将检测任务迁移python

python-inference-service/models/smoke_detector.py

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+import numpy as np
+import cv2
+from typing import List, Dict, Any, Tuple
+
+class Model:
+    """
+    Smoke detection model implementation
+    
+    This is a simple example that could be replaced with an actual
+    TensorFlow, PyTorch, or other ML framework implementation.
+    """
+    
+    def __init__(self):
+        """Initialize smoke detection model"""
+        # In a real implementation, you would load your model here
+        print("Smoke detection model initialized")
+        
+        # Define smoke class IDs
+        self.smoke_classes = {
+            0: "smoke",
+            1: "fire"
+        }
+    
+    def preprocess(self, image: np.ndarray) -> np.ndarray:
+        """Preprocess image for model input"""
+        # Convert BGR to grayscale for smoke detection
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        # Convert back to 3 channels to match model expected input shape
+        gray_3ch = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
+        
+        # In a real implementation, you would do normalization, etc.
+        return gray_3ch
+    
+    def predict(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Run smoke detection on the image
+        
+        This is a simplified example that uses basic image processing
+        In a real implementation, you would use your ML model
+        """
+        # Convert to grayscale for processing
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        
+        # Apply Gaussian blur to reduce noise
+        blurred = cv2.GaussianBlur(gray, (15, 15), 0)
+        
+        # Simple thresholding to find potential smoke regions
+        # In a real implementation, you'd use a trained model
+        _, thresh = cv2.threshold(blurred, 100, 255, cv2.THRESH_BINARY)
+        
+        # Find contours in the thresholded image
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        # Process contours to find potential smoke regions
+        detections = []
+        height, width = image.shape[:2]
+        
+        for contour in contours:
+            # Get bounding box
+            x, y, w, h = cv2.boundingRect(contour)
+            
+            # Filter small regions
+            if w > width * 0.05 and h > height * 0.05:
+                # Calculate area ratio
+                area = cv2.contourArea(contour)
+                rect_area = w * h
+                fill_ratio = area / rect_area if rect_area > 0 else 0
+                
+                # Smoke tends to have irregular shapes
+                # This is just for demonstration purposes
+                if fill_ratio > 0.2 and fill_ratio < 0.8:
+                    # Normalize coordinates
+                    x_norm = x / width
+                    y_norm = y / height
+                    w_norm = w / width
+                    h_norm = h / height
+                    
+                    # Determine if it's smoke or fire (just a simple heuristic for demo)
+                    # In a real model, this would be determined by the model prediction
+                    class_id = 0  # Default to smoke
+                    
+                    # Check if the region has high red values (fire)
+                    roi = image[y:y+h, x:x+w]
+                    if roi.size > 0:  # Make sure ROI is not empty
+                        avg_color = np.mean(roi, axis=(0, 1))
+                        if avg_color[2] > 150 and avg_color[2] > avg_color[0] * 1.5:  # High red, indicating fire
+                            class_id = 1  # Fire
+                    
+                    # Calculate confidence based on fill ratio
+                    # This is just for demonstration
+                    confidence = 0.5 + fill_ratio * 0.3
+                    
+                    # Add to detections
+                    detections.append({
+                        'bbox': (x_norm, y_norm, w_norm, h_norm),
+                        'class_id': class_id,
+                        'confidence': confidence
+                    })
+        
+        # For demo purposes, if no smoke detected by algorithm,
+        # add a small chance of random detection
+        if not detections and np.random.random() < 0.1:  # 10% chance
+            # Random smoke detection
+            x = np.random.random() * 0.7
+            y = np.random.random() * 0.7
+            w = 0.1 + np.random.random() * 0.2
+            h = 0.1 + np.random.random() * 0.2
+            confidence = 0.5 + np.random.random() * 0.3
+            
+            detections.append({
+                'bbox': (x, y, w, h),
+                'class_id': 0,  # Smoke
+                'confidence': confidence
+            })
+            
+        return detections
+    
+    @property
+    def applies_nms(self) -> bool:
+        """Model does not apply NMS internally"""
+        return False
+    
+    def close(self):
+        """Release resources"""
+        # In a real implementation, you would release model resources here
+        pass