mirror of
https://github.com/opencv/opencv.git
synced 2024-12-16 02:19:12 +08:00
331 lines
9.4 KiB
C++
331 lines
9.4 KiB
C++
#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;
|
|
} |