nagadomi/ZoeDepth_iw3

Metric depth estimation from a single image

ZoeDepth: Combining relative and metric depth (Official implementation)

ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, Matthias Müller

[Paper]

Table of Contents

• Usage
  • Using torch hub
  • Using local copy
    • Using local torch hub
    • or load the models manually
  • Using ZoeD models to predict depth
• Environment setup
• Sanity checks (Recommended)
• Model files
• Evaluation
  • Evaluating offical models
  • Evaluating local checkpoint
• Training
• Gradio demo
• Citation

Usage

It is recommended to fetch the latest MiDaS repo via torch hub before proceeding:

import torch

torch.hub.help("intel-isl/MiDaS", "DPT_BEiT_L_384", force_reload=True)  # Triggers fresh download of MiDaS repo

ZoeDepth models

Using torch hub

import torch

repo = "isl-org/ZoeDepth"
# Zoe_N
model_zoe_n = torch.hub.load(repo, "ZoeD_N", pretrained=True)

# Zoe_K
model_zoe_k = torch.hub.load(repo, "ZoeD_K", pretrained=True)

# Zoe_NK
model_zoe_nk = torch.hub.load(repo, "ZoeD_NK", pretrained=True)

Using local copy

Clone this repo:

git clone https://github.com/isl-org/ZoeDepth.git && cd ZoeDepth

Using local torch hub

You can use local source for torch hub to load the ZoeDepth models, for example:

import torch

# Zoe_N
model_zoe_n = torch.hub.load(".", "ZoeD_N", source="local", pretrained=True)

or load the models manually

from zoedepth.models.builder import build_model
from zoedepth.utils.config import get_config

# ZoeD_N
conf = get_config("zoedepth", "infer")
model_zoe_n = build_model(conf)

# ZoeD_K
conf = get_config("zoedepth", "infer", config_version="kitti")
model_zoe_k = build_model(conf)

# ZoeD_NK
conf = get_config("zoedepth_nk", "infer")
model_zoe_nk = build_model(conf)

Using ZoeD models to predict depth

##### sample prediction
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
zoe = model_zoe_n.to(DEVICE)


# Local file
from PIL import Image
image = Image.open("/path/to/image.jpg").convert("RGB")  # load
depth_numpy = zoe.infer_pil(image)  # as numpy

depth_pil = zoe.infer_pil(image, output_type="pil")  # as 16-bit PIL Image

depth_tensor = zoe.infer_pil(image, output_type="tensor")  # as torch tensor



# Tensor 
from zoedepth.utils.misc import pil_to_batched_tensor
X = pil_to_batched_tensor(image).to(DEVICE)
depth_tensor = zoe.infer(X)



# From URL
from zoedepth.utils.misc import get_image_from_url

# Example URL
URL = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS4W8H_Nxk_rs3Vje_zj6mglPOH7bnPhQitBH8WkqjlqQVotdtDEG37BsnGofME3_u6lDk&usqp=CAU"


image = get_image_from_url(URL)  # fetch
depth = zoe.infer_pil(image)

# Save raw
from zoedepth.utils.misc import save_raw_16bit
fpath = "/path/to/output.png"
save_raw_16bit(depth, fpath)

# Colorize output
from zoedepth.utils.misc import colorize

colored = colorize(depth)

# save colored output
fpath_colored = "/path/to/output_colored.png"
Image.fromarray(colored).save(fpath_colored)

Environment setup

The project depends on :

• pytorch (Main framework)
• timm (Backbone helper for MiDaS)
• pillow, matplotlib, scipy, h5py, opencv (utilities)

Install environment using environment.yml :

Using mamba (fastest):

mamba env create -n zoe --file environment.yml
mamba activate zoe

Using conda :

conda env create -n zoe --file environment.yml
conda activate zoe

Sanity checks (Recommended)

Check if models can be loaded:

python sanity_hub.py

Try a demo prediction pipeline:

python sanity.py

This will save a file pred.png in the root folder, showing RGB and corresponding predicted depth side-by-side.

Model files

Models are defined under models/ folder, with models/<model_name>_<version>.py containing model definitions and models/config_<model_name>.json containing configuration.

Single metric head models (Zoe_N and Zoe_K from the paper) have the common definition and are defined under models/zoedepth while as the multi-headed model (Zoe_NK) is defined under models/zoedepth_nk.

Evaluation

Download the required dataset and change the DATASETS_CONFIG dictionary in utils/config.py accordingly.

Evaluating offical models

On NYU-Depth-v2 for example:

For ZoeD_N:

python evaluate.py -m zoedepth -d nyu

For ZoeD_NK:

python evaluate.py -m zoedepth_nk -d nyu

Evaluating local checkpoint

python evaluate.py -m zoedepth --pretrained_resource="local::/path/to/local/ckpt.pt" -d nyu

Pretrained resources are prefixed with url:: to indicate weights should be fetched from a url, or local:: to indicate path is a local file. Refer to models/model_io.py for details.

The dataset name should match the corresponding key in utils.config.DATASETS_CONFIG .

Training

Download training datasets as per instructions given here. Then for training a single head model on NYU-Depth-v2 :

python train_mono.py -m zoedepth --pretrained_resource=""

For training the Zoe-NK model:

python train_mix.py -m zoedepth_nk --pretrained_resource=""

Gradio demo

We provide a UI demo built using gradio. To get started, install UI requirements:

pip install -r ui/ui_requirements.txt

Then launch the gradio UI:

python -m ui.app

The UI is also hosted on HuggingFace🤗 here

Citation

@misc{https://doi.org/10.48550/arxiv.2302.12288,
  doi = {10.48550/ARXIV.2302.12288},
  
  url = {https://arxiv.org/abs/2302.12288},
  
  author = {Bhat, Shariq Farooq and Birkl, Reiner and Wofk, Diana and Wonka, Peter and Müller, Matthias},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}