Dynamic View Synthesis from Dynamic Monocular Video

Overview

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:

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:

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

MiDaS was trained on 10 datasets (ReDWeb, DIML, Movies, MegaDepth, WSVD, TartanAir, HRWSI, ApolloScape, BlendedMVS, IRS) with multi-objective optimization. The original model that was trained on 5 datasets (MIX 5 in the paper) can be found here.

Changelog

Setup

  1. Pick one or more models and download corresponding weights to the weights folder:
  • For highest quality: dpt_large
  • For moderately less quality, but better speed on CPU and slower GPUs: dpt_hybrid
  • For real-time applications on resource-constrained devices: midas_v21_small
  • Legacy convolutional model: midas_v21
  1. Set up dependencies:

    conda install pytorch torchvision opencv
    pip install timm

    The code was tested with Python 3.7, PyTorch 1.8.0, OpenCV 4.5.1, and timm 0.4.5.

Usage

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

  2. Run the model:

    python run.py --model_type dpt_large
    python run.py --model_type dpt_hybrid 
    python run.py --model_type midas_v21_small
    python run.py --model_type midas_v21
  3. The resulting inverse depth maps are written to the output folder.

via Docker

  1. Make sure you have installed Docker and the NVIDIA Docker runtime.

  2. Build the Docker image:

    docker build -t midas .
  3. Run inference:

    docker run --rm --gpus all -v $PWD/input:/opt/MiDaS/input -v $PWD/output:/opt/MiDaS/output 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

via TensorFlow or ONNX

See README in the tf subdirectory.

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

via Mobile (iOS / Android)

See README in the mobile subdirectory.

via ROS1 (Robot Operating System)

See README in the ros subdirectory.

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

Accuracy

Zero-shot error (the lower - the better) and speed (FPS):

Model DIW, WHDR Eth3d, AbsRel Sintel, AbsRel Kitti, δ>1.25 NyuDepthV2, δ>1.25 TUM, δ>1.25 Speed, FPS
Small models: iPhone 11
MiDaS v2 small 0.1248 0.1550 0.3300 21.81 15.73 17.00 0.6
MiDaS v2.1 small URL 0.1344 0.1344 0.3370 29.27 13.43 14.53 30
Big models: GPU RTX 3090
MiDaS v2 large URL 0.1246 0.1290 0.3270 23.90 9.55 14.29 51
MiDaS v2.1 large URL 0.1295 0.1155 0.3285 16.08 8.71 12.51 51
MiDaS v3.0 DPT-Hybrid URL 0.1106 0.0934 0.2741 11.56 8.69 10.89 46
MiDaS v3.0 DPT-Large URL 0.1082 0.0888 0.2697 8.46 8.32 9.97 47

Citation

Please cite our paper if you use this code or any of the models:

@article{Ranftl2020,
	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 (TPAMI)},
	year      = {2020},
}

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   = {ArXiv preprint},
	year      = {2021},
}

Acknowledgements

Our work builds on and uses code from timm. We'd like to thank the author for making these libraries available.

License

MIT License

Comments
  • How could I convert it to onnx model?

    How could I convert it to onnx model?

    Trying to convert the model to onnx model, but got error

    File "to_onnx.py", line 72, in export_model(model, img_input, export_model_name) File "to_onnx.py", line 30, in export_model torch.onnx.export(model, input, export_model_name, verbose=False, export_params=True, opset_version=11) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\onnx_init_.py", line 148, in export strip_doc_string, dynamic_axes, keep_initializers_as_inputs) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\onnx\utils.py", line 66, in export dynamic_axes=dynamic_axes, keep_initializers_as_inputs=keep_initializers_as_inputs) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\onnx\utils.py", line 416, in _export fixed_batch_size=fixed_batch_size) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\onnx\utils.py", line 279, in _model_to_graph graph, torch_out = _trace_and_get_graph_from_model(model, args, training) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\onnx\utils.py", line 236, in _trace_and_get_graph_from_model trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(model, args, _force_outplace=True, return_inputs_states=True) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\jit_init.py", line 277, in _get_trace_graph outs = ONNXTracedModule(f, _force_outplace, return_inputs, return_inputs_states)(*args, **kwargs) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\nn\modules\module.py", line 532, in call result = self.forward(*input, **kwargs) File "C:\Users\yyyy\Anaconda3\envs\torchreid\lib\site-packages\torch\jit_init.py", line 332, in forward in_vars, in_desc = _flatten(args) RuntimeError: Only tuples, lists and Variables supported as JIT inputs/outputs. Dictionaries and strings are also accepted but their usage is not recommended. But got unsupported type numpy.ndarray

    to_onnx.py

    import os
    import glob
    import torch
    import utils
    import cv2
    
    from torchvision.transforms import Compose
    from models.midas_net import MidasNet
    from models.transforms import Resize, NormalizeImage, PrepareForNet
    
    import onnx
    import onnxruntime
    
    def test_model_accuracy(export_model_name, raw_output, input):    
        ort_session = onnxruntime.InferenceSession(export_model_name)
    
        def to_numpy(tensor):
            return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
    
        # compute ONNX Runtime output prediction
        ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(input)}
        ort_outs = ort_session.run(None, ort_inputs)	
    
        # compare ONNX Runtime and PyTorch results
        np.testing.assert_allclose(to_numpy(raw_output), ort_outs[0], rtol=1e-03, atol=1e-05)
    
        print("Exported model has been tested with ONNXRuntime, and the result looks good!")		
    
    def export_model(model, input, export_model_name):
        torch.onnx.export(model, input, export_model_name, verbose=False, export_params=True, opset_version=11)	
        onnx_model = onnx.load(export_model_name)    
        onnx.checker.check_model(onnx_model)
        graph_output = onnx.helper.printable_graph(onnx_model.graph)
        with open("graph_output.txt", mode="w") as fout:
            fout.write(graph_output)
    		
    device = torch.device("cpu")
    
     # load network
    model_path = "model.pt"
    model = MidasNet(model_path, non_negative=True)
    
    transform = Compose(
            [
                Resize(
                    384,
                    384,
                    resize_target=None,
                    keep_aspect_ratio=True,
                    ensure_multiple_of=32,
                    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(),
            ]
    )
    
    model.to(device)
    model.eval()
    
    img = utils.read_image("input/line_up_00.jpg")
    img_input = transform({"image": img})["image"]
    
    # compute
    #with torch.no_grad():
    sample = torch.from_numpy(img_input).to(device).unsqueeze(0)
    print("sample type = ", type(sample), ", shape of sample = ", sample.shape)
    print(sample)	
    prediction = model.forward(sample)
    export_model_name = "midas.onnx"	
    export_model(model, img_input, export_model_name)
    

    Environment:

    pytorch 1.4.0(installed by anaconda) os is windows 10 64bits

    opened by stereomatchingkiss 10
  • Slow image transformation

    Slow image transformation

    Hej, I dont think this is a issue, sorry for posting like this. But the image that goes through you model is really slow. Do you have a method for speeding it up? Sorry again for posting it as a issue, but dont know how else to make contact.

    opened by nickteim 8
  • Depth in float32 in meters units

    Depth in float32 in meters units

    Hello! Thanks for you work!

    I have two questions:

    1. What is the .pmf format and what is it used for?
    2. While opening .png depth maps how to convert them into float32 in meters units?
    opened by n-kasatkin 8
  • How to get a maximum depth by numeric, and x,y axis?

    How to get a maximum depth by numeric, and x,y axis?

    Hi, Before launching your code, I would like to understand how to get a maximum depth by numeric, and the x/y axis. Could I have your advice on it? If you could tell me the specific key function where generates the maximum depth, it would be really helpful.

    Allow me to add one more question, is your code available to use on the google colaboratory?

    I'm looking forward to hearing from you soon!

    opened by ramuneblue 7
  • Installation problem

    Installation problem

    Hi! I have runtime problems:

    ...
    File "/home/etienne/.cache/torch/hub/facebookresearch_WSL-Images_master/hubconf.py", line 23, in _resnext
        model = ResNet(block, layers, **kwargs)
    TypeError: __init__() got an unexpected keyword argument 'groups'
    

    Searching on this, it seems it's because of a bad torchvision version. I do not succeed in solving all version conflicts. Is it possible to have a minimal set of command lines to install all in the right version?

    opened by EtienneAb3d 7
  • What does the predicted depth signify?

    What does the predicted depth signify?

    A "prediction" gives the following:

    [[2496.0127 2495.973 2495.9888 ... 855.7698 855.57666 856.0468 ] [2495.9575 2495.9158 2495.9329 ... 855.4036 855.20917 855.68256] [2495.9797 2495.9387 2495.9556 ... 855.55426 855.36035 855.83234] ... [3245.7551 3245.7756 3245.7664 ... 2852.5774 2852.4922 2852.702 ] [3245.7275 3245.7478 3245.739 ... 2852.4827 2852.397 2852.6072 ] [3245.7974 3245.8179 3245.809 ... 2852.7156 2852.6309 2852.8398 ]]

    What are the units of these numbers m, mm, ft? Of course those numbers aren't disparities (since the images aren't that wide). So what do these numbers represent? How to convert this prediction to actual depth given camera intrinsics? Thanks

    opened by zendevil 7
  • About getting results in meters unit

    About getting results in meters unit

    @ranftlr Thank you for the work. I'm trying to apply it with Myriad X VPU. So I would like to ask whether the unknown scale and shift mentioned in #36 are linear parameters? For example, in each frame, I can find a linear equation like "P = D * scale + shift" to project the values of depth maps "D" to the physical absolute measurements "P" according to putting a known scale ruler in the view, right ?

    opened by nightheronry 6
  • How to eval on MiDaS?

    How to eval on MiDaS?

    Hi, is there any evaluation code for MiDaS?

    MiDaS can predict a robust inverse-depth of a single image, but how can I eval on datasets with ground truth depth like KITTI? Should I convert predict disparity to depth and evaluate in the depth space, or convert ground truth depth to disparity and evaluate in the disparity space?

    I downloaded the official validation set of KITTI, and convert gt_depth to gt_disparity with:

    gt_disparity = 1 / (gt_depth + 1e-8)
    gt_disparity[gt_depth==0]=0
    

    after performing alignment in disparity space and evaluating on midas_v3.0_dpt-large on KITTI, I got the performance:

    KITTI AbsRel : 10.0
    KITTI delta > 1.25 : 10.1
    

    It seems that my evaluation code is not so accurate. Could you please provide your evaluation code for MiDas? Thank you so much.

    opened by guangkaixu 5
  • nan value in the output of model

    nan value in the output of model

    Thanks very much for the work. It's much accurate than before.

    But in some cases, there are "nan" values in the output of the model. image

    Seems the old version doesn't have this issue.

    opened by mathmax12 5
  • Loss functions

    Loss functions

    Hi, the loss functions when training midas are very simple, i.e., ptrim(l1) and gradient loss. Have you tried other loss functions like normal loss or BerHu? Or have you tried these loss functions but they didn't work well?

    Thanks.

    opened by Tord-Zhang 5
  • Training code

    Training code

    Do you plan to release your training code sometime in the future? It would be really helpful to advance the research on monocular depth estimation!

    If not, can you explain how the Pareto optimatility is ensured during training? It seems like there will also have to be an undo step in the training pipeline such that whenever the Pareto optimum is reached and the next backpropagation update disturbs this state, this update will have to be reversed.

    opened by tarashakhurana 5
  • PUBKEY error building the docker image

    PUBKEY error building the docker image

    Hi,when building the Docker image with :docker build -t midas . the following error occured: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC #6 3.925 E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease' is not signed.

    try fix with: sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys A4B469963BF863CC but failed.

    Do you have any idea? 2022-10-18 (2)

    opened by wangbohan6aa 0
  • Video > Sequence > Depth (Uneven Render)

    Video > Sequence > Depth (Uneven Render)

    So basically what I’ve tried doing is converting a 2D video into a 3D video with anaglyph.

    1. I render a video clip into a PNG sequence using Adobe Media Encoder
    2. I run the PNG sequence using MiDas
    3. I insert the both DepthMap and the normal sequence in after effects as PNG sequence
    4. I Displace Map the depth and Right shift +5 and left shift -5 on another copy, creating 2 different perspectives
    5. I color the right one Cyan and the left one Magenta and apply Multiply blending mode
    6. Color correcting using adjustment layer
    7. Then render to final result

    the issue is that the Depth sequence rendered with MiDas is uneven, it’s flickering and it’s slight light changes, although the original clip has a flat exposure throughout the video.

    I’m using the Large MiDas file to render. Was wondering if there is another more ”accurate” way to do this? It seems to me the results other people are getting is way more on-point whilst mines is kind of blurry and uneven throughout the sequence!

    opened by Joakimgreenday 0
  • no timm despite pip install

    no timm despite pip install

    Hello, In my dedicated python environment, I tried with latest timm and forced 0.4.5 without luck:

    pip install timm==0.4.5
    Defaulting to user installation because normal site-packages is not writeable
    Collecting timm==0.4.5
      Downloading timm-0.4.5-py3-none-any.whl (287 kB)
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 287.4/287.4 kB 2.8 MB/s eta 0:00:00
    Requirement already satisfied: torch>=1.4 in /home/pm/.local/lib/python3.10/site-packages (from timm==0.4.5) (1.12.1)
    Requirement already satisfied: torchvision in /home/pm/.local/lib/python3.10/site-packages (from timm==0.4.5) (0.13.1)
    Requirement already satisfied: typing-extensions in /home/pm/.local/lib/python3.10/site-packages (from torch>=1.4->timm==0.4.5) (4.3.0)
    Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/lib/python3/dist-packages (from torchvision->timm==0.4.5) (9.0.1)
    Requirement already satisfied: numpy in /usr/lib/python3/dist-packages (from torchvision->timm==0.4.5) (1.21.5)
    Requirement already satisfied: requests in /usr/lib/python3/dist-packages (from torchvision->timm==0.4.5) (2.25.1)
    Installing collected packages: timm
    Successfully installed timm-0.4.5
    (midas) [email protected]:~/Documents/github$ python run.py --model_type dpt_large
    Traceback (most recent call last):
      File "run.py", line 11, in <module>
        from midas.dpt_depth import DPTDepthModel
      File "/home/pm/Documents/github/midas/dpt_depth.py", line 6, in <module>
        from .blocks import (
      File "/home/pm/Documents/github/midas/blocks.py", line 4, in <module>
        from .vit import (
      File "/home/pm/Documents/github/midas/vit.py", line 3, in <module>
        import timm
    ModuleNotFoundError: No module named 'timm'
    

    Any idea?

    opened by j2l 0
  • How do I define a cache directory for models to download and load from?

    How do I define a cache directory for models to download and load from?

    I'm having trouble figuring out how to define a location to store downloaded models, and load them. Every run it downloads models, and the probability of connections ended by peers, and other stuff is high. I'd like to prevent this, and keep bandwidth consumption load. I couldn't find anything in the documentation regarding model paths.

    opened by WASasquatch 0
  • device

    device "mps" for Apple silicon - strange output

    I've tried to run MiDaS on Apple silicon with pytorch nightly with mps (metal performance shader) support.

    I've change two lines of code to get this working: 28 - device = torch.device("cuda" if torch.cuda.is_available() else "mps") and 127 - mode="bilinear",

    The change to "bilinear" is necessary because bicubic is not supported currently for the M1 native implementation. Influence on output quality is neglectable in my experience (I did a few comparing tests on "CPU" with only this parameter changed)

    This way I could speed up inference by a whopping factor of 6. But: Output quality detoriates to not-usable. I've got virtually no experience with python and pytorch - I'm basically a hobbyist creator and dad who wants to have a depth map on selected gopro footage of his son to fake depth of field into it. So i do not know where to look at.

    Legacy Midas V2 seems to work okay and delivers the same output on CPU and MPS: 000727v2cpu CPU 000727v2mps MPS

    DPT however, output becomes unusable with mps: 000727dptmps MPS

    000727dptcpu CPU

    Any ideas? Can someone give me a hint?

    opened by RaceBo 0
  • torch.device(

    torch.device("cuda")?

    I have been testing around loading models at float16 via CUDA and by cpu. Is it best to use map_location=torch.device("cpu")? Or can we use torch.device("cuda:0")? Was MIDAS trained on cpu making this a situation where map_location='cpu' is needed?

    I also have been attempting to run MiDaS on CPU to save VRAM for other models. It runs very slow though. Does anyone have advice about optimizing that way? I could swear that I see better results in 32bit mode anyhow...?

    Thanks for your input! :)

    opened by ford442 1
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Intelligent Systems Lab Org
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