如何用python实现对DM二维码的矫正，并计算其中心坐标和偏转角度，和二维码内容的0-1矩阵

要实现对DM二维码的矫正和计算其中心坐标和偏转角度，可以使用OpenCV库和NumPy库。下面是一般的步骤：

1. 导入所需库

import cv2
import numpy as np

2. 读取图片并进行二值化处理

img = cv2.imread('dmcode.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

3. 找到二维码定位符

contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    area = cv2.contourArea(contour)
    if area < 1000:
        continue
    rect = cv2.minAreaRect(contour)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

4. 矫正二维码

width, height = rect[1]
if width > height:
    width, height = height, width
    angle = rect[2] + 90
else:
    angle = rect[2]
M = cv2.getRotationMatrix2D(rect[0], angle, 1)
img_rotated = cv2.warpAffine(img, M, (int(width), int(height)))

5. 计算二维码中心坐标和偏转角度

rows, cols = img_rotated.shape[:2]
center_x, center_y = cols // 2, rows // 2
angle = -angle

6. 提取二维码内容的0-1矩阵

gray = cv2.cvtColor(img_rotated, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
rows, cols = thresh.shape
matrix = np.zeros((rows, cols), dtype=np.int)
for i in range(rows):
    for j in range(cols):
        if thresh[i, j] == 255:
            matrix[i, j] = 1

完整代码如下：

import cv2
import numpy as np

img = cv2.imread('dmcode.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    area = cv2.contourArea(contour)
    if area < 1000:
        continue
    rect = cv2.minAreaRect(contour)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

width, height = rect[1]
if width > height:
    width, height = height, width
    angle = rect[2] + 90
else:
    angle = rect[2]
M = cv2.getRotationMatrix2D(rect[0], angle, 1)
img_rotated = cv2.warpAffine(img, M, (int(width), int(height)))

rows, cols = img_rotated.shape[:2]
center_x, center_y = cols // 2, rows // 2
angle = -angle

gray = cv2.cvtColor(img_rotated, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
rows, cols = thresh.shape
matrix = np.zeros((rows, cols), dtype=np.int)
for i in range(rows):
    for j in range(cols):
        if thresh[i, j] == 255:
            matrix[i, j] = 1

print('中心坐标：({},{})'.format(center_x, center_y))
print('偏转角度：{}度'.format(angle))
print('0-1矩阵：\n', matrix)
cv2.imshow('img', img_rotated)
cv2.waitKey(0)
cv2.destroyAllWindows()