在OpenCV中使用Hough变换检测垂直线

Question

在OpenCV中使用Hough变换检测垂直线

pythonopencv

5

我正在尝试使用OpenCV（Python）中的Hough变换来去除方框（垂直和水平线）。问题是没有任何垂直线被检测到。我已经尝试查找轮廓和层次结构，但是在这张图像中有太多轮廓，我不知道该如何使用它们。

经过查看相关的帖子，我已经尝试调整阈值和rho参数，但这并没有起到作用。我附上代码以获取更多细节。为什么Hough变换无法在图像中找到垂直线呢？欢迎提出任何解决此任务的建议。谢谢。

输入图像：

经Hough变换处理后的图像：

轮廓图像：

import cv2
import numpy as np
import pdb


img = cv2.imread('/home/user/Downloads/cropped/robust_blaze_cpp-300-0000046A-02-HW.jpg')

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 140, 255, 0)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,0,255), 2)

edges = cv2.Canny(gray,50,150,apertureSize = 3)
minLineLength = 5
maxLineGap = 100
lines = cv2.HoughLinesP(edges,rho=1,theta=np.pi/180,threshold=100,minLineLength=minLineLength,maxLineGap=maxLineGap)
for x1,y1,x2,y2 in lines[0]:
    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

cv2.imwrite('probHough.jpg',img)

- Dheeraj Peri

1

问题的一部分可能是初始阈值太低了。检查中间图像。我并没有真正回答你最初的问题，但我发布了一种可行的替代方法。 - Dan Mašek

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Dan Mašek · Accepted Answer

说实话，与其找线条，我宁愿寻找白色的盒子。

Preparation
```
import cv2
import numpy as np
```
Load the image
```
img = cv2.imread("digitbox.jpg", 0)
```

Binarize it, so that both the boxes and the digits are black, rest is white

_, thresh = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY)
cv2.imwrite('digitbox_step1.png', thresh)

Find contours. In this example image, it's fine to just look for external contours.

_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

Process the contours, filtering out any with too small an area. Find convex hull of each contour, create a mask of all areas outside the contour. Store the bounding boxes of each found contour, sorted by x coordinate.

mask = np.ones_like(img) * 255

boxes = []

for contour in contours:
    if cv2.contourArea(contour) > 100:
        hull = cv2.convexHull(contour)
        cv2.drawContours(mask, [hull], -1, 0, -1)
        x,y,w,h = cv2.boundingRect(contour)
        boxes.append((x,y,w,h))

boxes = sorted(boxes, key=lambda box: box[0])

cv2.imwrite('digitbox_step2.png', mask)

Dilate the mask (to shrink the black parts), to clip off any remains the the gray frames.

mask = cv2.dilate(mask, np.ones((5,5),np.uint8))

cv2.imwrite('digitbox_step3.png', mask)

Fill all the masked pixels with white, to erase the frames.

img[mask != 0] = 255

cv2.imwrite('digitbox_step4.png', img)

Process the digits as you desire -- i'll just draw the bounding boxes.

result = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

for n,box in enumerate(boxes):
    x,y,w,h = box
    cv2.rectangle(result,(x,y),(x+w,y+h),(255,0,0),2)
    cv2.putText(result, str(n),(x+5,y+17), cv2.FONT_HERSHEY_SIMPLEX, 0.6,(255,0,0),2,cv2.LINE_AA)

cv2.imwrite('digitbox_step5.png', result)

整个脚本在一个文件中的代码:

import cv2
import numpy as np

img = cv2.imread("digitbox.jpg", 0)

_, thresh = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY)
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

mask = np.ones_like(img) * 255
boxes = []

for contour in contours:
    if cv2.contourArea(contour) > 100:
        hull = cv2.convexHull(contour)
        cv2.drawContours(mask, [hull], -1, 0, -1)
        x,y,w,h = cv2.boundingRect(contour)
        boxes.append((x,y,w,h))

boxes = sorted(boxes, key=lambda box: box[0])

mask = cv2.dilate(mask, np.ones((5,5),np.uint8))

img[mask != 0] = 255

result = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

for n,box in enumerate(boxes):
    x,y,w,h = box
    cv2.rectangle(result,(x,y),(x+w,y+h),(255,0,0),2)
    cv2.putText(result, str(n),(x+5,y+17), cv2.FONT_HERSHEY_SIMPLEX, 0.6,(255,0,0),2,cv2.LINE_AA)

cv2.imwrite('digitbox_result.png', result)