如何从opencv中的图像中删除多余的空格？

Question

我有以下图像，它是收据图像，收据周围有很多空白区域。我想裁剪空白区域。我无法手动裁剪它，所以我正在寻找一种方法来做到这一点。

剪下一张：

从以下帖子中尝试了此代码：How to remove whitespace from an image in OpenCV?

gray = load_image(IMG_FILE) # image file
gray = 255*(gray < 128).astype(np.uint8)
coords = cv2.findNonZero(gray) # Find all non-zero points (text)
x, y, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box
rect = load_image(IMG_FILE)[y:y+h, x:x+w] # Crop the image - note we do this on the original image

它正在裁剪白色空间的一小部分。

Answer 1

这是一个简单的方法：

1. 获取二值图像。加载图像，转换为灰度，应用大的高斯模糊，然后是大津阈值

2. 进行形态学操作。我们首先用一个小内核 morph open 来去除噪声，然后用一个大内核 morph close 来组合轮廓

3. 找到封闭的边界框并裁剪 ROI。我们找到所有非零点的坐标，找到边界矩形，并裁剪 ROI。

这是检测到的 ROI 以绿色突出显示

裁剪的投资回报率

import cv2

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (25,25), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Perform morph operations, first open to remove noise, then close to combine
noise_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, noise_kernel, iterations=2)
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7,7))
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, close_kernel, iterations=3)

# Find enclosing boundingbox and crop ROI
coords = cv2.findNonZero(close)
x,y,w,h = cv2.boundingRect(coords)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
crop = original[y:y+h, x:x+w]

cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('image', image)
cv2.imshow('crop', crop)
cv2.waitKey()

Answer 2

我是您尝试的代码的原始作者。它不起作用的原因是因为文本周围有一些嘈杂的像素，从而导致了算法的失败。如果通过简单的开形态学运算去除噪声，您就可以获得所需的结果。事实上，这是在我的答案的第二个版本中完成的，不幸的是您没有尝试：

import cv2
import numpy as np

gray = load_image(IMG_FILE) # image file

# Threshold the image so that black text is white
gray = 255*(gray < 128).astype(np.uint8)

# Additionally do an opening operation with a 2 x 2 kernel
O = np.ones(2, dtype=np.uint8)
gray_morph = cv2.morphologyEx(gray, cv2.MORPH_OPEN, O)

# Continue where we left off
coords = cv2.findNonZero(gray_morph) # Find all non-zero points (text)
x, y, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box
rect = load_image(IMG_FILE)[y:y+h, x:x+w] # Crop the image

由此我们得到：