app.py

import cv2
import os
import pytesseract
import numpy as np

# Set the path to the Tesseract executable
pytesseract.pytesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"  # Update this path if needed

# Haar cascade file path (absolute path)
haar_cascade_path = r"D:\seta new num board code\venv\cars.xml"  # Corrected path to match the actual location

# Verify that the Haar cascade file exists
if not os.path.exists(haar_cascade_path):
    print(f"Error: Haar cascade file not found at {haar_cascade_path}")
    exit(1)

# Load the Haar cascade
car_cascade = cv2.CascadeClassifier(haar_cascade_path)
if car_cascade.empty():
    print(f"Error: Failed to load Haar cascade from {haar_cascade_path}")
    exit(1)

# Function to detect vehicles using Haar cascades
def detect_vehicles_haar(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    vehicles = car_cascade.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=3,
        minSize=(30, 30)
    )
    print(f"Detected {len(vehicles)} vehicles.")  # Debugging
    return vehicles

# Function to extract and display number plate text
def extract_number_plate_text(vehicle_region):
    # Convert the region to grayscale for better OCR accuracy
    gray = cv2.cvtColor(vehicle_region, cv2.COLOR_BGR2GRAY)

    # Apply thresholding to enhance the text region
    _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)

    # Debugging: Display the thresholded image
    cv2.imshow("Thresholded Number Plate", thresh)

    # Use Tesseract OCR to extract text
    text = pytesseract.image_to_string(thresh, config='--psm 8')  # PSM 8: Treat the image as a single word
    text = text.strip().upper()  # Clean and format the text
    return text

# Main function
if __name__ == "__main__":
    input_path = input("Enter the path to the image or video file: ").strip()
    if not os.path.exists(input_path):
        print(f"Error: The file {input_path} does not exist.")
        exit(1)

    try:
        # Process the input file
        if input_path.lower().endswith(('.png', '.jpg', '.jpeg')):
            frame = cv2.imread(input_path)
            if frame is None:
                print(f"Error: Could not open image file {input_path}")
                exit(1)
            vehicles = detect_vehicles_haar(frame)
            for (x, y, w, h) in vehicles:
                vehicle_region = frame[y:y + h, x:x + w]
                number_plate_text = extract_number_plate_text(vehicle_region)
                print(f"Detected Number Plate: {number_plate_text}")
                cv2.imshow("Vehicle Detection", frame)
                cv2.waitKey(0)
        else:
            cap = cv2.VideoCapture(input_path)
            if not cap.isOpened():
                print(f"Error: Could not open video file {input_path}")
                exit(1)

            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                vehicles = detect_vehicles_haar(frame)
                for (x, y, w, h) in vehicles:
                    vehicle_region = frame[y:y + h, x:x + w]
                    number_plate_text = extract_number_plate_text(vehicle_region)
                    print(f"Detected Number Plate: {number_plate_text}")
                    cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
                cv2.imshow("Vehicle Detection", frame)
                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break
            cap.release()
        cv2.destroyAllWindows()
    except Exception as e:
        print(f"An error occurred: {e}")
        cv2.destroyAllWindows()