白细胞分离问题分析与解决方案

def calculate_overlap_slices(image_size, slice_size, overlap_ratio=0.25):
    """
    计算重叠切片坐标

    参数:
    - image_size: 原始图像尺寸 (width, height)
    - slice_size: 切片尺寸 (width, height)
    - overlap_ratio: 重叠比例，默认25%

    返回:
    - 切片坐标列表 [(x, y), ...]
    """
    overlap_pixels_x = int(slice_size[0] * overlap_ratio)
    overlap_pixels_y = int(slice_size[1] * overlap_ratio)

    step_x = slice_size[0] - overlap_pixels_x
    step_y = slice_size[1] - overlap_pixels_y

    coords = []
    for x in range(0, image_size[0], step_x):
        for y in range(0, image_size[1], step_y):
            coords.append((x, y))

    return coords

def detect_cross_boundary_cells(detection_results, slice_bounds):
    """
    检测跨切片的细胞

    参数:
    - detection_results: 检测结果列表
    - slice_bounds: 切片边界信息

    返回:
    - 边界细胞列表
    """
    boundary_cells = []

    for result in detection_results:
        cell_box = result.bounding_box
        slice_info = result.slice_info

        # 检查是否接近切片边界
        if is_near_boundary(cell_box, slice_info, threshold=50):
            boundary_cells.append({
                'cell_id': result.cell_id,
                'box': cell_box,
                'slice_id': slice_info['slice_id'],
                'confidence': result.confidence,
                'class': result.class_id
            })

    return boundary_cells

def is_near_boundary(cell_box, slice_info, threshold=50):
    """
    判断细胞是否接近切片边界

    参数:
    - cell_box: 细胞边界框 [x1, y1, x2, y2]
    - slice_info: 切片信息
    - threshold: 边界阈值（像素）

    返回:
    - 是否接近边界
    """
    slice_width = slice_info['width']
    slice_height = slice_info['height']

    # 计算到各边界的距离
    dist_left = cell_box[0]
    dist_right = slice_width - cell_box[2]
    dist_top = cell_box[1]
    dist_bottom = slice_height - cell_box[3]

    # 如果任一方向距离小于阈值，则为边界细胞
    return (dist_left < threshold or dist_right < threshold or
            dist_top < threshold or dist_bottom < threshold)

def merge_duplicate_detections(detections, iou_threshold=0.7):
    """
    合并重复检测结果

    参数:
    - detections: 检测结果列表
    - iou_threshold: IoU阈值

    返回:
    - 去重后的检测结果
    """
    if not detections:
        return []

    # 按置信度排序
    sorted_detections = sorted(detections, key=lambda x: x.confidence, reverse=True)
    merged_results = []

    for current_det in sorted_detections:
        is_duplicate = False

        for existing_det in merged_results:
            iou = calculate_iou(current_det.bounding_box, existing_det.bounding_box)

            if iou > iou_threshold:
                # 发现重复，选择置信度更高的
                if current_det.confidence > existing_det.confidence:
                    # 替换现有结果
                    merged_results.remove(existing_det)
                    merged_results.append(current_det)
                is_duplicate = True
                break

        if not is_duplicate:
            merged_results.append(current_det)

    return merged_results

def calculate_iou(box1, box2):
    """
    计算两个边界框的IoU

    参数:
    - box1, box2: 边界框 [x1, y1, x2, y2]

    返回:
    - IoU值 (0-1)
    """
    # 计算交集区域
    x1 = max(box1[0], box2[0])
    y1 = max(box1[1], box2[1])
    x2 = min(box1[2], box2[2])
    y2 = min(box1[3], box2[3])

    if x2 <= x1 or y2 <= y1:
        return 0.0

    intersection = (x2 - x1) * (y2 - y1)

    # 计算并集区域
    area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
    area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
    union = area1 + area2 - intersection

    return intersection / union if union > 0 else 0.0

def optimize_memory_usage(slice_queue, max_queue_size=10000):
    """
    优化内存使用

    参数:
    - slice_queue: 切片队列
    - max_queue_size: 最大队列大小
    """
    while slice_queue.qsize() > max_queue_size:
        # 等待处理进程消费队列
        time.sleep(0.1)

        # 如果等待时间过长，可以考虑丢弃低优先级任务
        if time.time() - start_time > 30:
            # 丢弃队列末尾的低优先级任务
            try:
                slice_queue.get_nowait()
            except:
                pass

def optimize_parallel_processing(num_processes, image_size):
    """
    优化并行处理参数

    参数:
    - num_processes: 进程数量
    - image_size: 图像尺寸

    返回:
    - 优化的进程配置
    """
    # 根据图像大小动态调整进程数
    if image_size[0] * image_size[1] > 100000000:  # 大于100MP
        optimal_processes = min(8, num_processes * 2)
    elif image_size[0] * image_size[1] > 50000000:  # 大于50MP
        optimal_processes = num_processes
    else:
        optimal_processes = max(2, num_processes // 2)

    return optimal_processes

# 边缘白细胞检测配置
EDGE_DETECTION_MARGIN = 100  # 边缘检测的像素范围
BOUNDARY_EXTENSION = 50      # 边界框扩展的像素数

def is_edge_cell(box, image_shape):
    """
    判断检测到的细胞是否在图像边缘

    参数:
    - box: 细胞边界框 [left, top, right, bottom]
    - image_shape: 图像尺寸 (height, width, channels)

    返回:
    - 是否在边缘区域
    """
    left, top, right, bottom = box
    height, width = image_shape[:2]

    # 检查是否在边缘区域
    is_left_edge = left < EDGE_DETECTION_MARGIN
    is_right_edge = right > (width - EDGE_DETECTION_MARGIN)
    is_top_edge = top < EDGE_DETECTION_MARGIN
    is_bottom_edge = bottom > (height - EDGE_DETECTION_MARGIN)

    return is_left_edge or is_right_edge or is_top_edge or is_bottom_edge

def extend_boundary_box(box, image_shape, extension_pixels=50):
    """
    扩展边界框，确保包含完整的白细胞

    参数:
    - box: 原始边界框 [left, top, right, bottom]
    - image_shape: 图像尺寸
    - extension_pixels: 扩展像素数，默认50像素

    返回:
    - 扩展后的边界框
    """
    left, top, right, bottom = box
    height, width = image_shape[:2]

    # 扩展边界框，确保不超出图像边界
    new_left = max(0, left - extension_pixels)
    new_top = max(0, top - extension_pixels)
    new_right = min(width, right + extension_pixels)
    new_bottom = min(height, bottom + extension_pixels)

    return (new_left, new_top, new_right, new_bottom)

def merge_overlapping_boxes(boxes, scores, classes):
    """
    合并重叠的边界框，避免重复检测

    参数:
    - boxes: 边界框列表
    - scores: 置信度列表
    - classes: 类别列表

    返回:
    - 去重后的结果
    """
    if len(boxes) <= 1:
        return boxes, scores, classes

    merged_boxes = []
    merged_scores = []
    merged_classes = []

    for i, (box, score, cls) in enumerate(zip(boxes, scores, classes)):
        is_duplicate = False

        for j, (existing_box, existing_score, existing_cls) in enumerate(zip(merged_boxes, merged_scores, merged_classes)):
            # 计算IoU
            intersection_left = max(box[0], existing_box[0])
            intersection_top = max(box[1], existing_box[1])
            intersection_right = min(box[2], existing_box[2])
            intersection_bottom = min(box[3], existing_box[3])

            if intersection_right > intersection_left and intersection_bottom > intersection_top:
                intersection_area = (intersection_right - intersection_left) * (intersection_bottom - intersection_top)
                box_area = (box[2] - box[0]) * (box[3] - box[1])
                existing_area = (existing_box[2] - existing_box[0]) * (existing_box[3] - existing_box[1])

                union_area = box_area + existing_area - intersection_area
                iou = intersection_area / union_area if union_area > 0 else 0

                # 如果IoU大于0.5，认为是重复检测
                if iou > 0.5:
                    is_duplicate = True
                    # 保留置信度更高的检测结果
                    if score > existing_score:
                        merged_boxes[j] = box
                        merged_scores[j] = score
                        merged_classes[j] = cls
                    break

        if not is_duplicate:
            merged_boxes.append(box)
            merged_scores.append(score)
            merged_classes.append(cls)

    return merged_boxes, merged_scores, merged_classes

def predict_with_edge_detection(cut_queue, model_path, cell_queue, thread_id):
    """
    增强的预测函数，包含边缘白细胞检测和边界框扩展
    """
    option = InferOption()
    option.enable_swap_rb()
    model = DetectModel(model_path, option)
    nec_cell = [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 4, 2, 43, 46, 47, 48, 49, 50, 51, 52, 55]

    while True:
        try:
            x, y, item = cut_queue.get(timeout=5)
            result = model.predict(item)
            cell_boxes = [(box.left, box.top, box.right, box.bottom) for box in result.boxes]

            # 处理检测结果，包括边缘白细胞检测和边界框扩展
            processed_boxes = []
            processed_scores = []
            processed_classes = []

            for i, (box, score, cls) in enumerate(zip(cell_boxes, result.scores, result.classes)):
                if cls in nec_cell:  # 只处理需要的细胞类型
                    # 检查是否为边缘细胞
                    if is_edge_cell(box, item.shape):
                        # 扩展边界框以包含完整的白细胞
                        extended_box = extend_boundary_box(box, item.shape)
                        processed_boxes.append(extended_box)
                        processed_scores.append(score)
                        processed_classes.append(cls)
                    else:
                        # 非边缘细胞，保持原边界框
                        processed_boxes.append(box)
                        processed_scores.append(score)
                        processed_classes.append(cls)

            # 合并重叠的边界框
            if processed_boxes:
                final_boxes, final_scores, final_classes = merge_overlapping_boxes(
                    processed_boxes, processed_scores, processed_classes
                )

                # 将处理后的结果放入队列
                if final_boxes:
                    cell_queue.put((final_classes, final_scores, final_boxes, item, x, y))

        except Exception as e:
            print(f"小图队列为空，进程 {thread_id} 停止: {str(e)}")
            break

def extract_cell_image_with_extended_box(image, box, original_box):
    """
    根据扩展后的边界框提取细胞图像

    参数:
    - image: 原始切片图像
    - box: 扩展后的边界框
    - original_box: 原始检测边界框

    返回:
    - 提取的细胞图像和扩展信息
    """
    left, top, right, bottom = box
    cell_img = image[top:bottom, left:right]

    # 记录扩展信息，用于后续分析
    extension_info = {
        'original_box': original_box,
        'extended_box': box,
        'extension_pixels': {
            'left': original_box[0] - left,
            'top': original_box[1] - top,
            'right': right - original_box[2],
            'bottom': bottom - original_box[3]
        }
    }

    return cell_img, extension_info

def optimize_edge_detection(image_shape, cell_size_estimate):
    """
    根据图像尺寸和细胞大小估计优化边缘检测参数

    参数:
    - image_shape: 图像尺寸
    - cell_size_estimate: 估计的细胞大小（像素）

    返回:
    - 优化的边缘检测参数
    """
    # 根据细胞大小动态调整边缘检测范围
    optimal_margin = max(50, min(200, cell_size_estimate // 2))

    # 根据图像尺寸调整扩展像素数
    optimal_extension = max(25, min(100, cell_size_estimate // 4))

    return {
        'edge_margin': optimal_margin,
        'extension_pixels': optimal_extension
    }

def optimize_memory_usage_for_edge_detection(cell_queue, max_queue_size=8000):
    """
    为边缘检测优化的内存管理

    参数:
    - cell_queue: 细胞检测队列
    - max_queue_size: 最大队列大小
    """
    while cell_queue.qsize() > max_queue_size:
        # 等待处理进程消费队列
        time.sleep(0.1)

        # 如果等待时间过长，考虑丢弃低优先级任务
        if time.time() - start_time > 20:
            try:
                cell_queue.get_nowait()
            except:
                pass

def analyze_cell_density(slide_image, window_size=(512, 512)):
    """
    分析图像中的细胞密度分布

    参数:
    - slide_image: 幻灯片图像
    - window_size: 分析窗口大小

    返回:
    - 密度分布图和热点区域
    """
    height, width = slide_image.shape[:2]
    density_map = np.zeros((height // window_size[1], width // window_size[0]))

    for y in range(0, height, window_size[1]):
        for x in range(0, width, window_size[0]):
            window = slide_image[y:y+window_size[1], x:x+window_size[0]]
            # 使用图像处理技术估计细胞密度
            density = estimate_cell_density_in_window(window)
            density_map[y//window_size[1], x//window_size[0]] = density

    return density_map, find_hotspots(density_map)

def estimate_cell_density_in_window(window):
    """
    估计窗口内的细胞密度
    """
    # 转换为灰度图
    gray = cv2.cvtColor(window, cv2.COLOR_RGB2GRAY)

    # 使用形态学操作检测细胞样结构
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    morph = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)

    # 计算细胞样结构的数量
    contours, _ = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 过滤掉太小的结构
    valid_contours = [c for c in contours if cv2.contourArea(c) > 100]

    return len(valid_contours)

def generate_adaptive_slices(slide_image, density_map, min_slice_size=(1024, 768)):
    """
    根据细胞密度生成自适应切片

    参数:
    - slide_image: 幻灯片图像
    - density_map: 细胞密度分布图
    - min_slice_size: 最小切片尺寸

    返回:
    - 优化的切片坐标列表
    """
    height, width = slide_image.shape[:2]
    slices = []

    # 使用密度信息优化切片边界
    for y in range(0, height, min_slice_size[1]):
        for x in range(0, width, min_slice_size[0]):
            # 分析当前区域的密度特征
            region_density = analyze_region_density(density_map, x, y, min_slice_size)

            # 根据密度调整切片大小
            optimal_size = calculate_optimal_slice_size(region_density, min_slice_size)

            # 生成优化的切片
            slice_coords = optimize_slice_boundaries(x, y, optimal_size, slide_image)
            slices.append(slice_coords)

    return slices

def optimize_slice_boundaries(x, y, size, image):
    """
    优化切片边界，避免切割重要结构
    """
    # 在边界附近寻找最佳切割线
    best_boundaries = find_optimal_boundaries(x, y, size, image)

    return {
        'x': x,
        'y': y,
        'width': size[0],
        'height': size[1],
        'boundaries': best_boundaries
    }

def multi_scale_boundary_analysis(slice_image, scales=[0.5, 1.0, 2.0]):
    """
    多尺度边界分析，提高边界检测精度

    参数:
    - slice_image: 切片图像
    - scales: 分析尺度列表

    返回:
    - 边界检测结果
    """
    results = []

    for scale in scales:
        # 缩放图像
        if scale != 1.0:
            scaled_image = cv2.resize(slice_image, None, fx=scale, fy=scale)
        else:
            scaled_image = slice_image

        # 在不同尺度下进行边界检测
        boundary_result = detect_boundaries_at_scale(scaled_image, scale)
        results.append(boundary_result)

    # 融合多尺度结果
    return fuse_multi_scale_results(results)

def detect_boundaries_at_scale(image, scale):
    """
    在特定尺度下检测边界
    """
    # 使用Canny边缘检测
    edges = cv2.Canny(image, 50, 150)

    # 使用霍夫变换检测直线
    lines = cv2.HoughLines(edges, 1, np.pi/180, threshold=100)

    # 分析边界特征
    boundary_features = analyze_boundary_features(edges, lines)

    return {
        'scale': scale,
        'edges': edges,
        'lines': lines,
        'features': boundary_features
    }

class BoundaryOptimizer:
    """
    基于机器学习的边界优化器
    """
    def __init__(self, model_path=None):
        self.model = self.load_model(model_path) if model_path else None
        self.feature_extractor = self.create_feature_extractor()

    def optimize_boundaries(self, slice_image, detected_cells):
        """
        优化切片边界
        """
        # 提取边界特征
        boundary_features = self.extract_boundary_features(slice_image, detected_cells)

        # 使用ML模型预测最佳边界
        if self.model:
            optimal_boundaries = self.model.predict(boundary_features)
        else:
            optimal_boundaries = self.rule_based_optimization(boundary_features)

        return optimal_boundaries

    def extract_boundary_features(self, image, cells):
        """
        提取边界特征
        """
        features = []

        for cell in cells:
            # 提取细胞周围的边界特征
            cell_features = self.extract_cell_boundary_features(image, cell)
            features.extend(cell_features)

        return np.array(features)

def parallel_adaptive_processing(slide_path, num_workers=8):
    """
    并行自适应处理
    """
    with ThreadPoolExecutor(max_workers=num_workers) as executor:
        # 并行分析不同区域
        future_to_region = {
            executor.submit(analyze_region, region): region
            for region in divide_slide_into_regions(slide_path)
        }

        results = []
        for future in as_completed(future_to_region):
            region = future_to_region[future]
            try:
                result = future.result()
                results.append(result)
            except Exception as exc:
                print(f'区域 {region} 处理出错: {exc}')

    return merge_region_results(results)

def optimize_memory_for_adaptive_slicing(slide_image, max_memory_usage=0.8):
    """
    为自适应切片优化内存使用
    """
    # 监控内存使用
    memory_usage = psutil.virtual_memory().percent / 100

    if memory_usage > max_memory_usage:
        # 减少并发处理数量
        optimal_workers = max(2, int(8 * (1 - memory_usage)))
        return optimal_workers

    return 8

class MultiScaleFeatureExtractor(nn.Module):
    """
    多尺度特征提取网络
    """
    def __init__(self, backbone='resnet50'):
        super().__init__()
        self.backbone = self.create_backbone(backbone)
        self.fpn = FeaturePyramidNetwork([256, 512, 1024, 2048], 256)
        self.attention = MultiScaleAttention(256)

    def forward(self, x):
        # 提取多尺度特征
        features = self.backbone(x)

        # 特征金字塔网络
        fpn_features = self.fpn(features)

        # 多尺度注意力机制
        enhanced_features = self.attention(fpn_features)

        return enhanced_features

    def create_backbone(self, backbone_name):
        """
        创建骨干网络
        """
        if backbone_name == 'resnet50':
            model = models.resnet50(pretrained=True)
            return nn.Sequential(*list(model.children())[:-2])
        elif backbone_name == 'efficientnet':
            model = timm.create_model('efficientnet_b0', pretrained=True)
            return model
        else:
            raise ValueError(f"不支持的骨干网络: {backbone_name}")

class MultiScaleAttention(nn.Module):
    """
    多尺度注意力机制
    """
    def __init__(self, feature_dim):
        super().__init__()
        self.attention_layers = nn.ModuleList([
            nn.MultiheadAttention(feature_dim, num_heads=8)
            for _ in range(3)
        ])
        self.fusion = nn.Conv2d(feature_dim * 3, feature_dim, 1)

    def forward(self, features):
        enhanced_features = []

        for i, (scale_name, feature) in enumerate(features.items()):
            # 应用注意力机制
            attended_feature = self.apply_attention(feature, self.attention_layers[i])
            enhanced_features.append(attended_feature)

        # 融合多尺度特征
        fused_features = torch.cat(enhanced_features, dim=1)
        return self.fusion(fused_features)

class CellDetectionHead(nn.Module):
    """
    细胞检测头网络
    """
    def __init__(self, in_channels, num_classes, num_anchors=9):
        super().__init__()
        self.num_classes = num_classes
        self.num_anchors = num_anchors

        # 分类分支
        self.cls_conv = nn.Sequential(
            nn.Conv2d(in_channels, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, num_anchors * num_classes, 1)
        )

        # 回归分支
        self.reg_conv = nn.Sequential(
            nn.Conv2d(in_channels, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, num_anchors * 4, 1)  # x, y, w, h
        )

        # 中心点分支
        self.ctr_conv = nn.Sequential(
            nn.Conv2d(in_channels, 256, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, num_anchors, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        cls_output = self.cls_conv(x)
        reg_output = self.reg_conv(x)
        ctr_output = self.ctr_conv(x)

        return cls_output, reg_output, ctr_output

class MultiTaskLoss(nn.Module):
    """
    多任务损失函数
    """
    def __init__(self, alpha=1.0, beta=1.0, gamma=1.0):
        super().__init__()
        self.alpha = alpha  # 分类损失权重
        self.beta = beta    # 回归损失权重
        self.gamma = gamma  # 中心点损失权重

        self.cls_loss = FocalLoss()
        self.reg_loss = IoULoss()
        self.ctr_loss = nn.BCELoss()

    def forward(self, cls_pred, cls_target, reg_pred, reg_target, ctr_pred, ctr_target):
        """
        计算多任务损失
        """
        # 分类损失
        cls_loss = self.cls_loss(cls_pred, cls_target)

        # 回归损失
        reg_loss = self.reg_loss(reg_pred, reg_target)

        # 中心点损失
        ctr_loss = self.ctr_loss(ctr_pred, ctr_target)

        # 总损失
        total_loss = (self.alpha * cls_loss +
                     self.beta * reg_loss +
                     self.gamma * ctr_loss)

        return total_loss, {
            'cls_loss': cls_loss.item(),
            'reg_loss': reg_loss.item(),
            'ctr_loss': ctr_loss.item(),
            'total_loss': total_loss.item()
        }

class FocalLoss(nn.Module):
    """
    Focal Loss for 分类任务
    """
    def __init__(self, alpha=1, gamma=2):
        super().__init__()
        self.alpha = alpha
        self.gamma = gamma

    def forward(self, inputs, targets):
        ce_loss = F.cross_entropy(inputs, targets, reduction='none')
        pt = torch.exp(-ce_loss)
        focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss
        return focal_loss.mean()

class IoULoss(nn.Module):
    """
    IoU Loss for 回归任务
    """
    def __init__(self, loss_type='iou'):
        super().__init__()
        self.loss_type = loss_type

    def forward(self, pred, target):
        # 计算IoU
        iou = self.calculate_iou(pred, target)

        if self.loss_type == 'iou':
            return 1 - iou
        elif self.loss_type == 'giou':
            return 1 - self.calculate_giou(pred, target)
        else:
            return 1 - iou

    def calculate_iou(self, pred, target):
        # IoU计算实现
        pass

class AdvancedDataAugmentation:
    """
    高级数据增强策略
    """
    def __init__(self):
        self.transforms = A.Compose([
            # 几何变换
            A.RandomRotate90(p=0.5),
            A.Flip(p=0.5),
            A.Transpose(p=0.5),
            A.ShiftScaleRotate(
                shift_limit=0.0625,
                scale_limit=0.2,
                rotate_limit=45,
                p=0.5
            ),

            # 颜色变换
            A.OneOf([
                A.RandomBrightnessContrast(p=1),
                A.RandomGamma(p=1),
                A.HueSaturationValue(p=1),
            ], p=0.5),

            # 噪声和模糊
            A.OneOf([
                A.GaussNoise(p=1),
                A.GaussianBlur(p=1),
                A.MotionBlur(p=1),
            ], p=0.3),

            # 弹性变换
            A.ElasticTransform(
                alpha=120,
                sigma=120 * 0.05,
                alpha_affine=120 * 0.03,
                p=0.3
            ),

            # 网格变换
            A.GridDistortion(p=0.3),
            A.OpticalDistortion(p=0.3),
        ])

    def __call__(self, image, bboxes=None, labels=None):
        """
        应用数据增强
        """
        if bboxes is not None and labels is not None:
            # 有标注框的情况
            transformed = self.transforms(
                image=image,
                bboxes=bboxes,
                labels=labels
            )
            return transformed['image'], transformed['bboxes'], transformed['labels']
        else:
            # 无标注框的情况
            transformed = self.transforms(image=image)
            return transformed['image']

class AdaptiveAugmentation:
    """
    自适应数据增强
    """
    def __init__(self, model):
        self.model = model
        self.augmentation_pool = self.create_augmentation_pool()

    def adaptive_augment(self, image, target):
        """
        根据模型性能自适应选择增强策略
        """
        # 评估当前样本的难度
        difficulty_score = self.assess_sample_difficulty(image, target)

        # 根据难度选择增强策略
        if difficulty_score > 0.8:
            # 困难样本，使用强增强
            augmentation = self.select_strong_augmentation()
        elif difficulty_score < 0.3:
            # 简单样本，使用弱增强
            augmentation = self.select_weak_augmentation()
        else:
            # 中等样本，使用标准增强
            augmentation = self.select_standard_augmentation()

        return augmentation(image, target)

    def assess_sample_difficulty(self, image, target):
        """
        评估样本难度
        """
        # 使用模型预测结果评估难度
        with torch.no_grad():
            prediction = self.model(image.unsqueeze(0))
            confidence = prediction['confidence'].max().item()

        # 根据置信度和目标复杂度计算难度
        complexity = self.calculate_target_complexity(target)
        difficulty = (1 - confidence) * 0.6 + complexity * 0.4

        return difficulty

class KnowledgeDistillation:
    """
    知识蒸馏训练
    """
    def __init__(self, teacher_model, student_model, temperature=4.0, alpha=0.7):
        self.teacher_model = teacher_model
        self.student_model = student_model
        self.temperature = temperature
        self.alpha = alpha

        # 冻结教师模型
        for param in self.teacher_model.parameters():
            param.requires_grad = False

    def distill_loss(self, student_output, teacher_output, labels, temperature=4.0):
        """
        计算蒸馏损失
        """
        # 软标签损失
        soft_loss = F.kl_div(
            F.log_softmax(student_output / temperature, dim=1),
            F.softmax(teacher_output / temperature, dim=1),
            reduction='batchmean'
        ) * (temperature ** 2)

        # 硬标签损失
        hard_loss = F.cross_entropy(student_output, labels)

        # 总损失
        total_loss = self.alpha * soft_loss + (1 - self.alpha) * hard_loss

        return total_loss, soft_loss, hard_loss

class ModelQuantization:
    """
    模型量化优化
    """
    def __init__(self, model):
        self.model = model
        self.quantized_model = None

    def quantize_model(self, calibration_data):
        """
        量化模型
        """
        # 设置量化配置
        self.model.eval()
        self.model.qconfig = torch.quantization.get_default_qconfig('fbgemm')

        # 准备量化
        torch.quantization.prepare(self.model, inplace=True)

        # 校准
        with torch.no_grad():
            for data in calibration_data:
                self.model(data)

        # 转换为量化模型
        self.quantized_model = torch.quantization.convert(self.model, inplace=False)

        return self.quantized_model

    def benchmark_performance(self, test_data):
        """
        性能基准测试
        """
        # 原始模型性能
        original_time = self.benchmark_model(self.model, test_data)

        # 量化模型性能
        quantized_time = self.benchmark_model(self.quantized_model, test_data)

        # 计算加速比
        speedup = original_time / quantized_time

        return {
            'original_time': original_time,
            'quantized_time': quantized_time,
            'speedup': speedup
        }