Extremely unstable library to work with coral reef splats.
You can swim with a few GoPros around a reef (e.g. [wildflow.ai/protocol](https://wildflow.ai/protocol)) and then turn the footage into 3D models (e.g. [wildflow.ai/demo](https://wildflow.ai/demo)) to track changes over time, run different analysis on top of it, and ultimately see which conservation/restoration methods work best.
# Installation
### From PyPI
```bash
pip install wildflow
```
### From GitHub (latest development version)
```bash
pip install git+https://github.com/wildflowai/splat.git
```
**Note**: Installing from GitHub requires Rust to be installed on your system.
## Flow
```mermaid
flowchart TD
%% Inputs
style A fill:#1E90FF,stroke:#000,stroke-width:1px,color:#fff
style B fill:#1E90FF,stroke:#000,stroke-width:1px,color:#fff
style C fill:#1E90FF,stroke:#000,stroke-width:1px,color:#fff
A([corrected images])
B([colmap cameras])
C([colmap point cloud])
%% Transformations
style F fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
style D fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
style E fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
style G fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
style H fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
style I fill:#6C757D,stroke:#000,stroke-width:1px,color:#fff
F[plan GPU split]
D[split cameras]
E[split point cloud]
G([train each patch on GPU])
H[cleanup patches]
I[merge patches]
%% Outputs
style J1 fill:#006400,stroke:#000,stroke-width:1px,color:#fff
style J2 fill:#006400,stroke:#000,stroke-width:1px,color:#fff
style J3 fill:#006400,stroke:#000,stroke-width:1px,color:#fff
J1([massive splat model])
J2([splat ortho])
J3([artifacts to publish])
%% Flow connections
B --> F
F --> D
F --> E
D --> G
E --> G
A --> G
G --> H
H --> I
I --> J1
I --> J2
I --> J3
C --> E
```
## Using the library
```py
from wildflow import splat
# split into multiple training patches so each patch fits into GPU VRAM
gpu_patches = splat.patches(cameras_2d, max_cameras=1400, buffer_meters=2)
# split colmap images.bin and cameras.bin into patches to train on GPU
splat.split_cameras({
"input_path": input_path,
"save_points3d": False, # we don't want tie points to see splats
"patches": [
{**patch, "output_path": f"{output_path}/p{i}/sparse/0"}
for i, patch in enumerate(gpu_patches)
]
})
# use 5% of the dense point cloud to seed splat initial positions
splat.split_point_cloud({
"input_file": input_path,
"sample_percentage": 5,
"patches": [
{**coords(patch), "output_file": f"{output_path}/p{i}/sparse/0/points3D.bin"}
for i, patch in enumerate(gpu_patches)
]
})
# train all patches on GPU
# get rid of wacky splats
splat.cleanup_splats(...)
# merge everything into one ply file
splat.merge_ply_files(...)
# create orthomosaic
# publish
```
Raw data
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