完成表opencv中的缺失行

我正在嘗試檢測賬單影像中的單元格：

我有這張圖片

洗掉了帶有此代碼的標記：

import cv2
import numpy as np

# read image
img = cv2.imread('dummy1.PNG')

# threshold on yellow
lower = (0, 200, 200)
upper = (100, 255, 255)
thresh = cv2.inRange(img, lower, upper)

# apply dilate morphology
kernel = np.ones((9, 9), np.uint8)
mask = cv2.morphologyEx(thresh, cv2.MORPH_DILATE, kernel)

# get largest contour
contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(big_contour)

# draw filled white contour on input
result = img.copy()
cv2.drawContours(result, [big_contour], 0, (255, 255, 255), -1)


cv2.imwrite('removed.png', result)

# show the images
cv2.imshow("RESULT", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

并獲得了這張圖片：

然后應用灰度，反轉，檢測垂直和水平內核并通過這個 main.py 合并：

# Imports
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv

try:
    from PIL import Image
except ImportError:
    import Image
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
#################################################################################################


# Read your file
file = 'removed.png'
img = cv2.imread(file, 0)
img.shape


# thresholding the image to a binary image
thresh, img_bin = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

# inverting the image
img_bin = 255 - img_bin
cv2.imwrite(r'C:\Users\marou\Desktop\cv_inverted.png', img_bin)

# Plotting the image to see the output
plotting = plt.imshow(img_bin, cmap='gray')
plt.show()

# Define a kernel to detect rectangular boxes

# Length(width) of kernel as 100th of total width
kernel_len = np.array(img).shape[1] // 100
# Defining a vertical kernel to detect all vertical lines of image
ver_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_len))
# Defining a horizontal kernel to detect all horizontal lines of image
hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_len, 1))
# A kernel of 2x2
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))

#### Vertical LINES ####
# Use vertical kernel to detect and save the vertical lines in a jpg
image_1 = cv2.erode(img_bin, ver_kernel, iterations=5)
vertical_lines = cv2.dilate(image_1, ver_kernel, iterations=5)
cv2.imwrite(r'C:\Users\marou\Desktop\vertical.jpg', vertical_lines)
# Plot the generated image
plotting = plt.imshow(image_1, cmap='gray')
plt.show()

#### HORTIZONAL LINES ####
# Use horizontal kernel to detect and save the horizontal lines in a jpg
image_2 = cv2.erode(img_bin, hor_kernel, iterations=5)
horizontal_lines = cv2.dilate(image_2, hor_kernel, iterations=5)
cv2.imwrite(r'C:\Users\marou\Desktop\horizontal.jpg', horizontal_lines)
# Plot the generated image
plotting = plt.imshow(image_2, cmap='gray')
plt.show()



# Combining both H and V
# Combine horizontal and vertical lines in a new third image, with both having same weight.
img_vh = cv2.addWeighted(vertical_lines, 0.5, horizontal_lines, 0.5, 0.0)
# Eroding and thesholding the image
img_vh = cv2.erode(~img_vh, kernel, iterations=2)
thresh, img_vh = cv2.threshold(img_vh, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
cv2.imwrite(r'C:\Users\marou\Desktop\img_vh.jpg', img_vh)
plotting = plt.imshow(img_vh, cmap='gray')
plt.show()

要得到這個：

現在我正在嘗試填補由于去除水印而導致的行中的空白，以便能夠應用正確的 OCR。我嘗試按照此

uj5u.com熱心網友回復：

我已經概述了一種使用第二張影像作為輸入來填充表格中缺失行的方法。

image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV   cv2.THRESH_OTSU)[1]

現在為水平線創建一個單獨的蒙版：

h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,1))
# contains only the horizontal lines
h_mask = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, h_kernel, iterations=1)

# performing repeated iterations to join lines
h_mask = cv2.dilate(h_mask, h_kernel, iterations=7)

以及垂直線的單獨掩碼：

v_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
v_mask = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, v_kernel, iterations=1)

結合上述結果，我們得到以下結果：

joined_lines = cv2.bitwise_or(v_mask, h_mask)

上面的結果不是你所期望的，線條已經超出了表格的邊界。為了避免這種情況，我創建了一個分隔表格區域的單獨掩碼。

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations=1)

現在在上圖中找到最大的輪廓并將其繪制在另一個二值影像上以創建蒙版。

contours, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
c = max(contours, key = cv2.contourArea)           # contour with largest area
black = np.zeros((image.shape[0], image.shape[1]), np.uint8)
mask = cv2.drawContours(black, [c], 0, 255, -1)    # --> -1 to fill the contour

使用上面的影像作為上面創建的連接線的掩碼

fin = cv2.bitwise_and(joined_lines, joined_lines, mask = mask)

筆記：

您可以對形態學操作執行更多迭代，以更好地連接不連續的線

標籤：Python opencv 图像处理 opencv3.0 python-正方体

上一篇：問：兩張不同的ARUCO7X7_1000影像可以渲染相同的ID嗎？

下一篇：根據下拉選擇更新內容