app.py

from flask import Flask, request, jsonify
import cv2
import numpy as np
import torch
import yolov5
from yolov5 import YOLOv5

app = Flask(__name__)

# Load YOLOv5 model
model = YOLOv5('yolov5s.pt')  # Replace with your model path

def detect_number_plate(frame):
    results = model(frame)
    detections = results.pandas().xyxy[0]
    plates = []

    for _, row in detections.iterrows():
        if row['name'] == 'number_plate':  # Adjust class name
            plates.append({
                'class': row['name'],
                'confidence': row['confidence'],
                'x_min': row['xmin'],
                'y_min': row['ymin'],
                'x_max': row['xmax'],
                'y_max': row['ymax']
            })
    
    return plates

def detect_smoke(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (21, 21), 0)
    _, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
    
    smoke_intensity = np.sum(thresh) / (thresh.shape[0] * thresh.shape[1])
    smoke_detected = smoke_intensity > 0.1  # Adjust this threshold
    
    return smoke_detected, smoke_intensity

def process_frame(frame):
    plates = detect_number_plate(frame)
    smoke_detected, smoke_intensity = detect_smoke(frame)
    return {
        'smoke_detected': smoke_detected,
        'smoke_intensity': smoke_intensity,
        'number_plates': plates
    }

@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        return jsonify({'error': 'No file part'}), 400
    
    file = request.files['file']
    
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400
    
    if file:
        in_memory_file = file.read()
        np_arr = np.frombuffer(in_memory_file, np.uint8)
        frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
        results = process_frame(frame)
        return jsonify(results)

if __name__ == '__main__':
    app.run(port=5000, debug=True)