depth-estimation 0.0.3

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

git clone https://github.com/shriarul5273/depth_estimation.git
cd depth_estimation
pip install -e .

# With dev dependencies
pip install -e ".[dev]"

# With all optional model dependencies
pip install -e ".[all]"

Core Dependencies

Package	Min Version
Python	3.9
PyTorch	2.0
torchvision	0.15
Pillow	9.0
NumPy	1.24
matplotlib	3.6
opencv-python	4.8
huggingface-hub	0.16
timm	0.9.1
einops	0.6
addict	—

Optional Dependencies

Group	Package	Required for
transformers	transformers>=4.30	MiDaS, ZoeDepth
diffusers	diffusers>=0.25	Marigold-DC

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

Depth Anything v1

Variant ID	Backbone	Source
depth-anything-v1-vits	ViT-S	LiheYoung/depth-anything-small
depth-anything-v1-vitb	ViT-B	LiheYoung/depth-anything-base
depth-anything-v1-vitl	ViT-L	LiheYoung/depth-anything-large

Depth Anything v2

Variant ID	Backbone	Source
depth-anything-v2-vits	ViT-S	depth-anything/Depth-Anything-V2-Small
depth-anything-v2-vitb	ViT-B	depth-anything/Depth-Anything-V2-Base
depth-anything-v2-vitl	ViT-L	depth-anything/Depth-Anything-V2-Large

Depth Anything v3

Variant ID	Source
depth-anything-v3-small	depth-anything/DA3-SMALL
depth-anything-v3-base	depth-anything/DA3-BASE
depth-anything-v3-large	depth-anything/DA3-LARGE
depth-anything-v3-giant	depth-anything/DA3-GIANT
depth-anything-v3-nested-giant-large	depth-anything/DA3NESTED-GIANT-LARGE
depth-anything-v3-metric-large	depth-anything/DA3METRIC-LARGE
depth-anything-v3-mono-large	depth-anything/DA3MONO-LARGE

ZoeDepth (Metric)

Variant ID	Source
zoedepth	Intel/zoedepth-nyu-kitti

MiDaS

Variant ID	Source
midas-dpt-large	Intel/dpt-large
midas-dpt-hybrid	Intel/dpt-hybrid-midas
midas-beit-large	Intel/dpt-beit-large-512

Apple DepthPro (Metric)

Variant ID	Source
depth-pro	apple/DepthPro

Pixel-Perfect Depth

Variant ID	Source
pixel-perfect-depth	gangweix/Pixel-Perfect-Depth

Marigold-DC (Depth Completion)

Variant ID	Source
marigold-dc	prs-eth/marigold-depth-v1-0

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