python 实现对二维码(qr码)的检测与定位

在Python中，可以使用OpenCV库来实现对二维码的检测与定位。下面是一个基本的流程：

1. 导入必要的库

import cv2
import numpy as np
from pyzbar.pyzbar import decode

2. 读取图像并转为灰度图

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

3. 对图像进行二值化处理

ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

4. 对二值化后的图像进行轮廓检测

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

5. 遍历轮廓并筛选出二维码区域

for contour in contours:
    area = cv2.contourArea(contour)
    if area > 1000:
        perimeter = cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        if len(approx) == 4:
            x, y, w, h = cv2.boundingRect(contour)
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

6. 解码二维码

decoded = decode(gray)
for d in decoded:
    x, y, w, h = d.rect
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
    barcodeData = d.data.decode("utf-8")

完整代码如下：

import cv2
import numpy as np
from pyzbar.pyzbar import decode

# 读取图像并转为灰度图
img = cv2.imread('qrcode.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 对图像进行二值化处理
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 对二值化后的图像进行轮廓检测
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 遍历轮廓并筛选出二维码区域
for contour in contours:
    area = cv2.contourArea(contour)
    if area > 1000:
        perimeter = cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
        if len(approx) == 4:
            x, y, w, h = cv2.boundingRect(contour)
            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

# 解码二维码
decoded = decode(gray)
for d in decoded:
    x, y, w, h = d.rect
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
    barcodeData = d.data.decode("utf-8")

# 显示结果
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

在运行完代码后，可以看到二维码被框出，并在下方输出二维码的内容。