Depth estimation task guide (#22205)

* added doc to toc, auto tip with  supported models, mention of task guide in model docs

* make style

* removed "see also"

* minor fix

docs/source/en/_toctree.yml

@@ -85,6 +85,8 @@
       title: Zero-shot object detection
     - local: tasks/zero_shot_image_classification
       title: Zero-shot image classification
+    - local: tasks/monocular_depth_estimation
+      title: Depth estimation
     title: Computer Vision
   - sections:
     - local: tasks/image_captioning

docs/source/en/model_doc/dpt.mdx

@@ -33,7 +33,8 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DPT.
 
 - Demo notebooks for [`DPTForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DPT).
-- See also: [Semantic segmentation task guide](./tasks/semantic_segmentation)
+- [Semantic segmentation task guide](../tasks/semantic_segmentation)
+- [Monocular depth estimation task guide](../tasks/monocular_depth_estimation.mdx)
 
 If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
 

docs/source/en/model_doc/glpn.mdx

@@ -45,6 +45,7 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GLPN.
 
 - Demo notebooks for [`GLPNForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GLPN).
+- [Monocular depth estimation task guide](../tasks/monocular_depth_estimation.mdx)
 
 ## GLPNConfig
 

docs/source/en/tasks/monocular_depth_estimation.mdx

@@ -0,0 +1,147 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Monocular depth estimation
+
+Monocular depth estimation is a computer vision task that involves predicting the depth information of a scene from a
+single image. In other words, it is the process of estimating the distance of objects in a scene from
+a single camera viewpoint.
+
+Monocular depth estimation has various applications, including 3D reconstruction, augmented reality, autonomous driving,
+and robotics. It is a challenging task as it requires the model to understand the complex relationships between objects
+in the scene and the corresponding depth information, which can be affected by factors such as lighting conditions,
+occlusion, and texture.
+
+<Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+In this guide you'll learn how to:
+
+* create a depth estimation pipeline
+* run depth estimation inference by hand
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```bash
+pip install -q transformers
+```
+
+## Depth estimation pipeline
+
+The simplest way to try out inference with a model supporting depth estimation is to use the corresponding [`pipeline`].
+Instantiate a pipeline from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads):
+
+```py
+>>> from transformers import pipeline
+
+>>> checkpoint = "vinvino02/glpn-nyu"
+>>> depth_estimator = pipeline("depth-estimation", model=checkpoint)
+```
+
+Next, choose an image to analyze:
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/>
+</div>
+
+Pass the image to the pipeline.
+
+```py
+>>> predictions = depth_estimator(image)
+```
+
+The pipeline returns a dictionary with two entries. The first one, called `predicted_depth`, is a tensor with the values
+being the depth expressed in meters for each pixel.
+The second one, `depth`, is a PIL image that visualizes the depth estimation result.
+
+Let's take a look at the visualized result:
+
+```py
+>>> predictions["depth"]
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
+</div>
+
+## Depth estimation inference by hand
+
+Now that you've seen how to use the depth estimation pipeline, let's see how we can replicate the same result by hand.
+
+Start by loading the model and associated processor from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads).
+Here we'll use the same checkpoint as before:
+
+```py
+>>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+
+>>> checkpoint = "vinvino02/glpn-nyu"
+
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
+>>> model = AutoModelForDepthEstimation.from_pretrained(checkpoint)
+```
+
+Prepare the image input for the model using the `image_processor` that will take care of the necessary image transformations
+such as resizing and normalization:
+
+```py
+>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
+```
+
+Pass the prepared inputs through the model:
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+...     outputs = model(pixel_values)
+...     predicted_depth = outputs.predicted_depth
+```
+
+Visualize the results:
+
+```py
+>>> import numpy as np
+
+>>> # interpolate to original size
+>>> prediction = torch.nn.functional.interpolate(
+...     predicted_depth.unsqueeze(1),
+...     size=image.size[::-1],
+...     mode="bicubic",
+...     align_corners=False,
+... ).squeeze()
+>>> output = prediction.numpy()
+
+>>> formatted = (output * 255 / np.max(output)).astype("uint8")
+>>> depth = Image.fromarray(formatted)
+>>> depth
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
+</div>

utils/check_task_guides.py

@@ -70,6 +70,7 @@ TASK_GUIDE_TO_MODELS = {
     "translation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
     "video_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
     "document_question_answering.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
+    "monocular_depth_estimation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
 }
 
 # This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any