isl-org/ZoeDepth

import torch

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

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)

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

import torch

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

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)

##### 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)

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

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

python sanity_hub.py

python sanity.py

python evaluate.py -m zoedepth -d nyu

python evaluate.py -m zoedepth_nk -d nyu

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

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

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

pip install -r ui/ui_requirements.txt

python -m ui.app

@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}
}