mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 12:40:05 +08:00
337 lines
9.4 KiB
C++
337 lines
9.4 KiB
C++
#include <iostream>
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#include <vector>
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#include "opencv2/core.hpp"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/video.hpp"
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#include "opencv2/cudaoptflow.hpp"
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#include "opencv2/cudaimgproc.hpp"
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#include "opencv2/cudaarithm.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::cuda;
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static void download(const GpuMat& d_mat, vector<Point2f>& vec)
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{
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vec.resize(d_mat.cols);
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Mat mat(1, d_mat.cols, CV_32FC2, (void*)&vec[0]);
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d_mat.download(mat);
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}
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static void download(const GpuMat& d_mat, vector<uchar>& vec)
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{
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vec.resize(d_mat.cols);
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Mat mat(1, d_mat.cols, CV_8UC1, (void*)&vec[0]);
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d_mat.download(mat);
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}
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static void drawArrows(Mat& frame, const vector<Point2f>& prevPts, const vector<Point2f>& nextPts, const vector<uchar>& status, Scalar line_color = Scalar(0, 0, 255))
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{
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for (size_t i = 0; i < prevPts.size(); ++i)
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{
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if (status[i])
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{
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int line_thickness = 1;
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Point p = prevPts[i];
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Point q = nextPts[i];
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double angle = atan2((double) p.y - q.y, (double) p.x - q.x);
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double hypotenuse = sqrt( (double)(p.y - q.y)*(p.y - q.y) + (double)(p.x - q.x)*(p.x - q.x) );
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if (hypotenuse < 1.0)
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continue;
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// Here we lengthen the arrow by a factor of three.
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q.x = (int) (p.x - 3 * hypotenuse * cos(angle));
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q.y = (int) (p.y - 3 * hypotenuse * sin(angle));
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// Now we draw the main line of the arrow.
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line(frame, p, q, line_color, line_thickness);
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// Now draw the tips of the arrow. I do some scaling so that the
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// tips look proportional to the main line of the arrow.
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p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4));
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line(frame, p, q, line_color, line_thickness);
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p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
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line(frame, p, q, line_color, line_thickness);
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}
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}
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}
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inline bool isFlowCorrect(Point2f u)
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{
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return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
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}
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static Vec3b computeColor(float fx, float fy)
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{
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static bool first = true;
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// relative lengths of color transitions:
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// these are chosen based on perceptual similarity
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// (e.g. one can distinguish more shades between red and yellow
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// than between yellow and green)
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const int RY = 15;
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const int YG = 6;
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const int GC = 4;
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const int CB = 11;
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const int BM = 13;
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const int MR = 6;
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const int NCOLS = RY + YG + GC + CB + BM + MR;
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static Vec3i colorWheel[NCOLS];
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if (first)
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{
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int k = 0;
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for (int i = 0; i < RY; ++i, ++k)
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colorWheel[k] = Vec3i(255, 255 * i / RY, 0);
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for (int i = 0; i < YG; ++i, ++k)
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colorWheel[k] = Vec3i(255 - 255 * i / YG, 255, 0);
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for (int i = 0; i < GC; ++i, ++k)
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colorWheel[k] = Vec3i(0, 255, 255 * i / GC);
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for (int i = 0; i < CB; ++i, ++k)
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colorWheel[k] = Vec3i(0, 255 - 255 * i / CB, 255);
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for (int i = 0; i < BM; ++i, ++k)
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colorWheel[k] = Vec3i(255 * i / BM, 0, 255);
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for (int i = 0; i < MR; ++i, ++k)
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colorWheel[k] = Vec3i(255, 0, 255 - 255 * i / MR);
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first = false;
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}
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const float rad = sqrt(fx * fx + fy * fy);
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const float a = atan2(-fy, -fx) / (float)CV_PI;
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const float fk = (a + 1.0f) / 2.0f * (NCOLS - 1);
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const int k0 = static_cast<int>(fk);
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const int k1 = (k0 + 1) % NCOLS;
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const float f = fk - k0;
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Vec3b pix;
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for (int b = 0; b < 3; b++)
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{
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const float col0 = colorWheel[k0][b] / 255.0f;
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const float col1 = colorWheel[k1][b] / 255.0f;
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float col = (1 - f) * col0 + f * col1;
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if (rad <= 1)
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col = 1 - rad * (1 - col); // increase saturation with radius
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else
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col *= .75; // out of range
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pix[2 - b] = static_cast<uchar>(255.0 * col);
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}
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return pix;
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}
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static void drawOpticalFlow(const Mat_<float>& flowx, const Mat_<float>& flowy, Mat& dst, float maxmotion = -1)
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{
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dst.create(flowx.size(), CV_8UC3);
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dst.setTo(Scalar::all(0));
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// determine motion range:
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float maxrad = maxmotion;
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if (maxmotion <= 0)
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{
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maxrad = 1;
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for (int y = 0; y < flowx.rows; ++y)
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{
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for (int x = 0; x < flowx.cols; ++x)
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{
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Point2f u(flowx(y, x), flowy(y, x));
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if (!isFlowCorrect(u))
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continue;
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maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y));
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}
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}
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}
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for (int y = 0; y < flowx.rows; ++y)
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{
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for (int x = 0; x < flowx.cols; ++x)
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{
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Point2f u(flowx(y, x), flowy(y, x));
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if (isFlowCorrect(u))
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dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad);
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}
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}
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}
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static void showFlow(const char* name, const GpuMat& d_flow)
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{
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GpuMat planes[2];
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cuda::split(d_flow, planes);
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Mat flowx(planes[0]);
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Mat flowy(planes[1]);
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Mat out;
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drawOpticalFlow(flowx, flowy, out, 10);
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imshow(name, out);
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}
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template <typename T> inline T clamp (T x, T a, T b)
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{
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return ((x) > (a) ? ((x) < (b) ? (x) : (b)) : (a));
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}
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template <typename T> inline T mapValue(T x, T a, T b, T c, T d)
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{
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x = clamp(x, a, b);
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return c + (d - c) * (x - a) / (b - a);
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}
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int main(int argc, const char* argv[])
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{
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const char* keys =
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"{ h help | | print help message }"
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"{ l left | ../data/pic1.png | specify left image }"
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"{ r right | ../data/pic2.png | specify right image }"
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"{ flow | sparse | specify flow type [PyrLK] }"
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"{ gray | | use grayscale sources [PyrLK Sparse] }"
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"{ win_size | 21 | specify windows size [PyrLK] }"
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"{ max_level | 3 | specify max level [PyrLK] }"
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"{ iters | 30 | specify iterations count [PyrLK] }"
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"{ points | 4000 | specify points count [GoodFeatureToTrack] }"
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"{ min_dist | 0 | specify minimal distance between points [GoodFeatureToTrack] }";
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CommandLineParser cmd(argc, argv, keys);
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if (cmd.has("help") || !cmd.check())
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{
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cmd.printMessage();
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cmd.printErrors();
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return 0;
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}
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string fname0 = cmd.get<string>("left");
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string fname1 = cmd.get<string>("right");
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if (fname0.empty() || fname1.empty())
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{
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cerr << "Missing input file names" << endl;
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return -1;
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}
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string flow_type = cmd.get<string>("flow");
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bool is_sparse = true;
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if (flow_type == "sparse")
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{
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is_sparse = true;
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}
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else if (flow_type == "dense")
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{
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is_sparse = false;
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}
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else
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{
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cerr << "please specify 'sparse' or 'dense' as flow type" << endl;
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return -1;
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}
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bool useGray = cmd.has("gray");
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int winSize = cmd.get<int>("win_size");
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int maxLevel = cmd.get<int>("max_level");
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int iters = cmd.get<int>("iters");
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int points = cmd.get<int>("points");
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double minDist = cmd.get<double>("min_dist");
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Mat frame0 = imread(fname0);
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Mat frame1 = imread(fname1);
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if (frame0.empty() || frame1.empty())
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{
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cout << "Can't load input images" << endl;
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return -1;
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}
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if(is_sparse)
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{
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namedWindow("PyrLK [Sparse]", WINDOW_NORMAL);
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}
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else
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{
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namedWindow("PyrLK [Dense] Flow Field", WINDOW_NORMAL);
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}
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cout << "Image size : " << frame0.cols << " x " << frame0.rows << endl;
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cout << "Points count : " << points << endl;
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cout << endl;
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Mat frame0Gray;
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cv::cvtColor(frame0, frame0Gray, COLOR_BGR2GRAY);
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Mat frame1Gray;
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cv::cvtColor(frame1, frame1Gray, COLOR_BGR2GRAY);
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// goodFeaturesToTrack
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GpuMat d_frame0Gray(frame0Gray);
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GpuMat d_prevPts;
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Ptr<cuda::CornersDetector> detector = cuda::createGoodFeaturesToTrackDetector(d_frame0Gray.type(), points, 0.01, minDist);
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detector->detect(d_frame0Gray, d_prevPts);
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GpuMat d_frame0(frame0);
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GpuMat d_frame1(frame1);
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GpuMat d_frame1Gray(frame1Gray);
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GpuMat d_nextPts;
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GpuMat d_status;
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GpuMat d_flow(frame0.size(), CV_32FC2);
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if (is_sparse)
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{
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// Sparse
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Ptr<cuda::SparsePyrLKOpticalFlow> d_pyrLK_sparse = cuda::SparsePyrLKOpticalFlow::create(
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Size(winSize, winSize), maxLevel, iters);
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d_pyrLK_sparse->calc(useGray ? d_frame0Gray : d_frame0, useGray ? d_frame1Gray : d_frame1, d_prevPts, d_nextPts, d_status);
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// Draw arrows
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vector<Point2f> prevPts(d_prevPts.cols);
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download(d_prevPts, prevPts);
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vector<Point2f> nextPts(d_nextPts.cols);
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download(d_nextPts, nextPts);
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vector<uchar> status(d_status.cols);
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download(d_status, status);
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drawArrows(frame0, prevPts, nextPts, status, Scalar(255, 0, 0));
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imshow("PyrLK [Sparse]", frame0);
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}
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else {
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// Dense
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Ptr<cuda::DensePyrLKOpticalFlow> d_pyrLK_dense = cuda::DensePyrLKOpticalFlow::create(
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Size(winSize, winSize), maxLevel, iters);
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d_pyrLK_dense->calc(d_frame0Gray, d_frame1Gray, d_flow);
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// Draw flows
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showFlow("PyrLK [Dense] Flow Field", d_flow);
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}
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waitKey(0);
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return 0;
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} |