Update training pipeline to HDF5, add data access scripts, and readme updates

- Updated training pipeline for HDF5-based dataset workflow
  - Implemented convert_tif_to_npy.py for TIF to NPY conversion with validation
  - Modified dataset classes and dataloaders to use HDF5 storage format
  - Fixed classifier code and model architectures for NPY array handling
  - Updated training scripts to work with new data format

- Added user-friendly data access scripts
  - Created quickstart.py for dataset loading demonstration
  - Developed download_dataset.py for automated dataset download from Dropbox
  - Added get_dataloaders.py for simple PyTorch DataLoader generation
  - Updated README with clear usage instructions

- Updated package configuration for UV compatibility
  - Updated pyproject.toml dependencies and structure
  - Added proper dependency groups (dev, processing, logging)

- Added development infrastructure
  - GitHub Actions CI pipeline for automated testing
  - Pre-commit hooks with Black code formatting
  - Basic test suite with test_imports.py
  - Added baseline results: RGB 53.5%, HSI 27.3%, LiDAR 11.5%

Author: Ritesh313

.github/workflows/ci.yml

@@ -0,0 +1,62 @@
+name: CI
+
+on:
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main, develop ]
+
+jobs:
+  test:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ['3.9', '3.10', '3.11', '3.12']
+
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install uv
+      run: pip install uv
+
+    - name: Install dependencies
+      run: |
+        uv sync --all-extras
+
+    - name: Test imports
+      run: |
+        uv run python -c "
+        import neon_tree_classification
+        from neon_tree_classification import NeonCrownDataset
+        from scripts.get_dataloaders import get_dataloaders
+        print('All imports successful!')
+        "
+
+    - name: Run tests
+      run: uv run pytest tests/ -v --tb=short
+
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+
+    - name: Install uv
+      run: pip install uv
+
+    - name: Install dependencies
+      run: |
+        uv sync --extra dev
+
+    - name: Check code formatting
+      run: uv run black --check neon_tree_classification/ examples/ scripts/ tests/

.gitignore

@@ -30,6 +30,9 @@ dist/
 .venv/
 uv.lock
 
+# Downloaded dataset files
+_neon_tree_classification_dataset_files/
+
 # IDE/Editor
 .vscode/
 
@@ -63,4 +66,4 @@ uv.lock
 .Spotlight-V100
 .Trashes
 ehthumbs.db
-Thumbs.db
+Thumbs.db

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v6.0.0
+  hooks:
+    - id: trailing-whitespace
+    - id: end-of-file-fixer
+    - id: check-yaml
+    - id: check-added-large-files
+      args: ['--maxkb=1000']
+
+- repo: https://github.com/psf/black
+  rev: 25.1.0
+  hooks:
+    - id: black
+      files: |
+        (?x)^(
+          neon_tree_classification/.*\.py|
+          examples/.*\.py|
+          scripts/.*\.py|
+          tests/.*\.py
+        )$

CONTRIBUTING.md

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+# Contributing to NEON Tree Classification
+
+Thank you for your interest in contributing! We welcome contributions from researchers at all technical levels.
+
+## 🚀 Quick Start for Contributors
+
+### Option 1: UV Setup (Recommended)
+UV is faster and ensures consistent dependencies across all users.
+
+```bash
+# 1. Fork the repository on GitHub (click the Fork button)
+# 2. Clone your fork
+git clone https://github.com/yourusername/NeonTreeClassification.git
+cd NeonTreeClassification
+
+# 3. Install UV if you don't have it
+curl -LsSf https://astral.sh/uv/install.sh | sh
+# or: pip install uv
+
+# 4. Install the package
+uv sync
+```
+
+### Option 2: Pip Setup (Alternative)
+If you prefer traditional pip or can't install UV:
+
+```bash
+# After cloning (steps 1-2 above)
+pip install -e .
+```
+
+> **Note**: UV is preferred because it ensures all contributors have the same dependency versions using the `uv.lock` file.
+
+## 🤝 How to Contribute
+
+### 1. Found a Bug?
+- Open an [issue](https://github.com/Ritesh313/NeonTreeClassification/issues) 
+- Describe what you expected vs what happened
+- Include any error messages
+
+### 2. Have a Feature Idea?
+- Open an [issue](https://github.com/Ritesh313/NeonTreeClassification/issues) to discuss it first
+- We can help figure out the best approach
+
+### 3. Want to Contribute Code?
+1. **Create a branch**: `git checkout -b your-feature-name`
+2. **Make changes**: Edit files, add features, fix bugs
+3. **Test it works**: `uv run python -c "import neon_tree_classification; print('Works!')"`
+4. **Submit**: Create a Pull Request on GitHub
+
+> **For advanced testing**: Use `uv sync --all-extras` to install development tools, then run `uv run pytest tests/`
+
+## 💡 Easy Ways to Help
+
+**No coding required:**
+- Report bugs or issues you encounter
+- Suggest improvements to documentation
+- Share example use cases
+- Test the package with your data and report results
+
+**Light coding:**
+- Fix typos in documentation
+- Add examples or tutorials
+- Improve error messages
+
+**More involved:**
+- Add new model architectures
+- Improve data processing tools
+- Add visualization features
+
+## 🆘 Need Help?
+
+- **First time with Git/GitHub?** Check out [GitHub's guides](https://guides.github.com/)
+- **Python packaging confusion?** Just ask in an issue - we're happy to help!
+- **Not sure where to start?** Look for issues labeled "good first issue"
+
+## 📝 Guidelines
+
+- **Be descriptive** in commit messages and pull requests
+- **Test your changes** - make sure imports still work
+- **Ask questions** - we'd rather help than have you struggle alone
+
+We appreciate all contributions, whether it's fixing a typo or adding a major feature!

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Ritesh Chowdhry
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.