How to Use Depth Pro: Sharp Monocular Metric Depth in Less Than a Second!

Welcome to this user-friendly guide on using Depth Pro, the revolutionary model for zero-shot metric monocular depth estimation. With Depth Pro, you can achieve sharp and high-resolution depth maps in no time. Let’s dive into how to use this powerful tool!

Overview of Depth Pro

Depth Pro synthesizes high-resolution depth maps with incredible sharpness and high-frequency details. It achieves metric predictions without needing camera metadata and operates remarkably fast, generating 2.25-megapixel depth maps in only 0.3 seconds using a standard GPU.

Depth Pro incorporates several technical advancements:

• Efficient multi-scale vision transformer for accurate predictions.
• A training protocol that merges real and synthetic datasets for improved accuracy.
• Evaluation metrics dedicated to boundary accuracy in estimated depth maps.
• State-of-the-art focal length estimation from a single image.

Depth Pro was introduced in the paper Depth Pro: Sharp Monocular Metric Depth in Less Than a Second by esteemed researchers including Aleksei Bochkovskii and Vladlen Koltun.

How to Get Started

Follow these steps to set up your environment and run Depth Pro:

• Visit the official code repository to set up your environment.
• Download the checkpoint from the _Files and versions_ tab or use the Hugging Face Hub CLI with the following command:

pip install huggingface-hub
huggingface-cli download --local-dir checkpoints appleDepthPro

Running Depth Pro from the Command Line

You can easily run Depth Pro using the command line. Here’s how:

# Run prediction on a single image:
depth-pro-run -i .data/example.jpg
# Use `depth-pro-run -h` for available options.

Running Depth Pro from Python

If you prefer using Python, here’s a quick guide:

from PIL import Image
import depth_pro

# Load model and preprocessing transform
model, transform = depth_pro.create_model_and_transforms()
model.eval()

# Load and preprocess an image
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)

# Run inference
prediction = model.infer(image, f_px=f_px)
depth = prediction[depth]  # Depth in [m].
focal_length_px = prediction[focal_length_px]  # Focal length in pixels.

Evaluation with Boundary Metrics

If you want to assess the boundary metrics of your depth estimates, you can utilize the following functions:

# For a depth-based dataset
boundary_f1 = SI_boundary_F1(predicted_depth, target_depth)

# For a mask-based dataset (image matting or segmentation)
boundary_recall = SI_boundary_Recall(predicted_depth, target_mask)

Understanding the Code with an Analogy

Imagine you are an artist tasked with painting a landscape scene. You have a detailed image and a set of brushes at your disposal. Each brush represents a different function within the code, allowing you to create the depth map.

• Loading the Model: Like preparing your canvas, loading the model sets up your workspace where the depth estimation happens.
• Transforming the Image: Applying transformations to the image is akin to applying a base coat on your canvas, preparing it for further details.
• Running Inference: Running inference is like applying layers of paint to simulate depth and texture, revealing the final landscape of depth perception.
• Evaluating Metrics: Assessing boundary metrics is similar to stepping back to analyze your work, ensuring every aspect of the landscape meets your artistic vision.

Troubleshooting

If you encounter issues while using Depth Pro, here are a few troubleshooting tips:

• Incorrect Installation: Make sure all dependencies are correctly installed. Consult the code repository for setup guidance.
• Performance Issues: If the model is running slowly or not producing expected results, check the hardware specifications and ensure you meet the minimum requirements.
• Errors in Predictions: If you’re not getting the right depth predictions, double-check the input image path and make sure you’re using a valid image format.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.