将检测任务迁移python

python-inference-service/models/classes.txt

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+垃圾

python-inference-service/models/garbage_model.py

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+import os
+import numpy as np
+import cv2
+from typing import List, Dict, Any
+import torch
+
+class Model:
+    """
+    垃圾识别模型 - 直接加载 PyTorch 模型文件
+    """
+    
+    def __init__(self):
+        """初始化模型"""
+        # 获取当前文件所在目录路径
+        model_dir = os.path.dirname(os.path.abspath(__file__))
+        # 模型文件路径
+        model_path = os.path.join(model_dir, "best.pt")
+        
+        print(f"正在加载垃圾识别模型: {model_path}")
+        
+        # 加载 PyTorch 模型
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"使用设备: {self.device}")
+        
+        # 使用 YOLOv5 或通用方式加载模型
+        try:
+            # 尝试使用 YOLOv5 加载
+            import sys
+            sys.path.append(os.path.dirname(model_dir))  # 添加父目录到路径
+            
+            try:
+                # 方法1: 如果安装了 YOLOv5
+                import yolov5
+                self.model = yolov5.load(model_path, device=self.device)
+                self.yolov5_api = True
+                print("使用 YOLOv5 包加载模型")
+            except (ImportError, ModuleNotFoundError):
+                # 方法2: 直接加载 YOLO 代码
+                from models.yolov5_utils import attempt_load
+                self.model = attempt_load(model_path, device=self.device)
+                self.yolov5_api = False
+                print("使用内置 YOLOv5 工具加载模型")
+                
+        except Exception as e:
+            # 方法3: 通用 PyTorch 加载
+            print(f"YOLOv5 加载失败: {e}")
+            print("使用通用 PyTorch 加载")
+            self.model = torch.load(model_path, map_location=self.device)
+            if isinstance(self.model, dict) and 'model' in self.model:
+                self.model = self.model['model']
+            self.yolov5_api = False
+            
+            # 如果是 ScriptModule，设置为评估模式
+            if isinstance(self.model, torch.jit.ScriptModule):
+                self.model.eval()
+            elif hasattr(self.model, 'eval'):
+                self.model.eval()
+                
+        # 加载类别名称
+        self.classes = []
+        classes_path = os.path.join(model_dir, "classes.txt")
+        if os.path.exists(classes_path):
+            with open(classes_path, 'r', encoding='utf-8') as f:
+                self.classes = [line.strip() for line in f.readlines() if line.strip()]
+            print(f"已加载 {len(self.classes)} 个类别")
+        else:
+            # 如果模型自带类别信息
+            if hasattr(self.model, 'names') and self.model.names:
+                self.classes = self.model.names
+                print(f"使用模型自带类别，共 {len(self.classes)} 个类别")
+            else:
+                print("未找到类别文件，将使用数字索引作为类别名")
+        
+        # 设置识别参数
+        self.conf_threshold = 0.25  # 置信度阈值
+        self.img_size = 640        # 默认输入图像大小
+        
+        print("垃圾识别模型加载完成")
+    
+    def preprocess(self, image: np.ndarray) -> np.ndarray:
+        """预处理图像"""
+        # 如果是使用 YOLOv5 API，不需要预处理
+        if hasattr(self, 'yolov5_api') and self.yolov5_api:
+            return image
+            
+        # 默认预处理：调整大小并归一化
+        img = cv2.resize(image, (self.img_size, self.img_size))
+        
+        # BGR 转 RGB
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        
+        # 归一化 [0, 255] -> [0, 1]
+        img = img / 255.0
+        
+        # HWC -> CHW (高度，宽度，通道 -> 通道，高度，宽度)
+        img = img.transpose(2, 0, 1)
+        
+        # 转为 torch tensor
+        img = torch.from_numpy(img).float()
+        
+        # 添加批次维度
+        img = img.unsqueeze(0)
+        
+        # 移至设备
+        img = img.to(self.device)
+        
+        return img
+    
+    def predict(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """模型推理"""
+        original_height, original_width = image.shape[:2]
+        
+        try:
+            # 如果使用 YOLOv5 API
+            if hasattr(self, 'yolov5_api') and self.yolov5_api:
+                # YOLOv5 API 直接处理图像
+                results = self.model(image)
+                
+                # 提取检测结果
+                predictions = results.pred[0]  # 第一批次的预测
+                
+                detections = []
+                for *xyxy, conf, cls_id in predictions.cpu().numpy():
+                    x1, y1, x2, y2 = xyxy
+                    
+                    # 转换为归一化坐标 (x, y, w, h)
+                    x = x1 / original_width
+                    y = y1 / original_height
+                    w = (x2 - x1) / original_width
+                    h = (y2 - y1) / original_height
+                    
+                    # 整数类别 ID
+                    cls_id = int(cls_id)
+                    
+                    # 获取类别名称
+                    class_name = f"cls{cls_id}"
+                    if 0 <= cls_id < len(self.classes):
+                        class_name = self.classes[cls_id]
+                    
+                    # 添加检测结果
+                    if conf >= self.conf_threshold:
+                        detections.append({
+                            'bbox': (x, y, w, h),
+                            'class_id': cls_id,
+                            'confidence': float(conf)
+                        })
+                        
+                return detections
+                
+            else:
+                # 通用 PyTorch 模型处理
+                # 预处理图像
+                img = self.preprocess(image)
+                
+                # 推理
+                with torch.no_grad():
+                    outputs = self.model(img)
+                
+                # 后处理结果（这里需要根据模型输出格式调整）
+                detections = []
+                
+                # 假设输出格式是 YOLO 风格：[batch_idx, x1, y1, x2, y2, conf, cls_id]
+                if isinstance(outputs, torch.Tensor) and outputs.dim() == 2 and outputs.size(1) >= 6:
+                    for *xyxy, conf, cls_id in outputs.cpu().numpy():
+                        if conf >= self.conf_threshold:
+                            x1, y1, x2, y2 = xyxy
+                            
+                            # 转换为归一化坐标 (x, y, w, h)
+                            x = x1 / original_width
+                            y = y1 / original_height
+                            w = (x2 - x1) / original_width
+                            h = (y2 - y1) / original_height
+                            
+                            # 整数类别 ID
+                            cls_id = int(cls_id)
+                            
+                            detections.append({
+                                'bbox': (x, y, w, h),
+                                'class_id': cls_id,
+                                'confidence': float(conf)
+                            })
+                # 处理其他可能的输出格式
+                else:
+                    # 这里需要根据模型的实际输出格式进行适配
+                    print("警告：无法识别的模型输出格式，请检查模型类型")
+                    
+                return detections
+                
+        except Exception as e:
+            print(f"推理过程中出错: {str(e)}")
+            # 出错时返回空结果
+            return []
+    
+    @property
+    def applies_nms(self) -> bool:
+        """模型是否内部应用了 NMS"""
+        # YOLOv5 会自动应用 NMS
+        return True
+    
+    def close(self):
+        """释放资源"""
+        if hasattr(self, 'model'):
+            # 删除模型以释放 GPU 内存
+            del self.model
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        print("垃圾识别模型已关闭") 

python-inference-service/models/models.json

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+[
+  {
+    "name": "yolov8_detector",
+    "path": "models/yolov8_model.py",
+    "size": [640, 640],
+    "comment": "YOLOv8检测模型，确保将训练好的best.pt文件放在models目录下"
+  }
+] 

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 

python-inference-service/models/yolov5_utils.py

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+import torch
+import torch.nn as nn
+import sys
+import os
+
+def attempt_load(weights, device=''):
+    """尝试加载YOLOv5模型"""
+    # 加载模型
+    model = torch.load(weights, map_location=device)
+    
+    # 确定模型格式
+    if isinstance(model, dict):
+        if 'model' in model:  # state_dict格式
+            model = model['model']
+        elif 'state_dict' in model:  # state_dict格式
+            model = model['state_dict']
+    
+    # 如果是state_dict，则需要创建模型架构
+    if isinstance(model, dict):
+        print("警告：加载的是权重字典，尝试创建默认模型结构")
+        from models.yolov5_model import YOLOv5
+        model_arch = YOLOv5()
+        model_arch.load_state_dict(model)
+        model = model_arch
+    
+    # 设置为评估模式
+    if isinstance(model, nn.Module):
+        model.eval()
+    
+    # 检查是否有类别信息
+    if not hasattr(model, 'names') or not model.names:
+        print("模型没有类别信息，尝试加载默认类别")
+        # 设置通用类别
+        model.names = ['object']
+        
+    return model
+
+class YOLOv5:
+    """简化版YOLOv5模型结构，用于加载权重"""
+    def __init__(self):
+        super(YOLOv5, self).__init__()
+        self.names = []  # 类别名称
+        # 这里应该添加真实的网络结构
+        # 但为了简单起见，我们只提供一个占位符
+        # 在实际使用中，您应该实现完整的网络架构
+        
+    def forward(self, x):
+        # 这里应该是实际的前向传播逻辑
+        # 这只是一个占位符
+        raise NotImplementedError("这是一个占位符模型，请使用完整的YOLOv5模型实现")
+        
+    def load_state_dict(self, state_dict):
+        print("尝试加载模型权重")
+        # 实际的权重加载逻辑
+        # 这只是一个占位符
+        return self 

python-inference-service/models/yolov8_model.py

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+import os
+import numpy as np
+import cv2
+from typing import List, Dict, Any
+import torch
+
+class Model:
+    """
+    YOLOv8 模型包装类 - 使用 Ultralytics YOLO
+    """
+    
+    def __init__(self):
+        """初始化YOLOv8模型"""
+        # 获取当前文件所在目录路径
+        model_dir = os.path.dirname(os.path.abspath(__file__))
+        # 模型文件路径
+        model_path = os.path.join(model_dir, "best.pt")
+        
+        print(f"正在加载YOLOv8模型: {model_path}")
+        
+        # 检查设备
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"使用设备: {self.device}")
+        
+        # 使用 Ultralytics YOLO 加载模型
+        try:
+            from ultralytics import YOLO
+            self.model = YOLO(model_path)
+            print("使用 Ultralytics YOLO 加载模型成功")
+        except ImportError:
+            raise ImportError("请安装 ultralytics: pip install ultralytics>=8.0.0")
+        except Exception as e:
+            raise Exception(f"加载YOLOv8模型失败: {str(e)}")
+                
+        # 加载类别名称
+        self.classes = []
+        classes_path = os.path.join(model_dir, "classes.txt")
+        if os.path.exists(classes_path):
+            with open(classes_path, 'r', encoding='utf-8') as f:
+                self.classes = [line.strip() for line in f.readlines() if line.strip()]
+            print(f"已加载 {len(self.classes)} 个类别")
+        else:
+            # 使用模型自带的类别信息
+            if hasattr(self.model, 'names') and self.model.names:
+                self.classes = list(self.model.names.values()) if isinstance(self.model.names, dict) else self.model.names
+                print(f"使用模型自带类别，共 {len(self.classes)} 个类别")
+            else:
+                print("未找到类别文件，将使用数字索引作为类别名")
+        
+        # 设置识别参数
+        self.conf_threshold = 0.25  # 置信度阈值
+        self.img_size = 640        # 默认输入图像大小
+        
+        print("YOLOv8模型加载完成")
+    
+    def preprocess(self, image: np.ndarray) -> np.ndarray:
+        """预处理图像 - YOLOv8会自动处理，这里直接返回"""
+        return image
+    
+    def predict(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """模型推理"""
+        original_height, original_width = image.shape[:2]
+        
+        try:
+            # YOLOv8推理
+            results = self.model(
+                image, 
+                conf=self.conf_threshold,
+                device=self.device,
+                verbose=False
+            )
+            
+            detections = []
+            
+            # 解析结果
+            for result in results:
+                # 获取检测框
+                boxes = result.boxes
+                
+                if boxes is None or len(boxes) == 0:
+                    continue
+                
+                # 遍历每个检测框
+                for box in boxes:
+                    # 获取坐标 (xyxy格式)
+                    xyxy = box.xyxy[0].cpu().numpy()
+                    x1, y1, x2, y2 = xyxy
+                    
+                    # 转换为归一化坐标 (x, y, w, h)
+                    x = x1 / original_width
+                    y = y1 / original_height
+                    w = (x2 - x1) / original_width
+                    h = (y2 - y1) / original_height
+                    
+                    # 获取置信度
+                    conf = float(box.conf[0].cpu().numpy())
+                    
+                    # 获取类别ID
+                    cls_id = int(box.cls[0].cpu().numpy())
+                    
+                    # 获取类别名称
+                    class_name = f"cls{cls_id}"
+                    if 0 <= cls_id < len(self.classes):
+                        class_name = self.classes[cls_id]
+                    
+                    # 添加检测结果
+                    if conf >= self.conf_threshold:
+                        detections.append({
+                            'bbox': (x, y, w, h),
+                            'class_id': cls_id,
+                            'confidence': conf
+                        })
+                        
+            return detections
+                
+        except Exception as e:
+            print(f"推理过程中出错: {str(e)}")
+            import traceback
+            traceback.print_exc()
+            return []
+    
+    @property
+    def applies_nms(self) -> bool:
+        """模型是否内部应用了 NMS"""
+        # YOLOv8会自动应用 NMS
+        return True
+    
+    def close(self):
+        """释放资源"""
+        if hasattr(self, 'model'):
+            # 删除模型以释放 GPU 内存
+            del self.model
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+        print("YOLOv8模型已关闭")