Removing Horizontal Lines in image (OpenCV, Python, Matplotlib)

Question

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...

Answer 1

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

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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)

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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()