depth-estimation 0.0.5

A Transformers-style Python library for monocular depth estimation

depth_estimation

A Python library for monocular depth estimation.

Provides a unified, modular API for running inference, comparing, and integrating depth estimation models — supporting 8 model families with 20 variants and designed to accommodate new models with minimal friction.

Installation

pip install depth-estimation

# With dev dependencies
pip install "depth-estimation[dev]"

# With all optional model dependencies
pip install "depth-estimation[all]"

For a full list of core and optional dependencies, see docs/dependencies.md.

Quick Start

Pipeline API (Recommended)

from depth_estimation import pipeline

pipe = pipeline("depth-estimation", model="depth-anything-v2-vitb")
result = pipe("image.jpg")

depth_map = result.depth            # np.ndarray, float32, (H, W)
colored   = result.colored_depth    # np.ndarray, uint8, (H, W, 3)
meta      = result.metadata         # dict with model info

Auto Classes

from depth_estimation import AutoDepthModel, AutoProcessor

# Works with any of the 20 supported variants
model     = AutoDepthModel.from_pretrained("zoedepth")
processor = AutoProcessor.from_pretrained("zoedepth")

inputs = processor("image.jpg")
with torch.no_grad():
    depth = model(inputs["pixel_values"])

result = processor.postprocess(depth, inputs["original_sizes"])

Batch Inference

results = pipe(["img1.jpg", "img2.jpg", "img3.jpg"])
for r in results:
    print(r.depth.shape)

Supported Models

8 model families · 20 variants — see docs/models.md for the full list.

Architecture

The library follows the HuggingFace Transformers modular design philosophy:

• Single model, single file — each model's architecture is self-contained
• Shared processor — preprocessing/postprocessing is not duplicated
• Registry-based auto-loading — new models self-register, no core changes needed
• Config inheritance — configs override only what differs from the base

Input → Processor.preprocess() → Model.forward() → Processor.postprocess() → DepthOutput

Adding a New Model

1. Create src/depth_estimation/models/your_model/
2. Add configuration_your_model.py (inherit BaseDepthConfig)
3. Add modeling_your_model.py (inherit BaseDepthModel, single file)
4. Add __init__.py with MODEL_REGISTRY.register(...)

That's it — AutoDepthModel, AutoProcessor, and pipeline() will automatically resolve your model.

Running Tests

pip install -e ".[dev]"
pytest tests/ -v

Acknowledgments

This library builds upon the incredible work of the following research teams:

Model	Repository
Depth Anything v1	github.com/LiheYoung/Depth-Anything
Depth Anything v2	github.com/DepthAnything/Depth-Anything-V2
Depth Anything v3	github.com/DepthAnything/Depth-Anything-V3
DINOv2	github.com/facebookresearch/dinov2
DepthPro	github.com/apple/ml-depth-pro
ZoeDepth	github.com/isl-org/ZoeDepth
MiDaS	github.com/isl-org/MiDaS
Pixel-Perfect Depth	github.com/gangweix/Pixel-Perfect-Depth
Marigold-DC	github.com/prs-eth/Marigold-DC

License

MIT