用python实现在复杂背景下对A4纸的定位识别与裁剪

在Python中可以使用OpenCV库实现在复杂背景下对A4纸的定位识别与裁剪。下面是实现的基本步骤：

1. 导入必要的库：

import cv2
import numpy as np

2. 读取图片并进行预处理：

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

3. 检测并绘制轮廓：

contours, hierarchy = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
img_contours = img.copy()
cv2.drawContours(img_contours, contours, -1, (0, 255, 0), 3)

4. 筛选出符合A4纸大小的轮廓：

for contour in contours:
    perimeter = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
    if len(approx) == 4:
        x, y, w, h = cv2.boundingRect(approx)
        aspect_ratio = float(w) / h
        if aspect_ratio >= 0.9 and aspect_ratio <= 1.1:
            cv2.rectangle(img_contours, (x, y), (x + w, y + h), (0, 0, 255), 3)
            A4 = img[y:y + h, x:x + w]

5. 显示结果：

cv2.imshow('Original Image', img)
cv2.imshow('Contour Image', img_contours)
cv2.imshow('A4 Image', A4)
cv2.waitKey(0)
cv2.destroyAllWindows()

完整代码如下：

import cv2
import numpy as np

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)

contours, hierarchy = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
img_contours = img.copy()
cv2.drawContours(img_contours, contours, -1, (0, 255, 0), 3)

for contour in contours:
    perimeter = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
    if len(approx) == 4:
        x, y, w, h = cv2.boundingRect(approx)
        aspect_ratio = float(w) / h
        if aspect_ratio >= 0.9 and aspect_ratio <= 1.1:
            cv2.rectangle(img_contours, (x, y), (x + w, y + h), (0, 0, 255), 3)
            A4 = img[y:y + h, x:x + w]

cv2.imshow('Original Image', img)
cv2.imshow('Contour Image', img_contours)
cv2.imshow('A4 Image', A4)
cv2.waitKey(0)
cv2.destroyAllWindows()