Setting Up Depth Estimation on Ubuntu using PyTorch

This guide provides detailed instructions on setting up a Depth Estimation system on Ubuntu using PyTorch. Depth estimation is the process of predicting the distance to objects in a scene based on a 2D image.

1. Install System Prerequisites

First, ensure your Ubuntu system is updated and has Python and Pip installed. Open a terminal and run the following commands:

sudo apt update
sudo apt upgrade
sudo apt install python3 python3-pip git
    

This will install the necessary system dependencies.

2. Install PyTorch

To install PyTorch, run the following command. Ensure that you have the appropriate version of CUDA installed if you want to utilize GPU acceleration. You can find the specific command for your system at PyTorch Install Guide.

pip install torch torchvision torchaudio
    

If you don't have CUDA, you can install the CPU-only version by omitting CUDA from the command.

3. Install Additional Libraries

To handle image processing and visualization, install the following libraries:

pip install opencv-python matplotlib
    

These libraries are necessary for loading images, performing image transformations, and displaying results.

4. Download a Pre-trained Depth Estimation Model

For depth estimation, we can use a pre-trained model like MiDaS (Monocular Depth Estimation). Clone the MiDaS repository, which contains pre-trained models and inference scripts:

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

5. Create a Python Script for Depth Estimation

Create a new Python script named depth_estimation.py that will load the MiDaS model and perform depth estimation on an input image:

nano depth_estimation.py
    

Paste the following code into the file:

import torch
import cv2
import numpy as np
import matplotlib.pyplot as plt
from torchvision.transforms import Compose, Normalize, ToTensor

# Load the pre-trained MiDaS model
def load_model():
    model = torch.hub.load("isl-org/MiDaS", "MiDaS_small")
    model.eval()
    return model

# Preprocess the input image
def preprocess_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # Resize image to fit the input size expected by the model
    img = cv2.resize(img, (256, 256))

    # Define preprocessing transformations
    transform = Compose([ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

    input_tensor = transform(img).unsqueeze(0)  # Add batch dimension
    return input_tensor, img

# Estimate depth
def estimate_depth(model, input_tensor):
    with torch.no_grad():
        depth_map = model(input_tensor)
    return depth_map.squeeze().cpu().numpy()

# Display results
def display_depth_map(depth_map, original_img):
    plt.subplot(1, 2, 1)
    plt.title("Original Image")
    plt.imshow(original_img)

    plt.subplot(1, 2, 2)
    plt.title("Estimated Depth Map")
    plt.imshow(depth_map, cmap="inferno")

    plt.show()

# Main function
if __name__ == "__main__":
    model = load_model()
    
    image_path = "path/to/your/image.jpg"  # Replace with your image path
    input_tensor, original_img = preprocess_image(image_path)
    
    depth_map = estimate_depth(model, input_tensor)
    
    display_depth_map(depth_map, original_img)
    

This script performs the following steps:

• Loads a pre-trained MiDaS model from Torch Hub.
• Loads and preprocesses an input image for the model.
• Performs depth estimation using the model.
• Displays the original image alongside the estimated depth map.

6. Download and Prepare the Model Weights

To use the MiDaS model, Torch Hub will automatically download the necessary weights during execution. Ensure you have a stable internet connection when you run the script for the first time.

7. Run the Depth Estimation Script

Once the script is ready, you can run it using the following command:

python3 depth_estimation.py
    

Replace path/to/your/image.jpg with the path to the image you want to analyze. The script will display both the original image and the estimated depth map.

8. Troubleshooting

If you encounter issues, ensure that:

• All libraries (PyTorch, OpenCV, Matplotlib) are correctly installed.
• The input image path is correctly specified in the script.
• You have internet access during the model weights download.

9. Conclusion

You have successfully set up a Depth Estimation system on Ubuntu using PyTorch and a pre-trained MiDaS model. You can now apply this system to various applications such as 3D reconstruction, scene understanding, or autonomous driving.