process(gray);
vector<Rect> result;
fd.getObjects(result);
for(size_t i=0; i < result.size(); i++)
{
Rect r=result[i];
CV_Assert(r.area() > 0);
Point tl=r.tl();
Point br=r.br();
Scalar color=Scalar(0, 250, 0);
rectangle(m, tl, br, color, 3);
}
}
char outfilename[256];
for(int n=1; n <= num_images; n++)
{
snprintf(outfilename, sizeof(outfilename), outfilepattern, n);
LOGD("outfilename='%s'", outfilename);
m=images[n-1];
imwrite(outfilename, m);
}
fd.stop();
return 0;
}
int main(int argc, char *argv[])
{
return test_FaceDetector(argc, argv);
}
#else // #if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(ANDROID)
#include <stdio.h>
int main()
{
printf("This sample works for UNIX or ANDROID only\n");
return 0;
}
#endif