人工智能/Stable Diffusion/sd-webui-controlnet-main/annotator/zoe/zoedepth/models/base_models/midas_repo

File OriginalPic	EditTime	Size
input	2023-04-27 15:29:02	0 B
midas	2023-04-27 15:29:02	72.48 KB
output	2023-04-27 15:29:02	0 B
ros	2023-04-27 15:29:02	35.62 KB
tf	2023-04-27 15:29:02	24.19 KB
weights	2023-04-27 15:29:02	0 B
Dockerfile	2023-04-24 13:51:43	951 B
environment.yaml	2023-04-24 13:51:43	310 B
hubconf.py	2023-04-24 13:51:43	13.86 KB
LICENSE	2023-04-24 13:51:43	1.09 KB
README.md	2023-04-24 13:51:43	21.3 KB
run.py	2023-04-24 13:51:43	11.28 KB
utils.py	2023-04-24 13:51:43	4.96 KB

## Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

This repository contains code to compute depth from a single image. It accompanies our [paper](https://arxiv.org/abs/1907.01341v3):

>Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer  
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun

and our [preprint](https://arxiv.org/abs/2103.13413):

> Vision Transformers for Dense Prediction  
> René Ranftl, Alexey Bochkovskiy, Vladlen Koltun

MiDaS was trained on up to 12 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS, KITTI, NYU Depth V2) with
multi-objective optimization. 
The original model that was trained on 5 datasets  (`MIX 5` in the paper) can be found [here](https://github.com/isl-org/MiDaS/releases/tag/v2).
The figure below shows an overview of the different MiDaS models; the bubble size scales with number of parameters.

![](figures/Improvement_vs_FPS.png)

### Setup

1) Pick one or more models and download the corresponding weights to the `weights` folder:

MiDaS 3.1
- For highest quality: [dpt_beit_large_512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt)
- For moderately less quality, but better speed-performance trade-off: [dpt_swin2_large_384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt)
- For embedded devices: [dpt_swin2_tiny_256](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt), [dpt_levit_224](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_levit_224.pt)
- For inference on Intel CPUs, OpenVINO may be used for the small legacy model: openvino_midas_v21_small [.xml](https://github.com/isl-org/MiDaS/releases/download/v3_1/openvino_midas_v21_small_256.xml), [.bin](https://github.com/isl-org/MiDaS/releases/download/v3_1/openvino_midas_v21_small_256.bin)

MiDaS 3.0: Legacy transformer models [dpt_large_384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt) and [dpt_hybrid_384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid_384.pt)

MiDaS 2.1: Legacy convolutional models [midas_v21_384](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_384.pt) and [midas_v21_small_256](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_small_256.pt)

1) Set up dependencies:

```shell
    conda env create -f environment.yaml
    conda activate midas-py310
    ```

#### optional

For the Next-ViT model, execute

```shell
git submodule add https://github.com/isl-org/Next-ViT midas/external/next_vit
```

For the OpenVINO model, install

```shell
pip install openvino
```
    
### Usage

1) Place one or more input images in the folder `input`.

2) Run the model with

```shell
 python run.py --model_type <model_type> --input_path input --output_path output
 ```
 where ```<model_type>``` is chosen from [dpt_beit_large_512](#model_type), [dpt_beit_large_384](#model_type),
 [dpt_beit_base_384](#model_type), [dpt_swin2_large_384](#model_type), [dpt_swin2_base_384](#model_type),
 [dpt_swin2_tiny_256](#model_type), [dpt_swin_large_384](#model_type), [dpt_next_vit_large_384](#model_type),
 [dpt_levit_224](#model_type), [dpt_large_384](#model_type), [dpt_hybrid_384](#model_type),
 [midas_v21_384](#model_type), [midas_v21_small_256](#model_type), [openvino_midas_v21_small_256](#model_type).
 
3) The resulting depth maps are written to the `output` folder.

#### optional

1) By default, the inference resizes the height of input images to the size of a model to fit into the encoder. This
   size is given by the numbers in the model names of the [accuracy table](#accuracy). Some models do not only support a single
   inference height but a range of different heights. Feel free to explore different heights by appending the extra 
   command line argument `--height`. Unsupported height values will throw an error. Note that using this argument may
   decrease the model accuracy.
2) By default, the inference keeps the aspect ratio of input images when feeding them into the encoder if this is
   supported by a model (all models except for Swin, Swin2, LeViT). In order to resize to a square resolution,
   disregarding the aspect ratio while preserving the height, use the command line argument `--square`.

#### via Camera

If you want the input images to be grabbed from the camera and shown in a window, leave the input and output paths
   away and choose a model type as shown above:

```shell
 python run.py --model_type <model_type> --side
 ```

The argument `--side` is optional and causes both the input RGB image and the output depth map to be shown 
   side-by-side for comparison.

#### via Docker

1) Make sure you have installed Docker and the
   [NVIDIA Docker runtime](https://github.com/NVIDIA/nvidia-docker/wiki/Installation-$Native-GPU-Support$).

2) Build the Docker image:

```shell
    docker build -t midas .
    ```

3) Run inference:

```shell
    docker run --rm --gpus all -v $PWD/input:/opt/MiDaS/input -v $PWD/output:/opt/MiDaS/output -v $PWD/weights:/opt/MiDaS/weights midas
    ```

This command passes through all of your NVIDIA GPUs to the container, mounts the
   `input` and `output` directories and then runs the inference.

#### via PyTorch Hub

The pretrained model is also available on [PyTorch Hub](https://pytorch.org/hub/intelisl_midas_v2/)

#### via TensorFlow or ONNX

See [README](https://github.com/isl-org/MiDaS/tree/master/tf) in the `tf` subdirectory.

Currently only supports MiDaS v2.1.

#### via Mobile (iOS / Android)

See [README](https://github.com/isl-org/MiDaS/tree/master/mobile) in the `mobile` subdirectory.

#### via ROS1 (Robot Operating System)

See [README](https://github.com/isl-org/MiDaS/tree/master/ros) in the `ros` subdirectory.

Currently only supports MiDaS v2.1. DPT-based models to be added.

### Accuracy

We provide a **zero-shot error** $\epsilon_d$ which is evaluated for 6 different datasets
(see [paper](https://arxiv.org/abs/1907.01341v3)). **Lower error values are better**. 
$\color{green}{\textsf{Overall model quality is represented by the improvement}}$ ([Imp.](#improvement)) with respect to
MiDaS 3.0 DPTL-384. The models are grouped by the height used for inference, whereas the square training resolution is given by 
the numbers in the model names. The table also shows the **number of parameters** (in millions) and the 
**frames per second** for inference at the training resolution (for GPU RTX 3090):

| MiDaS Model | DIW WHDR | Eth3d AbsRel | Sintel AbsRel | TUM δ1 | KITTI δ1 | NYUv2 δ1 | $\color{green}{\textsf{Imp.}}$ % | Par.M | FPS  |
|-----------------------------------------------------------------------------------------------------------------------|-------------------------:|-----------------------------:|------------------------------:|-------------------------:|-------------------------:|-------------------------:|-------------------------------------------------:|----------------------:|--------------------------:|
| **Inference height 512** | | | | | | | | | |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt) | 0.1137 | 0.0659 | 0.2366 | **6.13** | 11.56* | **1.86*** | $\color{green}{\textsf{19}}$ | **345** | **5.7** |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt)$\tiny{\square}$ | **0.1121** | **0.0614** | **0.2090** | 6.46 | **5.00*** | 1.90* | $\color{green}{\textsf{34}}$ | **345** | **5.7** |
| | | | | | | | | | |
| **Inference height 384** | | | | | | | | | |
| [v3.1 BEiTL-512](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt) | 0.1245 | 0.0681 | **0.2176** | **6.13** | 6.28* | **2.16*** | $\color{green}{\textsf{28}}$ | 345 | 12 |
| [v3.1 Swin2L-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt)$\tiny{\square}$ | 0.1106 | 0.0732 | 0.2442 | 8.87 | **5.84*** | 2.92* | $\color{green}{\textsf{22}}$ | 213 | 41 |
| [v3.1 Swin2B-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt)$\tiny{\square}$ | 0.1095 | 0.0790 | 0.2404 | 8.93 | 5.97* | 3.28* | $\color{green}{\textsf{22}}$ | 102 | 39 |
| [v3.1 SwinL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin_large_384.pt)$\tiny{\square}$ | 0.1126 | 0.0853 | 0.2428 | 8.74 | 6.60* | 3.34* | $\color{green}{\textsf{17}}$ | 213 | 49 |
| [v3.1 BEiTL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_384.pt) | 0.1239 | **0.0667** | 0.2545 | 7.17 | 9.84* | 2.21* | $\color{green}{\textsf{17}}$ | 344 | 13 |
| [v3.1 Next-ViTL-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_next_vit_large_384.pt) | **0.1031** | 0.0954 | 0.2295 | 9.21 | 6.89* | 3.47* | $\color{green}{\textsf{16}}$ | **72** | 30 |
| [v3.1 BEiTB-384](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt) | 0.1159 | 0.0967 | 0.2901 | 9.88 | 26.60* | 3.91* | $\color{green}{\textsf{-31}}$ | 112 | 31 |
| [v3.0 DPTL-384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt) | 0.1082 | 0.0888 | 0.2697 | 9.97 | 8.46 | 8.32 | $\color{green}{\textsf{0}}$ | 344 | **61** |
| [v3.0 DPTH-384](https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid_384.pt) | 0.1106 | 0.0934 | 0.2741 | 10.89 | 11.56 | 8.69 | $\color{green}{\textsf{-10}}$ | 123 | 50 |
| [v2.1 Large384](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_384.pt) | 0.1295 | 0.1155 | 0.3285 | 12.51 | 16.08 | 8.71 | $\color{green}{\textsf{-32}}$ | 105 | 47 |
| | | | | | | | | | |
| **Inference height 256** | | | | | | | | | |
| [v3.1 Swin2T-256](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt)$\tiny{\square}$ | **0.1211** | **0.1106** | **0.2868** | **13.43** | **10.13*** | **5.55*** | $\color{green}{\textsf{-11}}$ | 42 | 64 |
| [v2.1 Small256](https://github.com/isl-org/MiDaS/releases/download/v2_1/midas_v21_small_256.pt) | 0.1344 | 0.1344 | 0.3370 | 14.53 | 29.27 | 13.43 | $\color{green}{\textsf{-76}}$ | **21** | **90** |
| | | | | | | | | | |
| **Inference height 224** | | | | | | | | | |
| [v3.1 LeViT224](https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_levit_224.pt)$\tiny{\square}$ | **0.1314** | **0.1206** | **0.3148** | **18.21** | **15.27*** | **8.64*** | $\color{green}{\textsf{-40}}$ | **51** | **73** |

* No zero-shot error, because models are also trained on KITTI and NYU Depth V2\
$\square$ Validation performed at **square resolution**, either because the transformer encoder backbone of a model 
does not support non-square resolutions (Swin, Swin2, LeViT) or for comparison with these models. All other 
validations keep the aspect ratio. A difference in resolution limits the comparability of the zero-shot error and the
improvement, because these quantities are averages over the pixels of an image and do not take into account the 
advantage of more details due to a higher resolution.\
Best values per column and same validation height in bold

#### Improvement

The improvement in the above table is defined as the relative zero-shot error with respect to MiDaS v3.0 
DPTL-384 and averaging over the datasets. So, if $\epsilon_d$ is the zero-shot error for dataset $d$, then
the $\color{green}{\textsf{improvement}}$ is given by $100(1-(1/6)\sum_d\epsilon_d/\epsilon_{d,\rm{DPT_{L-384}}})$%.

Note that the improvements of 10% for MiDaS v2.0 → v2.1 and 21% for MiDaS v2.1 → v3.0 are not visible from the
improvement column (Imp.) in the table but would require an evaluation with respect to MiDaS v2.1 Large384
and v2.0 Large384 respectively instead of v3.0 DPTL-384.

### Depth map comparison

Zoom in for better visibility
![](figures/Comparison.png)

### Speed on Camera Feed

Test configuration	
- Windows 10	
- 11th Gen Intel Core i7-1185G7 3.00GHz	
- 16GB RAM	
- Camera resolution 640x480	
- openvino_midas_v21_small_256

Speed: 22 FPS

### Changelog

* [Dec 2022] Released MiDaS v3.1:
 - New models based on 5 different types of transformers ([BEiT](https://arxiv.org/pdf/2106.08254.pdf), [Swin2](https://arxiv.org/pdf/2111.09883.pdf), [Swin](https://arxiv.org/pdf/2103.14030.pdf), [Next-ViT](https://arxiv.org/pdf/2207.05501.pdf), [LeViT](https://arxiv.org/pdf/2104.01136.pdf))
 - Training datasets extended from 10 to 12, including also KITTI and NYU Depth V2 using [BTS](https://github.com/cleinc/bts) split
 - Best model, BEiTLarge 512, with resolution 512x512, is on average about [28% more accurate](#Accuracy) than MiDaS v3.0
 - Integrated live depth estimation from camera feed
* [Sep 2021] Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/DPT-Large).
* [Apr 2021] Released MiDaS v3.0:
 - New models based on [Dense Prediction Transformers](https://arxiv.org/abs/2103.13413) are on average [21% more accurate](#Accuracy) than MiDaS v2.1
 - Additional models can be found [here](https://github.com/isl-org/DPT)
* [Nov 2020] Released MiDaS v2.1:
	- New model that was trained on 10 datasets and is on average about [10% more accurate](#Accuracy) than [MiDaS v2.0](https://github.com/isl-org/MiDaS/releases/tag/v2)
	- New light-weight model that achieves [real-time performance](https://github.com/isl-org/MiDaS/tree/master/mobile) on mobile platforms.
	- Sample applications for [iOS](https://github.com/isl-org/MiDaS/tree/master/mobile/ios) and [Android](https://github.com/isl-org/MiDaS/tree/master/mobile/android)
	- [ROS package](https://github.com/isl-org/MiDaS/tree/master/ros) for easy deployment on robots
* [Jul 2020] Added TensorFlow and ONNX code. Added [online demo](http://35.202.76.57/).
* [Dec 2019] Released new version of MiDaS - the new model is significantly more accurate and robust
* [Jul 2019] Initial release of MiDaS ([Link](https://github.com/isl-org/MiDaS/releases/tag/v1))

### Citation

Please cite our paper if you use this code or any of the models:
```
@ARTICLE {Ranftl2022,
    author  = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun",
    title   = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer",
    journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
    year    = "2022",
    volume  = "44",
    number  = "3"
}
```

If you use a DPT-based model, please also cite:

```
@article{Ranftl2021,
	author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
	title     = {Vision Transformers for Dense Prediction},
	journal   = {ICCV},
	year      = {2021},
}
```

### Acknowledgements

Our work builds on and uses code from [timm](https://github.com/rwightman/pytorch-image-models) and [Next-ViT](https://github.com/bytedance/Next-ViT). 
We'd like to thank the authors for making these libraries available.

### License

MIT License

马斯克的赛博云

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