Merge pull request #3 from EthoML/dev

dev

.github/workflows/release-vame-package.yaml

@@ -0,0 +1,31 @@
+name: Build and Release
+
+on:
+  workflow_run:
+    workflows: [Testing]
+    branches: [main]
+    types:
+      - completed
+jobs:
+  pypi-release:
+    name: Release VAME in pypi
+    runs-on: ubuntu-latest
+    if: ${{ github.event.workflow_run.conclusion == 'success' }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+          pip install twine
+      - name: Build and publish to PyPI
+        run: |
+          python -m build
+          twine upload dist/*
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}

.github/workflows/testing.yaml

@@ -0,0 +1,39 @@
+name: Testing
+
+on:
+  push:
+    branches:
+      - main
+      - dev
+
+
+jobs:
+  run:
+    name: ${{ matrix.os }} Python ${{ matrix.python-version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        os: [ubuntu-latest, macos-13, windows-latest]
+    steps:
+      - uses: actions/checkout@v4
+      - run: git fetch --prune --unshallow --tags
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install tests dependencies
+        run: |
+          python -m pip install -U pip
+          pip install . --no-cache-dir
+          pip install -r tests/requirements-tests.txt --no-cache-dir
+
+      - name: Run tests.
+        run: pytest --cov=src/vame --cov-report=xml --cov-report=term-missing -v
+
+      - name: Upload coverage reports to Codecov
+        uses: codecov/[email protected]
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -1,11 +1,18 @@
+# Sample files
+examples/*.mp4
+
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class
 
+
+
 # C extensions
 *.so
 
+
 # Distribution / packaging
 .Python
 env/
@@ -91,6 +98,7 @@ celerybeat-schedule
 .venv
 venv/
 ENV/
+venv_docs/
 
 # Spyder project settings
 .spyderproject

Dockerfile

@@ -0,0 +1,22 @@
+# Dockerfile for VAME pipeline
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /vame
+
+# Copy the vame package to the working directory
+COPY requirements.txt /vame
+COPY pyproject.toml /vame
+COPY src/ /vame/src
+
+# Copy the demo docker example to working dir
+COPY ./examples/demo_docker.py /vame
+
+# Install vame
+RUN pip install --no-cache-dir .
+
+
+CMD ["python", "demo_docker.py"]
+
+
+

MANIFEST.in

@@ -0,0 +1,4 @@
+prune tests
+prune docs
+prune examples
+prune Images

README.md

@@ -1,8 +1,16 @@
+![image](https://github.com/EthoML/VAME/assets/844306/0f08424f-06ab-48e4-8094-da0f0c78a08d)
+
+<p align="center">
+<a href="https://codecov.io/gh/EthoML/VAME" >
+ <img src="https://codecov.io/gh/EthoML/VAME/graph/badge.svg?token=J1CUXB4N0E"/>
+ </a>
+</p>
+
 🌟 Welcome to EthoML/VAME (Variational Animal Motion Encoding), an open-source machine learning tool for behavioral segmentation and analyses.
 
 We are a group of behavioral enthusiasts, comprising the original VAME developers Kevin Luxem and Pavol Bauer, behavioral neuroscientists Stephanie R. Miller and Jorge J. Palop, and computer scientists and statisticians Alex Pico, Reuben Thomas, and Katie Ly). Our aim is to provide scalable, unbiased and sensitive approaches for assessing mouse behavior using computer vision and machine learning approaches.
 
-We are focused on the expanding the analytical capabilities of VAME segmentation by providing curated scripts for VAME implementation and tools for data processing, visualization, and statistical analyses. 
+We are focused on the expanding the analytical capabilities of VAME segmentation by providing curated scripts for VAME implementation and tools for data processing, visualization, and statistical analyses.
 
 ## Recent Improvements to VAME
 * Curated scripts for VAME implementation

VAME.yaml

@@ -2,29 +2,11 @@
 #
 # install: conda env create -f VAME.yaml
 # update:  conda env update -f VAME.yaml
-name: VAME
+name: vame
 channels:
-  - pytorch
   - defaults
 dependencies:
-  - python=3.7
+  - python=3.11
   - pip
-  - torchvision
-  - jupyter
-  - nb_conda
   - pip:
-    - pytest-shutil
-    - scipy
-    - numpy
-    - matplotlib
-    - pathlib
-    - pandas
-    - ruamel.yaml
-    - sklearn
-    - pyyaml
-    - opencv-python-headless
-    - h5py
-    - umap-learn
-    - networkx    
-    - tqdm
-    - hmmlearn
+      - -r requirements.txt

examples/README.md

@@ -0,0 +1,42 @@
+# Running the first demo (demo.py)
+
+The first demo is a simple example of how to use the library.
+
+## 1. Downloading the necessary resources:
+
+To run the demo you will need vame package installed, follow the installation guide [here](/README.md#Installation) .
+Also you will need two files to properly run the demo:
+- `video-1.mp4`: A video file that will be used as input, for the demo you can download from [this link](https://drive.google.com/file/d/1w6OW9cN_-S30B7rOANvSaR9c3O5KeF0c/view)
+- `video-1.csv`: the pose estimation results for the video file. You can use the video-1.csv file that is in the examples folder [video](/examples/video-1.csv)
+
+## 2. Setting the demo variables
+To start the demo you must define 4 variables. In order to do that, open the `demo.py` file and edit the following:
+
+**The values below are just examples. You must set the variables according to your needs.**
+```python
+# The directory where the project will be saved
+working_directory = './'
+
+# The name you want for the project
+project = 'first_vame_project'
+
+# A list of paths to the videos file
+videos =  ['./video-1.mp4']
+
+# A list of paths to the poses estimations files.
+# Important: The name (without the extension) of the video file and the pose estimation file must be the same. E.g. `video-1.mp4` and `video-1.csv`
+poses_estimations = ['./video-1.csv']
+```
+
+## 3. Running the demo
+After setting the variables, you can run the demo by running the following code:
+
+```python
+python demo.py
+```
+
+The demo will create a project folder in the working directory with the name you defined in the `project` variable and a date suffix, e.g: `first_name-May-9-2024`.
+
+In this folder you can find a config file called `config.yaml` where you can set the parameters for the VAME algorithm. The videos and poses estimations files will be copied to the project videos folder. If everything is ok, the workflow will run and the logs will be displayed in your terminal. The image below shows the VAME workflow.
+
+![demo workflow](/Images/vame-workflow-diagram.jpg)