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

• Apply for a trial license of Dynamsoft Barcode Reader SDK.

Creating a Jupyter Notebook for Python Barcode Scanning

1. Run Jupyter notebook:

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

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)

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

Try it in Colab.

Source Code

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

Originally published at https://www.dynamsoft.com on December 19, 2021.