本篇讲解使用opencv提供的流光法算法接口,实现物体跟踪。范例代码为参考修改tvl1_optical_flow.cpp实现。
#include <iostream> #include <fstream> #include "opencv2/video/tracking.hpp" #include "opencv2/highgui/highgui.hpp" using namespace cv; using namespace std; inline bool isFlowCorrect(Point2f u) { return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.y) < 1e9; } static Vec3b computeColor(float fx, float fy) { static bool first = true; // relative lengths of color transitions: // these are chosen based on perceptual similarity // (e.g. one can distinguish more shades between red and yellow // than between yellow and green) const int RY = 15; const int YG = 6; const int GC = 4; const int CB = 11; const int BM = 13; const int MR = 6; const int NCOLS = RY + YG + GC + CB + BM + MR; static Vec3i colorWheel[NCOLS]; if (first){ int k = 0; for (int i = 0; i < RY; ++i, ++k) colorWheel[k] = Vec3i(255, 255 * i / RY, 0); for (int i = 0; i < YG; ++i, ++k) colorWheel[k] = Vec3i(255 - 255 * i / YG, 255, 0); for (int i = 0; i < GC; ++i, ++k) colorWheel[k] = Vec3i(0, 255, 255 * i / GC); for (int i = 0; i < CB; ++i, ++k) colorWheel[k] = Vec3i(0, 255 - 255 * i / CB, 255); for (int i = 0; i < BM; ++i, ++k) colorWheel[k] = Vec3i(255 * i / BM, 0, 255); for (int i = 0; i < MR; ++i, ++k) colorWheel[k] = Vec3i(255, 0, 255 - 255 * i / MR); first = false; } const float rad = sqrt(fx * fx + fy * fy); const float a = atan2(-fy, -fx) / (float)CV_PI; const float fk = (a + 1.0f) / 2.0f * (NCOLS - 1); const int k0 = static_cast<int>(fk); const int k1 = (k0 + 1) % NCOLS; const float f = fk - k0; Vec3b pix; for (int b = 0; b < 3; b++) { const float col0 = colorWheel[k0][b] / 255.f; const float col1 = colorWheel[k1][b] / 255.f; float col = (1 - f) * col0 + f * col1; if (rad <= 1) col = 1 - rad * (1 - col); // increase saturation with radius else col *= .75; // out of range pix[2 - b] = static_cast<uchar>(255.f * col); } return pix; } static void drawOpticalFlow(const Mat_<Point2f>& flow, Mat& dst, float maxmotion = -1) { dst.create(flow.size(), CV_8UC3); dst.setTo(Scalar::all(0)); // determine motion range: float maxrad = maxmotion; if (maxmotion <= 0) { maxrad = 1; for (int y = 0; y < flow.rows; ++y) { for (int x = 0; x < flow.cols; ++x) { Point2f u = flow(y, x); if (!isFlowCorrect(u)) continue; maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y)); } } } for (int y = 0; y < flow.rows; ++y) { for (int x = 0; x < flow.cols; ++x) { Point2f u = flow(y, x); if (isFlowCorrect(u)) dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad); } } } int main(int argc, const char* argv[]) { Mat frame0; Mat frame1; Mat_<Point2f> flow; Ptr<DenseOpticalFlow> tvl1 = createOptFlow_DualTVL1(); Mat out; if (argc < 2){ cerr << "Usage : " << argv[0] << "<video>" << endl; return -1; } VideoCapture cap; cap.open(argv[1]); while(1){ cap >> frame0; if(frame0.empty()){ cerr<< "video is over!!" << endl; break; } cvtColor(frame0, frame0, CV_BGR2GRAY); if(!frame1.empty()){ const double start = (double)getTickCount(); tvl1->calc(frame0, frame1, flow); const double timeSec = (getTickCount() - start) / getTickFrequency(); cout << "calcOpticalFlowDual_TVL1 : " << timeSec << " sec" << endl; drawOpticalFlow(flow, out); imshow("out", out); imshow("src", frame0); waitKey(10); } frame0.copyTo(frame1); } waitKey(); return 0; }
1、创建了一个DenseOpticalFlow实例,同时获得打开了需要跟踪处理的video视频到cap中。
Ptr<DenseOpticalFlow> tvl1 = createOptFlow_DualTVL1(); Mat out; if (argc < 2){ cerr << "Usage : " << argv[0] << "<video>" << endl; return -1; } VideoCapture cap; cap.open(argv[1]);
2、在循环中,不断的读取video的帧数据到frame0中,接着cvtColor将frame0中的数据,灰阶化。判断到存储前一帧数据为空,也就是表示 刚刚读取到第一帧数据时候,不进入处理函数中,直接跳过。最后将frame0中的帧数据,保存到frame1中。frame0进入下一次循环,获得新一帧 数据。
while(1){ cap >> frame0; if(frame0.empty()){ cerr<< "video is over!!" << endl; break; } cvtColor(frame0, frame0, CV_BGR2GRAY); if(!frame1.empty()){ ........... ........... } frame0.copyTo(frame1); }
3、当检测到frame1保存了前一帧数据之后,进入到流光法计算中。首先获得当前时钟getTickCount。使用tvl1->calc分别传入当前 帧(frame0)和前一帧(frame1),将获得的位置偏移保存到flow中。接着计算出calc函数处理花费的时间,之后使用函数 drawOpticalFlow,利用flow中的位置偏移,根据偏移位置的方向和速度,从而在out图像,对应位置赋予不同的颜色和饱和度。最后将 当前帧图像和处理之后的out图像分别显示出来。
const double start = (double)getTickCount(); tvl1->calc(frame0, frame1, flow); const double timeSec = (getTickCount() - start) / getTickFrequency(); cout << "calcOpticalFlowDual_TVL1 : " << timeSec << " sec" << endl; drawOpticalFlow(flow, out); imshow("out", out); imshow("src", frame0); waitKey(10);
对应的效果演示如下:
原文地址:http://blog.csdn.net/u011630458/article/details/46535905