How to Scan Barcodes in Jupyter Notebook

Jupyter notebook is a web-based interactive computing environment that enables you to create and share documents that contain live code, equations, visualizations and narrative text. If you have installed Anaconda, Jupyter notebook is installed by default. This article demonstrates how to write Python code to scan barcodes in Jupyter notebook.

Pre-requisites

Creating a Jupyter Notebook for Python Barcode Scanning

  1. Run Jupyter notebook:

    Jupyter notebook

  2. Open http://localhost:8888/ in your browser to create a barcode_scanning.ipynb file.

    new Jupyter notebook

  3. Insert a new cell to install Dynamsoft Barcode Reader, OpenCV, and Matplotlib:

     !pip install dbr opencv-python matplotlib
    
  4. Initialize the license of barcode SDK and barcode reader object:

     from dbr import *
     license_key = "LICENSE-KEY"
     reader = BarcodeReader()
     reader.init_license(license_key)
    
  5. Create an image upload button in Jupyter notebook:

     from ipywidgets import FileUpload
     def on_upload_change(change):
         if not change.new:
             return
         up = change.owner
        
         up.value.clear()
    
     uploader = FileUpload(accept='image/*', multiple=False)
     uploader.observe(on_upload_change)
     uploader
    
  6. Convert the image data to numpy array and then decode the image with OpenCV API:

     import cv2 as cv
     import numpy as np
    
     up = change.owner
     for _, data in up.value.items():
         image_buffer = np.frombuffer(data['content'], dtype=np.uint8)
         img = cv.imdecode(image_buffer, 1)
            
    
     up.value.clear()
    
  7. Scan barcodes with Dynamsoft Barcode API:

     def decode(frame):
    
         before = time.time()
         results = reader.decode_buffer(frame)
         after = time.time()
        
         COLOR_RED = (0,0,255)
         thickness = 2
         margin = 1
         text_x = 10; text_y = 20
         if results != None:
             found = len(results)
             for result in results:
                 print("Format: %s, Text: %s" % (result.barcode_format_string, result.barcode_text))
                 text = result.barcode_text 
                 points = result.localization_result.localization_points
                 data = np.array([[points[0][0], points[0][1]], [points[1][0], points[1][1]], [points[2][0], points[2][1]], [points[3][0], points[3][1]]])
                 cv.drawContours(image=frame, contours=[data], contourIdx=-1, color=COLOR_RED, thickness=thickness, lineType=cv.LINE_AA)
        
             cv.putText(frame, '%.2f s, barcode found: %d' % (after - before, found), (text_x, text_y), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)
         else:
             cv.putText(frame, '%.2f s, barcode found: %d' % (after - before, 0), (text_x, text_y), cv.FONT_HERSHEY_SIMPLEX, 0.5, COLOR_RED)
    
     img = cv.imdecode(image_buffer, 1)
            
     new_img = img.copy()
     # barcode recognition
     decode(new_img)
    
  8. Display the barcode recognition results with Matplotlib:

     import matplotlib.pyplot as plt
    
     def show_image(img1, img2):
         fig = plt.figure(figsize=(25, 10))
         ax1 = fig.add_subplot(1, 2, 1) 
         plt.title('Input image', fontsize=16)
         ax1.axis('off')
         ax2 = fig.add_subplot(1, 2, 2)
         plt.title('Barcode Recognition', fontsize=16)
         ax2.axis('off')
         ax1.imshow(img1)
         ax2.imshow(img2)
    
     img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
     new_img = cv.cvtColor(new_img, cv.COLOR_BGR2RGB)
     show_image(img, new_img)
    

    Scan barcodes in Jupyter Notebook

So far, the Jupyter notebook is done. To make it accessible to other users, we can share it via Google Drive.

Source Code

https://github.com/yushulx/jupyter-notebook-barcode-scanning