By scanning the WeChatQRCode to identify the QR code

原创

Swing Dunn

发布于 2025-10-23 17:16:10

1080

文章被收录于专栏：Some studies in imgsSome studies in imgs

WeChat has released its QR code detection and recognition algorithm (based on C++) and the OpenCV 4.5+ version has integrated this algorithm (the cv::wechat_qrcode::WeChatQRCode class), which supports QR code detection, positioning and decoding. Since loading the model file is required during the initialization of this class (which takes a long time), using the singleton pattern (Singleton) can ensure that only one instance is created globally, avoiding the repeated loading of the model and improving efficiency.

Version Requirements:OpenCV 4.5+ or python-opencv 4.5+

pip install numpy==1.24.3 -i https://mirrors.aliyun.com/pypi/simple/
pip install opencv-python==4.5.5.64 opencv-contrib-python==4.5.5.64 -i https://mirrors.aliyun.com/pypi/simple/

1.Similarly, read the image, perform some preprocessing at different scales, and enhance the image quality.

 # 递归遍历文件夹
    for root, dirs, files in os.walk(folder_path): 
        for filename in files:
            if filename.lower().endswith('.jpg'):
                paper_num += 1

                file_path = os.path.join(root, filename)
                ori_img = cv2.imread(file_path, cv2.IMREAD_ANYCOLOR)  # 目标图像
                if len(ori_img.shape) == 3 and ori_img.shape[2] == 3:
                    gray_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2GRAY)
                elif len(ori_img.shape) == 2:
                    gray_img = ori_img.copy()
                else:
                    print('error: bad image')
                    return   

                ori_bar_area = ori_img[bar_range[1]:bar_range[3], bar_range[0]:bar_range[2]]
                bar_area = gray_img[bar_range[1]:bar_range[3], bar_range[0]:bar_range[2]]

                find = False
                lt_sacle = [1.0, 1.25, 1.5, 2.0, 2.25, 2.5, 3.0]
                for scale_factor in lt_sacle:
                    resized_image = cv2.resize(bar_area, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR)
                    
                    blurred_image = cv2.GaussianBlur(resized_image, (7, 7), 0)
                   # blurred_image = cv2.medianBlur(blurred_image, 5)
                    #g_threshold, binary = cv2.threshold(resized_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

2.Model files: The WeChat algorithm requires 4 pre-trained model files (which can be obtained from the OpenCV source code or official channels):

detect.prototxt

detect.caffemodel

sr.prototxt

sr.caffemodel

3.Using the model file, create a singleton for the detector.

  # 使用
    detector = get_wechat_qrcode_detector("./model/detect.prototxt",'./model/detect.caffemodel','./model/sr.prototxt', './model/sr.caffemodel')


def get_wechat_qrcode_detector(detect_prototxt, detect_caffemodel, sr_prototxt, sr_caffemodel):
    global _detector
    if _detector is None:
        _detector = cv2.wechat_qrcode_WeChatQRCode(
            detect_prototxt, detect_caffemodel,
            sr_prototxt, sr_caffemodel
        )
    return _detector

4.Use the detector to identify and visualize the results

results, points = detector.detectAndDecode(blurred_image)
                    if len(points) > 0:  # 若检测到二维码
                        for i in range(len(points)):
                            # points[i] 是当前二维码的4个顶点坐标（形状：(4, 2)）
                            # 将坐标转为整数并reshape为适合polylines的格式
                            pts = np.int32(points[i]).reshape(-1, 1, 2)
                            
                            # 绘制闭合轮廓（绿色，线宽2）
                            cv2.polylines( ori_bar_area, [pts], isClosed=True, color=(0, 255, 0), thickness=2)
                        if results:
                            x, y = (10,140)
                            cv2.putText( ori_bar_area,results[i], (x, y), cv2.FONT_HERSHEY_SIMPLEX,0.6,(0, 0, 255), 1 )

                        show_image(ori_bar_area)
                        find =  True
                        break

5.Statistical results of recognition accuracy and the time consumption

Compare with pyzbar, the same group of imgs:

Source code:

import cv2
import os
import numpy as np
import time
# 模块级变量：全局唯一检测器
_detector = None

# 读取目标图像和模板图像
def show_image(img):
    cv2.namedWindow("1",cv2.WINDOW_AUTOSIZE )
    cv2.imshow("1",img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def get_wechat_qrcode_detector(detect_prototxt, detect_caffemodel, sr_prototxt, sr_caffemodel):
    global _detector
    if _detector is None:
        _detector = cv2.wechat_qrcode_WeChatQRCode(
            detect_prototxt, detect_caffemodel,
            sr_prototxt, sr_caffemodel
        )
    return _detector

def main():
    folder_path = './imgs8'
    #模板定位点信息
    bar_range = (1377, 152, 1536, 293)
    paper_num = 0
    error_paper_num = 0

    # 使用
    detector = get_wechat_qrcode_detector("./model/detect.prototxt",'./model/detect.caffemodel','./model/sr.prototxt', './model/sr.caffemodel')
    
    start = time.perf_counter()       
    # 递归遍历文件夹
    for root, dirs, files in os.walk(folder_path): 
        for filename in files:
            if filename.lower().endswith('.jpg'):
                paper_num += 1

                file_path = os.path.join(root, filename)
                ori_img = cv2.imread(file_path, cv2.IMREAD_ANYCOLOR)  # 目标图像
                if len(ori_img.shape) == 3 and ori_img.shape[2] == 3:
                    gray_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2GRAY)
                elif len(ori_img.shape) == 2:
                    gray_img = ori_img.copy()
                else:
                    print('error: bad image')
                    return   

                ori_bar_area = ori_img[bar_range[1]:bar_range[3], bar_range[0]:bar_range[2]]
                bar_area = gray_img[bar_range[1]:bar_range[3], bar_range[0]:bar_range[2]]
                
                single_start = time.perf_counter()   
                find = False
                lt_sacle = [1.0, 1.25, 1.5, 2.0, 2.25, 2.5, 3.0]
                for scale_factor in lt_sacle:
                    resized_image = cv2.resize(bar_area, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR)
                    
                    blurred_image = cv2.GaussianBlur(resized_image, (7, 7), 0)
                    blurred_image = cv2.medianBlur(blurred_image, 5)
                    #g_threshold, binary = cv2.threshold(resized_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
                    #show_image(blurred_image)
                    results, points = detector.detectAndDecode(blurred_image)
                    if len(points) > 0:  # 若检测到二维码
                        for i in range(len(points)):
                            # points[i] 是当前二维码的4个顶点坐标（形状：(4, 2)）
                            # 将坐标转为整数并reshape为适合polylines的格式
                            pts = np.int32(points[i]).reshape(-1, 1, 2)
                            
                            # 绘制闭合轮廓（绿色，线宽2）
                            cv2.polylines( ori_bar_area, [pts], isClosed=True, color=(0, 255, 0), thickness=2)
                        if results:
                            x, y = (10,140)
                            cv2.putText( ori_bar_area,results[i], (x, y), cv2.FONT_HERSHEY_SIMPLEX,0.6,(0, 0, 255), 1 )

                        #show_image(ori_bar_area)
                        find =  True
                        break
                if not find : 
                    error_paper_num += 1
                    #show_image(ori_bar_area)
                    pass
                          
                print('识别总份数： ', paper_num)
                print('未识别到二维码份数： ', error_paper_num)
                print('二维码识别率： ', str(1 - error_paper_num / paper_num))
                  
                singel_end = time.perf_counter()
                print(f"单份识别耗时耗时：{singel_end - single_start:.6f} 秒")
            
                print() 

    end = time.perf_counter()
    print(f"总耗时：{end - start:.6f} 秒")

if __name__ == '__main__':
    main()

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

opencv-python

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

opencv-python

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