341 lines
12 KiB
C++
341 lines
12 KiB
C++
// The "Square Detector" program.
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// It loads several images sequentially and tries to find squares in
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// each image
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#include "opencv2/core/core.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/ocl/ocl.hpp"
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#include <iostream>
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#include <math.h>
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#include <string.h>
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using namespace cv;
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using namespace std;
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#define ACCURACY_CHECK
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#ifdef ACCURACY_CHECK
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// check if two vectors of vector of points are near or not
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// prior assumption is that they are in correct order
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static bool checkPoints(
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vector< vector<Point> > set1,
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vector< vector<Point> > set2,
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int maxDiff = 5)
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{
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if(set1.size() != set2.size())
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{
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return false;
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}
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for(vector< vector<Point> >::iterator it1 = set1.begin(), it2 = set2.begin();
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it1 < set1.end() && it2 < set2.end(); it1 ++, it2 ++)
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{
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vector<Point> pts1 = *it1;
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vector<Point> pts2 = *it2;
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if(pts1.size() != pts2.size())
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{
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return false;
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}
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for(size_t i = 0; i < pts1.size(); i ++)
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{
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Point pt1 = pts1[i], pt2 = pts2[i];
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if(std::abs(pt1.x - pt2.x) > maxDiff ||
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std::abs(pt1.y - pt2.y) > maxDiff)
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{
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return false;
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}
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}
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}
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return true;
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}
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#endif
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int thresh = 50, N = 11;
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const char* wndname = "OpenCL Square Detection Demo";
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// helper function:
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// finds a cosine of angle between vectors
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// from pt0->pt1 and from pt0->pt2
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static double angle( Point pt1, Point pt2, Point pt0 )
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{
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double dx1 = pt1.x - pt0.x;
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double dy1 = pt1.y - pt0.y;
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double dx2 = pt2.x - pt0.x;
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double dy2 = pt2.y - pt0.y;
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return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
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}
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// returns sequence of squares detected on the image.
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// the sequence is stored in the specified memory storage
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static void findSquares( const Mat& image, vector<vector<Point> >& squares )
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{
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squares.clear();
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Mat pyr, timg, gray0(image.size(), CV_8U), gray;
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// down-scale and upscale the image to filter out the noise
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pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
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pyrUp(pyr, timg, image.size());
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vector<vector<Point> > contours;
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// find squares in every color plane of the image
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for( int c = 0; c < 3; c++ )
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{
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int ch[] = {c, 0};
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mixChannels(&timg, 1, &gray0, 1, ch, 1);
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// try several threshold levels
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for( int l = 0; l < N; l++ )
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{
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// hack: use Canny instead of zero threshold level.
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// Canny helps to catch squares with gradient shading
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if( l == 0 )
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{
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// apply Canny. Take the upper threshold from slider
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// and set the lower to 0 (which forces edges merging)
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Canny(gray0, gray, 0, thresh, 5);
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// dilate canny output to remove potential
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// holes between edge segments
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dilate(gray, gray, Mat(), Point(-1,-1));
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}
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else
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{
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// apply threshold if l!=0:
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// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
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cv::threshold(gray0, gray, (l+1)*255/N, 255, THRESH_BINARY);
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}
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// find contours and store them all as a list
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findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
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vector<Point> approx;
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// test each contour
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for( size_t i = 0; i < contours.size(); i++ )
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{
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// approximate contour with accuracy proportional
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// to the contour perimeter
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approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
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// square contours should have 4 vertices after approximation
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// relatively large area (to filter out noisy contours)
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// and be convex.
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// Note: absolute value of an area is used because
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// area may be positive or negative - in accordance with the
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// contour orientation
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if( approx.size() == 4 &&
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fabs(contourArea(Mat(approx))) > 1000 &&
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isContourConvex(Mat(approx)) )
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{
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double maxCosine = 0;
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for( int j = 2; j < 5; j++ )
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{
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// find the maximum cosine of the angle between joint edges
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double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
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maxCosine = MAX(maxCosine, cosine);
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}
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// if cosines of all angles are small
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// (all angles are ~90 degree) then write quandrange
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// vertices to resultant sequence
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if( maxCosine < 0.3 )
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squares.push_back(approx);
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}
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}
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}
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}
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}
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// returns sequence of squares detected on the image.
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// the sequence is stored in the specified memory storage
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static void findSquares_ocl( const Mat& image, vector<vector<Point> >& squares )
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{
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squares.clear();
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Mat gray;
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cv::ocl::oclMat pyr_ocl, timg_ocl, gray0_ocl, gray_ocl;
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// down-scale and upscale the image to filter out the noise
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ocl::pyrDown(ocl::oclMat(image), pyr_ocl);
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ocl::pyrUp(pyr_ocl, timg_ocl);
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vector<vector<Point> > contours;
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vector<cv::ocl::oclMat> gray0s;
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ocl::split(timg_ocl, gray0s); // split 3 channels into a vector of oclMat
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// find squares in every color plane of the image
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for( int c = 0; c < 3; c++ )
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{
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gray0_ocl = gray0s[c];
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// try several threshold levels
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for( int l = 0; l < N; l++ )
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{
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// hack: use Canny instead of zero threshold level.
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// Canny helps to catch squares with gradient shading
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if( l == 0 )
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{
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// do canny on OpenCL device
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// apply Canny. Take the upper threshold from slider
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// and set the lower to 0 (which forces edges merging)
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cv::ocl::Canny(gray0_ocl, gray_ocl, 0, thresh, 5);
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// dilate canny output to remove potential
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// holes between edge segments
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ocl::dilate(gray_ocl, gray_ocl, Mat(), Point(-1,-1));
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gray = Mat(gray_ocl);
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}
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else
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{
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// apply threshold if l!=0:
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// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
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cv::ocl::threshold(gray0_ocl, gray_ocl, (l+1)*255/N, 255, THRESH_BINARY);
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gray = gray_ocl;
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}
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// find contours and store them all as a list
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findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
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vector<Point> approx;
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// test each contour
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for( size_t i = 0; i < contours.size(); i++ )
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{
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// approximate contour with accuracy proportional
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// to the contour perimeter
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approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
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// square contours should have 4 vertices after approximation
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// relatively large area (to filter out noisy contours)
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// and be convex.
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// Note: absolute value of an area is used because
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// area may be positive or negative - in accordance with the
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// contour orientation
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if( approx.size() == 4 &&
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fabs(contourArea(Mat(approx))) > 1000 &&
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isContourConvex(Mat(approx)) )
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{
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double maxCosine = 0;
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for( int j = 2; j < 5; j++ )
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{
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// find the maximum cosine of the angle between joint edges
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double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
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maxCosine = MAX(maxCosine, cosine);
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}
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// if cosines of all angles are small
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// (all angles are ~90 degree) then write quandrange
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// vertices to resultant sequence
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if( maxCosine < 0.3 )
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squares.push_back(approx);
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}
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}
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}
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}
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}
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// the function draws all the squares in the image
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static void drawSquares( Mat& image, const vector<vector<Point> >& squares )
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{
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for( size_t i = 0; i < squares.size(); i++ )
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{
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const Point* p = &squares[i][0];
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int n = (int)squares[i].size();
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polylines(image, &p, &n, 1, true, Scalar(0,255,0), 3, CV_AA);
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}
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}
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// draw both pure-C++ and ocl square results onto a single image
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static Mat drawSquaresBoth( const Mat& image,
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const vector<vector<Point> >& sqsCPP,
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const vector<vector<Point> >& sqsOCL
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)
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{
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Mat imgToShow(Size(image.cols * 2, image.rows), image.type());
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Mat lImg = imgToShow(Rect(Point(0, 0), image.size()));
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Mat rImg = imgToShow(Rect(Point(image.cols, 0), image.size()));
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image.copyTo(lImg);
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image.copyTo(rImg);
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drawSquares(lImg, sqsCPP);
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drawSquares(rImg, sqsOCL);
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float fontScale = 0.8f;
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Scalar white = Scalar::all(255), black = Scalar::all(0);
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putText(lImg, "C++", Point(10, 20), FONT_HERSHEY_COMPLEX_SMALL, fontScale, black, 2);
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putText(rImg, "OCL", Point(10, 20), FONT_HERSHEY_COMPLEX_SMALL, fontScale, black, 2);
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putText(lImg, "C++", Point(10, 20), FONT_HERSHEY_COMPLEX_SMALL, fontScale, white, 1);
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putText(rImg, "OCL", Point(10, 20), FONT_HERSHEY_COMPLEX_SMALL, fontScale, white, 1);
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return imgToShow;
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}
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int main(int argc, char** argv)
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{
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const char* keys =
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"{ i | input | | specify input image }"
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"{ o | output | squares_output.jpg | specify output save path}"
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"{ h | help | false | print help message }";
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CommandLineParser cmd(argc, argv, keys);
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string inputName = cmd.get<string>("i");
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string outfile = cmd.get<string>("o");
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if(cmd.get<bool>("help"))
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{
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cout << "Usage : squares [options]" << endl;
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cout << "Available options:" << endl;
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cmd.printParams();
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return EXIT_SUCCESS;
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}
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int iterations = 10;
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namedWindow( wndname, CV_WINDOW_AUTOSIZE );
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vector<vector<Point> > squares_cpu, squares_ocl;
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Mat image = imread(inputName, 1);
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if( image.empty() )
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{
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cout << "Couldn't load " << inputName << endl;
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return EXIT_FAILURE;
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}
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int j = iterations;
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int64 t_ocl = 0, t_cpp = 0;
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//warm-ups
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cout << "warming up ..." << endl;
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findSquares(image, squares_cpu);
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findSquares_ocl(image, squares_ocl);
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#ifdef ACCURACY_CHECK
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cout << "Checking ocl accuracy ... " << endl;
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cout << (checkPoints(squares_cpu, squares_ocl) ? "Pass" : "Failed") << endl;
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#endif
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do
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{
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int64 t_start = cv::getTickCount();
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findSquares(image, squares_cpu);
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t_cpp += cv::getTickCount() - t_start;
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t_start = cv::getTickCount();
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findSquares_ocl(image, squares_ocl);
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t_ocl += cv::getTickCount() - t_start;
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cout << "run loop: " << j << endl;
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}
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while(--j);
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cout << "cpp average time: " << 1000.0f * (double)t_cpp / getTickFrequency() / iterations << "ms" << endl;
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cout << "ocl average time: " << 1000.0f * (double)t_ocl / getTickFrequency() / iterations << "ms" << endl;
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Mat result = drawSquaresBoth(image, squares_cpu, squares_ocl);
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imshow(wndname, result);
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imwrite(outfile, result);
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cvWaitKey(0);
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return EXIT_SUCCESS;
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}
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