如何从opencv中的图像中删除多余的空格？ [复制]

Question

我有以下图像，它是一张收据图像，并且收据周围有很多空白区域。我想裁剪空白区域。我不能手动裁剪它，所以我正在寻找一种可以做到的方法。

裁剪一个：

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

它正在裁剪一小部分空白区域。

Answer 1

我是您尝试的代码的原作者。它不起作用的原因是因为文本周围有一些嘈杂的像素，这些像素会影响算法。如果你用一个简单的开放形态学操作去除噪声，你会得到你需要的结果。这实际上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

因此我们得到：

Answer 2

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 Otsu 的阈值

2. 执行形态学操作。我们首先用一个小核变形打开以去除噪声，然后用一个大核变形关闭以组合轮廓

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，然后裁剪 ROI。

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这是检测到的要裁剪的 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()