OpenCV HoughLines 产生太多线 Python

Question

我一直致力于使用 python 和计算机视觉来检测名为 Go. Based on the data collected here, I planned to base my implementation off of this paper's algorithm(s). However, I ran into trouble when I got to section 3.1.2 in the paper and had to compute a Hough Transform on my image. I tried using OpenCV's Hough Line function 的游戏中游戏板的棋盘状态，但得到的图像充满了我无法看到的线条原图。

我为以前的功能尝试了各种线条粗细和不同的阈值，但我似乎总是以太多的线条或几乎没有线条而告终。例如，当使用顶部图像时，我得到了它下面的图像以及我粘贴在最底部的代码

我假设虽然 HoughLines 函数只是产生了太多的线条，以至于它覆盖了屏幕，但我似乎无法获得正常数量的线条。我不确定这个位是否有用，但与我可以在网上找到的任何教程或示例相比，我必须达到极高的阈值以避免几乎完全红屏，但即使这样也只显示 5 行。我可以不使用 HoughLines 函数，但论文的下一步取决于这个结果，所以我要么必须解决这个问题，要么找到一个完全不同的实现。任何帮助表示赞赏。谢谢！

import cv2
import numpy as np

img = cv2.imread(pathreal, 1)
middlex = img.shape[1]/2
middley = img.shape[0]/2

def gaussianweights(image):
   newarr = [[0 for i in range(image.shape[1])] for j in range(image.shape[0])]
   for i in range(image.shape[1]):
      for j in range(image.shape[0]):
         x,y = i,j
         filtered = np.exp(((x-middlex)**2)/((middlex**2)/2)+((y-middley)**2)/((middley**2)/2))
         newarr[j][i] = image[j][i]*filtered
   return newarr

img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

weighted_img = gaussianweights(img_gray)
filtered_img = cv2.filter2D(img_gray,-1, np.array([[1,1,1,1,1],[1,1,1,1,1],[1,1,-24,1,1],[1,1,1,1,1],[1,1,1,1,1]]))
            
dst = cv2.Canny(filtered_img, 600, 800, None, 3)


lines = cv2.HoughLines(dst,1,np.pi/180,100)
for line in lines:
   for rho,theta in line:
      a = np.cos(theta)
      b = np.sin(theta)
      x0 = a*rho
      y0 = b*rho
      x1 = int(x0 + 1000*(-b))
      y1 = int(y0 + 1000*(a))
      x2 = int(x0 - 1000*(-b))
      y2 = int(y0 - 1000*(a))

      cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)

 cv2.imshow("Hough Image",img)
 cv2.waitKey(0)

编辑：

有人问了，这里是夏令时

Answer 1

代码在OpenCV's Hough Line function I acheive this :

也许你可以从那个代码开始...

OpenCV HoughLines 产生太多线 Python

OpenCV HoughLines Produces Too Many Lines Python

python

opencv

numpy

computer-vision

hough-transform