回答问题

使用以下代码,我可以删除图像中的水平线。见下面的结果。

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,不是完美但很好。我想要实现的是这里显示的。我正在使用这个代码。

源图..

我的一个问题是:如何在没有应用灰色效果的情况下保存Sobel X?作为原始但经过处理..

另外,有没有更好的方法呢?

编辑

对源图像使用以下代码很好。效果很好。

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()

但如果我有这个图像?

我尝试执行上面的代码,结果真的很差......

我正在处理的其他图像是这些......

Answers

1. 获取二值图像。加载图像,转换为灰度,然后Otsu 的阈值得到二值黑白图像。

2. **检测并移除水平线。**为了检测水平线,我们创建了一个特殊的水平内核和morph open来检测水平轮廓。从这里我们在mask上找到轮廓和“填充”检测到的水平轮廓用白色有效去除线条

3. **修复图像。**此时如果水平线穿过字符,图像可能会有间隙。为了修复文本,我们创建一个垂直内核和morph close来扭转损坏

---

转换为灰度后,我们用Otsu的阈值得到二值图像

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来检测水平线。我们将这些线条绘制到蒙版上,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

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,我们构建了一个垂直内核来修复图像

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

注意根据图像,内核的大小会发生变化。你可以把内核想象成(horizontal, vertical)。例如,为了检测更长的行,我们可以使用(50,1)内核。如果我们想要更粗的线条,我们可以增加第二个参数,比如(50,2)。

这是其他图像的结果

检测到的线

! zwz 100077 zwz 100078 zwz 100076 ! zwz 100080 zwz 100081 zwz 100079

原->已移除

---

检测到的线

原->已移除

! zwz 100095 zwz 100096 zwz 100094 ! zwz 100098 zwz 100099 zwz 100097

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()