接入图像预处理，画出预处理流程图，完善处理前程序用户交互逻辑。

.gitignore

@@ -201,3 +201,6 @@ __marimo__/
 
 # Streamlit
 .streamlit/secrets.toml
+
+测试数据/
+model/

.vscode/launch.json

@@ -0,0 +1,12 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Run Main",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/main.py",  // 修改为你的主文件路径
+            "console": "integratedTerminal"
+        }
+    ]
+}

MainWindow/mainwindow.py

@@ -0,0 +1,127 @@
+from PySide6.QtWidgets import (QMainWindow, QWidget, QGridLayout, 
+                              QPushButton, QSizePolicy, QSplitter, QToolBar)
+from PySide6.QtGui import QFontDatabase
+from PySide6.QtCore import Signal, Qt
+
+import os
+from info_core.defines import *
+from info_core.MyQtClass import ConfigComboBoxGroup, FolderDropWidget
+
+
+class ReportGeneratorUI(QMainWindow):
+    send_baogao_choose_info = Signal(list[str])
+    
+    def __init__(self):
+        super().__init__()
+        # 加载字体
+        self.load_font()
+        
+        # 设置窗口属性
+        self.setWindowTitle("报告生成器")
+        self.setMinimumSize(WINDOW_MIN_WIDTH, WINDOW_MIN_HEIGHT)
+        
+        # 主窗口部件
+        self.central_widget = QWidget()
+        self.setCentralWidget(self.central_widget)
+        
+        # 主布局
+        self.main_layout = QGridLayout(self.central_widget)
+        self.main_layout.setSpacing(MAIN_LAYOUT_SPACING)
+        self.main_layout.setContentsMargins(*MAIN_LAYOUT_MARGINS)
+        
+        # 初始化UI
+        self.init_ui()
+        
+    
+    def load_font(self):
+        """加载自定义字体"""
+        if os.path.exists(FONT_PATH):
+            font_id = QFontDatabase.addApplicationFont(FONT_PATH)
+            if font_id == -1:
+                print("字体加载失败，将使用系统默认字体")
+        else:
+            print(f"字体文件未找到: {FONT_PATH}，将使用系统默认字体")
+        
+    def init_ui(self):
+        """初始化所有UI组件"""
+        # 第一行：项目信息和人员配置
+        self.project_group = ConfigComboBoxGroup("项目基本信息")
+        self.staff_group = ConfigComboBoxGroup("单次检查配置信息", is_project=False)
+        # 第二行：导入图片路径、填写机组信息
+        self.picture_group = FolderDropWidget()
+        # self.image_analysis = 
+        # self.main_layout.addWidget(self.image_analysis, 1, 1)
+        # 第三行：生成报告按钮（跨两列）
+        self.fill_turbine_info_button()
+        self.fill_btn.clicked.connect(self.on_fill_clicked)
+        
+        # 创建一个垂直分割器
+        self.splitter = QSplitter(Qt.Vertical)
+        self.splitter.setStyleSheet(SPLITTER_STYLE)
+        # 创建顶部和底部容器
+        top_container = QWidget()
+        top_container.setLayout(QGridLayout())
+        top_container.layout().addWidget(self.project_group, 0, 0)
+        top_container.layout().addWidget(self.staff_group, 0, 1)
+
+        middle_container = QWidget()
+        middle_container.setLayout(QGridLayout())
+        middle_container.layout().addWidget(self.picture_group, 0, 0)
+
+        # 添加部件到分割器
+        self.splitter.addWidget(top_container)
+        self.splitter.addWidget(middle_container)
+
+        # 设置主布局
+        self.main_layout.addWidget(self.splitter, 0, 0, 2, 2)  # 占据前两行两列
+        self.main_layout.addWidget(self.fill_btn, 2, 0, 1, 2)
+
+        # 设置分割器初始比例
+        self.splitter.setStretchFactor(0, 1)
+        self.splitter.setStretchFactor(1, 4)
+        
+        self.toolbar = QToolBar()
+        self.addToolBar(self.toolbar)
+        self.toolbar.setMovable(False)
+        self.toolbar.setFloatable(False)
+        new_action = self.toolbar.addAction("重置布局比例")
+        self.toolbar.addSeparator()
+        new_action.triggered.connect(self.reset_splitter)
+    
+    def reset_splitter(self):
+        """重置分割器的比例"""
+        total_size = sum(self.splitter.sizes())  # 获取当前总大小
+        self.splitter.setSizes([
+            int(total_size * 0.2),  # 第一部分占 20%（比例 1:4）
+            int(total_size * 0.8)   # 第二部分占 80%
+        ])
+
+    def on_fill_clicked(self):
+        """填写信息"""
+        # 读取各个配置信息
+        turbine_file_list = self.picture_group.get_selected_folders()
+        print(turbine_file_list)
+    
+    # search_file_list = []
+    # if self.image_analysis.check_is_waibu:
+    #     search_file_list.append("外汇总")
+    # if self.image_analysis.check_is_neibu:
+    #     search_file_list.append("内汇总")
+    # if self.image_analysis.check_is_fanglei:
+    #     search_file_list.append("防汇总")
+    # self.send_baogao_choose_info.emit(search_file_list)
+    
+    def create_button(self, text):
+        """创建统一风格的按钮"""
+        btn = QPushButton(text)
+        btn.setStyleSheet(BUTTON_STYLE)
+        btn.setFixedSize(BUTTON_WIDTH, BUTTON_HEIGHT)
+        return btn
+    
+    def fill_turbine_info_button(self):
+        """创建生成报告按钮"""
+        self.fill_btn = QPushButton("开始填写各个机组信息")
+        self.fill_btn.setStyleSheet(PRIMARY_BUTTON_STYLE)
+        self.fill_btn.setFixedHeight(50)
+        self.fill_btn.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
+    

README.md

@@ -3,5 +3,7 @@
 - 数据预处理：阴暗处亮度增加，细节增强。
 - 数据报告生成：基于模板批量生成报告。
 
-
+# 项目架构图
-![项目架构](工具流程.png)
+![项目架构](工具流程.png)
+# 预处理流程图
+![预处理流程](原始数据预处理流程图.png)

depth_anything_v2/dinov2.py

@@ -0,0 +1,416 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from .dinov2_layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
+        w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
+        # w0, h0 = w0 + 0.1, h0 + 0.1
+        
+        sqrt_N = math.sqrt(N)
+        sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
+            scale_factor=(sx, sy),
+            # (int(w0), int(h0)), # to solve the upsampling shape issue
+            mode="bicubic"
+        )
+        #     antialias=self.interpolate_antialias
+        # )
+        
+        assert int(w0) == patch_pos_embed.shape[-2]
+        assert int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def DINOv2(model_name):
+    model_zoo = {
+        "vits": vit_small, 
+        "vitb": vit_base, 
+        "vitl": vit_large, 
+        "vitg": vit_giant2
+    }
+    
+    return model_zoo[model_name](
+        img_size=518,
+        patch_size=14,
+        init_values=1.0,
+        ffn_layer="mlp" if model_name != "vitg" else "swiglufused",
+        block_chunks=0,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1
+    )

depth_anything_v2/dinov2_layers/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention

depth_anything_v2/dinov2_layers/attention.py

@@ -0,0 +1,83 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import memory_efficient_attention, unbind, fmha
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+        

depth_anything_v2/dinov2_layers/block.py

@@ -0,0 +1,252 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+from typing import Callable, List, Any, Tuple, Dict
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import fmha
+    from xformers.ops import scaled_index_add, index_select_cat
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError

depth_anything_v2/dinov2_layers/drop_path.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)

depth_anything_v2/dinov2_layers/layer_scale.py

@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma

depth_anything_v2/dinov2_layers/mlp.py

@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x

depth_anything_v2/dinov2_layers/patch_embed.py

@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops

depth_anything_v2/dinov2_layers/swiglu_ffn.py

@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+try:
+    from xformers.ops import SwiGLU
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )

depth_anything_v2/dpt.py

@@ -0,0 +1,221 @@
+import cv2
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.transforms import Compose
+
+from .dinov2 import DINOv2
+from .util.blocks import FeatureFusionBlock, _make_scratch
+from .util.transform import Resize, NormalizeImage, PrepareForNet
+
+
+def _make_fusion_block(features, use_bn, size=None):
+    return FeatureFusionBlock(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        size=size,
+    )
+
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_feature, out_feature):
+        super().__init__()
+        
+        self.conv_block = nn.Sequential(
+            nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(out_feature),
+            nn.ReLU(True)
+        )
+    
+    def forward(self, x):
+        return self.conv_block(x)
+
+
+class DPTHead(nn.Module):
+    def __init__(
+        self, 
+        in_channels, 
+        features=256, 
+        use_bn=False, 
+        out_channels=[256, 512, 1024, 1024], 
+        use_clstoken=False
+    ):
+        super(DPTHead, self).__init__()
+        
+        self.use_clstoken = use_clstoken
+        
+        self.projects = nn.ModuleList([
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channel,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ) for out_channel in out_channels
+        ])
+        
+        self.resize_layers = nn.ModuleList([
+            nn.ConvTranspose2d(
+                in_channels=out_channels[0],
+                out_channels=out_channels[0],
+                kernel_size=4,
+                stride=4,
+                padding=0),
+            nn.ConvTranspose2d(
+                in_channels=out_channels[1],
+                out_channels=out_channels[1],
+                kernel_size=2,
+                stride=2,
+                padding=0),
+            nn.Identity(),
+            nn.Conv2d(
+                in_channels=out_channels[3],
+                out_channels=out_channels[3],
+                kernel_size=3,
+                stride=2,
+                padding=1)
+        ])
+        
+        if use_clstoken:
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(
+                    nn.Sequential(
+                        nn.Linear(2 * in_channels, in_channels),
+                        nn.GELU()))
+        
+        self.scratch = _make_scratch(
+            out_channels,
+            features,
+            groups=1,
+            expand=False,
+        )
+        
+        self.scratch.stem_transpose = None
+        
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+        
+        head_features_1 = features
+        head_features_2 = 32
+        
+        self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
+        self.scratch.output_conv2 = nn.Sequential(
+            nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True),
+            nn.Identity(),
+        )
+    
+    def forward(self, out_features, patch_h, patch_w):
+        out = []
+        for i, x in enumerate(out_features):
+            if self.use_clstoken:
+                x, cls_token = x[0], x[1]
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+            else:
+                x = x[0]
+            
+            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
+            
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            
+            out.append(x)
+        
+        layer_1, layer_2, layer_3, layer_4 = out
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+        
+        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])        
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv1(path_1)
+        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+        out = self.scratch.output_conv2(out)
+        
+        return out
+
+
+class DepthAnythingV2(nn.Module):
+    def __init__(
+        self, 
+        encoder='vitl', 
+        features=256, 
+        out_channels=[256, 512, 1024, 1024], 
+        use_bn=False, 
+        use_clstoken=False
+    ):
+        super(DepthAnythingV2, self).__init__()
+        
+        self.intermediate_layer_idx = {
+            'vits': [2, 5, 8, 11],
+            'vitb': [2, 5, 8, 11], 
+            'vitl': [4, 11, 17, 23], 
+            'vitg': [9, 19, 29, 39]
+        }
+        
+        self.encoder = encoder
+        self.pretrained = DINOv2(model_name=encoder)
+        
+        self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+    
+    def forward(self, x):
+        patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14
+        
+        features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True)
+        
+        depth = self.depth_head(features, patch_h, patch_w)
+        depth = F.relu(depth)
+        
+        return depth.squeeze(1)
+    
+    @torch.no_grad()
+    def infer_image(self, raw_image, input_size=518):
+        image, (h, w) = self.image2tensor(raw_image, input_size)
+        
+        depth = self.forward(image)
+        
+        depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0]
+        
+        return depth.cpu().numpy()
+    
+    def image2tensor(self, raw_image, input_size=518):        
+        transform = Compose([
+            Resize(
+                width=input_size,
+                height=input_size,
+                resize_target=False,
+                keep_aspect_ratio=True,
+                ensure_multiple_of=14,
+                resize_method='lower_bound',
+                image_interpolation_method=cv2.INTER_CUBIC,
+            ),
+            NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+            PrepareForNet(),
+        ])
+        
+        h, w = raw_image.shape[:2]
+        
+        image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0
+        
+        image = transform({'image': image})['image']
+        image = torch.from_numpy(image).unsqueeze(0)
+        
+        DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+        image = image.to(DEVICE)
+        
+        return image, (h, w)

depth_anything_v2/util/blocks.py

@@ -0,0 +1,148 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+        
+        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(
+        self, 
+        features, 
+        activation, 
+        deconv=False, 
+        bn=False, 
+        expand=False, 
+        align_corners=True,
+        size=None
+    ):
+        """Init.
+        
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size=size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
+        
+        output = self.out_conv(output)
+
+        return output

depth_anything_v2/util/transform.py

@@ -0,0 +1,158 @@
+import numpy as np
+import cv2
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
+        
+        # resize sample
+        sample["image"] = cv2.resize(sample["image"], (width, height), interpolation=self.__image_interpolation_method)
+
+        if self.__resize_target:
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(sample["mask"].astype(np.float32), (width, height), interpolation=cv2.INTER_NEAREST)
+        
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+        
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        return sample

info_core/MyQtClass.py

@@ -740,8 +740,8 @@ class FolderDropWidget(QWidget):
 
     def init_ui(self):
         main_layout = QVBoxLayout()
-        main_layout.setSpacing(15)
+        main_layout.setSpacing(5)
-        main_layout.setContentsMargins(15, 15, 15, 15)
+        main_layout.setContentsMargins(0, 0, 0, 0)
 
         # 顶部按钮区域
         btn_layout = QHBoxLayout()
@@ -750,12 +750,24 @@ class FolderDropWidget(QWidget):
         self.reset_btn.setFixedSize(120, 40)
         self.reset_btn.setVisible(False)
         
-        self.clear_btn = QPushButton("清空选中机组列表")
+        self.clear_btn = QPushButton("清空")
         self.clear_btn.clicked.connect(self.clear_selected)
         self.clear_btn.setFixedSize(120, 40)
         self.clear_btn.setVisible(False)
         
+        self.full_select_btn = QPushButton("全选")
+        self.full_select_btn.clicked.connect(self.select_all_folders)
+        self.full_select_btn.setFixedSize(120, 40)
+        self.full_select_btn.setVisible(False)
+
+        # 添加返回按钮
+        self.back_btn = QPushButton("返回项目选择")
+        self.back_btn.clicked.connect(lambda: (self.stacked_layout.setCurrentIndex(1) or self.clear_selected() or self.reset_btn.setVisible(True) or self.back_btn.setVisible(False)))
+        self.back_btn.setVisible(False)
+
         btn_layout.addWidget(self.reset_btn)
+        btn_layout.addWidget(self.back_btn)
+        btn_layout.addWidget(self.full_select_btn)
         btn_layout.addWidget(self.clear_btn)
         btn_layout.addStretch()
         main_layout.addLayout(btn_layout)
@@ -776,6 +788,8 @@ class FolderDropWidget(QWidget):
         # 状态2: 显示第一层文件夹 (项目选择)
         self.level1_container = QWidget()
         level1_main_layout = QVBoxLayout()
+        level1_main_layout.setSpacing(0)
+        level1_main_layout.setContentsMargins(0, 0, 0, 0)
         
         # 添加状态标签
         self.level1_title = QLabel("项目选择")
@@ -797,6 +811,8 @@ class FolderDropWidget(QWidget):
         # 状态3: 显示第二层文件夹 (机组选择)
         self.level2_container = QWidget()
         level2_main_layout = QVBoxLayout()
+        level2_main_layout.setSpacing(0)
+        level2_main_layout.setContentsMargins(0, 0, 0, 0)
         
         # 添加状态标签
         self.level2_title = QLabel("机组选择")
@@ -811,11 +827,6 @@ class FolderDropWidget(QWidget):
         self.level2_list.setSpacing(10)
         level2_main_layout.addWidget(self.level2_list)
         
-        # 添加返回按钮
-        self.back_btn = QPushButton("返回项目选择")
-        self.back_btn.clicked.connect(lambda: self.stacked_layout.setCurrentIndex(1))
-        level2_main_layout.addWidget(self.back_btn)
-        
         self.level2_container.setLayout(level2_main_layout)
         self.stacked_layout.addWidget(self.level2_container)
 
@@ -836,6 +847,13 @@ class FolderDropWidget(QWidget):
 
         self.setLayout(main_layout)
 
+    def select_all_folders(self):
+        self.selected_folders.update(
+            set(item.data(Qt.ItemDataRole.UserRole) for item in self.level2_list.findItems("", Qt.MatchFlag.MatchContains))
+        )
+        self.update_checkbox_states()
+        self.selection_changed.emit(self.get_selected_folders())
+
     def apply_styles(self):
         self.prompt_label.setStyleSheet(f"""
             {PATH_DISPLAY_STYLE}
@@ -872,6 +890,7 @@ class FolderDropWidget(QWidget):
         """)
         
         self.selected_group.setStyleSheet(GROUP_BOX_STYLE)
+        self.full_select_btn.setStyleSheet(BUTTON_STYLE)
         self.reset_btn.setStyleSheet(BUTTON_STYLE)
         self.clear_btn.setStyleSheet(BUTTON_STYLE)
         self.back_btn.setStyleSheet(BUTTON_STYLE)
@@ -890,6 +909,7 @@ class FolderDropWidget(QWidget):
         self.stacked_layout.setCurrentIndex(0)  # 显示提示
         self.reset_btn.setVisible(False)
         self.clear_btn.setVisible(False)
+        self.back_btn.setVisible(False)
         self.selected_group.setVisible(False)
         self.selection_changed.emit([])
 
@@ -904,7 +924,9 @@ class FolderDropWidget(QWidget):
         self.load_level1_folders(path)
         self.stacked_layout.setCurrentIndex(1)  # 显示第一层
         self.reset_btn.setVisible(True)
+        self.back_btn.setVisible(False)
         self.clear_btn.setVisible(True)
+        self.full_select_btn.setVisible(True)
         self.selected_group.setVisible(True)
 
     def dragEnterEvent(self, event: QDragEnterEvent):
@@ -933,6 +955,8 @@ class FolderDropWidget(QWidget):
 
     def show_level2_folders(self, item):
         self.level2_list.clear()
+        self.reset_btn.setVisible(False)
+        self.back_btn.setVisible(True)
         folder_path = item.data(Qt.ItemDataRole.UserRole)
 
         for sub_item in os.listdir(folder_path):

info_core/defines.py

@@ -84,7 +84,7 @@ COMBO_BOX_STYLE = f"""
 GROUP_BOX_MIN_WIDTH = 300
 GROUP_BOX_MIN_HEIGHT = 120
 GROUP_BOX_SPACING = 5
-GROUP_BOX_MARGINS = (5, 5, 5, 5)
+GROUP_BOX_MARGINS = (1, 1, 1, 1)
 GROUP_BOX_STYLE = f"""
     QGroupBox {{
         font-family: "{FONT_FAMILY}";

main.py

@@ -1,127 +1,5 @@
-from PySide6.QtWidgets import (QApplication, QMainWindow, QWidget, QGridLayout, 
+from PySide6.QtWidgets import QApplication
-                              QPushButton, QSizePolicy, QSplitter, QToolBar)
+from MainWindow.mainwindow import ReportGeneratorUI
-from PySide6.QtGui import QFontDatabase
-from PySide6.QtCore import Signal, Qt
-
-import os
-from info_core.defines import *
-from info_core.MyQtClass import ConfigComboBoxGroup, FolderDropWidget, DraggableLine
-
-
-class ReportGeneratorUI(QMainWindow):
-    send_baogao_choose_info = Signal(list[str])
-    
-    def __init__(self):
-        super().__init__()
-        # 加载字体
-        self.load_font()
-        
-        # 设置窗口属性
-        self.setWindowTitle("报告生成器")
-        self.setMinimumSize(WINDOW_MIN_WIDTH, WINDOW_MIN_HEIGHT)
-        
-        # 主窗口部件
-        self.central_widget = QWidget()
-        self.setCentralWidget(self.central_widget)
-        
-        # 主布局
-        self.main_layout = QGridLayout(self.central_widget)
-        self.main_layout.setSpacing(MAIN_LAYOUT_SPACING)
-        self.main_layout.setContentsMargins(*MAIN_LAYOUT_MARGINS)
-        
-        # 初始化UI
-        self.init_ui()
-        
-    
-    def load_font(self):
-        """加载自定义字体"""
-        if os.path.exists(FONT_PATH):
-            font_id = QFontDatabase.addApplicationFont(FONT_PATH)
-            if font_id == -1:
-                print("字体加载失败，将使用系统默认字体")
-        else:
-            print(f"字体文件未找到: {FONT_PATH}，将使用系统默认字体")
-        
-    def init_ui(self):
-        """初始化所有UI组件"""
-        # 第一行：项目信息和人员配置
-        self.project_group = ConfigComboBoxGroup("项目基本信息")
-        self.staff_group = ConfigComboBoxGroup("单次检查配置信息", is_project=False)
-        # 第二行：导入图片路径、填写机组信息
-        self.picture_group = FolderDropWidget()
-        # self.image_analysis = 
-        # self.main_layout.addWidget(self.image_analysis, 1, 1)
-        # 第三行：生成报告按钮（跨两列）
-        self.create_generate_button()
-        self.generate_btn.setEnabled(False)
-        
-        # 创建一个垂直分割器
-        self.splitter = QSplitter(Qt.Vertical)
-        self.splitter.setStyleSheet(SPLITTER_STYLE)
-        # 创建顶部和底部容器
-        top_container = QWidget()
-        top_container.setLayout(QGridLayout())
-        top_container.layout().addWidget(self.project_group, 0, 0)
-        top_container.layout().addWidget(self.staff_group, 0, 1)
-
-        middle_container = QWidget()
-        middle_container.setLayout(QGridLayout())
-        middle_container.layout().addWidget(self.picture_group, 0, 0)
-
-        # 添加部件到分割器
-        self.splitter.addWidget(top_container)
-        self.splitter.addWidget(middle_container)
-
-        # 设置主布局
-        self.main_layout.addWidget(self.splitter, 0, 0, 2, 2)  # 占据前两行两列
-        self.main_layout.addWidget(self.generate_btn, 2, 0, 1, 2)
-
-        # 设置分割器初始比例
-        self.splitter.setStretchFactor(0, 1)
-        self.splitter.setStretchFactor(1, 4)
-        
-        self.toolbar = QToolBar()
-        self.addToolBar(self.toolbar)
-        self.toolbar.setMovable(False)
-        self.toolbar.setFloatable(False)
-        new_action = self.toolbar.addAction("重置布局比例")
-        self.toolbar.addSeparator()
-        new_action.triggered.connect(self.reset_splitter)
-    
-    def reset_splitter(self):
-        """重置分割器的比例"""
-        total_size = sum(self.splitter.sizes())  # 获取当前总大小
-        self.splitter.setSizes([
-            int(total_size * 0.2),  # 第一部分占 20%（比例 1:4）
-            int(total_size * 0.8)   # 第二部分占 80%
-        ])
-
-    def on_generate_path_selected(self, path):
-        self.generate_btn.setEnabled(True)
-    
-    # search_file_list = []
-    # if self.image_analysis.check_is_waibu:
-    #     search_file_list.append("外汇总")
-    # if self.image_analysis.check_is_neibu:
-    #     search_file_list.append("内汇总")
-    # if self.image_analysis.check_is_fanglei:
-    #     search_file_list.append("防汇总")
-    # self.send_baogao_choose_info.emit(search_file_list)
-    
-    def create_button(self, text):
-        """创建统一风格的按钮"""
-        btn = QPushButton(text)
-        btn.setStyleSheet(BUTTON_STYLE)
-        btn.setFixedSize(BUTTON_WIDTH, BUTTON_HEIGHT)
-        return btn
-    
-    def create_generate_button(self):
-        """创建生成报告按钮"""
-        self.generate_btn = QPushButton("生成报告")
-        self.generate_btn.setStyleSheet(PRIMARY_BUTTON_STYLE)
-        self.generate_btn.setFixedHeight(50)
-        self.generate_btn.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
-    
 
 if __name__ == "__main__":
     app = QApplication([])

tool/lighter.py

@@ -0,0 +1,176 @@
+import cv2
+import numpy as np
+# import rawpy
+# from PIL import Image
+# from tqdm import tqdm
+# import tkinter as tk
+#from tkinter import filedialog
+#from concurrent.futures  import ThreadPoolExecutor
+#import argparse
+#import os
+#import torch
+#from depth_anything_v2.dpt import DepthAnythingV2
+#from utils import specify_name_group_blade,get_name
+#parser = argparse.ArgumentParser(description='Depth Anything V2')
+# parser.add_argument('--input-size', type=int, default=518)
+# parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
+# args = parser.parse_args()
+
+def extraction_win_lamina_mask(raw_image, depth_anything):
+    # 前面读的都是RGB图像，深度估计需要BGR，即下面是把RGB→BGR
+    raw_image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
+
+    depth = depth_anything.infer_image(raw_image)
+
+    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+    depth = depth.astype(np.uint8)
+
+    _, otsu_mask = cv2.threshold(depth, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    return otsu_mask
+
+
+def check_overexposure(image, lamina_mask,threshold):
+    """
+    检查图像中是否有过曝区域
+    :param image: 输入图像
+    :param threshold: 亮度值的阈值，像素值超过该值则认为是过曝区域
+    :return: 一个二值图像，过曝区域为白色，其它区域为黑色
+    """
+    # 转换为浮动数据类型并归一化
+    image_float = image.astype(np.float32)
+
+    # 获取每个像素的亮度值（可以使用 YUV 或 RGB 亮度）
+    gray_image = cv2.cvtColor(image_float, cv2.COLOR_BGR2GRAY)
+
+    # 标记过曝区域，亮度大于阈值的像素为过曝区域
+    overexposure_mask = gray_image > threshold
+
+    overexposure_mask = (lamina_mask == 255) & overexposure_mask  #只取叶片的亮光区mask
+
+    overexposure_number = np.sum(overexposure_mask * 1)
+    overexposed_pixels = gray_image[overexposure_mask]
+    # 计算曝光区域的平均亮度
+    if overexposed_pixels.size > 0:
+        avg_overexposed_value = np.mean(overexposed_pixels)
+    else:
+        avg_overexposed_value = 0  # 如果没有曝光区域，返回 0
+
+    return overexposure_mask, avg_overexposed_value,overexposure_number
+
+def check_shawn(image,lamina_mask, threshold):
+    """
+    检查图像中是否有阴影区域
+    :param image: 输入图像
+    :param threshold: 亮度值的阈值，像素值小于该值则认为是过阴影域
+    :return: 一个二值图像，过曝区域为白色，其它区域为黑色
+    """
+
+    # 转换为浮动数据类型并归一化
+    image_float = image.astype(np.float32)
+    # 获取每个像素的亮度值（可以使用 YUV 或 RGB 亮度）
+    gray_image = cv2.cvtColor(image_float, cv2.COLOR_BGR2GRAY)
+    # 标记阴影区域，亮度小于阈值的像素为阴影区域
+    shawn_mask = gray_image < threshold
+
+    shawn_mask = (lamina_mask == 255) & shawn_mask
+
+    shawn_number = np.sum(shawn_mask * 1)
+    shawn_pixels = gray_image[shawn_mask]
+    # 计算阴影区域的平均亮度
+    if shawn_pixels.size > 0:
+        avg_shawn_value = np.mean(shawn_pixels)
+    else:
+        avg_shawn_value = 0
+
+    return shawn_mask, avg_shawn_value,shawn_number
+
+def smooth_overexposed_regions(image, overexposure_mask, kernel_size=(15, 15)):
+    """
+    对过曝区域进行平滑处理，修复与周围区域的过渡
+    :param image: 输入图像
+    :param overexposure_mask: 过曝区域的掩码
+    :param kernel_size: 高斯核大小
+    :return: 平滑过曝区域后的图像
+    """
+    # 使用高斯模糊平滑过曝区域和周围区域
+    smoothed_image = cv2.GaussianBlur(image, kernel_size, 0)
+
+    # 将平滑后的图像和原始图像合成，修复过曝区域
+    fixed_image = np.where(overexposure_mask[..., None] == 255, image,smoothed_image)
+
+    return fixed_image
+
+def bilateral_filter_adjustment(image, high_light_mask, d=15, sigma_color=75, sigma_space=75):
+    """
+    使用双边滤波器平滑高光区域与周围区域的过渡
+    :param image: 输入图像
+    :param high_light_mask: 高光区域的掩码
+    :param d: 邻域的直径
+    :param sigma_color: 颜色空间的标准差
+    :param sigma_space: 坐标空间的标准差
+    :return: 平滑后的图像
+    """
+    # 对整个图像应用双边滤波
+    filtered_image = cv2.bilateralFilter(image, d, sigma_color, sigma_space)
+
+    # 合成平滑后的图像和原图，保留非高光部分
+    final_image = np.where(high_light_mask[..., None] == 0, image, filtered_image)
+
+    return final_image
+
+def adjust_highlights_shadows(image, threshold_shawn,threshold, depth_anything):
+
+    lamina_mask = extraction_win_lamina_mask(image, depth_anything)
+
+    shawn_mask,avg_shawn_value,shawn_number = check_shawn(image,lamina_mask,threshold_shawn)
+    # 调整亮度，gamma < 1 时变亮，gamma > 1 时变暗
+    gamma = 1 - (threshold_shawn-avg_shawn_value)/255.0
+    # print('阴影区调整的gama: '+str(gamma))#+str('\n'))
+    lookup_table = np.array([((i / 255.0) ** gamma) * 255 for i in range(256)]).astype('uint8')
+    # 应用 gamma 校正
+    if shawn_number !=0:
+        image[shawn_mask == True] = cv2.LUT(image[shawn_mask == True], lookup_table)
+        gamma_corrected_image = image
+    else:
+        gamma_corrected_image = image
+    #gamma_corrected_image = cv2.LUT(image, lookup_table)
+
+    #寻找过爆区域
+    overexposure_mask,avg_overexposed_value,overexposure_number = check_overexposure(gamma_corrected_image,lamina_mask,threshold)
+    reduction_factor = 1-(avg_overexposed_value-threshold) / 255
+    #reduction_factor = (avg_overexposed_value/255)*scale
+    # print("降低曝光区比例：" + str(reduction_factor))
+
+    # 调整亮度，gamma < 1 时变亮（越小越亮），gamma > 1 时变暗(越大越暗)，
+    print((1+reduction_factor))
+    lookup_table = np.array([((i / 255.0) ** (1+reduction_factor)) * 255 for i in range(256)]).astype('uint8')
+    # 应用 gamma 校正
+    if overexposure_number !=0:
+        gamma_corrected_image[overexposure_mask == True] = cv2.LUT(gamma_corrected_image[overexposure_mask == True], lookup_table)
+
+
+    #gamma_corrected_image[overexposure_mask == True] = np.clip(gamma_corrected_image[overexposure_mask == True] * reduction_factor, 0, 255)
+
+
+    #smoothed_image = smooth_overexposed_regions(gamma_corrected_image, overexposure_mask)
+    #smoothed_image = bilateral_filter_adjustment(gamma_corrected_image, overexposure_mask, d=15, sigma_color=75, sigma_space=75)
+
+    return gamma_corrected_image
+
+# path=r'/home/dtyx/下载/1_涂层损伤_叶尖3m_2m_一般_紧急_一般_尽快打磨维修.jpg'
+# from time import time
+# if __name__ == "__main__":
+#     # input_dir = input("请输入处理图片路径: ")
+#     # output_dir = input("请输入保存图片路径: ")
+#     time_start = time()
+#     imgpath = path
+#     img = cv2.imdecode(np.fromfile(imgpath, dtype=np.uint8), cv2.IMREAD_COLOR)
+#     cv2.imshow('img',cv2.resize(img,(800,600)))
+#     mask=adjust_highlights_shadows(img,180,253)
+#     mask=cv2.resize(mask,(800,600))
+#     time_end = time()
+#     print("Time used:", time_end - time_start)
+#     cv2.imshow('mask',mask)
+#     cv2.waitKey(0)
+#     cv2.destroyAllWindows()
+#     # gamma = float(input("请输入 gamma 值: "))

tool/model_start.py

@@ -0,0 +1,18 @@
+import torch
+from depth_anything_v2.dpt import DepthAnythingV2
+
+def model_start(model_path):
+    DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+
+    depth_anything = DepthAnythingV2()
+    depth_anything.load_state_dict(
+        torch.load(f'{model_path}', map_location='cpu'))
+    depth_anything = depth_anything.to(DEVICE).eval()
+    print("model loaded")
+    return depth_anything

tool/process_image.py

@@ -0,0 +1,141 @@
+import os
+import numpy as np
+import cv2
+from tool.lighter import adjust_highlights_shadows
+from concurrent.futures import ThreadPoolExecutor
+from PIL import Image
+import piexif
+
+# parser = argparse.ArgumentParser(description='Depth Anything V2')
+# parser.add_argument('--input-paths', type=str, nargs='+', required=True,
+#                         help='输入文件夹列表（多个路径，用空格分隔）')
+# parser.add_argument('--output-path', type=str, default='./output', 
+#                         help="按输出路径的结构输出处理好的图片")
+# parser.add_argument('--model-path', type=str, default='./model/depth_anything_v2_vitl.pth',
+#                         help='模型路径')
+# args = parser.parse_args()
+
+
+def process_single_image(img_path, depth_anything):
+    """处理单个图片
+    Args:
+        img_path: 图片路径
+    Returns:
+        tuple: (处理后的图片numpy数组, 原始图片路径)
+    """
+    # 使用OpenCV读取图片
+    img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+    if img is None:
+        raise ValueError(f"无法读取图片: {img_path}")
+    
+    # 处理图片 - 这里假设adjust_highlights_shadows能处理OpenCV格式的图片
+    processed_img = adjust_highlights_shadows(img, 180, 253, depth_anything)
+    return processed_img, img_path
+
+def save_image_with_exif(input_path, output_path, processed_img):
+    """保存图片并保留EXIF信息
+    Args:
+        input_path: 原始图片路径
+        output_path: 输出图片路径
+        processed_img: 处理后的numpy数组图片
+    """
+    # 确保输出目录存在
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    
+    # 读取原始图片的EXIF信息
+    exif_dict = None
+    try:
+        with exif_lock:  # 使用锁防止多线程同时读取EXIF
+            with Image.open(input_path) as img:
+                if 'exif' in img.info:
+                    exif_dict = piexif.load(img.info['exif'])
+    except Exception as e:
+        print(f"Warning: 无法读取 {input_path} 的EXIF信息: {str(e)}")
+    
+    # 将OpenCV格式转换为PIL格式以便保存EXIF
+    processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
+    pil_img = Image.fromarray(processed_img_rgb)
+    
+    # 保存图片
+    try:
+        if exif_dict:
+            pil_img.save(output_path, exif=piexif.dump(exif_dict))
+        else:
+            pil_img.save(output_path)
+    except Exception as e:
+        print(f"Error: 无法保存图片 {output_path}: {str(e)}")
+        raise
+
+def process_single_file(input_path, output_root, input_base, depth_anything):
+    """处理单个文件"""
+    try:
+        # 计算相对路径以保持文件夹结构
+        rel_path = os.path.relpath(os.path.dirname(input_path), start=input_base)
+        output_dir = os.path.join(output_root, rel_path)
+        output_path = os.path.join(output_dir, os.path.basename(input_path))
+        
+        # 处理图片
+        processed_img, _ = process_single_image(input_path, depth_anything)
+        
+        # 保存图片
+        save_image_with_exif(input_path, output_path, processed_img)
+        print(f"Processed and saved: {output_path}")
+    except Exception as e:
+        print(f"Error processing {input_path}: {str(e)}")
+
+def process_images(input_paths, output_root, depth_anything, workers=4):
+    """处理所有图片并保持原文件夹结构（多线程版本）
+    Args:
+        input_paths: 输入路径列表
+        output_root: 输出根目录
+        workers: 线程池大小
+    """
+    # 收集所有需要处理的文件
+    all_files = []
+    for input_path in input_paths:
+        for root, dirs, files in os.walk(input_path):
+            for file in files:
+                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff')):
+                    input_file_path = os.path.join(root, file)
+                    all_files.append((input_file_path, input_path))
+    
+    # 使用线程池处理文件
+    with ThreadPoolExecutor(max_workers=workers) as executor:
+        futures = []
+        for file_path, input_base in all_files:
+            futures.append(
+                executor.submit(
+                    process_single_file,
+                    file_path,
+                    output_root,
+                    input_base,
+                    depth_anything
+                )
+            )
+        
+        # 等待所有任务完成
+        for future in futures:
+            try:
+                future.result()
+            except Exception as e:
+                print(f"Error in processing: {str(e)}")
+# if __name__ == '__main__':
+    # model_path = args.model_path
+    # input_paths = args.input_paths
+    # output_path = args.output_path
+
+    # # 线程锁，防止多线程同时访问EXIF数据时出现问题
+    # exif_lock = threading.Lock()
+
+    # input_paths = ["/home/dtyx/桌面/yhh/ReportGenerator/测试数据/山东国华无棣风电场叶片外部数据/A1（31301）",]
+    # model_path = "./model/depth_anything_v2_vitl.pth"
+    # output_path = "./output"
+    # #启动模型
+    # depth_anything = model_start(model_path)
+    # # 创建输出目录
+    # os.makedirs(output_path, exist_ok=True)
+    # # 处理所有图片
+    # process_images(input_paths, output_path, depth_anything)
+
+
+