app.py

import cv2
import imghdr
import pytesseract

def extract_number_plate(image_path):
    # Load the image
    image = cv2.imread(image_path)
    
    # Check if the image is valid
    if image is None:
        print("Invalid image file!")
        return
    
    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(gray, (7, 7), 0)
    
    # Perform edge detection using Canny algorithm
    edges = cv2.Canny(blurred, 30, 150)
    
    # Find contours in the edge-detected image
    contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    # Filter contours based on area to select potential number plates
    number_plate_contours = []
    for contour in contours:
        x, y, w, h = cv2.boundingRect(contour)
        area = cv2.contourArea(contour)
        if area > 1000 and w > h:
            number_plate_contours.append(contour)
    
    # Draw bounding rectangles around the number plates
    for contour in number_plate_contours:
        x, y, w, h = cv2.boundingRect(contour)
        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
        
        # Extract the region of interest (number plate)
        plate = gray[y:y+h, x:x+w]
        
        # Apply OCR to the number plate region
        plate_text = pytesseract.image_to_string(plate, config='--psm 7')
        
        # Print the extracted text
        print("Number Plate Text:", plate_text)
    
    # Display the image with bounding rectangles
    cv2.imshow("Number Plates", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# Path to the input image
image_path = "cars/car2.jpg"

# Check if the file is an image
if imghdr.what(image_path) is not None:
    # Extract the number plates and print the text
    extract_number_plate(image_path)
else:
    print("Invalid image file format!")