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538e847c06
* Edited zoedepth model card according to specifications. * Edited Zoedepth model file * made suggested changes.
125 lines
4.8 KiB
Markdown
125 lines
4.8 KiB
Markdown
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# ZoeDepth
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[ZoeDepth](https://huggingface.co/papers/2302.12288) 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.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/zoedepth_architecture_bis.png"
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alt="drawing" width="600"/>
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You can find all the original ZoeDepth checkpoints under the [Intel](https://huggingface.co/Intel?search=zoedepth) organization.
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The example below demonstrates how to estimate depth with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import requests
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import torch
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from transformers import pipeline
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from PIL import Image
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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pipeline = pipeline(
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task="depth-estimation",
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model="Intel/zoedepth-nyu-kitti",
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torch_dtype=torch.float16,
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device=0
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)
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results = pipeline(image)
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results["depth"]
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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import requests
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from PIL import Image
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from transformers import AutoModelForDepthEstimation, AutoImageProcessor
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image_processor = AutoImageProcessor.from_pretrained(
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"Intel/zoedepth-nyu-kitti"
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)
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model = AutoModelForDepthEstimation.from_pretrained(
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"Intel/zoedepth-nyu-kitti",
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device_map="auto"
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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inputs = image_processor(image, return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model(inputs)
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# interpolate to original size and visualize the prediction
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## ZoeDepth dynamically pads the input image, so pass the original image size as argument
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## to `post_process_depth_estimation` to remove the padding and resize to original dimensions.
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post_processed_output = image_processor.post_process_depth_estimation(
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outputs,
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source_sizes=[(image.height, image.width)],
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)
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predicted_depth = post_processed_output[0]["predicted_depth"]
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depth = (predicted_depth - predicted_depth.min()) / (predicted_depth.max() - predicted_depth.min())
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depth = depth.detach().cpu().numpy() * 255
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Image.fromarray(depth.astype("uint8"))
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```
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</hfoption>
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</hfoptions>
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## Notes
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- In the [original implementation](https://github.com/isl-org/ZoeDepth/blob/edb6daf45458569e24f50250ef1ed08c015f17a7/zoedepth/models/depth_model.py#L131) 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.
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```py
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with torch.no_grad():
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outputs = model(pixel_values)
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outputs_flipped = model(pixel_values=torch.flip(inputs.pixel_values, dims=[3]))
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post_processed_output = image_processor.post_process_depth_estimation(
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outputs,
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source_sizes=[(image.height, image.width)],
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outputs_flipped=outputs_flipped,
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)
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```
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## Resources
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- Refer to this [notebook](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ZoeDepth) for an inference example.
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## ZoeDepthConfig
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[[autodoc]] ZoeDepthConfig
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## ZoeDepthImageProcessor
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[[autodoc]] ZoeDepthImageProcessor
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- preprocess
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## ZoeDepthForDepthEstimation
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[[autodoc]] ZoeDepthForDepthEstimation
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- forward |