python - 在OpenCV中删除图像中的噪声而不会丢失数据

Question

我用这个代码:

    horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1))
    horizontal = cv2.erode(horizontal, horizontalStructure, (-1, -1))
    horizontal = cv2.dilate(horizontal, horizontalStructure, (-1, -1))

删除线。

以及一些过滤器，用于删除噪点并加粗字体:

 blur = cv2.GaussianBlur(img, (11, 11), 0)
 thresh = cv2.threshold(blur, 80, 255, cv2.THRESH_BINARY)[1]
 kernel = np.ones((2,1), np.uint8)
 dilation = cv2.erode(thresh, kernel, iterations=1)
 dilation = cv2.bitwise_not(dilation)

尽管有阈值和其他方法，您仍然可以看到很多噪音

，这是我想要达到的结果:

您知道一个OpenCV过滤器可以帮助我实现这一结果吗？

Answer 1

以下解决方案不是完美的解决方案，也不是通用的解决方案，但我希望它足以满足您的需求。

为了删除该行，我建议使用cv2.connectedComponentsWithStats查找簇，并屏蔽宽或长簇。

该解决方案使用以下阶段:

将图像转换为灰度。

应用阈值并反转极性。
通过应用标志cv2.THRESH_OTSU使用自动阈值。

使用“关闭”形态操作来缩小小差距。

查找具有统计信息的连接的组件(集群)。

遍历集群，并删除具有较大宽度和较大高度的集群。
去除非常小的簇-被认为是噪音。

“手动”清洁顶部和左侧。

这是代码:

import numpy as np
import cv2

img = cv2.imread('Heshbonit.jpg')  # Read input image

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to Grayscale.

ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)  # Convert to binary and invert polarity

# Use "close" morphological operation to close small gaps
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]));
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]).T);

nlabel,labels,stats,centroids = cv2.connectedComponentsWithStats(thresh, connectivity=8)

thresh_size = 100

# Delete all lines by filling wide and long lines with zeros.
# Delete very small clusters (assumes to be noise).
for i in range(1, nlabel):
    #
    if (stats[i, cv2.CC_STAT_WIDTH] > thresh_size) or (stats[i, cv2.CC_STAT_HEIGHT] > thresh_size):
        thresh[labels == i] = 0
    if stats[i, cv2.CC_STAT_AREA] < 4:
        thresh[labels == i] = 0

# Clean left and top margins "manually":
thresh[:, 0:30] = 0
thresh[0:10, :] = 0

# Inverse polarity
thresh = 255 - thresh

# Write result to file
cv2.imwrite('thresh.png', thresh)