問題描述

使用以下代碼，我可以刪除圖像中的水平線.見下面的結(jié)果.

導(dǎo)入 cv2從 matplotlib 導(dǎo)入 pyplot 作為 pltimg = cv2.imread('image.png',0)拉普拉斯= cv2.拉普拉斯(img，cv2.CV_64F)sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)plt.subplot(2,2,1),plt.imshow(img,cmap = '灰色')plt.title('Original'), plt.xticks([]), plt.yticks([])plt.subplot(2,2,2),plt.imshow(拉普拉斯,cmap = '灰色')plt.title('拉普拉斯'), plt.xticks([]), plt.yticks([])plt.subplot(2,2,3),plt.imshow(sobelx,cmap = '灰色')plt.title('Sobel X'), plt.xticks([]), plt.yticks([])plt.show()

結(jié)果還不錯(cuò)，不是很完美，但是很好.我想要實(shí)現(xiàn)的是

我的一個(gè)問題是:如何在不應(yīng)用灰色效果的情況下保存 Sobel X ?作為原始但經(jīng)過處理..

另外，有沒有更好的方法呢?

編輯

對(duì)源圖像使用以下代碼很好.效果很好.

導(dǎo)入 cv2將 numpy 導(dǎo)入為 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 = 水平形狀#反轉(zhuǎn)圖像，使線條為黑色以進(jìn)行遮罩Horizo??ntal_inv = cv2.bitwise_not(水平)#perform bitwise_and 用提供的掩碼掩碼行masked_img = cv2.bitwise_and(img, img, mask=horizo??ntal_inv)#反轉(zhuǎn)圖像恢復(fù)正常masked_img_inv = cv2.bitwise_not(masked_img)cv2.imshow("屏蔽的圖像", masked_img_inv)cv2.imwrite("result2.jpg", masked_img_inv)cv2.waitKey(0)cv2.destroyAllWindows()水平尺寸 = int(cols/30)水平結(jié)構(gòu) = cv2.getStructuringElement(cv2.MORPH_RECT, (horizo??ntalsize,1))水平= cv2.erode(水平，水平結(jié)構(gòu)，(-1，-1))水平 = cv2.擴(kuò)張(水平，水平結(jié)構(gòu)，(-1，-1))cv2.imshow(水平"，水平)cv2.imwrite("horizo??ntal.jpg", 水平)cv2.waitKey(0)cv2.destroyAllWindows()垂直尺寸 = int(行數(shù)/30)verticalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (1, verticalsize))垂直= cv2.erode(垂直，垂直結(jié)構(gòu)，(-1，-1))垂直= cv2.dilate(垂直，垂直結(jié)構(gòu)，(-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()#步驟1邊緣 = cv2.adaptiveThreshold(垂直，255，cv2.ADAPTIVE_THRESH_MEAN_C，cv2.THRESH_BINARY，3，-2)cv2.imshow(邊緣"，邊緣)cv2.imwrite("edges.jpg", 邊緣)cv2.waitKey(0)cv2.destroyAllWindows()#第2步kernel = np.ones((2, 2), dtype = "uint8")擴(kuò)張= cv2.擴(kuò)張(邊緣，內(nèi)核)cv2.imshow(擴(kuò)張"，擴(kuò)張)cv2.imwrite(擴(kuò)張的.jpg"，擴(kuò)張)cv2.waitKey(0)cv2.destroyAllWindows()# 第三步平滑 = vertical.copy()#步驟4平滑 = cv2.blur(平滑，(4,4))cv2.imshow("平滑", 平滑)cv2.imwrite("smooth.jpg", 平滑)cv2.waitKey(0)cv2.destroyAllWindows()#步驟 5(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()

但是如果我有這張圖片呢?

我嘗試執(zhí)行上面的代碼，結(jié)果真的很差……

我正在處理的其他圖像是這些......

這是一種方法

• 將圖像轉(zhuǎn)換為

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

  复制代码

  接下來我們創(chuàng)建一個(gè)特殊的水平內(nèi)核來檢測(cè)水平線.我們將這些線條繪制到蒙版上，然后在蒙版上找到輪廓.為了去除線條，我們用白色填充輪廓

  檢測(cè)到的線

  面具

  填充輪廓

  # 刪除水平水平內(nèi)核 = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))檢測(cè)到的線= cv2.morphologyEx(thresh，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 否則 cnts[1]對(duì)于 cnts 中的 c:cv2.drawContours(圖像，[c]，-1，(255,255,255)，2)

  复制代码

  圖像當(dāng)前有間隙.為了解決這個(gè)問題，我們構(gòu)建了一個(gè)垂直內(nèi)核來修復(fù)圖像

  # 修復(fù)圖片repair_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))結(jié)果 = 255 - cv2.morphologyEx(255 - 圖像，cv2.MORPH_CLOSE，repair_kernel，迭代 = 1)

  复制代码

  <塊引用>

  注意根據(jù)映像，內(nèi)核的大小會(huì)有所變化.例如，為了檢測(cè)更長(zhǎng)的行，我們可以使用 (50,1) 內(nèi)核.如果我們想要更粗的線條，我們可以增加第二個(gè)參數(shù)為 (50,2).

  這是其他圖片的結(jié)果

  檢測(cè)到的線

  原始(左)，刪除(右)

  ---

  檢測(cè)到的線

  原始(左)，刪除(右)

  完整代碼

  導(dǎo)入 cv2圖像 = cv2.imread('1.png')灰色 = cv2.cvtColor(圖像,cv2.COLOR_BGR2GRAY)thresh = cv2.threshold(灰色, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]# 刪除水平水平內(nèi)核 = cv2.getStructuringElement(cv2.MORPH_RECT, (25,1))檢測(cè)到的線= cv2.morphologyEx(thresh，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 否則 cnts[1]對(duì)于 cnts 中的 c:cv2.drawContours(圖像，[c]，-1，(255,255,255)，2)# 修復(fù)圖像repair_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,6))結(jié)果 = 255 - cv2.morphologyEx(255 - 圖像，cv2.MORPH_CLOSE，repair_kernel，迭代 = 1)cv2.imshow('thresh', thresh)cv2.imshow('detected_lines', detected_lines)cv2.imshow('圖像', 圖像)cv2.imshow('結(jié)果', 結(jié)果)cv2.waitKey()

  复制代码

  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()
  

  复制代码

  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()
  

  复制代码

  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...

  解決方案

  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]
  

  复制代码

  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)
  

  复制代码

  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)
  

  复制代码

  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()
  

  复制代码

  這篇關(guān)于刪除圖像中的水平線(OpenCV、Python、Matplotlib)的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！

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