删除图像中的水平线(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.请参见下面的结果.

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()

结果非常好，不是完美的，但是很好.我要实现的是此处显示的. 我正在使用此代码

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

源图像.

我的问题之一是:如何在不应用灰色效果的情况下保存Sobel X?原始但已处理..

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

还有，还有更好的方法吗?

Also, is there a better way to do it ?

编辑

使用以下代码作为源图像是很好的.效果很好.

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()

但是，如果我有这张图片?

But if I have this image ?

我试图执行上面的代码，结果确实很差...

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

我正在处理的其他图像是这些...

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

推荐答案

这是一种方法

• 将图像转换为灰度
• 大津的门槛
• 创建特殊的水平内核以检测水平线
• 在遮罩上找到轮廓
• 修复图片

转换为灰度后，我们以Otsu的阈值获取二进制图像

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]

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

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

检测到的线

面具

填充轮廓

# 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)

图像当前有间隙.为了解决这个问题，我们构造了一个垂直内核来修复图像

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)

请注意，取决于图像，内核的大小将改变.例如，要检测更长的行，我们可以改用(50,1)内核.如果我们想要更粗的线，我们可以增加第二个参数来表示(50,2).

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).

这是其他图片的结果

检测到的线

原始(左)，已移除(右)

Original (left), removed (right)

检测到的线

原始(左)，已移除(右)

Original (left), removed (right)

完整代码

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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