Opencv最强案例——使用摄像头、OpenCV和Python扫描条形码和二维码。

        Dynamsoft是唯一一家为Windows、Linux、macOS和Raspberry Pi OS提供企业级Python条形码和二维码SDK的公司。SDK允许开发人员快速构建健壮的命令行、web和桌面应用程序，这些应用程序可以扫描来自各种来源的条形码和QR码。在本文中，我们使用条形阅读器OpenCV和网络摄像头，用Python创建跨平台的桌面条形码和二维码扫描仪。

下载SDK

• 开放计算机视觉

  用于访问网络摄像头和拼接图像。

  pip install opencv-python

• Dynamsoft条形码阅读器

  用于解码图像中的条形码和QR码。

  pip install dbr

许可证激活

从获取桌面许可证密钥这里要激活Dynamsoft条形码阅读器:

BarcodeReader.init_license("LICENSE-KEY")

用Python构建条形码和QR码扫描仪的步骤

据知，Python的GIL(全局解释器锁)是多线程应用程序的性能瓶颈。因此，建议使用Python的多处理库来运行CPU密集型的条形码和二维码检测算法。示例代码视频_线程. py演示如何使用Python的多处理库。

以下是构建我们的条形码和QR码扫描仪的步骤:

1. 导入必要的包:

   import numpy as np
   import cv2 as cv
   
   from multiprocessing.pool import ThreadPool
   from collections import deque
   
   import dbr
   from dbr import *

2. 设置许可证密钥以激活和实例化Dynamsoft条形码读取器:

   threadn = 1 # cv.getNumberOfCPUs()
   pool = ThreadPool(processes = threadn)
   barcodeTasks = deque()

3. 使用您想要使用的进程数量创建一个线程池:

   threadn = 1 # cv.getNumberOfCPUs()
   pool = ThreadPool(processes = threadn)
   barcodeTasks = deque()

   注意:如果您使用所有CPU核心，CPU使用率将会很高。

4. 创建一个任务功能，从网络摄像头视频帧中检测条形码和QR码:

   def process_frame(frame):
       results = None
       try:
           results = reader.decode_buffer(frame)
       except BarcodeReaderError as bre:
           print(bre)
   
       return results
   
   while True:
       ret, frame = cap.read()
       while len(barcodeTasks) > 0 and barcodeTasks[0].ready():
           results = barcodeTasks.popleft().get()
           if results != None:
               for result in results:
                   points = result.localization_result.localization_points
                   cv.line(frame, points[0], points[1], (0,255,0), 2)
                   cv.line(frame, points[1], points[2], (0,255,0), 2)
                   cv.line(frame, points[2], points[3], (0,255,0), 2)
                   cv.line(frame, points[3], points[0], (0,255,0), 2)
                   cv.putText(frame, result.barcode_text, points[0], cv.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255))
   
       if len(barcodeTasks) < threadn:
           task = pool.apply_async(process_frame, (frame.copy(), ))
           barcodeTasks.append(task)
   
       cv.imshow('Barcode & QR Code Scanner', frame)
       ch = cv.waitKey(1)
       if ch == 27:
           break

5. 运行条形码和QR码扫描仪:

   ​

Dynamsoft条形码阅读器可以从一幅图像中检测多个条形码和QR码。然而，图像质量影响检测精度。正如你在上面的图像中看到的，为了捕捉所有的条形码和二维码，我们需要增加镜头的景深。这样，条形码和二维码可能会变得太小而无法读取。为了解决这一问题，我们将摄像头拉近以获得高质量的扫描图像，然后使用OpenCV拼接API将多个条形码和二维码图像拼接成一幅全景图。

将多个条形码和QR码图像拼接成全景图

OpenCV存储库包含一个stitching.py展示如何使用OpenCV缝合器API的文件。

要实现全景拼接:

1. 初始化stitcher对象:

   modes = (cv.Stitcher_PANORAMA, cv.Stitcher_SCANS)
   stitcher = cv.Stitcher.create(modes[1])
   stitcher.setPanoConfidenceThresh(0.5)

2. 为拼接包含条形码和QR码的图像创建新的任务功能:

   panoramaPool = ThreadPool(processes = threadn)
   panoramaTask = deque()
   
   def stitch_frame(self, frame):
       try:
           results = self.reader.decode_buffer(frame)
           if results != None:
               for result in results:
                   points = result.localization_result.localization_points
                   cv.line(frame, points[0], points[1], (0,255,0), 2)
                   cv.line(frame, points[1], points[2], (0,255,0), 2)
                   cv.line(frame, points[2], points[3], (0,255,0), 2)
                   cv.line(frame, points[3], points[0], (0,255,0), 2)
                   cv.putText(frame, result.barcode_text, points[0], cv.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255))
   
               self.panorama.append((frame, len(results)))
               print('Stitching .............')
               try:
                   all_images = [frame for frame, count in self.panorama]
                   status, image = self.stitcher.stitch(all_images)
   
                   if status != cv.Stitcher_OK:
                       print("Can't stitch images, error code = %d" % status)
                       return self.panorama[0][0]
                   else:
                       # Stop stitching if the output image is out of control
                       if image.shape[0] >= frame.shape[0] * 1.5:
                           self.isPanoramaDone = True
                           self.save_frame(all_images[0])
                           print('Stitching is done.............')
                           return None
   
                       # Drop the stitched image if its quality is not good enough
                       total = 0
                       for frame, count in self.panorama:
                           total += count
   
                       count_stitch = self.count_barcodes(image)
                       if count_stitch > total or count_stitch < self.panorama[0][1]:
                           return self.panorama[0][0]
   
                       # Wait for the next stitching and return the current stitched image
                       self.panorama = [(image, count_stitch)]
                       return image
               except Exception as e:
                   print(e)
                   return None
   
       except BarcodeReaderError as e:
           print(e)
           return None
   
       return None
   
   while len(panoramaTask) > 0 and panoramaTask[0].ready():
       image = panoramaTask.popleft().get()
       if image is not None:
           cv.imshow('panorama', image)
   
   if len(panoramaTask) < threadn:
       task = panoramaPool.apply_async(self.stitch_frame, (frame_cp, ))
       panoramaTask.append(task)

3. 运行代码以获得全景拼接结果。

   ​

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