我已经尝试用OpenCV 2.4.1-2.4.4移植Square检测，但结果似乎很慢。由于提供了新的功能，我渴望迁移到新版本的OpenCV，但结果非常缓慢。
2.4版本的OpenCV代码。X是:
// The "Square Detector" program.
// It loads several images sequentially and tries to find squares in
// each image
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <math.h>
#include <string.h>
using namespace cv;
using namespace std;
int thresh = 50, N = 11;
const char* wndname = "Square Detection Demo";
// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
static double angle( Point pt1, Point pt2, Point pt0 )
{
double dx1 = pt1.x - pt0.x;
double dy1 = pt1.y - pt0.y;
double dx2 = pt2.x - pt0.x;
double dy2 = pt2.y - pt0.y;
return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}
// returns sequence of squares detected on the image.
// the sequence is stored in the specified memory storage
static void findSquares( const Mat& image, vector<vector<Point> >& squares )
{
squares.clear();
Mat pyr, timg, gray0(image.size(), CV_8U), gray;
// down-scale and upscale the image to filter out the noise
pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
pyrUp(pyr, timg, image.size());
vector<vector<Point> > contours;
// find squares in every color plane of the image
for( int c = 0; c < 3; c++ )
{
int ch[] = {c, 0};
mixChannels(&timg, 1, &gray0, 1, ch, 1);
// try several threshold levels
for( int l = 0; l < N; l++ )
{
// hack: use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading
if( l == 0 )
{
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
Canny(gray0, gray, 0, thresh, 5);
// dilate canny output to remove potential
// holes between edge segments
dilate(gray, gray, Mat(), Point(-1,-1));
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
gray = gray0 >= (l+1)*255/N;
}
// find contours and store them all as a list
findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
vector<Point> approx;
// test each contour
for( size_t i = 0; i < contours.size(); i++ )
{
// approximate contour with accuracy proportional
// to the contour perimeter
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if( approx.size() == 4 &&
fabs(contourArea(Mat(approx))) > 1000 &&
isContourConvex(Mat(approx)) )
{
double maxCosine = 0;
for( int j = 2; j < 5; j++ )
{
// find the maximum cosine of the angle between joint edges
double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
maxCosine = MAX(maxCosine, cosine);
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if( maxCosine < 0.3 )
squares.push_back(approx);
}
}
}
}
}
// the function draws all the squares in the image
static void drawSquares( Mat& image, const vector<vector<Point> >& squares )
{
for( size_t i = 0; i < squares.size(); i++ )
{
const Point* p = &squares[i][0];
int n = (int)squares[i].size();
polylines(image, &p, &n, 1, true, Scalar(0,255,0), 3, CV_AA);
}
imshow(wndname, image);
}
int main()
{
VideoCapture cap;
cap.open(0);
Mat frame,image;
namedWindow( "Square Detection Demo", 1 );
vector<vector<Point> > squares;
for(;;)
{
cap >> frame;
if( frame.empty() ){
break;
}
frame.copyTo(image);
if( image.empty() )
{
cout << "Couldn't load image" << endl;
continue;
}
findSquares(image, squares);
drawSquares(image, squares);
//imshow("Window", image);
int c = waitKey(1);
if( (char)c == 27 )
break;
}
return 0;
}
你可以注意到代码是Webcam可视化和OpenCV 2.4.X提供的方块代码的简单混合。
但是，我现在要放的OpenCV 2.1版本的等效代码要快得多:
#include <cv.h>
#include <highgui.h>
int thresh = 50;
IplImage* img = 0;
IplImage* img0 = 0;
CvMemStorage* storage = 0;
// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
double angle( CvPoint* pt1, CvPoint* pt2, CvPoint* pt0 )
{
double dx1 = pt1->x - pt0->x;
double dy1 = pt1->y - pt0->y;
double dx2 = pt2->x - pt0->x;
double dy2 = pt2->y - pt0->y;
return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}
// returns sequence of squares detected on the image.
// the sequence is stored in the specified memory storage
CvSeq* findSquares4( IplImage* img, CvMemStorage* storage )
{
CvSeq* contours;
int i, c, l, N = 11;
CvSize sz = cvSize( img->width & -2, img->height & -2 );
IplImage* timg = cvCloneImage( img ); // make a copy of input image
IplImage* gray = cvCreateImage( sz, 8, 1 );
IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 );
IplImage* tgray;
CvSeq* result;
double s, t;
// create empty sequence that will contain points -
// 4 points per square (the square's vertices)
CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );
// select the maximum ROI in the image
// with the width and height divisible by 2
cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ));
//cvSetImageROI( timg, cvRect( 0,0,50, 50 ));
// down-scale and upscale the image to filter out the noise
cvPyrDown( timg, pyr, 7 );
cvPyrUp( pyr, timg, 7 );
tgray = cvCreateImage( sz, 8, 1 );
// find squares in every color plane of the image
for( c = 0; c < 3; c++ )
{
// extract the c-th color plane
cvSetImageCOI( timg, c+1 );
cvCopy( timg, tgray, 0 );
// try several threshold levels
for( l = 0; l < N; l++ )
{
// hack: use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading
if( l == 0 )
{
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
cvCanny( tgray, gray, 0, thresh, 5 );
// dilate canny output to remove potential
// holes between edge segments
cvDilate( gray, gray, 0, 1 );
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
cvThreshold( tgray, gray, (l+1)*255/N, 255, CV_THRESH_BINARY );
}
// find contours and store them all as a list
cvFindContours( gray, storage, &contours, sizeof(CvContour),
CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );
// test each contour
while( contours )
{
// approximate contour with accuracy proportional
// to the contour perimeter
result = cvApproxPoly( contours, sizeof(CvContour), storage,
CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if( result->total == 4 &&
cvContourArea(result,CV_WHOLE_SEQ,0) > 1000 &&
cvCheckContourConvexity(result) )
{
s = 0;
for( i = 0; i < 5; i++ )
{
// find minimum angle between joint
// edges (maximum of cosine)
if( i >= 2 )
{
t = fabs(angle(
(CvPoint*)cvGetSeqElem( result, i ),
(CvPoint*)cvGetSeqElem( result, i-2 ),
(CvPoint*)cvGetSeqElem( result, i-1 )));
s = s > t ? s : t;
}
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if( s < 0.3 )
for( i = 0; i < 4; i++ )
cvSeqPush( squares,
(CvPoint*)cvGetSeqElem( result, i ));
}
// take the next contour
contours = contours->h_next;
}
}
}
// release all the temporary images
cvReleaseImage( &gray );
cvReleaseImage( &pyr );
cvReleaseImage( &tgray );
cvReleaseImage( &timg );
return squares;
}
// the function draws all the squares in the image
void drawSquares( IplImage* img, CvSeq* squares )
{
CvSeqReader reader;
IplImage* cpy = cvCloneImage( img );
int i;
// initialize reader of the sequence
cvStartReadSeq( squares, &reader, 0 );
// read 4 sequence elements at a time (all vertices of a square)
for( i = 0; i < squares->total; i += 4 )
{
CvPoint pt[4], *rect = pt;
int count = 4;
// read 4 vertices
CV_READ_SEQ_ELEM( pt[0], reader );
CV_READ_SEQ_ELEM( pt[1], reader );
CV_READ_SEQ_ELEM( pt[2], reader );
CV_READ_SEQ_ELEM( pt[3], reader );
// draw the square as a closed polyline
cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 3, CV_AA, 0 );
}
// show the resultant image
cvShowImage( "Squares", cpy );
cvReleaseImage( &cpy );
}
int main(int argc, char** argv){
// Crea una ventana llamada Original Image con un tamaño predeterminado.
cvNamedWindow("Original Image", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Squares", CV_WINDOW_AUTOSIZE);
// Crea la conexion con la Webcam.
CvCapture* capture = cvCreateCameraCapture(0);
if( !capture ){
throw "Error when reading steam_avi";
}
storage = cvCreateMemStorage(0);
while(true)
{
// Pongo el frame capturado dentro de la imagen originalImg.
img0 = cvQueryFrame(capture);
if(!img0){
break;
}
img = cvCloneImage( img0 );
// find and draw the squares
drawSquares( img, findSquares4( img, storage ) );
cvShowImage("Original Image", img0);
cvReleaseImage(&img);
// clear memory storage - reset free space position
cvClearMemStorage( storage );
// Espero a que me pulsen el ESC para salir del bucle infinito.
char c = cvWaitKey(10);
if( c == 27 ) break;
}
//cvReleaseImage(&img);
cvReleaseImage(&img0);
// clear memory storage - reset free space position
cvClearMemStorage( storage );
// Destruye la ventana “Original Image”.
cvDestroyWindow("Original Image");
cvDestroyWindow("Squares");
// Libera la memoria utilizada por la variable capture.
cvReleaseCapture(&capture);
}
我知道我可以使用一个颜色通道来加速x3，并改变其他参数来加速，但不知道为什么等效代码给出如此不同的执行时间。
有什么基本的我错过了吗?
我试着把工作代码放在每个人都可以尝试的地方，这样就不会浪费任何人的时间在模糊的问题上，比如:
最后忽略Canny并检查正方形的面积是否低于某些值(小于图像面积的20%)，这样就不会检测到不需要的正方形。至于获得相同正方形的多个结果，我不太担心它，因为我可以输入给定的正方形尽可能的模板图像进行比较。现在进入正方形图像的识别。感谢Chris至少阅读了这篇评论(我不能给你分数作为答案，因为这只是一个评论，但无论如何，谢谢你)。