如何用cv2将封闭图像框出来并将直角框的角的坐标和框的大小排名打印出来

可以使用cv2.findContours和cv2.minAreaRect方法来框出封闭图像并获取直角框的角的坐标和框的大小，具体的步骤如下：

1. 读取图像并将其转换为灰度图像

import cv2

img = cv2.imread('image_path')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

2. 对灰度图像进行二值化

threshold_value = 127
max_value = 255
ret, binary = cv2.threshold(gray, threshold_value, max_value, cv2.THRESH_BINARY)

3. 查找图像中的轮廓

contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

4. 遍历轮廓并绘制矩形框

rects = []
for contour in contours:
    rect = cv2.minAreaRect(contour)
    rects.append(rect)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

5. 计算矩形框的大小和角的坐标并按照大小排名打印输出

for i, rect in enumerate(sorted(rects, key=lambda x: x[1][0] * x[1][1], reverse=True)):
    print(f'Rank {i+1}: Size={rect[1][0]*rect[1][1]:.2f}, TopLeft={rect[0]}, Width={rect[1][0]:.2f}, Height={rect[1][1]:.2f}, Angle={rect[2]:.2f}')

完整代码如下：

import cv2
import numpy as np

img = cv2.imread('image_path')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

threshold_value = 127
max_value = 255
ret, binary = cv2.threshold(gray, threshold_value, max_value, cv2.THRESH_BINARY)

contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

rects = []
for contour in contours:
    rect = cv2.minAreaRect(contour)
    rects.append(rect)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

for i, rect in enumerate(sorted(rects, key=lambda x: x[1][0] * x[1][1], reverse=True)):
    print(f'Rank {i+1}: Size={rect[1][0]*rect[1][1]:.2f}, TopLeft={rect[0]}, Width={rect[1][0]:.2f}, Height={rect[1][1]:.2f}, Angle={rect[2]:.2f}')

cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()