Image Depth Estimation Using Depth Prediction Transformers (DPTs)

Introduction

Image depth estimation is about figuring out how far away objects in an image are. It’s an important problem in computer vision because it helps with things like creating 3D models, augmented reality, and self-driving cars. In the past, people used techniques like stereo vision or special sensors to estimate depth. But now, there’s a new method called Depth Prediction Transformers (DPTs) that uses deep learning.

DPTs are a type of model that can learn to estimate depth by looking at images. In this article, we’ll learn more about how DPTs work using hands-on coding, why they’re useful, and what we can do with them in different applications.

Learning Objectives

• The concept of Dense Prediction Transformers (DPTs) and their role in image depth estimation.
• Explore the architecture of DPTs, including the combination of vision transformers and encoder-decoder frameworks.
• Implement a DPT task using the Hugging Face transformer library.
• Recognize the potential applications of DPTs in various domains.

This article was published as a part of the Data Science Blogathon.

Understanding Depth Prediction Transformers

Depth Prediction Transformers (DPTs) are a unique kind of deep learning model that is specifically designed to estimate the depth of objects in images. They make use of a special type of architecture called transformers, which were initially developed for processing language data. However, DPTs adapt and apply this architecture to handle visual data. One of the key strengths of DPTs is their ability to capture intricate relationships between various parts of an image and model dependencies that span across long distances. This enables DPTs to accurately predict the depth or distance of objects in an image.

The Architecture of Depth Prediction Transformers

Depth Prediction Transformers (DPTs) combine vision transformers with an encoder-decoder framework to estimate depth in images. The encoder component captures and encodes features using self-attention mechanisms, enhancing the understanding of relationships between different parts of the image. This improves feature resolution and allows for the capture of fine-grained details. The decoder component reconstructs dense depth predictions by mapping the encoded features back to the original image space, utilizing techniques like upsampling and convolutional layers. The architecture of DPTs enables the model to consider the global context of the scene and model dependencies between different image regions, resulting in accurate depth predictions.

In summary, DPTs leverage vision transformers and an encoder-decoder framework to estimate depth in images. The encoder captures features and encodes them using self-attention mechanisms, while the decoder reconstructs dense depth predictions. This architecture enables DPTs to capture fine-grained details, consider global context, and generate accurate depth predictions.

DPT Implementation Using Hugging Face Transformer

We will see a practical implementation of DPT using a Huggin Face pipeline. Find the entire code here.

Step 1: Installing Dependencies

We start by installing the transformers package from the GitHub repository by using the following command:

!pip install -q git+https://github.com/huggingface/transformers.git  # Install the transformers package from the Hugging Face GitHub repository

Execute !pip install command in Jupyter Notebook or JupyterLab cell to install packages directly within the notebook environment.

Step 2: Depth Estimation Model Definition

The provided code defines a depth estimation model using the DPT architecture from the Hugging Face Transformers library.

from transformers import DPTFeatureExtractor, DPTForDepthEstimation

# Create a DPT feature extractor
feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large")

# Create a DPT depth estimation model
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")

The code imports the necessary classes from the Transformers library i.e. DPTFeatureExtractor and DPTForDepthEstimation. Then, we created an instance of the DPT feature extractor by calling DPTFeatureExtractor.from_pretrained() and loading the pre-trained weights from the “Intel/dpt-large” model. In a similar manner, they create an instance of the DPT depth estimation model by using DPTForDepthEstimation.from_pretrained() and load the pre-trained weights from the same “Intel/dpt-large” model.

Step 3: Image Loading

Now we go on to provide a means of loading and preparing an image for further processing.

from PIL import Image
import requests

# Specify the URL of the image to download
url="https://img.freepik.com/free-photo/full-length-shot-pretty-healthy-young-lady-walking-morning-park-with-dog_171337-18880.jpg?w=360&t=st=1689213531~exp=1689214131~hmac=67dea8e3a9c9f847575bb27e690c36c3fec45b056e90a04b68a00d5b4ba8990e"

# Download and open the image using PIL
image = Image.open(requests.get(url, stream=True).raw)

We imported the necessary modules (Image from PIL and requests) to handle image processing and HTTP requests, respectively. It specifies the URL of the image to download and then uses requests.get() to retrieve the image data. Image.open() is used to open the downloaded image data as a PIL Image object.

Step 4: Forward Pass

import torch

# Use torch.no_grad() to disable gradient computation
with torch.no_grad():
    # Pass the pixel values through the model
    outputs = model(pixel_values)
    # Access the predicted depth values from the outputs
    predicted_depth = outputs.predicted_depth    

The above code performs the forward pass of the model to obtain predicted depth values for the input image. We use torch.no_grad() as a context manager to disable gradient computation, which helps to reduce memory usage during inference. They pass the pixel values tensor, pixel_values, through the model using model(pixel_values), and store the resulting outputs in the outputs variable. Next, they access the predicted depth values from outputs.predicted_depth and assign them to the predicted_depth variable.

Step 5: Interpolation and Visualization

We now perform interpolation of the predicted depth values to the original image size and convert the output into an image.

import numpy as np

# Interpolate the predicted depth values to the original size
prediction = torch.nn.functional.interpolate(
    predicted_depth.unsqueeze(1),
    size=image.size[::-1],
    mode="bicubic",
    align_corners=False,
).squeeze()

# Convert the interpolated depth values to a numpy array
output = prediction.cpu().numpy()

# Scale and format the depth values for visualization
formatted = (output * 255 / np.max(output)).astype('uint8')

# Create an image from the formatted depth values
depth = Image.fromarray(formatted)
depth

We use torch.nn.functional.interpolate() to interpolate the predicted depth values to the original size of the input image. The interpolated depth values are then converted to a numpy array using .cpu().numpy(). Next, the depth values are scaled and formatted to the range [0, 255] for visualization purposes. Finally, an image is created from the formatted depth values using Image.fromarray().

After executing this code, the `depth` variable will contain the depth image, which we display as the image depth.

Benefits and Advantages

Depth Prediction Transformers offer several benefits and advantages over traditional methods for image depth estimation. Here are some key points to understand about Depth Prediction Transformers (DPTs):

• Better Attention to Details: DPTs use a special part called the encoder to capture very small details and make the predictions more accurate.
• Understanding the Big Picture: DPTs are good at figuring out how different parts of an image are connected. This helps them understand the whole scene and estimate depth accurately.
• Diverse areas of Application: Use DPTs in lots of different things like making 3D models, adding things to the real world in augmented reality, and helping robots understand their surroundings.

• Ease of Integration: Combine DPTs with other tools in computer vision like picking out objects or dividing an image into different parts. This makes the depth estimation even better and more precise.

Potential Applications

Image Depth Estimation using Depth Prediction Transformers has many useful applications in different fields. Here are a few examples:

• Autonomous Navigation: Depth estimation is important for self-driving cars to understand their surroundings and navigate safely on the road.
• Augmented Reality: Depth estimation helps in overlaying virtual objects onto the real world in augmented reality apps, making them look realistic and interact with the environment correctly.
• 3D Reconstruction: Depth estimation is essential for creating 3D models of objects or scenes from regular 2D images, allowing us to visualize them in a three-dimensional space.
• Robotics: Depth estimation is valuable for robots to perform tasks like picking up objects, avoiding obstacles, and understanding the layout of their environment.

Conclusion

Image Depth Estimation using Depth Prediction Transformers provides a strong and precise method to estimate depth from 2D images. By using the transformer architecture and an encoder-decoder framework, DPTs can effectively capture intricate details, understand connections between different parts of the image, and generate accurate depth predictions. This technology has the potential for applying in various areas such as autonomous navigation, augmented reality, 3D reconstruction, and robotics, offering exciting possibilities for advancements in these fields. As computer vision progresses, Depth Prediction Transformers will continue to play a crucial role in achieving accurate and dependable depth estimation, leading to improvements and breakthroughs in numerous applications.

Key Takeaways

• Image Depth Estimation using Depth Prediction Transformers (DPTs) is a powerful and accurate approach to predicting depth from 2D images.
• DPTs leverage the transformer architecture and the encoder-decoder framework to capture fine-grained details, model long-range dependencies, and generate precise depth predictions.
• DPTs have potential applications in autonomous navigation, augmented reality, 3D reconstruction, and robotics, opening up new possibilities in various domains.
• As computer vision advances, Depth Prediction Transformers will continue to play a significant role in achieving precise and reliable depth estimation, contributing to advancements in numerous applications.

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