OpenCV点目标检测未找到所有目标，并且找到的圆圈偏移

2022-01-09 00:00:00 python opencv computer-vision image-processing c

问题描述

我正在尝试检测黑/白点目标的中心，如图所示.我尝试使用 cv2.HoughCircles 方法，但 1，只能检测 2 到 3 个目标，2，当我将找到的圆圈重新绘制到图像上时，它们总是略微偏移.

I'm trying to detect the center of black/white dot targets, like in this picture. I've tried to use the cv2.HoughCircles method but 1, am only able to detect 2 to 3 targets, and 2, when I plot the found circles back onto the image, they're always offset slightly.

我是否使用了错误的方法?我应该使用 findContours 还是完全不同的东西?

Am I using the wrong method? Should I be using the findContours or something completely different?

这是我的代码:

import cv2
from cv2 import cv
import os
import numpy as np

def showme(pic):
    cv2.imshow('window',pic)
    cv2.waitKey()
    cv2.destroyAllWindows()


im=cv2.imread('small_test.jpg')

gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)

#I've tried blur,bw,tr...  all give me poor results.

blur = cv2.GaussianBlur(gray,(3,3),0)
n,bw = cv2.threshold(blur,120,255,cv2.THRESH_BINARY)
tr=cv2.adaptiveThreshold(blur,255,0,1,11,2)

circles = cv2.HoughCircles(gray, cv.CV_HOUGH_GRADIENT, 3, 100, None, 200, 100, 5, 16)

try:
    n = np.shape(circles)
    circles=np.reshape(circles,(n[1],n[2]))
    print circles
    for circle in circles:
        cv2.circle(im,(circle[0],circle[1]),circle[2],(0,0,255))
    showme(im)
except:
    print "no cicles found"

这是我当前的输出:

解决方案

播放我在另一篇文章中写的代码，我能够取得稍微好一点的结果:

Playing the code I wrote in another post, I was able to achieve a slightly better result:

一切都与参数有关.始终如此.

您应该尝试在此程序中调用 3 个重要的函数:cvSmooth()、cvCanny() 和 cvHoughCircles().他们每个人都有可能彻底改变结果.

There are 3 important functions that are called in this program that you should experiment with: cvSmooth(), cvCanny(), and cvHoughCircles(). Each of them has the potential to change the result drastically.

这里是 C 代码:

IplImage* img = NULL;
if ((img = cvLoadImage(argv[1]))== 0)
{
    printf("cvLoadImage failed
");
}

IplImage* gray = cvCreateImage(cvGetSize(img), IPL_DEPTH_8U, 1);
CvMemStorage* storage = cvCreateMemStorage(0);

cvCvtColor(img, gray, CV_BGR2GRAY);

// This is done so as to prevent a lot of false circles from being detected
cvSmooth(gray, gray, CV_GAUSSIAN, 7, 9);

IplImage* canny = cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
IplImage* rgbcanny = cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,3);
cvCanny(gray, canny, 40, 240, 3);

CvSeq* circles = cvHoughCircles(gray, storage, CV_HOUGH_GRADIENT, 2, gray->height/8, 120, 10, 2, 25);
cvCvtColor(canny, rgbcanny, CV_GRAY2BGR);

for (size_t i = 0; i < circles->total; i++)
{
     // round the floats to an int
     float* p = (float*)cvGetSeqElem(circles, i);
     cv::Point center(cvRound(p[0]), cvRound(p[1]));
     int radius = cvRound(p[2]);

     // draw the circle center
     cvCircle(rgbcanny, center, 3, CV_RGB(0,255,0), -1, 8, 0 );

     // draw the circle outline
     cvCircle(rgbcanny, center, radius+1, CV_RGB(0,0,255), 2, 8, 0 );

     printf("x: %d y: %d r: %d
",center.x,center.y, radius);
}

cvNamedWindow("circles", 1);
cvShowImage("circles", rgbcanny);

cvSaveImage("out.png", rgbcanny);
cvWaitKey(0);

我相信你有能力将它移植到 Python.

I trust you have the skills to port this to Python.

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