python 中用 cv2 连接手绘图像角的线

Question

我想检测手绘图像上连接角的线 like this. I am using Harris Corner Detection to find the corners of the image. Next I am connecting all of the corners with lines and iterating though the points to see if they match the pixels from original image and setting a threshold for each line pixel cover to say what is acceptable to say it is a correct line connecting corners.Image of connected lines。它有效......但它非常慢。有没有更好的方法或我应该使用的不同方法？（霍夫线将无法工作，因为可能有曲线，我只想要连接角的线。

for i in c_corners: #corners thru harris and coorected with subpix
    x1,y1 = i.ravel()
    for k in c_corners:
        x2,y2 = k.ravel()
        if x1 != x2 and y1 != y2: #ignore vertical lines 
            linePoints = line_points(x1,y1, x2,y2) # function to get line pnts
            totalLinePoints = len(linePoints)
            coverPoints = 0

########## This is where I think the slow down is happening and could be optimized

            for m in originalImage: #image is dialated to help detection
                for n in linePoints:
                    match = np.all(m == n)
                    if match == True:
                        coverPoints += 1 
            print("Line Cover = ", (coverPoints/totalLinePoints))
            if (coverPoints/totalLinePoints) > .65:
                good_lines.append([x1,y1,x2,y2])

感谢任何帮助，谢谢！

Answer 1

我最初的方法是创建一个空白图像并在其上绘制每条线，然后使用 cv2.bitwise_and() 和二值（膨胀）图像来计算有多少像素一致，如果它们满足阈值，然后在原始图像上绘制这些线条。但是，为像素数设置阈值会惩罚细线。一个更好的指标是正确匹配与不正确匹配的数量之比（我现在意识到这就是你实际在做的事情）。此外，这对膨胀和您选择绘制线条的线条粗细更加稳健。

但是，您使用的一般方法对于绘图中的问题不是很稳健，在绘图中，像这一个一样，合成线可能能够轻松地适应它们不属于的线，因为许多绘制的曲线可能打一个线段。您可以在我的代码输出中看到这个问题：

我只是硬编码了一些角落估计，然后从那里开始。请注意使用 itertools 来帮助创建所有可能的点对来定义线段。

import cv2
import numpy as np
import itertools

img = cv2.imread('drawing.png')
bin_inv = cv2.bitwise_not(img) # flip image colors
bin_inv = cv2.cvtColor(bin_inv, cv2.COLOR_BGR2GRAY) # make one channel
bin_inv = cv2.dilate(bin_inv, np.ones((5,5)))

corners = ((517, 170),
    (438, 316),
    (574, 315),
    (444, 436),
    (586, 436))

lines = itertools.combinations(corners,2) # create all possible lines
line_img = np.ones_like(img)*255 # white image to draw line markings on
for line in lines: # loop through each line
    bin_line = np.zeros_like(bin_inv) # create a matrix to draw the line in
    start, end = line # grab endpoints
    cv2.line(bin_line, start, end, color=255, thickness=5) # draw line
    conj = (bin_inv/255 + bin_line/255) # create agreement image
    n_agree = np.sum(conj==2)
    n_wrong = np.sum(conj==1)
    if n_agree/n_wrong > .05: # high agreements vs disagreements
        cv2.line(line_img, start, end, color=[0,200,0], thickness=5) # draw onto original img

# combine the identified lines with the image
marked_img = cv2.addWeighted(img, .5, line_img, .5, 1)
cv2.imwrite('marked.png', marked_img)

我尝试了很多不同的设置（使用粗细、膨胀、不同的比率等），但无法让那条虚假的更长的线出现。它非常适合原始的黑色像素，所以我不确定如果你使用这种方法你将如何摆脱它。它有从右上角的线开始的曲线，以及它穿过的中间线，和右下角的曲线向该方向倾斜了一点。无论如何，运行只需要两秒钟，所以至少它比您当前的代码快。

python 中用 cv2 连接手绘图像角的线

Lines connecting corners over hand drawn image in python with cv2

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image-processing

corner-detection