Python二维码解析案例如何识别二维码

wen python案例 2026-06-29 166

本文目录导读：

使用pyzbar库（推荐）
使用OpenCV内置解码器
实战：批量处理文件夹中的二维码
高级：预处理提升识别率
使用建议

我来分享几个Python识别二维码的实用案例：

使用pyzbar库（推荐）

安装

pip install pyzbar pillow opencv-python

基础案例

from pyzbar.pyzbar import decode
from PIL import Image
import cv2
import numpy as np
# 从图片文件识别
def decode_qr_from_file(image_path):
    img = Image.open(image_path)
    codes = decode(img)
    for code in codes:
        print(f"二维码内容: {code.data.decode('utf-8')}")
        print(f"二维码类型: {code.type}")
        print(f"位置: {code.rect}")
    return codes
# 从OpenCV图像识别
def decode_qr_from_cv2(image):
    # 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 使用pyzbar解码
    codes = decode(gray)
    for code in codes:
        data = code.data.decode('utf-8')
        print(f"识别结果: {data}")
        # 绘制识别框
        (x, y, w, h) = code.rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
        # 显示内容
        cv2.putText(image, data, (x, y - 10), 
                   cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
    return image, codes
# 使用示例
if __name__ == "__main__":
    # 识别图片文件
    result = decode_qr_from_file("qrcode.png")
    # 识别摄像头捕获的图像
    cap = cv2.VideoCapture(0)
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        result_frame, codes = decode_qr_from_cv2(frame)
        cv2.imshow("QR Scanner", result_frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

使用OpenCV内置解码器

import cv2
import numpy as np
def decode_qr_opencv(image_path):
    # 初始化二维码检测器
    qr_detector = cv2.QRCodeDetector()
    # 读取图片
    img = cv2.imread(image_path)
    # 检测并解码
    data, points, straight_qrcode = qr_detector.detectAndDecode(img)
    if data:
        print(f"识别内容: {data}")
        print(f"位置点: {points}")
        # 绘制检测框
        if points is not None:
            points = points.astype(int)
            for i in range(len(points[0])):
                cv2.circle(img, tuple(points[0][i]), 5, (0, 255, 0), -1)
                cv2.line(img, tuple(points[0][i]), 
                        tuple(points[0][(i+1) % len(points[0])]), 
                        (0, 255, 0), 2)
        cv2.imshow("QR Code", img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    return data
# 批量识别多个二维码
def decode_multiple_qr(image_path):
    img = cv2.imread(image_path)
    qr_detector = cv2.QRCodeDetector()
    # 设置解码参数
    qr_detector.setEpsX(0.5)  # 精度参数
    qr_detector.setEpsY(0.5)
    data, points, _ = qr_detector.detectAndDecodeMulti(img)
    if data:
        for i, (d, p) in enumerate(zip(data, points)):
            print(f"二维码 {i+1}: {d}")
    else:
        print("未检测到二维码")
    return data, points
if __name__ == "__main__":
    decode_qr_opencv("test_qr.png")
    decode_multiple_qr("multi_qr.png")

实战：批量处理文件夹中的二维码

import os
from pathlib import Path
from pyzbar.pyzbar import decode
from PIL import Image
import pandas as pd
def batch_decode_qr(folder_path, output_file="results.csv"):
    results = []
    # 支持的图片格式
    extensions = ['.png', '.jpg', '.jpeg', '.bmp', '.gif']
    # 遍历文件夹
    for file_path in Path(folder_path).rglob('*'):
        if file_path.suffix.lower() in extensions:
            try:
                # 识别二维码
                img = Image.open(file_path)
                codes = decode(img)
                for code in codes:
                    result = {
                        'filename': file_path.name,
                        'path': str(file_path),
                        'content': code.data.decode('utf-8'),
                        'type': code.type,
                        'width': code.rect.width,
                        'height': code.rect.height
                    }
                    results.append(result)
                    print(f"✅ {file_path.name}: {result['content']}")
            except Exception as e:
                print(f"❌ {file_path.name}: 识别失败 - {str(e)}")
    # 保存结果到CSV
    if results:
        df = pd.DataFrame(results)
        df.to_csv(output_file, index=False, encoding='utf-8-sig')
        print(f"\n共识别 {len(results)} 个二维码，结果已保存到 {output_file}")
    return results
# 使用示例
if __name__ == "__main__":
    results = batch_decode_qr("./qrcodes/", "decoded_results.csv")

高级：预处理提升识别率

import cv2
import numpy as np
from pyzbar.pyzbar import decode
def preprocess_image(image):
    """图像预处理，提高二维码识别率"""
    # 1. 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 2. 高斯模糊去噪
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    # 3. 自适应阈值二值化
    binary = cv2.adaptiveThreshold(blurred, 255, 
                                   cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                   cv2.THRESH_BINARY_INV, 11, 2)
    # 4. 形态学操作（可选）
    kernel = np.ones((3, 3), np.uint8)
    morph = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
    return morph
def enhanced_qr_decode(image_path):
    """增强版二维码识别"""
    # 读取原图
    img = cv2.imread(image_path)
    # 方法1：直接识别
    codes1 = decode(img)
    # 方法2：预处理后识别
    processed = preprocess_image(img)
    codes2 = decode(processed)
    # 方法3：调整图像大小
    resized = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    codes3 = decode(resized)
    # 合并所有结果，去重
    all_codes = []
    seen = set()
    for codes in [codes1, codes2, codes3]:
        for code in codes:
            data = code.data.decode('utf-8')
            if data not in seen:
                seen.add(data)
                all_codes.append(code)
    return all_codes
# 使用示例
if __name__ == "__main__":
    codes = enhanced_qr_decode("blurry_qr.jpg")
    for code in codes:
        print(f"识别结果: {code.data.decode('utf-8')}")