本文目录导读：
我来帮你创建一个二维码识别脚本,以下是使用Python和OpenCV的实现方法：
使用OpenCV（推荐，支持实时视频）
import cv2
import numpy as np
from pyzbar.pyzbar import decode
import sys
def decode_qr_from_image(image_path):
"""从图片文件识别二维码"""
# 读取图片
img = cv2.imread(image_path)
if img is None:
print(f"无法读取图片: {image_path}")
return
# 解码二维码
decoded_objects = decode(img)
for obj in decoded_objects:
print(f"二维码类型: {obj.type}")
print(f"二维码数据: {obj.data.decode('utf-8')}")
# 在图片上绘制边框
points = obj.polygon
if len(points) > 4:
hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))
points = hull
n = len(points)
for j in range(n):
cv2.line(img, tuple(points[j]), tuple(points[(j+1) % n]), (0, 255, 0), 3)
# 显示解码内容
x, y, w, h = obj.rect
cv2.putText(img, obj.data.decode('utf-8'), (x, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# 显示结果
cv2.imshow("QR Code Decoder", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def decode_qr_from_camera():
"""从摄像头实时识别二维码"""
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("无法打开摄像头")
return
print("按 'q' 键退出")
while True:
ret, frame = cap.read()
if not ret:
break
# 解码二维码
decoded_objects = decode(frame)
for obj in decoded_objects:
# 绘制边框
points = obj.polygon
if len(points) > 4:
hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))
points = hull
n = len(points)
for j in range(n):
cv2.line(frame, tuple(points[j]), tuple(points[(j+1) % n]), (0, 255, 0), 3)
# 显示数据
x, y, w, h = obj.rect
data = obj.data.decode('utf-8')
cv2.putText(frame, data, (x, y-10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
print(f"识别到二维码: {data}")
# 显示视频
cv2.imshow("QR Code Scanner", frame)
# 按 'q' 退出
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def decode_qr_from_screen():
"""从屏幕截图识别二维码（Windows）"""
try:
from PIL import ImageGrab
import pyautogui
except ImportError:
print("需要安装 Pillow 和 pyautogui")
print("pip install Pillow pyautogui")
return
print("程序将运行5秒，请将二维码放在屏幕上")
print("按 Ctrl+C 退出")
import time
for i in range(5):
# 截取屏幕
screen = ImageGrab.grab()
screen_np = np.array(screen)
screen_bgr = cv2.cvtColor(screen_np, cv2.COLOR_RGB2BGR)
# 解码
decoded_objects = decode(screen_bgr)
for obj in decoded_objects:
data = obj.data.decode('utf-8')
print(f"识别到二维码: {data}")
# 绘制边框
points = obj.polygon
if len(points) > 4:
hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))
points = hull
n = len(points)
for j in range(n):
cv2.line(screen_bgr, tuple(points[j]), tuple(points[(j+1) % n]), (0, 255, 0), 3)
cv2.imshow("Screen QR Scanner", screen_bgr)
cv2.waitKey(1)
time.sleep(1)
cv2.destroyAllWindows()
if __name__ == "__main__":
if len(sys.argv) > 1:
# 命令行参数处理
if sys.argv[1] == "camera":
decode_qr_from_camera()
elif sys.argv[1] == "screen":
decode_qr_from_screen()
else:
decode_qr_from_image(sys.argv[1])
else:
print("使用方法:")
print(" 识别图片: python qr_scanner.py image.jpg")
print(" 摄像头识别: python qr_scanner.py camera")
print(" 屏幕识别: python qr_scanner.py screen")
使用zbarlight（轻量级）
import zbarlight
from PIL import Image
def decode_qr_with_zbarlight(image_path):
"""使用zbarlight库识别二维码"""
try:
# 打开图片
with open(image_path, 'rb') as image_file:
image = Image.open(image_file)
image = image.convert('L') # 转换为灰度图
# 解码所有二维码
codes = zbarlight.scan_codes('qrcode', image)
if codes:
for code in codes:
print(f"二维码内容: {code.decode('utf-8')}")
else:
print("未识别到二维码")
except Exception as e:
print(f"识别失败: {e}")
def batch_decode_qr(folder_path):
"""批量识别文件夹中的二维码图片"""
import os
for filename in os.listdir(folder_path):
if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
filepath = os.path.join(folder_path, filename)
print(f"处理: {filename}")
decode_qr_with_zbarlight(filepath)
print("-" * 30)
安装依赖
# 主要依赖 pip install opencv-python pip install pyzbar pip install Pillow pip install numpy # 以下为可选 pip install zbarlight # 轻量级二维码识别 pip install pyautogui # 屏幕截图功能
使用示例
# 单张图片识别 python qr_scanner.py qrcode.jpg # 摄像头实时识别 python qr_scanner.py camera # 屏幕识别 python qr_scanner.py screen
高级功能版本
import cv2
import numpy as np
from pyzbar.pyzbar import decode
import json
import requests
from urllib.parse import urlparse
class AdvancedQRScanner:
def __init__(self):
self.scanned_codes = set() # 避免重复识别
def decode_qr(self, image):
"""解码二维码并返回详细结果"""
results = []
decoded_objects = decode(image)
for obj in decoded_objects:
data = obj.data.decode('utf-8')
# 避免重复识别
if data in self.scanned_codes:
continue
self.scanned_codes.add(data)
# 检测是否为URL
is_url = self.is_valid_url(data)
result = {
'data': data,
'type': obj.type,
'is_url': is_url,
'rect': {
'x': obj.rect.left,
'y': obj.rect.top,
'width': obj.rect.width,
'height': obj.rect.height
},
'points': [(p.x, p.y) for p in obj.polygon]
}
results.append(result)
# 如果是URL，可以自动访问
if is_url:
self.check_url_accessibility(data)
return results
def is_valid_url(self, text):
"""检查是否为有效URL"""
try:
result = urlparse(text)
return all([result.scheme, result.netloc])
except:
return False
def check_url_accessibility(self, url):
"""检查URL是否可访问"""
try:
response = requests.head(url, timeout=5)
print(f"URL状态: {response.status_code}")
except:
print(f"无法访问URL: {url}")
def save_results(self, results, filename='qr_results.json'):
"""保存识别结果到文件"""
with open(filename, 'w', encoding='utf-8') as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print(f"结果已保存到 {filename}")
def visualize_results(self, image, results):
"""在图片上可视化识别结果"""
for result in results:
points = result['points']
if len(points) > 4:
hull = cv2.convexHull(np.array(points, dtype=np.float32))
points = hull
n = len(points)
for j in range(n):
cv2.line(image, tuple(points[j]), tuple(points[(j+1) % n]), (0, 255, 0), 3)
x, y = result['rect']['x'], result['rect']['y']
cv2.putText(image, result['data'][:20] + '...' if len(result['data']) > 20 else result['data'],
(x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
return image
# 使用示例
if __name__ == "__main__":
scanner = AdvancedQRScanner()
# 识别图片
img = cv2.imread("qrcode.jpg")
results = scanner.decode_qr(img)
for result in results:
print(f"识别到: {result['data']}")
print(f"类型: {result['type']}")
print(f"是否URL: {result['is_url']}")
# 保存结果
scanner.save_results(results)
这个脚本支持：
- 图片文件识别
- 摄像头实时识别
- 屏幕截图识别
- 批量处理
- 结果保存
- URL检测
- 避免重复识别
选择适合你需求的方案使用即可。
