Recognize QR codes by pyzbar

原创

Swing Dunn

发布于 2025-10-23 16:38:42

1380

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

Here we explains how to use pyzbar to recognize QR codes.

1.First, the image needs to be read and converted into a grayscale image.

To make it simpler, directly extract the approximate area where the QR code is located from the image.

                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]]

2.Adjust the size of the image, recognize it at different scales;reduce image noise and interference through preprocessing, and improve the image quality of the QR code area may help increase the accuracy of recognition.

                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)
                    qrcode_content = recognize_QRcode(blurred_image)

3.Identify the content of the QR code using pyzbar

def recognize_QRcode(img):
    # 使用 pyzbar 解码二维码
    decoded_objects = decode(img)

    # 遍历解码结果
    for obj in decoded_objects:
        points = obj.polygon
        if len(points) > 4:
            hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))
            points = hull

        for j in range(len(points)):
            cv2.line(img, tuple(points[j]), tuple(points[(j + 1) % len(points)]), (0, 255, 0), 3)
    if(len(decoded_objects) > 0):
        return obj.data.decode('utf-8')
    else:
        return None

4.Visualization of the decoding results:

 qrcode_content = recognize_QRcode(blurred_image)
                    if qrcode_content:
                        print("二维码缩放率：", scale_factor)
                        print('二维码内容： ', qrcode_content)
                        x, y = (10,140)
                        cv2.putText(ori_bar_area,qrcode_content, (x, y), cv2.FONT_HERSHEY_SIMPLEX,0.6,  (0, 0, 255), 1)
                        find = True
                        show_image(ori_bar_area)
                        break
                    else:
                        show_image(blurred_image)

5.Since the images of these QR codes in my group are rather blurry and of poor quality, there will also be cases where they cannot be recognized.

Like this:

6.Recognition accuracy rate

For nearly a thousand identical poor quality QR code identifications, the accuracy rates are as follows:

When the QR code is clear enough, pyzbar will be a reliable tool.

Source code:


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

# 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 recognize_QRcode(img):
    # 使用 pyzbar 解码二维码
    decoded_objects = decode(img)

    # 遍历解码结果
    for obj in decoded_objects:
        points = obj.polygon
        if len(points) > 4:
            hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))
            points = hull

        for j in range(len(points)):
            cv2.line(img, tuple(points[j]), tuple(points[(j + 1) % len(points)]), (0, 255, 0), 3)
    if(len(decoded_objects) > 0):
        return obj.data.decode('utf-8')
    else:
        return None
  

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

    # 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]]
                #show_image(bar_area)

                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)
                    qrcode_content = recognize_QRcode(blurred_image)
                    if qrcode_content:
                        print("二维码缩放率：", scale_factor)
                        print('二维码内容： ', qrcode_content)
                        x, y = (10,140)
                        cv2.putText(ori_bar_area,qrcode_content, (x, y), cv2.FONT_HERSHEY_SIMPLEX,0.6,  (0, 0, 255), 1)
                        find = True
                        #show_image(ori_bar_area)
                        break
                if not find : 
                    error_paper_num += 1
                    pass
                    #show_image(blurred_image)
              
                
                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))
                print()     


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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