使用 OpenCV(基于霍夫变换或其他功能)编写稳健(颜色和大小不变)的圆检测

I wrote the following very simple python code to find circles in an image:

import cv
import numpy as np

WAITKEY_DELAY_MS = 10
STOP_KEY = 'q'

cv.NamedWindow("image - press 'q' to quit", cv.CV_WINDOW_AUTOSIZE);
cv.NamedWindow("post-process", cv.CV_WINDOW_AUTOSIZE);

key_pressed = False
while key_pressed != STOP_KEY:

    # grab image
    orig = cv.LoadImage('circles3.jpg')

    # create tmp images
    grey_scale = cv.CreateImage(cv.GetSize(orig), 8, 1)
    processed = cv.CreateImage(cv.GetSize(orig), 8, 1)


    cv.Smooth(orig, orig, cv.CV_GAUSSIAN, 3, 3)

    cv.CvtColor(orig, grey_scale, cv.CV_RGB2GRAY)

    # do some processing on the grey scale image
    cv.Erode(grey_scale, processed, None, 10)
    cv.Dilate(processed, processed, None, 10)
    cv.Canny(processed, processed, 5, 70, 3)
    cv.Smooth(processed, processed, cv.CV_GAUSSIAN, 15, 15)

    storage = cv.CreateMat(orig.width, 1, cv.CV_32FC3)

    # these parameters need to be adjusted for every single image
    HIGH = 50
    LOW = 140

    try: 
        # extract circles
        cv.HoughCircles(processed, storage, cv.CV_HOUGH_GRADIENT, 2, 32.0, HIGH, LOW)

        for i in range(0, len(np.asarray(storage))):
            print "circle #%d" %i
            Radius = int(np.asarray(storage)[i][0][2])
            x = int(np.asarray(storage)[i][0][0])
            y = int(np.asarray(storage)[i][0][1])
            center = (x, y)

            # green dot on center and red circle around
            cv.Circle(orig, center, 1, cv.CV_RGB(0, 255, 0), -1, 8, 0)
            cv.Circle(orig, center, Radius, cv.CV_RGB(255, 0, 0), 3, 8, 0)

            cv.Circle(processed, center, 1, cv.CV_RGB(0, 255, 0), -1, 8, 0)
            cv.Circle(processed, center, Radius, cv.CV_RGB(255, 0, 0), 3, 8, 0)

    except:
        print "nothing found"
        pass

    # show images
    cv.ShowImage("image - press 'q' to quit", orig)
    cv.ShowImage("post-process", processed)

    cv_key = cv.WaitKey(WAITKEY_DELAY_MS)
    key_pressed = chr(cv_key & 255)

从以下两个例子可以看出，寻圈质量"差异很大:

import cv2
import math
import numpy as np

d_red = cv2.cv.RGB(150, 55, 65)
l_red = cv2.cv.RGB(250, 200, 200)

orig = cv2.imread("c.jpg")
img = orig.copy()
img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

detector = cv2.FeatureDetector_create('MSER')
fs = detector.detect(img2)
fs.sort(key = lambda x: -x.size)

def supress(x):
        for f in fs:
                distx = f.pt[0] - x.pt[0]
                disty = f.pt[1] - x.pt[1]
                dist = math.sqrt(distx*distx + disty*disty)
                if (f.size > x.size) and (dist<f.size/2):
                        return True

sfs = [x for x in fs if not supress(x)]

for f in sfs:
        cv2.circle(img, (int(f.pt[0]), int(f.pt[1])), int(f.size/2), d_red, 2, cv2.CV_AA)
        cv2.circle(img, (int(f.pt[0]), int(f.pt[1])), int(f.size/2), l_red, 1, cv2.CV_AA)

h, w = orig.shape[:2]
vis = np.zeros((h, w*2+5), np.uint8)
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
vis[:h, :w] = orig
vis[:h, w+5:w*2+5] = img

cv2.imshow("image", vis)
cv2.imwrite("c_o.jpg", vis)
cv2.waitKey()
cv2.destroyAllWindows()

P = {p1, ..., pn} 
P: search space
p1...pn: pixels

E(P) : enumerates the search space
V(p) : checks whether the item/pixel has the desirable properties, the items passing the check are added to the output list