去除图像中的水平线(OpenCV、Python、Matplotlib) [英] Removing Horizontal Lines in image (OpenCV, Python, Matplotlib)

问题描述

使用以下代码我可以删除图像中的水平线.见下面的结果.

导入 cv2从 matplotlib 导入 pyplot 作为 pltimg = cv2.imread('image.png',0)拉普拉斯算子 = cv2.拉普拉斯算子(img,cv2.CV_64F)sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')plt.title('Original'), plt.xticks([]), plt.yticks([])plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')plt.title('Laplacian'), plt.xticks([]), plt.yticks([])plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')plt.title('Sobel X'), plt.xticks([]), plt.yticks([])plt.show()

结果还不错，不完美但很好.我想要实现的是

我的一个问题是:如何在不应用灰色效果的情况下保存 Sobel X ?作为原创但经过处理..

另外，有没有更好的方法来做到这一点?

编辑

对源图像使用以下代码是好的.效果很好.

导入 cv2将 numpy 导入为 npimg = cv2.imread("image.png")img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)img = cv2.bitwise_not(img)th2 = cv2.adaptiveThreshold(img,255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,-2)cv2.imshow("th2", th2)cv2.imwrite("th2.jpg", th2)cv2.waitKey(0)cv2.destroyAllWindows()水平 = th2垂直 = th2行，列 = 水平.形状#反转图像，使线条为黑色以进行遮罩水平_inv = cv2.bitwise_not(水平)#perform bitwise_and 用提供的掩码掩码行masked_img = cv2.bitwise_and(img, img, mask=horizo​​ntal_inv)#反转图像恢复正常masked_img_inv = cv2.bitwise_not(masked_img)cv2.imshow("屏蔽图片", masked_img_inv)cv2.imwrite("result2.jpg", masked_img_inv)cv2.waitKey(0)cv2.destroyAllWindows()水平尺寸 = int(cols/30)水平结构 = cv2.getStructuringElement(cv2.MORPH_RECT, (horizo​​ntalsize,1))水平 = cv2.erode(水平，水平结构，(-1，-1))水平 = cv2.dilate(水平，水平结构，(-1，-1))cv2.imshow(水平"，水平)cv2.imwrite("horizo​​ntal.jpg", 水平)cv2.waitKey(0)cv2.destroyAllWindows()垂直大小 = 整数(行数/30)VerticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))垂直 = cv2.erode(垂直，垂直结构，(-1，-1))垂直 = cv2.dilate(垂直，垂直结构，(-1，-1))cv2.imshow(垂直"，垂直)cv2.imwrite("vertical.jpg", 垂直)cv2.waitKey(0)cv2.destroyAllWindows()垂直 = cv2.bitwise_not(垂直)cv2.imshow("vertical_bitwise_not", 垂直)cv2.imwrite("vertical_bitwise_not.jpg", 垂直)cv2.waitKey(0)cv2.destroyAllWindows()#步骤1边缘 = cv2.adaptiveThreshold(vertical,255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,3,-2)cv2.imshow(边缘"，边缘)cv2.imwrite("edges.jpg", 边缘)cv2.waitKey(0)cv2.destroyAllWindows()#第2步内核 = np.ones((2, 2), dtype = "uint8")扩张 = cv2.dilate(边，核)cv2.imshow(扩张"，扩张)cv2.imwrite("dilated.jpg", 膨胀)cv2.waitKey(0)cv2.destroyAllWindows()# 步骤 3平滑 = 垂直.copy()#步骤4平滑 = cv2.blur(smooth, (4,4))cv2.imshow(平滑"，平滑)cv2.imwrite("smooth.jpg", 平滑)cv2.waitKey(0)cv2.destroyAllWindows()#第5步(行，列)= np.where(img == 0)垂直[行，列] = 平滑[行，列]cv2.imshow("vertical_final", 垂直)cv2.imwrite("vertical_final.jpg", 垂直)cv2.waitKey(0)cv2.destroyAllWindows()

但是如果我有这张图片?

我尝试执行上面的代码，结果真的很糟糕...

我正在处理的其他图像是这些...

解决方案

这是一个方法

• 将图像转换为

  image = cv2.imread('1.png')灰色 = cv2.cvtColor(图像，cv2.COLOR_BGR2GRAY)thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

  接下来我们创建一个特殊的水平内核来检测水平线.我们将这些线绘制到蒙版上，然后在蒙版上找到轮廓.为了去除线条，我们用白色填充轮廓

  检测到的线条

  面具

  填充轮廓

  # 删除水平水平内核 = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))检测到的线 = cv2.morphologyEx(阈值，cv2.MORPH_OPEN，horizo​​ntal_kernel，迭代 = 2)cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] 如果 len(cnts) == 2 else cnts[1]对于 cnts 中的 c:cv2.drawContours(图像，[c]，-1，(255,255,255)，2)

  图像当前有间隙.为了解决这个问题，我们构建了一个垂直内核来修复图像

  #修复图片修复内核 = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))结果 = 255 - cv2.morphologyEx(255 - 图像，cv2.MORPH_CLOSE，repair_kernel，迭代次数 = 1)

  <块引用>

  注意根据图像，内核的大小会发生变化.例如，为了检测更长的行，我们可以使用 (50,1) 内核来代替.如果我们想要更粗的线条，我们可以增加第二个参数来表示 (50,2).

  这是其他图像的结果

  检测到的线条

  原始(左)，移除(右)

  ---

  检测到的线条

  原始(左)，移除(右)

  完整代码

  导入 cv2图像 = cv2.imread('1.png')灰色 = cv2.cvtColor(图像，cv2.COLOR_BGR2GRAY)thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]# 删除水平水平内核 = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))检测到的线 = cv2.morphologyEx(阈值，cv2.MORPH_OPEN，horizo​​ntal_kernel，迭代 = 2)cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] 如果 len(cnts) == 2 else cnts[1]对于 cnts 中的 c:cv2.drawContours(图像，[c]，-1，(255,255,255)，2)# 修复图像修复内核 = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))结果 = 255 - cv2.morphologyEx(255 - 图像，cv2.MORPH_CLOSE，repair_kernel，迭代次数 = 1)cv2.imshow('thresh', thresh)cv2.imshow('detected_lines',detected_lines)cv2.imshow('图像', 图像)cv2.imshow('结果', 结果)cv2.waitKey()

  Using the following code I can remove horizontal lines in images. See result below.

  import cv2
  from matplotlib import pyplot as plt
  
  img = cv2.imread('image.png',0)
  
  laplacian = cv2.Laplacian(img,cv2.CV_64F)
  sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
  
  plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
  plt.title('Original'), plt.xticks([]), plt.yticks([])
  plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
  plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
  plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
  plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
  
  plt.show()
  

  The result is pretty good, not perfect but good. What I want to achieve is the one showed here. I am using this code.

  Source image..

  One of my questions is: how to save the Sobel X without that grey effect applied ? As original but processed..

  Also, is there a better way to do it ?

  EDIT

  Using the following code for the source image is good. Works pretty well.

  import cv2
  import numpy as np
  
  img = cv2.imread("image.png")
  img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  
  img = cv2.bitwise_not(img)
  th2 = cv2.adaptiveThreshold(img,255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,-2)
  cv2.imshow("th2", th2)
  cv2.imwrite("th2.jpg", th2)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  horizontal = th2
  vertical = th2
  rows,cols = horizontal.shape
  
  #inverse the image, so that lines are black for masking
  horizontal_inv = cv2.bitwise_not(horizontal)
  #perform bitwise_and to mask the lines with provided mask
  masked_img = cv2.bitwise_and(img, img, mask=horizontal_inv)
  #reverse the image back to normal
  masked_img_inv = cv2.bitwise_not(masked_img)
  cv2.imshow("masked img", masked_img_inv)
  cv2.imwrite("result2.jpg", masked_img_inv)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  horizontalsize = int(cols / 30)
  horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize,1))
  horizontal = cv2.erode(horizontal, horizontalStructure, (-1, -1))
  horizontal = cv2.dilate(horizontal, horizontalStructure, (-1, -1))
  cv2.imshow("horizontal", horizontal)
  cv2.imwrite("horizontal.jpg", horizontal)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  verticalsize = int(rows / 30)
  verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))
  vertical = cv2.erode(vertical, verticalStructure, (-1, -1))
  vertical = cv2.dilate(vertical, verticalStructure, (-1, -1))
  cv2.imshow("vertical", vertical)
  cv2.imwrite("vertical.jpg", vertical)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  vertical = cv2.bitwise_not(vertical)
  cv2.imshow("vertical_bitwise_not", vertical)
  cv2.imwrite("vertical_bitwise_not.jpg", vertical)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  #step1
  edges = cv2.adaptiveThreshold(vertical,255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,3,-2)
  cv2.imshow("edges", edges)
  cv2.imwrite("edges.jpg", edges)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  #step2
  kernel = np.ones((2, 2), dtype = "uint8")
  dilated = cv2.dilate(edges, kernel)
  cv2.imshow("dilated", dilated)
  cv2.imwrite("dilated.jpg", dilated)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  # step3
  smooth = vertical.copy()
  
  #step 4
  smooth = cv2.blur(smooth, (4,4))
  cv2.imshow("smooth", smooth)
  cv2.imwrite("smooth.jpg", smooth)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  #step 5
  (rows, cols) = np.where(img == 0)
  vertical[rows, cols] = smooth[rows, cols]
  
  cv2.imshow("vertical_final", vertical)
  cv2.imwrite("vertical_final.jpg", vertical)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  

  But if I have this image ?

  I tried to execute the code above and the result is really poor...

  Other images which I am working on are these...

  解决方案

  Here's an approach

  • Convert image to grayscale

  • Otsu's threshold to get binary image

  • Create special horizontal kernel and morph open to detect horizontal lines

  • Find contours on mask and "fill in" the detected horizontal lines with white to effectively remove horizontal lines

  • Create vertical kernel and repair image with morph close

  ---

  After converting to grayscale, we Otsu's threshold to obtain a binary image

  image = cv2.imread('1.png')
  gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
  thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
  

  Next we create a special horizontal kernel to detect horizontal lines. We draw these lines onto a mask and then find contours on the mask. To remove the lines, we fill in the contours with white

  Detected lines

  Mask

  Filled in contours

  # Remove horizontal
  horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
  detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
  cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  cnts = cnts[0] if len(cnts) == 2 else cnts[1]
  for c in cnts:
      cv2.drawContours(image, [c], -1, (255,255,255), 2)
  

  The image currently has gaps. To fix this, we construct a vertical kernel to repair the image

  # Repair image
  repair_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))
  result = 255 - cv2.morphologyEx(255 - image, cv2.MORPH_CLOSE, repair_kernel, iterations=1)
  

  Note depending on the image, the size of the kernel will change. For instance, to detect longer lines, we could use a (50,1) kernel instead. If we wanted thicker lines, we could increase the 2nd parameter to say (50,2).

  Here's the results with the other images

  Detected lines

  Original (left), removed (right)

  ---

  Detected lines

  Original (left), removed (right)

  Full code

  import cv2
  
  image = cv2.imread('1.png')
  gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
  thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
  
  # Remove horizontal
  horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))
  detected_lines = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
  cnts = cv2.findContours(detected_lines, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  cnts = cnts[0] if len(cnts) == 2 else cnts[1]
  for c in cnts:
      cv2.drawContours(image, [c], -1, (255,255,255), 2)
  
  # Repair image
  repair_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))
  result = 255 - cv2.morphologyEx(255 - image, cv2.MORPH_CLOSE, repair_kernel, iterations=1)
  
  cv2.imshow('thresh', thresh)
  cv2.imshow('detected_lines', detected_lines)
  cv2.imshow('image', image)
  cv2.imshow('result', result)
  cv2.waitKey()
  

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