Delete DAP-main-2

DAP-main-2/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2025 Insta360 Research Team
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

DAP-main-2/README.md

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-<h1 align="center">
-Depth Any Panoramas:<br>
-A Foundation Model for Panoramic Depth Estimation
-</h1>
-
-
-<p align="center">
-  <a href="https://linxin0.github.io"><b>Xin Lin</b></a> ·
-  <a href="#"><b>Meixi Song</b></a> ·
-  <a href="#"><b>Dizhe Zhang</b></a> ·
-  <a href="#"><b>Wenxuan Lu</b></a> ·
-  <a href="https://haodong2000.github.io"><b>Haodong Li</b></a>
-  <br>
-  <a href="#"><b>Bo Du</b></a> ·
-  <a href="#"><b>Ming-Hsuan Yang</b></a> ·
-  <a href="#"><b>Truong Nguyen</b></a> ·
-  <a href="http://luqi.info"><b>Lu Qi</b></a>
-</p>
-
-
-
-<p align="center">
-  <a href='https://arxiv.org/abs/2512.16913'><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
-  <a href='https://insta360-research-team.github.io/DAP_website/'><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=insta360&logoColor=white' alt='Project Page'></a>
-  <a href=''><img src='https://img.shields.io/badge/%F0%9F%93%88%20Hugging%20Face-Dataset-yellow'></a>
-  <a href='https://huggingface.co/spaces/Insta360-Research/DAP'><img src='https://img.shields.io/badge/🚀%20Hugging%20Face-Demo-orange'></a>
-</p>
-
-![teaser](assets/depth_teaser2_00.png)
-
-
-
-## 🔨 Installation
-
-Clone the repo first:
-
-```Bash
-git clone https://github.com/Insta360-Research-Team/DAP
-cd DAP
-```
-
-(Optional) Create a fresh conda env:
-
-```Bash
-conda create -n dap python=3.12
-conda activate dap
-```
-
-Install necessary packages (torch > 2):
-
-```Bash
-# pytorch (select correct CUDA version, we test our code on torch==2.7.1 and torchvision==0.22.1)
-pip install torch==2.7.1 torchvision==0.22.1
-
-# other dependencies
-pip install -r requirements.txt
-```
-
-## 🖼️ Dataset
-
-The training dataset will be open soon.
-
-
-## 🤝 Pre-trained model
-
-Please download the pretrained model: https://huggingface.co/Insta360-Research/DAP-weights
-
-
-## 📒 Inference
-
-```Bash
-python test/infer.py 
-```
-
-
-## 🚀 Evaluation
-
-
-```Bash
-python test/eval.py 
-```
-
-
-
-
-## 🤝 Acknowledgement
-
-We appreciate the open source of the following projects:
-
-* [PanDA](https://caozidong.github.io/PanDA_Depth/)
-* [Depth-Anything-V2](https://github.com/DepthAnything/Depth-Anything-V2)
-
-
-## Citation
-```
-@article{lin2025dap,
-          title={Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation},
-          author={Lin, Xin and Song, Meixi and Zhang, Dizhe and Lu, Wenxuan and Li, Haodong and Du, Bo and Yang, Ming-Hsuan and Nguyen, Truong and Qi, Lu},
-          journal={arXiv},
-          year={2025}
-        }
-```
-

DAP-main-2/assets/depth_teaser2.pdf

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-version https://git-lfs.github.com/spec/v1
-oid sha256:28e8d4fecc3a5bd905ffead457d35f3602c98e78976c75e5da6317286ae4a385
-size 1872942

DAP-main-2/config/infer.yaml

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-model:
-  name: dap
-  args:
-    midas_model_type: vitl         
-    fine_tune_type: hypersim       
-    min_depth: 0.01
-    max_depth: 1.0
-    train_decoder: True
-
-median_align: False
-load_weights_dir: /home/tione/notebook/home/songmeixi_insta360.com/depth/panda_orgindual/ckpt_save/1111/trainw1_2_dualw1_2/weights_0
-input:
-  height: 512
-  width: 1024
-inference:
-  batch_size: 1
-  num_workers: 1
-  save_colormap: True       
-  colormap_type: jet        

DAP-main-2/config/test.yaml

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-test_dataset_1:
-  name: stanford2d3d
-  root_path: /home/tione/notebook/home/wenxuan/PanDA/data/stanford2d3d
-  list_path: datasets/stanford2d3d_test.txt
-  args:
-    height: 512
-    width: 1024
-    repeat: 1
-    augment_color: False
-    augment_flip: False
-    augment_rotation: False
-  batch_size: 32
-  num_workers: 64
-
-# test_dataset_1:
-#   name: insta23k
-#   root_path: /home/tione/notebook/nfs/MLUAV_Data
-#   list_path: datasets/instadata_list_test.txt
-#   args:
-#     height: 512
-#     width: 1024
-#     repeat: 1
-#     augment_color: False
-#     augment_flip: False
-#     augment_rotation: False
-#   batch_size: 32
-#   num_workers: 64
-
-# test_dataset_1:
-#   name: deep360
-#   root_path: /home/tione/notebook/home/wenxuan/PanDA/data/Deep360
-#   list_path: datasets/deep360_test_final.txt
-#   args:
-#     height: 512
-#     width: 1024
-#     repeat: 1
-#     augment_color: False
-#     augment_flip: False
-#     augment_rotation: False
-#   batch_size: 32
-#   num_workers: 24
-
-# test_dataset_1:
-#   name: m3d
-#   root_path: /home/tione/notebook/home/wenxuan/PanDA/data/M3D
-#   list_path: datasets/m3d_test.txt
-#   args:
-#     height: 512
-#     width: 1024
-#     repeat: 1
-#     augment_color: False
-#     augment_flip: False
-#     augment_rotation: False
-#   batch_size: 32
-#   num_workers: 24
-
-
-model:
-  name: dap
-  args:
-    midas_model_type: vitl
-    fine_tune_type: 
-    min_depth: 0.001
-    max_depth: 1.0             
-    train_decoder: True
-
-median_align: False
-
-
-
-load_weights_dir: /home/tione/notebook/home/songmeixi_insta360.com/depth/panda_orgindual/ckpt_save/1111/trainw1_2_dualw1_2/weights_0

DAP-main-2/datasets/M3D.py

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-from __future__ import print_function
-import os
-import cv2
-import numpy as np
-import random
-import pyexr
-import torch
-from torch.utils import data
-from torchvision import transforms
-from torchvision.transforms import Compose
-
-from PIL import Image, ImageOps, ImageFilter
-import torch.nn.functional as F
-from einops import rearrange
-
-def read_list(list_file):
-    rgb_depth_list = []
-    with open(list_file) as f:
-        lines = f.readlines()
-        for line in lines:
-            rgb_depth_list.append(line.strip().split(" "))
-    return rgb_depth_list
-
-class M3D(data.Dataset):
-    """The M3D Dataset"""
-
-    def __init__(self, root_dir, list_file, height=504, width=1008, color_augmentation=True,
-                 LR_filp_augmentation=True, yaw_rotation_augmentation=True, repeat=1, is_training=False):
-        """
-        Args:
-            root_dir (string): Directory of the Stanford2D3D Dataset.
-            list_file (string): Path to the txt file contain the list of image and depth files.
-            height, width: input size.
-            disable_color_augmentation, disable_LR_filp_augmentation,
-            disable_yaw_rotation_augmentation: augmentation options.
-            is_training (bool): True if the dataset is the training set.
-        """
-        self.root_dir = root_dir
-
-        self.w = width
-        self.h = height
-
-        self.max_depth_meters = 100.0
-        self.min_depth_meters = 0.01
-
-        self.color_augmentation = color_augmentation
-        self.LR_filp_augmentation = LR_filp_augmentation
-        self.yaw_rotation_augmentation = yaw_rotation_augmentation
-
-        if self.color_augmentation:
-            try:
-                self.brightness = (0.8, 1.2)
-                self.contrast = (0.8, 1.2)
-                self.saturation = (0.8, 1.2)
-                self.hue = (-0.1, 0.1)
-                self.color_aug= transforms.ColorJitter(
-                    self.brightness, self.contrast, self.saturation, self.hue)
-            except TypeError:
-                self.brightness = 0.2
-                self.contrast = 0.2
-                self.saturation = 0.2
-                self.hue = 0.1
-                self.color_aug = transforms.ColorJitter(
-                    self.brightness, self.contrast, self.saturation, self.hue)
-
-        self.is_training = is_training
-
-        self.to_tensor = transforms.ToTensor()
-        self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-        self.rgb_depth_list = read_list(list_file)
-
-    def __len__(self):
-        return len(self.rgb_depth_list)
-
-    def __getitem__(self, idx):
-
-        # Read and process the image file
-        rgb_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][0])
-        rgb = cv2.imread(rgb_name)
-        # cv2.imwrite('label_rgb.jpg', rgb)
-        rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
-        rgb = cv2.resize(rgb, dsize=(self.w, self.h), interpolation=cv2.INTER_CUBIC)
-        
-        # Read and process the depth file
-        depth_name = os.path.join(self.root_dir, self.rgb_depth_list[idx][1])
-        # gt_depth = cv2.imread(depth_name, -1)
-        # gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST)
-        # gt_depth = gt_depth.astype(float)/4000
-        # gt_depth[gt_depth > self.max_depth_meters+1] = self.max_depth_meters + 1
-
-        gt_depth = pyexr.open(depth_name).get()
-        gt_depth = gt_depth[:, :, 0]
-        gt_depth = cv2.resize(gt_depth, dsize=(self.w, self.h), interpolation=cv2.INTER_NEAREST)
-        gt_depth[gt_depth > self.max_depth_meters+1] = self.max_depth_meters + 1
-
-        if self.is_training and self.yaw_rotation_augmentation:
-            # random yaw rotation
-            roll_idx = random.randint(0, self.w)
-            rgb = np.roll(rgb, roll_idx, 1)
-            gt_depth = np.roll(gt_depth, roll_idx, 1)
-
-        if self.is_training and self.LR_filp_augmentation and random.random() > 0.5:
-            rgb = cv2.flip(rgb, 1)
-            gt_depth = cv2.flip(gt_depth, 1)
-
-        if self.is_training and self.color_augmentation and random.random() > 0.5:
-            aug_rgb = np.asarray(self.color_aug(transforms.ToPILImage()(rgb)))
-        else:
-            aug_rgb = rgb.copy()
-
-        aug_rgb = self.to_tensor(aug_rgb.copy())
-
-        gt_depth = torch.from_numpy(np.expand_dims(gt_depth, axis=0)).to(torch.float32)
-
-        val_mask = ((gt_depth > 0) & (gt_depth <= self.max_depth_meters)& ~torch.isnan(gt_depth))
-
-        # _min, _max = torch.quantile(gt_depth[val_mask], torch.tensor([0.02, 1 - 0.02]),)
-        # gt_depth = gt_depth / 2560.0
-        gt_depth_norm = gt_depth / 100.0
-        gt_depth_norm = torch.clip(gt_depth_norm, 0.001, 1.0)
-
-        # print(gt_depth_norm.shape)
-        # Conduct output
-        inputs = {}
-
-        inputs["rgb"] = self.normalize(aug_rgb)
-        inputs["gt_depth"] = gt_depth_norm 
-        inputs["val_mask"] = val_mask # 合法区域，不是全true，真把不能用的��域划出来了；其他参与训练的数据集是全true的（除了投影数据集）
-        inputs["mask_100"] = (gt_depth > 0) & (gt_depth <= 100)
-        # 对于这个数据集，mask_100设定为全true的，因为求不出来。大于100米的深度gt也有可能是玻璃镜子等物体，反正这个数据集也不参加训练
-        
-        # 这个数据集中，模型预测的mask100应该是被val_mask涵盖的，所以mask100理论上没有影响
-        # val_mask控制计算指标的区域
-        return inputs

DAP-main-2/datasets/__init__.py

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-from .stanford2d3d import Stanford2D3D
-from .deep360 import Deep360
-from .insta23k import Insta23k
-from .M3D import M3D