Detect and Read Barcodes with OpenCV in Python

A barcode is a graphical representation of data that is machine-readable. It consists of parallel lines or rectangles of varying widths and spacings, along with specific patterns, that encode information. Barcodes are widely used for automatic identification and tracking of products, assets, inventory, and more. In this article, we will see how we can detect and read barcodes with OpenCV in Python.

Detect and Read Barcodes with OpenCV in Python

Below is the step-by-step procedure by which we can detect and read barcodes with OpenCV in Python:

Step 1: Create a Virtual Environment

This is the first step in which we will create a virtual environment by using the following commands in your terminal:

python -m venv venv
.\venv\Scripts\activate 

Step 2: Installation

At first, we will install OpenCV, Matplotlib, and pyzbar by using the following command:

pip install opencv-python matplotlib pyzbar

Step 3: Import Libraries

In this step, we have imported the following libraries:

• cv2: This is the OpenCV library used for computer vision tasks.
• decode from pyzbar.pyzbar: This function is used to decode the barcode data from an image.
• matplotlib.pyplot as plt: This library is used for visualizing the image with detected barcodes.

import cv2
from pyzbar.pyzbar import decode
import matplotlib.pyplot as plt

Step 4: Define Function to Detect and Decode Barcodes

In this step, the function detect_and_decode_barcode aims to identify and decode barcodes present in an input image. It begins by converting the image to grayscale and then proceeds to detect barcodes using the decode function. For each detected barcode, it extracts the data and type, draws a rectangle around it, and annotates the image with the extracted information. Finally, it displays the annotated image with the detected barcodes using Matplotlib.

def detect_and_decode_barcode(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect barcodes in the grayscale image
    barcodes = decode(gray)

    # Loop over detected barcodes
    for barcode in barcodes:
        # Extract barcode data and type
        barcode_data = barcode.data.decode("utf-8")
        barcode_type = barcode.type

        # Print barcode data and type
        print("Barcode Data:", barcode_data)
        print("Barcode Type:", barcode_type)

        # Draw a rectangle around the barcode
        (x, y, w, h) = barcode.rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)

        # Put barcode data and type on the image
        cv2.putText(image, f"{barcode_data} ({barcode_type})",
                    (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

    # Convert image from BGR to RGB (Matplotlib uses RGB)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    plt.imshow(image_rgb)
    plt.axis('off')
    plt.show()

Step 5: Read Input Image and Call the Function

In this step, we are reading the input image and calling the function.

# Read input image
image = cv2.imread("your path:/barcode_image.png")

detect_and_decode_barcode(image)

Step 6: Run the Program

To run the program, type the following command:

python main.py

Barcode Used

Output:

Barcode Data: Wikipedia
Barcode Type: CODE128

Real-World Applications of Barcode Detection

• Retail: Barcodes streamline sales and inventory tracking in stores.
• Logistics: They track goods from production to delivery.
• Healthcare: Barcodes ensure accurate patient and medication identification.
• Transportation: Used for ticketing and boarding passes.
• Asset Management: Track equipment and resources efficiently.
• Manufacturing: Monitor production and manage inventory.
• Events: Barcodes serve as tickets for entry and access control.
• Food Packaging: Ensure product traceability and compliance.
• Education: Barcodes simplify library book management and student tracking.

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Article Tags :

• Python
• AI-ML-DS
• OpenCV

Practice Tags :

• python