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Dockerfile.txt

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+import cv2
+import numpy as np
+import torch
+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)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9af13581e91c59e8e2f16ccd179d2f7dbf1cfe16f0e2a6495118dffb9adf0268
+size 14449320

requirements.txt

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+flask
+opencv-python-headless
+numpy
+torch
+torchvision
+yolov5