#include <iostream> #include <vector> #include <opencv2/core.hpp> #include <opencv2/core/utility.hpp> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> #include <opencv2/video.hpp> #include <opencv2/cudaoptflow.hpp> #include <opencv2/cudaimgproc.hpp> #include <opencv2/cudaarithm.hpp> using namespace std; using namespace cv; using namespace cv::cuda; static void download(const GpuMat& d_mat, vector<Point2f>& vec) { vec.resize(d_mat.cols); Mat mat(1, d_mat.cols, CV_32FC2, (void*)&vec[0]); d_mat.download(mat); } static void download(const GpuMat& d_mat, vector<uchar>& vec) { vec.resize(d_mat.cols); Mat mat(1, d_mat.cols, CV_8UC1, (void*)&vec[0]); d_mat.download(mat); } static void drawArrows(Mat& frame, const vector<Point2f>& prevPts, const vector<Point2f>& nextPts, const vector<uchar>& status, Scalar line_color = Scalar(0, 0, 255)) { for (size_t i = 0; i < prevPts.size(); ++i) { if (status[i]) { int line_thickness = 1; Point p = prevPts[i]; Point q = nextPts[i]; double angle = atan2((double) p.y - q.y, (double) p.x - q.x); double hypotenuse = sqrt( (double)(p.y - q.y)*(p.y - q.y) + (double)(p.x - q.x)*(p.x - q.x) ); if (hypotenuse < 1.0) continue; // Here we lengthen the arrow by a factor of three. q.x = (int) (p.x - 3 * hypotenuse * cos(angle)); q.y = (int) (p.y - 3 * hypotenuse * sin(angle)); // Now we draw the main line of the arrow. line(frame, p, q, line_color, line_thickness); // Now draw the tips of the arrow. I do some scaling so that the // tips look proportional to the main line of the arrow. p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4)); p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4)); line(frame, p, q, line_color, line_thickness); p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4)); p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4)); line(frame, p, q, line_color, line_thickness); } } } inline bool isFlowCorrect(Point2f u) { return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && 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] / 255.0f; const float col1 = colorWheel[k1][b] / 255.0f; 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>(255.0 * col); } return pix; } static void drawOpticalFlow(const Mat_<float>& flowx, const Mat_<float>& flowy, Mat& dst, float maxmotion = -1) { dst.create(flowx.size(), CV_8UC3); dst.setTo(Scalar::all(0)); // determine motion range: float maxrad = maxmotion; if (maxmotion <= 0) { maxrad = 1; for (int y = 0; y < flowx.rows; ++y) { for (int x = 0; x < flowx.cols; ++x) { Point2f u(flowx(y, x), flowy(y, x)); if (!isFlowCorrect(u)) continue; maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y)); } } } for (int y = 0; y < flowx.rows; ++y) { for (int x = 0; x < flowx.cols; ++x) { Point2f u(flowx(y, x), flowy(y, x)); if (isFlowCorrect(u)) dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad); } } } static void showFlow(const char* name, const GpuMat& d_flow) { GpuMat planes[2]; cuda::split(d_flow, planes); Mat flowx(planes[0]); Mat flowy(planes[1]); Mat out; drawOpticalFlow(flowx, flowy, out, 10); imshow(name, out); } template <typename T> inline T clamp (T x, T a, T b) { return ((x) > (a) ? ((x) < (b) ? (x) : (b)) : (a)); } template <typename T> inline T mapValue(T x, T a, T b, T c, T d) { x = clamp(x, a, b); return c + (d - c) * (x - a) / (b - a); } int main(int argc, const char* argv[]) { const char* keys = "{ h help | | print help message }" "{ l left | ../data/pic1.png | specify left image }" "{ r right | ../data/pic2.png | specify right image }" "{ flow | sparse | specify flow type [PyrLK] }" "{ gray | | use grayscale sources [PyrLK Sparse] }" "{ win_size | 21 | specify windows size [PyrLK] }" "{ max_level | 3 | specify max level [PyrLK] }" "{ iters | 30 | specify iterations count [PyrLK] }" "{ points | 4000 | specify points count [GoodFeatureToTrack] }" "{ min_dist | 0 | specify minimal distance between points [GoodFeatureToTrack] }"; CommandLineParser cmd(argc, argv, keys); if (cmd.has("help") || !cmd.check()) { cmd.printMessage(); cmd.printErrors(); return 0; } string fname0 = cmd.get<string>("left"); string fname1 = cmd.get<string>("right"); if (fname0.empty() || fname1.empty()) { cerr << "Missing input file names" << endl; return -1; } string flow_type = cmd.get<string>("flow"); bool is_sparse = true; if (flow_type == "sparse") { is_sparse = true; } else if (flow_type == "dense") { is_sparse = false; } else { cerr << "please specify 'sparse' or 'dense' as flow type" << endl; return -1; } bool useGray = cmd.has("gray"); int winSize = cmd.get<int>("win_size"); int maxLevel = cmd.get<int>("max_level"); int iters = cmd.get<int>("iters"); int points = cmd.get<int>("points"); double minDist = cmd.get<double>("min_dist"); Mat frame0 = imread(fname0); Mat frame1 = imread(fname1); if (frame0.empty() || frame1.empty()) { cout << "Can't load input images" << endl; return -1; } cout << "Image size : " << frame0.cols << " x " << frame0.rows << endl; cout << "Points count : " << points << endl; cout << endl; Mat frame0Gray; cv::cvtColor(frame0, frame0Gray, COLOR_BGR2GRAY); Mat frame1Gray; cv::cvtColor(frame1, frame1Gray, COLOR_BGR2GRAY); // goodFeaturesToTrack GpuMat d_frame0Gray(frame0Gray); GpuMat d_prevPts; Ptr<cuda::CornersDetector> detector = cuda::createGoodFeaturesToTrackDetector(d_frame0Gray.type(), points, 0.01, minDist); detector->detect(d_frame0Gray, d_prevPts); GpuMat d_frame0(frame0); GpuMat d_frame1(frame1); GpuMat d_frame1Gray(frame1Gray); GpuMat d_nextPts; GpuMat d_status; GpuMat d_flow(frame0.size(), CV_32FC2); if (is_sparse) { // Sparse Ptr<cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cuda::SparsePyrLKOpticalFlow::create( Size(winSize, winSize), maxLevel, iters); d_pyrLK_sparse->calc(useGray ? d_frame0Gray : d_frame0, useGray ? d_frame1Gray : d_frame1, d_prevPts, d_nextPts, d_status); // Draw arrows vector<Point2f> prevPts(d_prevPts.cols); download(d_prevPts, prevPts); vector<Point2f> nextPts(d_nextPts.cols); download(d_nextPts, nextPts); vector<uchar> status(d_status.cols); download(d_status, status); namedWindow("PyrLK [Sparse]", WINDOW_NORMAL); drawArrows(frame0, prevPts, nextPts, status, Scalar(255, 0, 0)); imshow("PyrLK [Sparse]", frame0); } else { // Dense Ptr<cuda::DensePyrLKOpticalFlow> d_pyrLK_dense = cuda::DensePyrLKOpticalFlow::create( Size(winSize, winSize), maxLevel, iters); d_pyrLK_dense->calc(d_frame0Gray, d_frame1Gray, d_flow); // Draw flows namedWindow("PyrLK [Dense] Flow Field", WINDOW_NORMAL); showFlow("PyrLK [Dense] Flow Field", d_flow); } waitKey(0); return 0; }