This model was released on 2023-02-23 and added to Hugging Face Transformers on 2024-07-08.

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ZoeDepth

ZoeDepth is a depth estimation model that combines the generalization performance of relative depth estimation (how far objects are from each other) and metric depth estimation (precise depth measurement on metric scale) from a single image. It is pre-trained on 12 datasets using relative depth and 2 datasets (NYU Depth v2 and KITTI) for metric accuracy. A lightweight head with a metric bin module for each domain is used, and during inference, it automatically selects the appropriate head for each input image with a latent classifier.

You can find all the original ZoeDepth checkpoints under the Intel organization.

The example below demonstrates how to estimate depth with [Pipeline] or the [AutoModel] class.

import requests
from PIL import Image

from transformers import pipeline


url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
pipeline = pipeline(
    task="depth-estimation",
    model="Intel/zoedepth-nyu-kitti",
    device=0
)
results = pipeline(image)
results["depth"]

import requests
import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForDepthEstimation


image_processor = AutoImageProcessor.from_pretrained(
    "Intel/zoedepth-nyu-kitti"
)
model = AutoModelForDepthEstimation.from_pretrained(
    "Intel/zoedepth-nyu-kitti",
    device_map="auto"
)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(image, return_tensors="pt").to(model.device)

with torch.no_grad():
  outputs = model(inputs)

# interpolate to original size and visualize the prediction
## ZoeDepth dynamically pads the input image, so pass the original image size as argument
## to `post_process_depth_estimation` to remove the padding and resize to original dimensions.
post_processed_output = image_processor.post_process_depth_estimation(
    outputs,
    source_sizes=[(image.height, image.width)],
)

predicted_depth = post_processed_output[0]["predicted_depth"]
depth = (predicted_depth - predicted_depth.min()) / (predicted_depth.max() - predicted_depth.min())
depth = depth.detach().cpu().numpy() * 255
Image.fromarray(depth.astype("uint8"))

Notes

• In the original implementation ZoeDepth performs inference on both the original and flipped images and averages the results. The post_process_depth_estimation function handles this by passing the flipped outputs to the optional outputs_flipped argument as shown below.

  pyCopy

   with torch.no_grad():
       outputs = model(pixel_values)
       outputs_flipped = model(pixel_values=torch.flip(inputs.pixel_values, dims=[3]))
       post_processed_output = image_processor.post_process_depth_estimation(
           outputs,
           source_sizes=[(image.height, image.width)],
           outputs_flipped=outputs_flipped,
       )
  

Resources

• Refer to this notebook for an inference example.

ZoeDepthConfig

[[autodoc]] ZoeDepthConfig

ZoeDepthImageProcessor

[[autodoc]] ZoeDepthImageProcessor - preprocess

ZoeDepthImageProcessorPil

[[autodoc]] ZoeDepthImageProcessorPil - preprocess

ZoeDepthForDepthEstimation

[[autodoc]] ZoeDepthForDepthEstimation - forward