# 传统CV技术示例
import cv2
import numpy as np
from sklearn import svm
from skimage import feature

class TraditionalComputerVision:
    def __init__(self):
        self.feature_extractors = {}
    
    def extract_sift_features(self, image):
        """提取SIFT特征"""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        sift = cv2.SIFT_create()
        keypoints, descriptors = sift.detectAndCompute(gray, None)
        return keypoints, descriptors
    
    def extract_hog_features(self, image):
        """提取HOG特征"""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        hog_features = feature.hog(
            gray,
            orientations=9,
            pixels_per_cell=(8, 8),
            cells_per_block=(2, 2),
            visualize=False
        )
        return hog_features
    
    def extract_lbp_features(self, image):
        """提取LBP特征"""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        radius = 3
        n_points = 8 * radius
        lbp = feature.local_binary_pattern(gray, n_points, radius, method='uniform')
        hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3))
        hist = hist.astype("float")
        hist /= (hist.sum() + 1e-6)
        return hist
    
    def build_classifier(self, features, labels):
        """构建传统分类器"""
        # 使用SVM分类器
        clf = svm.SVC(kernel='rbf', C=1.0, gamma='scale')
        clf.fit(features, labels)
        return clf
    
    def object_detection_haar(self, image):
        """Haar级联检测器"""
        face_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
        )
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30, 30)
        )
        return faces

# 使用示例
traditional_cv = TraditionalComputerVision()
print("传统计算机视觉工具准备就绪")

import torch
import torch.nn as nn
import torchvision.models as models

class DeepLearningCV:
    def __init__(self):
        self.models = {}
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    def load_pretrained_model(self, model_name="resnet50"):
        """加载预训练模型"""
        model_dict = {
            "resnet18": models.resnet18(pretrained=True),
            "resnet50": models.resnet50(pretrained=True),
            "vgg16": models.vgg16(pretrained=True),
            "inception_v3": models.inception_v3(pretrained=True),
            "efficientnet_b0": models.efficientnet_b0(pretrained=True)
        }
        
        if model_name in model_dict:
            model = model_dict[model_name]
            model = model.to(self.device)
            model.eval()
            self.models[model_name] = model
            return model
        else:
            raise ValueError(f"未知模型: {model_name}")
    
    def extract_features(self, image_tensor, model_name="resnet50"):
        """提取深度特征"""
        if model_name not in self.models:
            self.load_pretrained_model(model_name)
        
        model = self.models[model_name]
        
        # 移除最后的分类层
        if "resnet" in model_name:
            feature_extractor = nn.Sequential(*list(model.children())[:-1])
        elif "vgg" in model_name:
            feature_extractor = model.features
        else:
            feature_extractor = model
        
        with torch.no_grad():
            features = feature_extractor(image_tensor)
            features = features.view(features.size(0), -1)
        
        return features.cpu().numpy()
    
    def build_custom_cnn(self, num_classes=10):
        """构建自定义CNN"""
        class CustomCNN(nn.Module):
            def __init__(self, num_classes):
                super().__init__()
                self.features = nn.Sequential(
                    # 卷积块1
                    nn.Conv2d(3, 32, kernel_size=3, padding=1),
                    nn.BatchNorm2d(32),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(32, 32, kernel_size=3, padding=1),
                    nn.BatchNorm2d(32),
                    nn.ReLU(inplace=True),
                    nn.MaxPool2d(kernel_size=2, stride=2),
                    
                    # 卷积块2
                    nn.Conv2d(32, 64, kernel_size=3, padding=1),
                    nn.BatchNorm2d(64),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(64, 64, kernel_size=3, padding=1),
                    nn.BatchNorm2d(64),
                    nn.ReLU(inplace=True),
                    nn.MaxPool2d(kernel_size=2, stride=2),
                    
                    # 卷积块3
                    nn.Conv2d(64, 128, kernel_size=3, padding=1),
                    nn.BatchNorm2d(128),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(128, 128, kernel_size=3, padding=1),
                    nn.BatchNorm2d(128),
                    nn.ReLU(inplace=True),
                    nn.MaxPool2d(kernel_size=2, stride=2),
                )
                
                self.classifier = nn.Sequential(
                    nn.Dropout(0.5),
                    nn.Linear(128 * 4 * 4, 512),
                    nn.ReLU(inplace=True),
                    nn.Dropout(0.5),
                    nn.Linear(512, num_classes)
                )
            
            def forward(self, x):
                x = self.features(x)
                x = x.view(x.size(0), -1)
                x = self.classifier(x)
                return x
        
        return CustomCNN(num_classes)
    
    def object_detection_yolo(self):
        """YOLO目标检测"""
        # 这里简化实现，实际使用需要加载YOLO模型
        class SimplifiedYOLO:
            def __init__(self):
                self.grid_size = 7
                self.num_boxes = 2
                self.num_classes = 20
            
            def predict(self, image):
                # 简化的YOLO预测逻辑
                height, width = image.shape[:2]
                cell_height = height / self.grid_size
                cell_width = width / self.grid_size
                
                # 模拟预测结果
                predictions = []
                for i in range(self.grid_size):
                    for j in range(self.grid_size):
                        # 每个网格预测两个边界框
                        for b in range(self.num_boxes):
                            # 边界框坐标（相对网格）
                            x = (j + 0.5) * cell_width
                            y = (i + 0.5) * cell_height
                            w = cell_width * 0.8
                            h = cell_height * 0.8
                            confidence = 0.8
                            class_id = np.random.randint(self.num_classes)
                            
                            predictions.append({
                                "bbox": [x, y, w, h],
                                "confidence": confidence,
                                "class_id": class_id
                            })
                
                return predictions
        
        return SimplifiedYOLO()

# 使用示例
dl_cv = DeepLearningCV()
print("深度学习计算机视觉工具准备就绪")

import torch
from transformers import ViTFeatureExtractor, ViTForImageClassification

class ModernComputerVision:
    def __init__(self):
        self.models = {}
        self.feature_extractors = {}
    
    def load_vision_transformer(self, model_name="google/vit-base-patch16-224"):
        """加载Vision Transformer"""
        feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
        model = ViTForImageClassification.from_pretrained(model_name)
        
        self.feature_extractors[model_name] = feature_extractor
        self.models[model_name] = model
        
        return feature_extractor, model
    
    def vit_inference(self, image, model_name="google/vit-base-patch16-224"):
        """ViT推理"""
        if model_name not in self.models:
            self.load_vision_transformer(model_name)
        
        feature_extractor = self.feature_extractors[model_name]
        model = self.models[model_name]
        
        # 预处理图像
        inputs = feature_extractor(images=image, return_tensors="pt")
        
        # 推理
        with torch.no_grad():
            outputs = model(**inputs)
            logits = outputs.logits
            predicted_class_idx = logits.argmax(-1).item()
        
        return model.config.id2label[predicted_class_idx]
    
    def build_swin_transformer(self, img_size=224, patch_size=4, embed_dim=96):
        """构建Swin Transformer"""
        import math
        
        class SwinTransformerBlock(nn.Module):
            def __init__(self, dim, num_heads, window_size=7, shift_size=0):
                super().__init__()
                self.dim = dim
                self.num_heads = num_heads
                self.window_size = window_size
                self.shift_size = shift_size
                
                # 多头自注意力
                self.attn = nn.MultiheadAttention(dim, num_heads)
                self.norm1 = nn.LayerNorm(dim)
                self.norm2 = nn.LayerNorm(dim)
                
                # MLP
                self.mlp = nn.Sequential(
                    nn.Linear(dim, dim * 4),
                    nn.GELU(),
                    nn.Linear(dim * 4, dim)
                )
            
            def window_partition(self, x):
                """将特征图划分为窗口"""
                B, H, W, C = x.shape
                x = x.view(
                    B,
                    H // self.window_size,
                    self.window_size,
                    W // self.window_size,
                    self.window_size,
                    C
                )
                windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
                windows = windows.view(-1, self.window_size, self.window_size, C)
                return windows
            
            def window_reverse(self, windows, H, W):
                """将窗口还原为特征图"""
                B = int(windows.shape[0] / (H * W / self.window_size / self.window_size))
                x = windows.view(
                    B,
                    H // self.window_size,
                    W // self.window_size,
                    self.window_size,
                    self.window_size,
                    -1
                )
                x = x.permute(0, 1, 3, 2, 4, 5).contiguous()
                x = x.view(B, H, W, -1)
                return x
            
            def forward(self, x):
                B, H, W, C = x.shape
                
                # 窗口划分
                if self.shift_size > 0:
                    shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
                else:
                    shifted_x = x
                
                x_windows = self.window_partition(shifted_x)
                x_windows = x_windows.view(-1, self.window_size * self.window_size, C)
                
                # 自注意力
                attn_windows, _ = self.attn(x_windows, x_windows, x_windows)
                attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
                
                # 窗口还原
                shifted_x = self.window_reverse(attn_windows, H, W)
                
                if self.shift_size > 0:
                    x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
                else:
                    x = shifted_x
                
                # 残差连接
                x = x + shifted_x
                x = self.norm1(x)
                
                # MLP
                x_mlp = self.mlp(x)
                x = x + x_mlp
                x = self.norm2(x)
                
                return x
        
        class SwinTransformer(nn.Module):
            def __init__(self, img_size, patch_size, embed_dim, depths, num_heads):
                super().__init__()
                self.img_size = img_size
                self.patch_size = patch_size
                self.embed_dim = embed_dim
                
                # 补丁嵌入
                self.patch_embed = nn.Conv2d(
                    3, embed_dim,
                    kernel_size=patch_size,
                    stride=patch_size
                )
                
                # Swin Transformer块
                self.blocks = nn.ModuleList()
                for i, depth in enumerate(depths):
                    for j in range(depth):
                        shift_size = 0 if j % 2 == 0 else patch_size // 2
                        block = SwinTransformerBlock(
                            dim=embed_dim * (2 ** i),
                            num_heads=num_heads[i],
                            window_size=7,
                            shift_size=shift_size
                        )
                        self.blocks.append(block)
                
                # 分类头
                self.norm = nn.LayerNorm(embed_dim * (2 ** (len(depths) - 1)))
                self.head = nn.Linear(embed_dim * (2 ** (len(depths) - 1)), 1000)
            
            def forward(self, x):
                # 补丁嵌入
                x = self.patch_embed(x)
                B, C, H, W = x.shape
                x = x.permute(0, 2, 3, 1)  # B, H, W, C
                
                # Swin Transformer块
                for block in self.blocks:
                    x = block(x)
                
                # 全局平均池化
                x = x.mean(dim=[1, 2])
                x = self.norm(x)
                x = self.head(x)
                
                return x
        
        # 简化配置
        depths = [2, 2, 6, 2]
        num_heads = [3, 6, 12, 24]
        
        return SwinTransformer(img_size, patch_size, embed_dim, depths, num_heads)
    
    def contrastive_learning(self):
        """对比学习实现"""
        class ContrastiveLearner:
            def __init__(self, encoder, temperature=0.07):
                self.encoder = encoder
                self.temperature = temperature
                self.projector = nn.Sequential(
                    nn.Linear(encoder.output_dim, 512),
                    nn.ReLU(),
                    nn.Linear(512, 128)
                )
            
            def info_nce_loss(self, features1, features2):
                """InfoNCE损失"""
                batch_size = features1.shape[0]
                
                # 归一化特征
                features1 = F.normalize(features1, dim=1)
                features2 = F.normalize(features2, dim=1)
                
                # 计算相似度矩阵
                logits = torch.matmul(features1, features2.T) / self.temperature
                
                # 创建标签（对角线为正样本）
                labels = torch.arange(batch_size, device=features1.device)
                
                # 计算交叉熵损失
                loss = F.cross_entropy(logits, labels)
                
                return loss
            
            def generate_augmentations(self, images):
                """生成数据增强"""
                augmentations = []
                
                for img in images:
                    # 随机裁剪
                    crop = transforms.RandomResizedCrop(224)(img)
                    
                    # 颜色抖动
                    color_jitter = transforms.ColorJitter(
                        brightness=0.8,
                        contrast=0.8,
                        saturation=0.8,
                        hue=0.2
                    )(crop)
                    
                    # 高斯模糊
                    blur = transforms.GaussianBlur(kernel_size=23)(color_jitter)
                    
                    # 随机灰度化
                    gray = transforms.RandomGrayscale(p=0.2)(blur)
                    
                    augmentations.append(gray)
                
                return torch.stack(augmentations)
        
        return ContrastiveLearner

# 使用示例
modern_cv = ModernComputerVision()
print("现代计算机视觉工具准备就绪")

class ObjectDetectionSystem:
    def __init__(self, model_type="yolo"):
        self.model_type = model_type
        self.detectors = {}
        self.trackers = {}
    
    def load_detector(self, model_name):
        """加载目标检测器"""
        if model_name == "yolov5":
            # 这里简化实现，实际需要加载YOLOv5模型
            class YOLOv5Detector:
                def __init__(self):
                    self.confidence_threshold = 0.5
                    self.iou_threshold = 0.45
                
                def detect(self, image):
                    # 简化的检测逻辑
                    height, width = image.shape[:2]
                    detections = []
                    
                    # 模拟检测结果
                    num_objects = np.random.randint(1, 5)
                    for i in range(num_objects):
                        x = np.random.randint(0, width - 100)
                        y = np.random.randint(0, height - 100)
                        w = np.random.randint(50, 150)
                        h = np.random.randint(50, 150)
                        confidence = np.random.uniform(0.6, 0.95)
                        class_id = np.random.randint(0, 80)
                        
                        detections.append({
                            "bbox": [x, y, w, h],
                            "confidence": confidence,
                            "class_id": class_id,
                            "class_name": f"object_{class_id}"
                        })
                    
                    return detections
            
            detector = YOLOv5Detector()
            self.detectors[model_name] = detector
            return detector
        
        elif model_name == "faster_rcnn":
            # Faster R-CNN检测器
            class FasterRCNNDetector:
                def __init__(self):
                    self.min_size = 600
                    self.max_size = 1000
                
                def detect(self, image):
                    # 简化的Faster R-CNN检测
                    detections = []
                    # 实际实现需要加载预训练模型
                    return detections
            
            detector = FasterRCNNDetector()
            self.detectors[model_name] = detector
            return detector
        
        else:
            raise ValueError(f"不支持的检测器: {model_name}")
    
    def load_tracker(self, tracker_type="sort"):
        """加载目标跟踪器"""
        class SORTTracker:
            """SORT (Simple Online and Realtime Tracking)"""
            def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
                self.max_age = max_age
                self.min_hits = min_hits
                self.iou_threshold = iou_threshold
                self.trackers = []
                self.frame_count = 0
                self.next_id = 1
            
            def update(self, detections):
                """更新跟踪器"""
                self.frame_count += 1
                
                # 获取当前帧的跟踪预测
                predicted_boxes = []
                for tracker in self.trackers:
                    predicted_box = tracker.predict()
                    predicted_boxes.append(predicted_box)
                
                # 关联检测和跟踪
                if len(detections) > 0:
                    # 计算IoU矩阵
                    iou_matrix = self._calculate_iou(predicted_boxes, detections)
                    
                    # 匈牙利算法匹配
                    matched_indices = self._hungarian_matching(iou_matrix)
                    
                    # 更新匹配的跟踪器
                    for det_idx, trk_idx in matched_indices:
                        if iou_matrix[trk_idx][det_idx] >= self.iou_threshold:
                            self.trackers[trk_idx].update(detections[det_idx])
                    
                    # 创建新跟踪器
                    unmatched_detections = [i for i in range(len(detections)) 
                                          if i not in [idx[1] for idx in matched_indices]]
                    
                    for idx in unmatched_detections:
                        new_tracker = KalmanBoxTracker(detections[idx])
                        new_tracker.id = self.next_id
                        self.next_id += 1
                        self.trackers.append(new_tracker)
                
                # 移除丢失的跟踪器
                self.trackers = [t for t in self.trackers 
                               if not t.time_since_update > self.max_age]
                
                # 返回活跃的跟踪结果
                active_tracks = []
                for tracker in self.trackers:
                    if tracker.time_since_update < 1 and tracker.hit_streak >= self.min_hits:
                        active_tracks.append({
                            "id": tracker.id,
                            "bbox": tracker.get_state(),
                            "age": tracker.age,
                            "hits": tracker.hit_streak
                        })
                
                return active_tracks
            
            def _calculate_iou(self, boxes1, boxes2):
                """计算IoU矩阵"""
                iou_matrix = np.zeros((len(boxes1), len(boxes2)))
                for i, box1 in enumerate(boxes1):
                    for j, box2 in enumerate(boxes2):
                        iou_matrix[i][j] = self._box_iou(box1, box2)
                return iou_matrix
            
            def _box_iou(self, box1, box2):
                """计算两个边界框的IoU"""
                # 简化的IoU计算
                return np.random.random()  # 实际需要实现完整的IoU计算
            
            def _hungarian_matching(self, cost_matrix):
                """匈牙利算法匹配"""
                # 简化的匹配逻辑
                matched = []
                rows, cols = cost_matrix.shape
                for i in range(min(rows, cols)):
                    if cost_matrix[i, i] > self.iou_threshold:
                        matched.append((i, i))
                return matched
        
        class KalmanBoxTracker:
            """卡尔曼滤波器跟踪单个目标"""
            def __init__(self, bbox):
                self.id = None
                self.age = 0
                self.hits = 0
                self.time_since_update = 0
                self.hit_streak = 0
                
                # 简化的卡尔曼滤波器
                self.state = bbox
                self.covariance = np.eye(4)
            
            def predict(self):
                """预测下一帧位置"""
                self.age += 1
                if self.time_since_update > 0:
                    self.hit_streak = 0
                self.time_since_update += 1
                
                # 简化的预测：假设目标匀速运动
                predicted_state = self.state.copy()
                return predicted_state
            
            def update(self, bbox):
                """用新检测更新状态"""
                self.time_since_update = 0
                self.hits += 1
                self.hit_streak += 1
                self.state = bbox  # 简化的更新
            
            def get_state(self):
                """获取当前状态"""
                return self.state
        
        tracker = SORTTracker()
        self.trackers[tracker_type] = tracker
        return tracker
    
    def real_time_detection(self, video_source=0):
        """实时目标检测"""
        import cv2
        
        class RealTimeDetector:
            def __init__(self, detector, tracker=None):
                self.detector = detector
                self.tracker = tracker
                self.cap = cv2.VideoCapture(video_source)
                self.fps = 30
                self.frame_count = 0
            
            def process_frame(self, frame):
                """处理单帧"""
                self.frame_count += 1
                
                # 目标检测
                detections = self.detector.detect(frame)
                
                # 目标跟踪
                if self.tracker:
                    tracks = self.tracker.update(detections)
                    results = tracks
                else:
                    results = detections
                
                # 绘制结果
                annotated_frame = self._draw_results(frame, results)
                
                return annotated_frame, results
            
            def _draw_results(self, frame, results):
                """在帧上绘制结果"""
                annotated = frame.copy()
                
                for result in results:
                    if "bbox" in result:
                        x, y, w, h = result["bbox"]
                        confidence = result.get("confidence", 0)
                        class_name = result.get("class_name", "object")
                        track_id = result.get("id")
                        
                        # 绘制边界框
                        color = (0, 255, 0)  # 绿色
                        cv2.rectangle(annotated, (x, y), (x+w, y+h), color, 2)
                        
                        # 绘制标签
                        label = f"{class_name}"
                        if confidence > 0:
                            label += f" {confidence:.2f}"
                        if track_id:
                            label += f" ID:{track_id}"
                        
                        cv2.putText(
                            annotated, label,
                            (x, y - 10),
                            cv2.FONT_HERSHEY_SIMPLEX,
                            0.5, color, 2
                        )
                
                # 显示FPS
                cv2.putText(
                    annotated, f"FPS: {self.fps}",
                    (10, 30),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    1, (0, 0, 255), 2
                )
                
                return annotated
            
            def run(self):
                """运行实时检测"""
                print("开始实时目标检测...")
                print("按 'q' 键退出")
                
                while True:
                    ret, frame = self.cap.read()
                    if not ret:
                        break
                    
                    # 处理帧
                    processed_frame, results = self.process_frame(frame)
                    
                    # 显示结果
                    cv2.imshow('Real-time Object Detection', processed_frame)
                    
                    # 按键退出
                    if cv2.waitKey(1) & 0xFF == ord('q'):
                        break
                
                self.cap.release()
                cv2.destroyAllWindows()
        
        return RealTimeDetector

# 使用示例
od_system = ObjectDetectionSystem()
print("目标检测与跟踪系统准备就绪")

class ImageSegmentationSystem:
    def __init__(self):
        self.segmenters = {}
    
    def semantic_segmentation(self, model_type="deeplabv3"):
        """语义分割"""
        class SemanticSegmenter:
            def __init__(self, num_classes=21):
                self.num_classes = num_classes
                
                # 简化的DeepLabV3架构
                class DeepLabV3(nn.Module):
                    def __init__(self, num_classes):
                        super().__init__()
                        # 这里简化实现，实际需要完整的DeepLabV3
                        self.backbone = nn.Sequential(
                            nn.Conv2d(3, 64, kernel_size=3, padding=1),
                            nn.BatchNorm2d(64),
                            nn.ReLU(),
                            nn.Conv2d(64, 64, kernel_size=3, padding=1),
                            nn.BatchNorm2d(64),
                            nn.ReLU(),
                            nn.MaxPool2d(2)
                        )
                        
                        # ASPP模块简化
                        self.aspp = nn.Sequential(
                            nn.Conv2d(64, 256, kernel_size=3, padding=6, dilation=6),
                            nn.BatchNorm2d(256),
                            nn.ReLU(),
                            nn.Conv2d(256, 256, kernel_size=1),
                            nn.BatchNorm2d(256),
                            nn.ReLU()
                        )
                        
                        self.decoder = nn.Sequential(
                            nn.Conv2d(256, 256, kernel_size=3, padding=1),
                            nn.BatchNorm2d(256),
                            nn.ReLU(),
                            nn.Conv2d(256, num_classes, kernel_size=1)
                        )
                    
                    def forward(self, x):
                        x = self.backbone(x)
                        x = self.aspp(x)
                        x = F.interpolate(x, scale_factor=8, mode='bilinear', align_corners=False)
                        x = self.decoder(x)
                        return x
                
                self.model = DeepLabV3(num_classes)
            
            def segment(self, image):
                """执行语义分割"""
                # 预处理图像
                image_tensor = self._preprocess(image)
                
                # 模型推理
                with torch.no_grad():
                    output = self.model(image_tensor)
                    prediction = torch.argmax(output, dim=1)
                
                # 后处理
                segmentation_map = prediction.squeeze().cpu().numpy()
                colored_map = self._colorize(segmentation_map)
                
                return {
                    "segmentation_map": segmentation_map,
                    "colored_map": colored_map,
                    "class_distribution": np.bincount(segmentation_map.flatten())
                }
            
            def _preprocess(self, image):
                """预处理图像"""
                # 简化的预处理
                transform = transforms.Compose([
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                       std=[0.229, 0.224, 0.225])
                ])
                return transform(image).unsqueeze(0)
            
            def _colorize(self, segmentation_map):
                """为分割图上色"""
                height, width = segmentation_map.shape
                colored = np.zeros((height, width, 3), dtype=np.uint8)
                
                # 简化的颜色映射
                color_map = {
                    0: [0, 0, 0],      # 背景 - 黑色
                    1: [128, 0, 0],    # 类别1 - 深红
                    2: [0, 128, 0],    # 类别2 - 深绿
                    3: [0, 0, 128],    # 类别3 - 深蓝
                    4: [128, 128, 0],  # 类别4 - 黄绿
                    5: [128, 0, 128],  # 类别5 - 紫色
                }
                
                for class_id, color in color_map.items():
                    mask = segmentation_map == class_id
                    colored[mask] = color
                
                return colored
        
        segmenter = SemanticSegmenter()
        self.segmenters["semantic"] = segmenter
        return segmenter
    
    def instance_segmentation(self, model_type="mask_rcnn"):
        """实例分割"""
        class InstanceSegmenter:
            def __init__(self):
                # 简化的Mask R-CNN
                class MaskRCNN(nn.Module):
                    def __init__(self, num_classes):
                        super().__init__()
                        self.num_classes = num_classes
                        
                        # 骨干网络
                        self.backbone = nn.Sequential(
                            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
                            nn.BatchNorm2d(64),
                            nn.ReLU(),
                            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
                        )
                        
                        # RPN (区域建议网络)
                        self.rpn = nn.Sequential(
                            nn.Conv2d(64, 256, kernel_size=3, padding=1),
                            nn.ReLU()
                        )
                        
                        # ROI Align
                        self.roi_align = nn.AdaptiveAvgPool2d((7, 7))
                        
                        # 分类和回归头
                        self.classifier = nn.Sequential(
                            nn.Linear(256 * 7 * 7, 1024),
                            nn.ReLU(),
                            nn.Linear(1024, num_classes + 1)  # +1 for background
                        )
                        
                        self.regressor = nn.Sequential(
                            nn.Linear(256 * 7 * 7, 1024),
                            nn.ReLU(),
                            nn.Linear(1024, 4 * num_classes)  # 4 coordinates per class
                        )
                        
                        # 掩码头
                        self.mask_head = nn.Sequential(
                            nn.Conv2d(256, 256, kernel_size=3, padding=1),
                            nn.ReLU(),
                            nn.Conv2d(256, 256, kernel_size=3, padding=1),
                            nn.ReLU(),
                            nn.Conv2d(256, num_classes, kernel_size=1)
                        )
                    
                    def forward(self, x):
                        features = self.backbone(x)
                        
                        # RPN生成建议
                        rpn_features = self.rpn(features)
                        
                        # 简化的ROI处理
                        rois = self._generate_rois(rpn_features)
                        
                        # ROI Align
                        roi_features = self.roi_align(features)
                        
                        # 分类和回归
                        roi_features_flat = roi_features.view(roi_features.size(0), -1)
                        class_logits = self.classifier(roi_features_flat)
                        box_regression = self.regressor(roi_features_flat)
                        
                        # 掩码预测
                        masks = self.mask_head(roi_features)
                        
                        return class_logits, box_regression, masks
                    
                    def _generate_rois(self, features):
                        """生成区域建议"""
                        # 简化的ROI生成
                        batch_size, channels, height, width = features.shape
                        num_rois = 100
                        
                        rois = []
                        for i in range(num_rois):
                            x = np.random.randint(0, width - 50)
                            y = np.random.randint(0, height - 50)
                            w = np.random.randint(20, 50)
                            h = np.random.randint(20, 50)
                            rois.append([x, y, x+w, y+h])
                        
                        return torch.tensor(rois, dtype=torch.float32)
                
                self.model = MaskRCNN(num_classes=10)
            
            def segment(self, image):
                """执行实例分割"""
                # 预处理
                image_tensor = self._preprocess(image)
                
                # 模型推理
                with torch.no_grad():
                    class_logits, box_regression, masks = self.model(image_tensor)
                
                # 后处理
                instances = self._postprocess(class_logits, box_regression, masks)
                
                return instances
            
            def _preprocess(self, image):
                """预处理图像"""
                transform = transforms.Compose([
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                       std=[0.229, 0.224, 0.225])
                ])
                return transform(image).unsqueeze(0)
            
            def _postprocess(self, class_logits, box_regression, masks):
                """后处理得到实例"""
                # 简化的后处理
                instances = []
                num_instances = min(10, class_logits.shape[0])
                
                for i in range(num_instances):
                    # 获取类别
                    class_probs = torch.softmax(class_logits[i], dim=0)
                    class_id = torch.argmax(class_probs).item()
                    confidence = class_probs[class_id].item()
                    
                    # 跳过背景
                    if class_id == 0 or confidence < 0.5:
                        continue
                    
                    # 获取边界框
                    bbox = box_regression[i, class_id*4:(class_id+1)*4].tolist()
                    
                    # 获取掩码
                    mask = masks[i, class_id].sigmoid().cpu().numpy()
                    mask = (mask > 0.5).astype(np.uint8)
                    
                    instances.append({
                        "class_id": class_id,
                        "confidence": confidence,
                        "bbox": bbox,
                        "mask": mask
                    })
                
                return instances
        
        segmenter = InstanceSegmenter()
        self.segmenters["instance"] = segmenter
        return segmenter

# 使用示例
seg_system = ImageSegmentationSystem()
print("图像分割系统准备就绪")

class ThreeDComputerVision:
    def __init__(self):
        self.methods = {}
    
    def point_cloud_processing(self):
        """点云处理"""
        class PointCloudProcessor:
            def __init__(self):
                self.pointnet = None
            
            def load_pointnet(self):
                """加载PointNet模型"""
                class PointNet(nn.Module):
                    def __init__(self, num_classes):
                        super().__init__()
                        self.mlp1 = nn.Sequential(
                            nn.Conv1d(3, 64, 1),
                            nn.BatchNorm1d(64),
                            nn.ReLU(),
                            nn.Conv1d(64, 64, 1),
                            nn.BatchNorm1d(64),
                            nn.ReLU()
                        )
                        
                        self.mlp2 = nn.Sequential(
                            nn.Conv1d(64, 128, 1),
                            nn.BatchNorm1d(128),
                            nn.ReLU(),
                            nn.Conv1d(128, 1024, 1),
                            nn.BatchNorm1d(1024),
                            nn.ReLU()
                        )
                        
                        self.classifier = nn.Sequential(
                            nn.Linear(1024, 512),
                            nn.BatchNorm1d(512),
                            nn.ReLU(),
                            nn.Dropout(0.3),
                            nn.Linear(512, 256),
                            nn.BatchNorm1d(256),
                            nn.ReLU(),
                            nn.Dropout(0.3),
                            nn.Linear(256, num_classes)
                        )
                    
                    def forward(self, x):
                        # x: (B, 3, N)
                        x = self.mlp1(x)
                        x = self.mlp2(x)
                        x = torch.max(x, 2)[0]  # 全局最大池化
                        x = self.classifier(x)
                        return x
                
                return PointNet(num_classes=40)  # ModelNet40数据集
            
            def voxel_based_processing(self, point_cloud, voxel_size=0.05):
                """体素化处理"""
                # 计算边界
                min_coords = point_cloud.min(axis=0)
                max_coords = point_cloud.max(axis=0)
                
                # 计算体素网格尺寸
                grid_size = ((max_coords - min_coords) / voxel_size).astype(int) + 1
                
                # 创建体素网格
                voxel_grid = np.zeros(grid_size, dtype=bool)
                
                # 将点分配到体素
                indices = ((point_cloud - min_coords) / voxel_size).astype(int)
                for idx in indices:
                    if all(0 <= i < s for i, s in zip(idx, grid_size)):
                        voxel_grid[tuple(idx)] = True
                
                return voxel_grid
            
            def surface_reconstruction(self, point_cloud):
                """表面重建"""
                # 使用泊松重建或Marching Cubes
                class SurfaceReconstructor:
                    def __init__(self):
                        self.method = "poisson"
                    
                    def reconstruct(self, points, normals=None):
                        """重建表面"""
                        if self.method == "poisson":
                            return self._poisson_reconstruction(points, normals)
                        else:
                            return self._marching_cubes(points)
                    
                    def _poisson_reconstruction(self, points, normals):
                        """泊松重建"""
                        # 简化的泊松重建
                        print("执行泊松表面重建...")
                        return {"mesh": "reconstructed_mesh", "vertices": 1000, "faces": 2000}
                    
                    def _marching_cubes(self, points):
                        """Marching Cubes算法"""
                        print("执行Marching Cubes表面重建...")
                        return {"mesh": "mc_mesh", "vertices": 800, "faces": 1600}
                
                reconstructor = SurfaceReconstructor()
                return reconstructor.reconstruct(point_cloud)
        
        processor = PointCloudProcessor()
        self.methods["point_cloud"] = processor
        return processor
    
    def neural_radiance_fields(self):
        """神经辐射场"""
        class NeRFSystem:
            def __init__(self):
                self.model = None
            
            def build_nerf(self):
                """构建NeRF模型"""
                class NeRF(nn.Module):
                    def __init__(self, hidden_dim=256):
                        super().__init__()
                        
                        # 位置编码
                        self.pos_encoder = PositionalEncoding(L=10)
                        self.dir_encoder = PositionalEncoding(L=4)
                        
                        # MLP网络
                        self.mlp = nn.Sequential(
                            nn.Linear(self.pos_encoder.output_dim, hidden_dim),
                            nn.ReLU(),
                            nn.Linear(hidden_dim, hidden_dim),
                            nn.ReLU(),
                            nn.Linear(hidden_dim, hidden_dim),
                            nn.ReLU(),
                            nn.Linear(hidden_dim, hidden_dim),
                            nn.ReLU()
                        )
                        
                        # 输出层
                        self.sigma_layer = nn.Linear(hidden_dim, 1)
                        self.feature_layer = nn.Linear(hidden_dim, hidden_dim)
                        
                        self.color_layer = nn.Sequential(
                            nn.Linear(hidden_dim + self.dir_encoder.output_dim, hidden_dim // 2),
                            nn.ReLU(),
                            nn.Linear(hidden_dim // 2, 3),
                            nn.Sigmoid()
                        )
                    
                    def forward(self, x, d):
                        # 位置编码
                        x_encoded = self.pos_encoder(x)
                        d_encoded = self.dir_encoder(d)
                        
                        # 通过MLP
                        h = self.mlp(x_encoded)
                        
                        # 体积密度
                        sigma = self.sigma_layer(h)
                        
                        # 颜色
                        features = self.feature_layer(h)
                        color_input = torch.cat([features, d_encoded], dim=-1)
                        color = self.color_layer(color_input)
                        
                        return color, sigma
                
                class PositionalEncoding(nn.Module):
                    def __init__(self, L):
                        super().__init__()
                        self.L = L
                        self.output_dim = 3 * (2 * L + 1)  # 对于位置编码
                    
                    def forward(self, x):
                        encoded = [x]
                        for i in range(self.L):
                            encoded.append(torch.sin(2 ** i * torch.pi * x))
                            encoded.append(torch.cos(2 ** i * torch.pi * x))
                        return torch.cat(encoded, dim=-1)
                
                return NeRF()
            
            def volume_rendering(self, colors, densities, t_vals):
                """体渲染"""
                # 计算透明度
                deltas = t_vals[1:] - t_vals[:-1]
                deltas = torch.cat([deltas, torch.tensor([1e10], device=deltas.device)])
                
                # 计算透射率
                alphas = 1 - torch.exp(-densities * deltas.unsqueeze(-1))
                
                # 累积透射率
                transmittance = torch.cumprod(1 - alphas + 1e-10, dim=0)
                
                # 计算权重
                weights = alphas * transmittance
                
                # 渲染颜色
                rendered_color = torch.sum(weights * colors, dim=0)
                
                return rendered_color
            
            def train_nerf(self, images, poses, focal_length):
                """训练NeRF模型"""
                print("开始训练NeRF模型...")
                # 这里简化训练过程
                return {"loss": 0.1, "psnr": 25.0, "training_time": "2小时"}
        
        nerf_system = NeRFSystem()
        self.methods["nerf"] = nerf_system
        return nerf_system

# 使用示例
td_cv = ThreeDComputerVision()
print("3D计算机视觉系统准备就绪")

class CVBenchmark:
    def __init__(self):
        self.datasets = {
            "分类": ["ImageNet", "CIFAR-10", "CIFAR-100", "MNIST"],
            "检测": ["COCO", "PASCAL VOC", "Open Images"],
            "分割": ["Cityscapes", "ADE20K", "Mapillary Vistas"],
            "3D": ["ModelNet", "ShapeNet", "ScanNet"],
            "人脸": ["LFW", "CelebA", "VGGFace2"]
        }
        
        self.metrics = {
            "分类": ["准确率", "Top-5准确率", "混淆矩阵"],
            "检测": ["mAP", "AP@50", "AP@75", "召回率"],
            "分割": ["mIoU", "像素准确率", "Dice系数"],
            "3D": ["Chamfer距离", "F-Score", "IoU 3D"]
        }
    
    def evaluate_model(self, model, dataset_type, metric_type):
        """评估模型性能"""
        if dataset_type not in self.datasets:
            raise ValueError(f"未知数据集类型: {dataset_type}")
        
        if metric_type not in self.metrics:
            raise ValueError(f"未知评估指标: {metric_type}")
        
        # 模拟评估结果
        evaluation_results = {
            "数据集": np.random.choice(self.datasets[dataset_type]),
            "评估指标": np.random.choice(self.metrics[metric_type]),
            "得分": np.random.uniform(0.7, 0.95),
            "排名": np.random.randint(1, 100),
            "参数量": np.random.randint(1, 100) * 1e6,
            "推理速度": np.random.uniform(10, 100)  # FPS
        }
        
        return evaluation_results
    
    def compare_models(self, models, dataset="ImageNet"):
        """比较多个模型"""
        comparisons = []
        
        for model_name in models:
            # 模拟不同任务的评估
            if "分类" in model_name:
                metrics = self.evaluate_model(None, "分类", "分类")
            elif "检测" in model_name:
                metrics = self.evaluate_model(None, "检测", "检测")
            elif "分割" in model_name:
                metrics = self.evaluate_model(None, "分割", "分割")
            else:
                metrics = {"得分": 0.0, "排名": 999}
            
            comparisons.append({
                "模型": model_name,
                "数据集": dataset,
                "得分": metrics["得分"],
                "排名": metrics["排名"],
                "效率得分": metrics["得分"] / (metrics["参数量"] / 1e6) if "参数量" in metrics else 0
            })
        
        # 按得分排序
        comparisons.sort(key=lambda x: -x["得分"])
        
        return comparisons

# 使用示例
benchmark = CVBenchmark()

models = ["ResNet-50", "EfficientNet-B4", "Vision Transformer", "Swin Transformer"]
comparisons = benchmark.compare_models(models, "ImageNet")

print("📊 模型性能比较:")
for i, comp in enumerate(comparisons, 1):
    print(f"{i}. {comp['模型']}: 得分={comp['得分']:.3f}, 排名={comp['排名']}, 效率={comp['效率得分']:.4f}")

class AutonomousDrivingVision:
    def __init__(self):
        self.modules = {}
    
    def build_perception_system(self):
        """构建感知系统"""
        class PerceptionSystem:
            def __init__(self):
                self.detector = ObjectDetectionSystem().load_detector("yolov5")
                self.segmenter = ImageSegmentationSystem().semantic_segmentation()
                self.tracker = ObjectDetectionSystem().load_tracker("sort")
                
                # 传感器融合
                self.sensor_fusion = SensorFusion()
                
                # 场景理解
                self.scene_understanding = SceneUnderstanding()
            
            def process_frame(self, camera_image, lidar_points=None):
                """处理单帧数据"""
                results = {}
                
                # 2D目标检测
                detections = self.detector.detect(camera_image)
                results["detections"] = detections
                
                # 语义分割
                segmentation = self.segmenter.segment(camera_image)
                results["segmentation"] = segmentation
                
                # 目标跟踪
                if hasattr(self, 'tracker'):
                    tracks = self.tracker.update(detections)
                    results["tracks"] = tracks
                
                # 传感器融合
                if lidar_points is not None:
                    fused_results = self.sensor_fusion.fuse(camera_image, lidar_points, detections)
                    results["fused"] = fused_results
                
                # 场景理解
                scene_info = self.scene_understanding.analyze(results)
                results["scene"] = scene_info
                
                return results
            
            def generate_occupancy_grid(self, results):
                """生成占据栅格"""
                grid_size = (100, 100)  # 100x100栅格
                occupancy = np.zeros(grid_size, dtype=float)
                
                # 基于检测结果更新占据栅格
                if "detections" in results:
                    for det in results["detections"]:
                        # 简化的占据更新
                        x, y, w, h = det.get("bbox", [0, 0, 0, 0])
                        # 将边界框映射到栅格
                        grid_x = int(x * grid_size[0] / 1920)  # 假设图像宽度1920
                        grid_y = int(y * grid_size[1] / 1080)  # 假设图像高度1080
                        
                        if 0 <= grid_x < grid_size[0] and 0 <= grid_y < grid_size[1]:
                            occupancy[grid_y, grid_x] = 1.0
                
                return occupancy
        
        perception = PerceptionSystem()
        self.modules["perception"] = perception
        return perception
    
    class SensorFusion:
        """传感器融合"""
        def fuse(self, camera_data, lidar_data, detections):
            """融合相机和激光雷达数据"""
            fused_objects = []
            
            # 简化的融合逻辑
            for det in detections:
                # 获取2D边界框
                bbox_2d = det.get("bbox", [0, 0, 0, 0])
                
                # 将2D边界框投影到3D（需要相机参数）
                bbox_3d = self._project_2d_to_3d(bbox_2d, lidar_data)
                
                # 关联激光雷达点云
                lidar_points_in_bbox = self._extract_lidar_points(bbox_3d, lidar_data)
                
                # 计算3D边界框
                if len(lidar_points_in_bbox) > 0:
                    bbox_3d_refined = self._fit_3d_bbox(lidar_points_in_bbox)
                    
                    fused_objects.append({
                        "class": det.get("class_name", "unknown"),
                        "confidence_2d": det.get("confidence", 0),
                        "bbox_2d": bbox_2d,
                        "bbox_3d": bbox_3d_refined,
                        "lidar_points": len(lidar_points_in_bbox),
                        "velocity": self._estimate_velocity(bbox_3d_refined)
                    })
            
            return fused_objects
        
        def _project_2d_to_3d(self, bbox_2d, lidar_data):
            """2D到3D投影"""
            # 简化的投影
            return {
                "x": bbox_2d[0] * 0.1,  # 简化比例
                "y": bbox_2d[1] * 0.1,
                "z": 0,
                "width": bbox_2d[2] * 0.1,
                "height": bbox_2d[3] * 0.1,
                "depth": 2.0  # 假设深度
            }
        
        def _extract_lidar_points(self, bbox_3d, lidar_data):
            """提取边界框内的激光雷达点"""
            # 简化的点提取
            points_in_bbox = []
            for point in lidar_data[:100]:  # 只检查前100个点
                if self._point_in_bbox(point, bbox_3d):
                    points_in_bbox.append(point)
            return points_in_bbox
        
        def _point_in_bbox(self, point, bbox):
            """判断点是否在边界框内"""
            x, y, z = point[:3]
            bx, by, bz = bbox["x"], bbox["y"], bbox["z"]
            bw, bh, bd = bbox["width"], bbox["height"], bbox["depth"]
            
            return (bx <= x <= bx + bw) and (by <= y <= by + bh) and (bz <= z <= bz + bd)
        
        def _fit_3d_bbox(self, points):
            """拟合3D边界框"""
            if len(points) == 0:
                return None
            
            points_array = np.array(points)
            min_coords = points_array.min(axis=0)
            max_coords = points_array.max(axis=0)
            
            return {
                "x": float(min_coords[0]),
                "y": float(min_coords[1]),
                "z": float(min_coords[2]),
                "width": float(max_coords[0] - min_coords[0]),
                "height": float(max_coords[1] - min_coords[1]),
                "depth": float(max_coords[2] - min_coords[2])
            }
        
        def _estimate_velocity(self, bbox_3d):
            """估计速度"""
            # 简化的速度估计
            return {
                "vx": 0.0,
                "vy": 0.0,
                "vz": 0.0,
                "speed": 0.0
            }
    
    class SceneUnderstanding:
        """场景理解"""
        def analyze(self, perception_results):
            """分析场景"""
            scene_info = {
                "road_condition": "clear",
                "traffic_density": "low",
                "weather": "clear",
                "time_of_day": "day",
                "risk_level": "low"
            }
            
            # 基于检测结果更新场景信息
            if "detections" in perception_results:
                detections = perception_results["detections"]
                
                # 统计各类目标数量
                object_counts = {}
                for det in detections:
                    class_name = det.get("class_name", "unknown")
                    object_counts[class_name] = object_counts.get(class_name, 0) + 1
                
                # 根据目标类型判断场景
                if object_counts.get("car", 0) > 10:
                    scene_info["traffic_density"] = "high"
                    scene_info["risk_level"] = "medium"
                
                if object_counts.get("pedestrian", 0) > 5:
                    scene_info["risk_level"] = "high"
                
                if object_counts.get("traffic_light", 0) > 0:
                    scene_info["has_traffic_light"] = True
            
            # 基于分割结果
            if "segmentation" in perception_results:
                segmentation = perception_results["segmentation"]
                if "class_distribution" in segmentation:
                    road_pixels = segmentation["class_distribution"].get(7, 0)  # 假设7是道路类别
                    total_pixels = segmentation["class_distribution"].sum()
                    
                    if road_pixels / total_pixels < 0.3:
                        scene_info["road_condition"] = "narrow"
            
            return scene_info

# 使用示例
adv = AutonomousDrivingVision()
perception = adv.build_perception_system()
print("自动驾驶视觉系统准备就绪")

class MedicalImageAnalysis:
    def __init__(self):
        self.modalities = ["CT", "MRI", "X-ray", "Ultrasound"]
        self.tasks = {
            "CT": ["肿瘤检测", "器官分割", "骨折检测"],
            "MRI": ["脑部分割", "肿瘤分级", "组织分类"],
            "X-ray": ["肺炎检测", "骨折检测", "结核筛查"],
            "Ultrasound": ["胎儿检测", "器官测量", "血流分析"]
        }
    
    def build_analysis_pipeline(self, modality, task):
        """构建分析流水线"""
        class MedicalAIPipeline:
            def __init__(self, modality, task):
                self.modality = modality
                self.task = task
                self.model = self._load_model(modality, task)
                self.preprocessor = MedicalImagePreprocessor()
                self.postprocessor = MedicalImagePostprocessor()
                self.explainability = ModelExplainability()
            
            def _load_model(self, modality, task):
                """加载医疗AI模型"""
                # 根据模态和任务选择模型
                model_configs = {
                    ("CT", "肿瘤检测"): "nnUNet",
                    ("CT", "器官分割"): "DeepLabV3+",
                    ("MRI", "脑部分割"): "U-Net",
                    ("X-ray", "肺炎检测"): "CheXNet",
                    ("X-ray", "骨折检测"): "DenseNet",
                    ("Ultrasound", "胎儿检测"): "YOLO-Medical"
                }
                
                model_name = model_configs.get((modality, task), "ResNet-50")
                print(f"加载模型: {model_name} for {modality} - {task}")
                
                # 简化的模型加载
                class MedicalModel:
                    def __init__(self, name):
                        self.name = name
                        self.confidence = 0.85
                    
                    def predict(self, image):
                        # 简化的预测
                        return {
                            "prediction": "positive" if np.random.random() > 0.5 else "negative",
                            "confidence": np.random.uniform(0.7, 0.95),
                            "segmentation_map": np.random.rand(*image.shape[:2]) if "分割" in task else None,
                            "bounding_boxes": [] if "检测" in task else None
                        }
                
                return MedicalModel(model_name)
            
            def analyze(self, medical_image, patient_info=None):
                """分析医疗影像"""
                # 预处理
                processed_image = self.preprocessor.process(medical_image, self.modality)
                
                # 模型推理
                raw_prediction = self.model.predict(processed_image)
                
                # 后处理
                final_results = self.postprocessor.process(
                    raw_prediction, 
                    self.modality, 
                    self.task,
                    patient_info
                )
                
                # 可解释性分析
                explanation = self.explainability.explain(
                    processed_image, 
                    raw_prediction, 
                    self.model
                )
                
                return {
                    "results": final_results,
                    "explanation": explanation,
                    "confidence": raw_prediction["confidence"],
                    "processing_time": np.random.uniform(0.1, 2.0)
                }
        
        class MedicalImagePreprocessor:
            """医疗影像预处理"""
            def process(self, image, modality):
                """预处理医疗影像"""
                preprocessing_steps = {
                    "CT": ["窗宽窗位调整", "归一化", "去噪"],
                    "MRI": ["偏置场校正", "强度归一化", "颅骨剥离"],
                    "X-ray": ["对比度增强", "归一化", "去噪"],
                    "Ultrasound": ["散斑抑制", "对比度增强", "归一化"]
                }
                
                steps = preprocessing_steps.get(modality, ["归一化"])
                print(f"预处理步骤: {', '.join(steps)}")
                
                # 简化的预处理
                processed = image.copy()
                if modality == "CT":
                    # CT窗宽窗位调整
                    processed = self._window_level_adjustment(processed, 40, 400)
                
                return processed
            
            def _window_level_adjustment(self, image, window, level):
                """CT窗宽窗位调整"""
                # 简化的窗宽窗位调整
                min_val = level - window / 2
                max_val = level + window / 2
                image = np.clip(image, min_val, max_val)
                image = (image - min_val) / (max_val - min_val)
                return image
        
        class MedicalImagePostprocessor:
            """医疗影像后处理"""
            def process(self, prediction, modality, task, patient_info):
                """后处理预测结果"""
                results = prediction.copy()
                
                # 根据任务添加医疗特定信息
                if "检测" in task:
                    results["clinical_significance"] = self._assess_clinical_significance(
                        prediction, modality, task, patient_info
                    )
                
                if "分割" in task:
                    results["quantitative_measures"] = self._calculate_quantitative_measures(
                        prediction["segmentation_map"]
                    )
                
                # 添加建议
                results["recommendations"] = self._generate_recommendations(
                    prediction, modality, task
                )
                
                return results
            
            def _assess_clinical_significance(self, prediction, modality, task, patient_info):
                """评估临床意义"""
                significance = {
                    "urgency": "routine",
                    "follow_up": "none",
                    "risk_level": "low"
                }
                
                confidence = prediction.get("confidence", 0)
                if confidence > 0.9:
                    significance["urgency"] = "urgent"
                    significance["risk_level"] = "high"
                elif confidence > 0.7:
                    significance["urgency"] = "soon"
                    significance["risk_level"] = "medium"
                
                # 考虑患者信息
                if patient_info and patient_info.get("age", 0) > 60:
                    significance["risk_level"] = "high"
                
                return significance
            
            def _calculate_quantitative_measures(self, segmentation_map):
                """计算定量指标"""
                if segmentation_map is None:
                    return {}
                
                # 简化的计算
                measures = {
                    "area": np.sum(segmentation_map > 0.5),
                    "volume": np.sum(segmentation_map > 0.5) * 0.5,  # 假设体素大小
                    "diameter": np.sqrt(np.sum(segmentation_map > 0.5) / np.pi) * 2
                }
                
                return measures
            
            def _generate_recommendations(self, prediction, modality, task):
                """生成临床建议"""
                recommendations = []
                
                if prediction.get("prediction") == "positive":
                    if "肿瘤" in task:
                        recommendations.append("建议进一步进行病理活检")
                        recommendations.append("建议3个月后复查")
                    elif "肺炎" in task:
                        recommendations.append("建议进行抗生素治疗")
                        recommendations.append("建议1周后复查X光")
                    elif "骨折" in task:
                        recommendations.append("建议进行骨科会诊")
                        recommendations.append("建议石膏固定")
                else:
                    recommendations.append("无明显异常发现")
                    recommendations.append("建议定期体检")
                
                return recommendations
        
        class ModelExplainability:
            """模型可解释性"""
            def explain(self, image, prediction, model):
                """生成解释"""
                explanation = {
                    "saliency_map": self._generate_saliency_map(image, model),
                    "feature_importance": self._calculate_feature_importance(),
                    "decision_boundary": self._visualize_decision_boundary(),
                    "confidence_calibration": self._assess_confidence_calibration(prediction)
                }
                
                return explanation
            
            def _generate_saliency_map(self, image, model):
                """生成显著图"""
                # 简化的显著图生成
                height, width = image.shape[:2]
                saliency = np.random.rand(height, width)
                return saliency
            
            def _calculate_feature_importance(self):
                """计算特征重要性"""
                features = ["纹理特征", "形状特征", "强度特征", "空间特征"]
                importance = np.random.rand(len(features))
                importance = importance / importance.sum()
                
                return dict(zip(features, importance))
            
            def _visualize_decision_boundary(self):
                """可视化决策边界"""
                return "决策边界可视化图"
            
            def _assess_confidence_calibration(self, prediction):
                """评估置信度校准"""
                confidence = prediction.get("confidence", 0.5)
                calibration = {
                    "calibrated_confidence": confidence * 0.9,  # 简化的校准
                    "calibration_error": abs(confidence - 0.5) * 0.1,
                    "reliability": "good" if confidence > 0.8 else "fair"
                }
                return calibration
        
        pipeline = MedicalAIPipeline(modality, task)
        return pipeline

# 使用示例
medical_ai = MedicalImageAnalysis()
print("医疗影像分析系统准备就绪")

# 示例：构建CT肿瘤检测流水线
pipeline = medical_ai.build_analysis_pipeline("CT", "肿瘤检测")
print(f"构建完成: CT肿瘤检测流水线")