我有一个图像如下:
谁能告诉我如何检测其中的圆数。我正在使用霍夫圆变换来实现这一点,这是我的代码:
# import the necessary packages
import numpy as np
import sys
import cv2
# load the image, clone it for output, and then convert it to grayscale
image = cv2.imread(str(sys.argv[1]))
output = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect circles in the image
circles = cv2.HoughCircles(gray, cv2.cv.CV_HOUGH_GRADIENT, 1.2, 5)
no_of_circles = 0
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
circles = np.round(circles[0, :]).astype("int")
no_of_circles = len(circles)
# loop over the (x, y) coordinates and radius of the circles
for (x, y, r) in circles:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
cv2.circle(output, (x, y), r, (0, 255, 0), 4)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
# show the output image
cv2.imshow("output", np.hstack([image, output]))
print 'no of circles',no_of_circles
我收到此代码的错误答案。有人可以告诉我我哪里出错了吗?
最佳答案
我尝试了一种技巧来检测所有圈子。
我手动找到了HoughCircles
参数
HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, 50, 40, 46, 0, 0 );
棘手的部分是
flip( src, flipped, 1 );
hconcat( src,flipped, flipped );
hconcat( flipped, src, src );
flip( src, flipped, 0 );
vconcat( src,flipped, flipped );
vconcat( flipped, src, src );
flip( src, src, -1 );
在检测之前将创建如下所示的模型。
结果是这样的
C++代码可以轻松转换为python
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, char** argv)
{
Mat src, src_gray, flipped, display;
if (argc < 2)
{
std::cerr<<"No input image specified\n";
return -1;
}
// Read the image
src = imread( argv[1], 1 );
if( src.empty() )
{
std::cerr<<"Invalid input image\n";
return -1;
}
flip( src, flipped, 1 );
hconcat( src,flipped, flipped );
hconcat( flipped, src, src );
flip( src, flipped, 0 );
vconcat( src,flipped, flipped );
vconcat( flipped, src, src );
flip( src, src, -1 );
// Convert it to gray
cvtColor( src, src_gray, COLOR_BGR2GRAY );
// Reduce the noise so we avoid false circle detection
GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );
// will hold the results of the detection
std::vector<Vec3f> circles;
// runs the actual detection
HoughCircles( src_gray, circles, HOUGH_GRADIENT, 1, 50, 40, 46, 0, 0 );
// clone the colour, input image for displaying purposes
display = src.clone();
Rect rect_src(display.cols / 3, display.rows / 3, display.cols / 3, display.rows / 3 );
rectangle( display, rect_src, Scalar(255,0,0) );
for( size_t i = 0; i < circles.size(); i++ )
{
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
Rect r = Rect( center.x-radius, center.y-radius, radius * 2, radius * 2 );
Rect intersection_rect = r & rect_src;
if( intersection_rect.width * intersection_rect.height > r.width * r.height / 3 )
{
// circle center
circle( display, center, 3, Scalar(0,255,0), -1, 8, 0 );
// circle outline
circle( display, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
}
// shows the results
imshow( "results", display(rect_src));
// get user key
waitKey();
return 0;
}
关于python - 使用OpenCV查找图像中的圆圈数,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/34216130/