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使用流光法实现物体跟踪

發(fā)布時(shí)間：2025/7/25 编程问答 12 豆豆

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簡介

　　本篇講解使用opencv提供的流光法算法接口，實(shí)現(xiàn)物體跟蹤。范例代碼為參考修改tvl1_optical_flow.cpp實(shí)現(xiàn)。

具體實(shí)現(xiàn)

實(shí)現(xiàn)代碼

#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 radiuselsecol *= .75; // out of rangepix[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; }

代碼講解

1、創(chuàng)建了一個(gè)DenseOpticalFlow實(shí)例，同時(shí)獲得打開了需要跟蹤處理的video視頻到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、在循環(huán)中，不斷的讀取video的幀數(shù)據(jù)到frame0中，接著cvtColor將frame0中的數(shù)據(jù)，灰階化。判斷到存儲(chǔ)前一幀數(shù)據(jù)為空，也就是表示剛剛讀取到第一幀數(shù)據(jù)時(shí)候，不進(jìn)入處理函數(shù)中，直接跳過。最后將frame0中的幀數(shù)據(jù)，保存到frame1中。frame0進(jìn)入下一次循環(huán)，獲得新一幀數(shù)據(jù)。 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、當(dāng)檢測(cè)到frame1保存了前一幀數(shù)據(jù)之后，進(jìn)入到流光法計(jì)算中。首先獲得當(dāng)前時(shí)鐘getTickCount。使用tvl1->calc分別傳入當(dāng)前幀(frame0)和前一幀(frame1)，將獲得的位置偏移保存到flow中。接著計(jì)算出calc函數(shù)處理花費(fèi)的時(shí)間，之后使用函數(shù) drawOpticalFlow，利用flow中的位置偏移，根據(jù)偏移位置的方向和速度，從而在out圖像，對(duì)應(yīng)位置賦予不同的顏色和飽和度。最后將當(dāng)前幀圖像和處理之后的out圖像分別顯示出來。 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);

效果演示

　對(duì)應(yīng)的效果演示如下:

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