CODEWITHSUNDEEP
pythoncopencvimage-processingcomputer-vision
hokiebird
hokiebird

Reputation: 280

OpenCV dot target detection not finding all targets, and found circles are offset

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.

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

Here is my code:

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"

And this is my current output:

Upvotes: 8

Views: 8902

Answers (3)

Manivannan Murugavel
Manivannan Murugavel

Reputation: 1588

Most Detect Circles using Python Code

import cv2
import numpy as np

img = cv2.imread('coin.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(7,9),6)
cimg = cv2.cvtColor(blur,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(blur,cv2.HOUGH_GRADIENT,1,50,
                            param1=120,param2=10,minRadius=2,maxRadius=30)


circles = np.uint16(np.around(circles))
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)

cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

Upvotes: 0

karlphillip
karlphillip

Reputation: 93468

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

It's all about the parameters. It always is.

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.

And here is the C code:

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

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\n",center.x,center.y, radius);
}

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

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

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

Upvotes: 8

jeff7
jeff7

Reputation: 2182

Since that circle pattern is fixed and well distinguished from the object, simple template matching should work reasonably well, check out cvMatchTemplate. For a more complex conditions (warping due to object shape or view geometry), you may try more robust features like SIFT or SURF (cvExtractSURF).

Upvotes: 0

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