OpenCV Crop Hough Circles Python无法正常工作

时间:2018-06-26 06:26:57

标签: python opencv computer-vision opencv-contour

Please Refer this Image for the post below 你好, 我正在尝试通过将OpenCV与Python一起构建模拟量规读取器。我使用了Hough Circles来减少编码。代码转载如下:

import cv2
import numpy as np

img = cv2.imread('gauge.jpg', 0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

height,width = img.shape
mask = np.zeros((height,width), np.uint8)

counter = 0

circles = cv2.HoughCircles(img,cv2.cv.CV_HOUGH_GRADIENT,1,20,
                            param1=200,param2=100,minRadius=0,maxRadius=0)

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)

    # Draw on mask
    cv2.circle(mask,(i[0],i[1]),i[2],(255,255,255),-1)
    masked_data = cv2.bitwise_and(cimg, cimg, mask=mask)    

    # Apply Threshold
    _,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)

    # Find Contour
    cnt = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]

    #print len(contours)
    x,y,w,h = cv2.boundingRect(cnt[0])

    # Crop masked_data
    crop = masked_data[y:y+h,x:x+w]

    # Write Files
    cv2.imwrite("output/crop"+str(counter)+".jpg", crop)

    counter +=1

print counter

cv2.imshow('detected circles',cimg)
cv2.imwrite("output/circled_img.jpg", cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

我的问题如下:

  1. 我没有得到单独的转盘,但是在每个图像中“ crop0.jpg”有1个,但是“ crop1.jpg”有2个,“ crop3.jpg”有4个。然后,我可以运行批处理模板匹配算法。

  2. 总结果为5,您可能需要注意。

1 个答案:

答案 0 :(得分:1)

在我看来,您在这里加班。在此行

circles = cv2.HoughCircles(img,cv2.cv.CV_HOUGH_GRADIENT,1,20, param1=200,param2=100,minRadius=0,maxRadius=0)

您实际上找到了要查找的圈子,但是由于某种原因,您尝试在随后的循环中再次找到它们。

您只需使用获得的坐标即可获取裁剪的图像。由于每个圆都有一个中心和一个半径,因此可以得到一个包含圆的边界框,然后(可能)对其应用蒙版。
我猜类似的东西会起作用:

for c in circles[0, :]:
    c = c.astype(int)
    # get the actual cropped images here
    crop = img_copy[c[1]-c[2]:c[1]+c[2], c[0]-c[2]:c[0]+c[2]]
    # create a mask and add each circle in it
    mask = np.zeros(crop.shape)
    mask = cv2.circle(mask, (c[2], c[2]), c[2], (255, 255, 255), -1)
    final_im = mask * crop

只需在添加图片之前先添加它,然后再过滤图片即可

img = cv2.imread('/home/gorfanidis/misc/gauge2.jpg', 0)
img_copy = img.copy()  # <- add this to have a copy of your original image

编辑:

如果由于某种原因而没有得到结果(返回类型为None)或得到零结果(中心和半径为0),则可以检查以下两种情况:

if circles is not None:  # checks that something actually was returned
    for c in circles[0, :]:
        c = c.astype(int)
        if not c[2]:  # just checks that radius is not zero to proceed
            continue
        ...