app.py

import streamlit as st
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
import tempfile
import os

# Title for the Streamlit App
st.title("Nepal Vehicle License Plate and Character Recognition")

# Description
st.write("Upload an image to detect license plates, segment characters, and recognize each character using advanced YOLO and CNN models.")

# Download YOLO and CNN model weights from Hugging Face
@st.cache_resource
def load_models():
    # Full license plate detection model
    full_plate_model_path = hf_hub_download(
        repo_id="krishnamishra8848/Nepal-Vehicle-License-Plate-Detection", filename="last.pt"
    )
    full_plate_model = YOLO(full_plate_model_path)

    # Character detection model
    character_model_path = hf_hub_download(
        repo_id="krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version3", filename="best.pt"
    )
    character_model = YOLO(character_model_path)

    # Character recognition model
    recognition_model_path = hf_hub_download(
        repo_id="krishnamishra8848/Nepal_Vehicle_License_Plates_Character_Recognisation", filename="model.h5"
    )
    recognition_model = load_model(recognition_model_path)

    return full_plate_model, character_model, recognition_model

# Load models
full_plate_model, character_model, recognition_model = load_models()

# Function to detect and crop license plates
def detect_and_crop_license_plate(image):
    img_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    results = full_plate_model(img_bgr)

    detected_image = img_bgr.copy()
    cropped_images = []
    for result in results:
        if hasattr(result, 'boxes') and result.boxes is not None:
            for box in result.boxes.xyxy:
                x1, y1, x2, y2 = map(int, box)
                cv2.rectangle(detected_image, (x1, y1), (x2, y2), (255, 0, 0), 2)  # Draw bounding box
                cropped_image = img_bgr[y1:y2, x1:x2]
                cropped_images.append(cropped_image)

    return cropped_images, detected_image

# Function to detect and crop characters
def detect_and_crop_characters(image):
    results = character_model(image)
    character_crops = []
    for result in results:
        if hasattr(result, 'boxes') and result.boxes is not None:
            for box in result.boxes.xyxy:
                x1, y1, x2, y2 = map(int, box)
                character_crops.append(image[y1:y2, x1:x2])
    return character_crops

# Function to recognize characters
def recognize_characters(character_crops):
    class_labels = [
        'क', 'को', 'ख', 'ग', 'च', 'ज', 'झ', 'ञ', 'डि', 'त', 'ना', 'प',
        'प्र', 'ब', 'बा', 'भे', 'म', 'मे', 'य', 'लु', 'सी', 'सु', 'से', 'ह',
        '०', '१', '२', '३', '४', '५', '६', '७', '८', '९'
    ]
    recognized_characters = []
    for char_crop in character_crops:
        # Preprocess the cropped character for recognition model
        resized = cv2.resize(char_crop, (64, 64))
        normalized = resized / 255.0
        reshaped = np.expand_dims(normalized, axis=0)  # Add batch dimension

        # Predict the character
        prediction = recognition_model.predict(reshaped)
        predicted_class = class_labels[np.argmax(prediction)]
        recognized_characters.append(predicted_class)
    return recognized_characters

# Upload an image file
uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Load image
    image = Image.open(uploaded_file)

    # Detect license plates and characters
    with st.spinner("Processing image..."):
        cropped_plates, detected_image = detect_and_crop_license_plate(image)

        # Show the image with detected license plates
        st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected License Plates", use_container_width=True)

        if cropped_plates:
            st.write(f"Detected {len(cropped_plates)} license plate(s).")
            for idx, cropped_image in enumerate(cropped_plates, 1):
                st.write(f"Processing License Plate {idx}:")

                # Detect and crop characters
                character_crops = detect_and_crop_characters(cropped_image)

                if character_crops:
                    # Recognize characters
                    recognized_characters = recognize_characters(character_crops)

                    # Show each cropped character and prediction
                    for i, char_crop in enumerate(character_crops):
                        st.image(cv2.cvtColor(char_crop, cv2.COLOR_BGR2RGB), caption=f"Character {i+1}")
                        st.write(f"Predicted Character: {recognized_characters[i]}")
                else:
                    st.write("No characters detected in this license plate.")
        else:
            st.write("No license plates detected.")

    st.success("Processing complete!")