7：灵波空间感知技术：基于MDM技术的高精度深度估计与透明物体感知

# MDM技术核心代码示例
class MaskedDepthModeling(nn.Module):
    def __init__(self, feature_extractor, mask_generator, depth_estimator):
        super().__init__()
        self.feature_extractor = feature_extractor
        self.mask_generator = mask_generator
        self.depth_estimator = depth_estimator
    
    def forward(self, image):
        """掩码深度建模"""
        # 提取多尺度特征
        features = self.feature_extractor(image)
        # 生成材质掩码
        masks = self.mask_generator(features)
        # 分层深度估计
        depth_maps = []
        for i, mask in enumerate(masks):
            # 应用掩码
            masked_feature = features * mask
            # 估计深度
            depth = self.depth_estimator(masked_feature, i)
            depth_maps.append(depth)
        # 融合深度图
        fused_depth = self.fuse_depth_maps(depth_maps, masks)
        # 材质感知优化
        optimized_depth = self.material_aware_optimization(fused_depth, masks)
        return optimized_depth, masks
    
    def fuse_depth_maps(self, depth_maps, masks):
        """融合不同材质的深度图"""
        # 初始化融合深度图
        fused = torch.zeros_like(depth_maps[0])
        # 权重计算
        weights = []
        for mask in masks:
            # 基于掩码计算权重
            weight = mask / (torch.sum(masks, dim=0) + 1e-8)
            weights.append(weight)
        # 加权融合
        for depth, weight in zip(depth_maps, weights):
            fused += depth * weight
        return fused

# 透明反光物体处理核心代码示例
class TransparentObjectProcessor(nn.Module):
    def __init__(self, refraction_model, reflection_remover):
        super().__init__()
        self.refraction_model = refraction_model
        self.reflection_remover = reflection_remover
    
    def process(self, image, depth_map, material_mask):
        """处理透明反光物体"""
        # 检测透明区域
        transparent_mask = material_mask == 1  # 假设1表示透明材质
        # 处理反射
        reflection_free_image = self.reflection_remover(image, transparent_mask)
        # 建模折射
        refracted_depth = self.refraction_model(
            image, depth_map, transparent_mask
        )
        # 更新深度图
        depth_map[transparent_mask] = refracted_depth[transparent_mask]
        return depth_map
    
    def estimate_refraction(self, image, depth, mask):
        """估计折射效应"""
        # 提取透明区域
        transparent_region = image * mask
        # 分析边缘信息
        edges = self.detect_edges(transparent_region)
        # 基于边缘和深度估计折射
        refraction_offset = self.refraction_model(edges, depth)
        # 应用折射校正
        corrected_depth = depth + refraction_offset
        return corrected_depth

# 单目深度估计核心代码示例
class MonocularDepthEstimator(nn.Module):
    def __init__(self, encoder, decoder):
        super().__init__()
        self.encoder = encoder  # 特征编码器
        self.decoder = decoder  # 深度解码器
    
    def forward(self, image):
        """单目深度估计"""
        # 提取特征
        features = self.encoder(image)
        # 解码深度
        depth = self.decoder(features)
        return depth
    
    def predict(self, image):
        """预测接口"""
        with torch.no_grad():
            depth = self.forward(image)
            # 后处理
            depth = self.post_process(depth)
        return depth
    
    def post_process(self, depth):
        """深度图后处理"""
        # 边缘保持滤波
        depth = self.edge_preserving_filter(depth)
        # 全局一致性优化
        depth = self.global_consistency_optimization(depth)
        return depth

# 推理优化核心代码示例
class OptimizedInferenceEngine:
    def __init__(self, model, quantization='fp16'):
        self.model = model
        self.quantization = quantization
        self.optimize_model()
    
    def optimize_model(self):
        """优化模型以提高推理速度"""
        # 应用量化
        if self.quantization == 'int8':
            self.model = self.quantize_to_int8(self.model)
        elif self.quantization == 'fp16':
            self.model = self.model.half()
        
        # 移动到合适的设备
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()
    
    def infer(self, images):
        """高效推理"""
        with torch.no_grad():
            # 准备输入
            if isinstance(images, list):
                # 批处理
                input_tensor = torch.stack([self.preprocess(img) for img in images])
            else:
                # 单张图像
                input_tensor = self.preprocess(images).unsqueeze(0)
            
            input_tensor = input_tensor.to(self.device)
            if self.quantization == 'fp16':
                input_tensor = input_tensor.half()
            
            # 推理
            output = self.model(input_tensor)
            
            # 后处理
            depth_maps = []
            for i in range(output.shape[0]):
                depth_map = self.postprocess(output[i])
                depth_maps.append(depth_map)
            
            return depth_maps
    
    def preprocess(self, image):
        """图像预处理"""
        # 调整大小
        image = self.resize(image)
        # 归一化
        image = self.normalize(image)
        # 转换为张量
        image = torch.from_numpy(image).permute(2, 0, 1).float()
        return image

import gradio as gr
import numpy as np
import torch
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from PIL import Image

# 加载模型
lingbot_depth_pipeline = pipeline(
    Tasks.depth_estimation,
    model='Robbyant/lingbot-depth'
)

# 处理函数
def estimate_depth(image):
    """深度估计"""
    # 执行深度估计
    result = lingbot_depth_pipeline({
        'image': image
    })
    
    # 获取深度图
    depth_map = result['depth_map']
    
    # 转换深度图为可视化格式
    depth_visualization = visualize_depth(depth_map)
    
    # 生成统计信息
    stats = f"深度图分辨率: {depth_map.shape[1]}x{depth_map.shape[0]}\n"
    stats += f"最小深度: {np.min(depth_map):.2f}\n"
    stats += f"最大深度: {np.max(depth_map):.2f}\n"
    stats += f"平均深度: {np.mean(depth_map):.2f}\n"
    stats += f"处理时间: {result.get('processing_time', 'N/A')}ms\n"
    
    return depth_visualization, stats

def visualize_depth(depth_map):
    """将深度图转换为可视化格式"""
    # 归一化深度图
    depth_normalized = (depth_map - np.min(depth_map)) / (np.max(depth_map) - np.min(depth_map))
    # 应用颜色映射
    depth_colored = (depth_normalized * 255).astype(np.uint8)
    # 转换为RGB
    depth_rgb = np.stack([depth_colored, depth_colored, depth_colored], axis=-1)
    # 转换为PIL图像
    depth_image = Image.fromarray(depth_rgb)
    return depth_image

# 创建Gradio界面
with gr.Blocks(title="灵波空间感知 - 深度估计") as demo:
    gr.Markdown("# 灵波空间感知 - 深度估计演示")
    gr.Markdown("上传一张包含透明或反光物体的图片，生成深度图")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="输入图像")
            estimate_btn = gr.Button("估计深度")
        
        with gr.Column(scale=2):
            depth_output = gr.Image(label="深度图")
            stats_output = gr.Textbox(label="深度统计", lines=5)
    
    # 绑定事件
    estimate_btn.click(
        fn=estimate_depth,
        inputs=[image_input],
        outputs=[depth_output, stats_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)

pytorch==2.0.1
modelscope==1.9.1
gradio==4.14.0
Pillow==10.1.0
numpy==1.24.4
opencv-python==4.8.1.78

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

EXPOSE 7860

CMD ["python", "app.py"]