pymoten

https://gallantlab.github.io/pymoten/
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A Python package that extracts motion energy features from video using pyramids of spatio-temporal...
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### Install pymoten using pip

Source: https://gallantlab.github.io/pymoten

Install the latest version of pymoten from GitHub or the most recent release from PyPI.

```bash
pip install git+https://github.com/gallantlab/pymoten.git
```

```bash
pip install pymoten
```

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### Enable GPU acceleration with PyTorch CUDA

Source: https://gallantlab.github.io/pymoten

Switch the computational backend to PyTorch with CUDA support for GPU acceleration. This requires PyTorch to be installed. The stimulus is moved to the GPU, projected, and then converted back to NumPy.

```python
import moten
from moten.backend import set_backend

# Switch to a GPU backend (returns the backend module)
backend = set_backend("torch_cuda")

pyramid = moten.get_default_pyramid(vhsize=(vdim, hdim), fps=24)

# Move the stimulus onto the GPU, project, then bring the result back
stimulus_gpu = backend.asarray(luminance_images)
moten_features = pyramid.project_stimulus_batched(stimulus_gpu)
moten_features = backend.to_numpy(moten_features)
```

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### Compute motion energy features from a video file

Source: https://gallantlab.github.io/pymoten

Stream a video file, convert it to luminance images, and compute motion energy features using a default pyramid of Gabor filters.

```python
import moten

# Stream and convert the RGB video into a sequence of luminance images
video_file = 'http://anwarnunez.github.io/downloads/avsnr150s24fps_tiny.mp4'
luminance_images = moten.io.video2luminance(video_file, nimages=100)

# Create a pyramid of spatio-temporal gabor filters
nimages, vdim, hdim = luminance_images.shape
pyramid = moten.get_default_pyramid(vhsize=(vdim, hdim), fps=24)

# Compute motion energy features
moten_features = pyramid.project_stimulus(luminance_images)
```

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### Compute motion energy features from synthetic data

Source: https://gallantlab.github.io/pymoten

Generate synthetic video data and compute motion energy features using a default pyramid of Gabor filters.

```python
import moten
import numpy as np

# Generate synthetic data
nimages, vdim, hdim = (100, 90, 180)
noise_movie = np.random.randn(nimages, vdim, hdim)

# Create a pyramid of spatio-temporal gabor filters
pyramid = moten.get_default_pyramid(vhsize=(vdim, hdim), fps=24)

# Compute motion energy features
moten_features = pyramid.project_stimulus(noise_movie)
```
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