feat: Set up comprehensive Python testing infrastructure

.gitignore

@@ -1,6 +1,64 @@
+# IDE and editor files
 .idea
-venv
-*pyc
+.vscode/
+*.swp
+*.swo
+*~
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+.venv/
+*.egg-info/
+dist/
+build/
+*.egg
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+coverage.xml
+*.cover
+.hypothesis/
+.tox/
+nosetests.xml
+
+# Claude
+.claude/*
+
+# Project specific
 online_prepared_data
 offline_prepared_data
-threedod/sample_data
+threedod/sample_data
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Jupyter
+.ipynb_checkpoints/
+*.ipynb_checkpoints
+
+# Logs
+*.log
+logs/
+
+# Model weights and checkpoints
+*.pth
+*.pt
+*.ckpt
+checkpoints/
+weights/
+
+# Data files
+*.h5
+*.hdf5
+*.npz
+*.npy

pyproject.toml

@@ -0,0 +1,138 @@
+[tool.poetry]
+name = "depth-upsampling-3dod"
+version = "0.1.0"
+description = "Depth upsampling and 3D object detection project"
+authors = ["Your Name <[email protected]>"]
+readme = "README.md"
+packages = [
+    { include = "depth_upsampling" },
+    { include = "threedod" }
+]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+numpy = "^1.20.3"
+opencv-python = "^4.5.1"
+pandas = "^1.3.3"
+matplotlib = "^3.4.3"
+torch = ">=1.9.1"
+torchvision = ">=0.10.1"
+tensorboard = "^2.0.0"
+addict = "^2.4.0"
+aiohttp = "^3.7.4"
+async-timeout = "^3.0.1"
+attrs = "^21.2.0"
+autobahn = "^21.3.1"
+Automat = "^20.2.0"
+cffi = "^1.14.6"
+chardet = "^4.0.0"
+constantly = "^15.1.0"
+cryptography = "^3.4.8"
+cycler = "^0.10.0"
+hyperlink = "^21.0.0"
+idna = "^3.2"
+incremental = "^21.3.0"
+joblib = "^1.0.1"
+kiwisolver = "^1.3.2"
+multidict = "^5.1.0"
+open3d = ">=0.13.0"
+Pillow = "^8.3.2"
+plyfile = "^0.7.4"
+pycparser = "^2.20"
+pyparsing = "^2.4.7"
+python-dateutil = "^2.8.2"
+pytz = "^2021.1"
+PyYAML = "^6.0"
+scikit-learn = "^1.0"
+scipy = "^1.7.1"
+six = "^1.16.0"
+threadpoolctl = "^2.2.0"
+tqdm = "^4.62.3"
+Twisted = "^21.7.0"
+txaio = "^21.2.1"
+typing-extensions = ">=4.0.0"
+vtk = "^9.0.3"
+wslink = "^1.0.7"
+yarl = "^1.6.3"
+zope-interface = "^5.4.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-cov = "^4.1.0"
+pytest-mock = "^3.11.0"
+
+
+[tool.poetry.scripts]
+test = "pytest:main"
+tests = "pytest:main"
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = [
+    "-ra",
+    "--strict-markers",
+    "--strict-config",
+    "--cov=depth_upsampling",
+    "--cov=threedod",
+    "--cov-branch",
+    "--cov-report=term-missing:skip-covered",
+    "--cov-report=html",
+    "--cov-report=xml",
+    "--cov-fail-under=80",
+]
+testpaths = ["tests"]
+python_files = ["test_*.py", "*_test.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+markers = [
+    "unit: Unit tests",
+    "integration: Integration tests",
+    "slow: Slow running tests",
+]
+filterwarnings = [
+    "error",
+    "ignore::UserWarning",
+    "ignore::DeprecationWarning",
+]
+
+[tool.coverage.run]
+source = ["depth_upsampling", "threedod"]
+branch = true
+parallel = true
+omit = [
+    "*/tests/*",
+    "*/test_*",
+    "*/__init__.py",
+    "*/setup.py",
+    "*/conf.py",
+    "*/.venv/*",
+    "*/venv/*",
+]
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "def __repr__",
+    "if self.debug:",
+    "if settings.DEBUG",
+    "raise AssertionError",
+    "raise NotImplementedError",
+    "if 0:",
+    "if __name__ == .__main__.:",
+    "if TYPE_CHECKING:",
+    "class .*\\bProtocol\\):",
+    "@(abc\\.)?abstractmethod",
+]
+show_missing = true
+skip_covered = true
+fail_under = 80
+
+[tool.coverage.html]
+directory = "htmlcov"
+
+[tool.coverage.xml]
+output = "coverage.xml"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

tests/conftest.py

@@ -0,0 +1,173 @@
+import os
+import shutil
+import tempfile
+from pathlib import Path
+from typing import Generator
+from unittest.mock import MagicMock
+
+import pytest
+import numpy as np
+import torch
+
+
+@pytest.fixture
+def temp_dir() -> Generator[Path, None, None]:
+    """Create a temporary directory that is cleaned up after the test."""
+    temp_path = Path(tempfile.mkdtemp())
+    yield temp_path
+    shutil.rmtree(temp_path)
+
+
+@pytest.fixture
+def mock_config() -> dict:
+    """Provide a mock configuration dictionary for testing."""
+    return {
+        "model": {
+            "name": "test_model",
+            "num_classes": 10,
+            "input_channels": 3,
+            "learning_rate": 0.001,
+        },
+        "data": {
+            "batch_size": 32,
+            "num_workers": 4,
+            "train_path": "/path/to/train",
+            "val_path": "/path/to/val",
+        },
+        "training": {
+            "epochs": 100,
+            "checkpoint_dir": "/path/to/checkpoints",
+            "log_interval": 10,
+        },
+    }
+
+
+@pytest.fixture
+def sample_image() -> np.ndarray:
+    """Create a sample image array for testing."""
+    return np.random.randint(0, 255, size=(480, 640, 3), dtype=np.uint8)
+
+
+@pytest.fixture
+def sample_depth_map() -> np.ndarray:
+    """Create a sample depth map for testing."""
+    return np.random.rand(480, 640).astype(np.float32) * 10.0
+
+
+@pytest.fixture
+def sample_point_cloud() -> np.ndarray:
+    """Create a sample 3D point cloud for testing."""
+    num_points = 1000
+    points = np.random.randn(num_points, 3).astype(np.float32)
+    return points
+
+
+@pytest.fixture
+def sample_torch_tensor() -> torch.Tensor:
+    """Create a sample PyTorch tensor for testing."""
+    return torch.randn(1, 3, 224, 224)
+
+
+@pytest.fixture
+def mock_model() -> MagicMock:
+    """Create a mock PyTorch model for testing."""
+    model = MagicMock()
+    model.eval = MagicMock(return_value=model)
+    model.train = MagicMock(return_value=model)
+    model.forward = MagicMock(return_value=torch.randn(1, 10))
+    model.parameters = MagicMock(return_value=[torch.randn(10, 10)])
+    return model
+
+
+@pytest.fixture
+def mock_dataset() -> MagicMock:
+    """Create a mock dataset for testing."""
+    dataset = MagicMock()
+    dataset.__len__ = MagicMock(return_value=100)
+    dataset.__getitem__ = MagicMock(
+        return_value=(torch.randn(3, 224, 224), torch.tensor(1))
+    )
+    return dataset
+
+
+@pytest.fixture
+def mock_dataloader(mock_dataset) -> MagicMock:
+    """Create a mock dataloader for testing."""
+    dataloader = MagicMock()
+    dataloader.__iter__ = MagicMock(
+        return_value=iter([(torch.randn(32, 3, 224, 224), torch.randint(0, 10, (32,)))])
+    )
+    dataloader.dataset = mock_dataset
+    return dataloader
+
+
+@pytest.fixture
+def sample_bounding_boxes() -> np.ndarray:
+    """Create sample bounding boxes for 3D object detection testing."""
+    # Format: [x_center, y_center, z_center, width, height, depth, rotation]
+    boxes = np.array([
+        [0.0, 0.0, 5.0, 2.0, 1.5, 4.0, 0.0],
+        [3.0, 0.0, 10.0, 2.5, 1.8, 4.5, np.pi/4],
+        [-2.0, 0.0, 8.0, 1.8, 1.6, 3.8, -np.pi/6],
+    ], dtype=np.float32)
+    return boxes
+
+
+@pytest.fixture
+def sample_lidar_data() -> np.ndarray:
+    """Create sample LiDAR data for testing."""
+    num_points = 5000
+    # Generate points in a cone pattern to simulate LiDAR
+    angles = np.random.uniform(0, 2*np.pi, num_points)
+    distances = np.random.uniform(0.5, 50.0, num_points)
+    heights = np.random.uniform(-2.0, 2.0, num_points)
+
+    x = distances * np.cos(angles)
+    y = distances * np.sin(angles)
+    z = heights
+    intensity = np.random.uniform(0, 1, num_points)
+
+    return np.column_stack([x, y, z, intensity]).astype(np.float32)
+
+
+@pytest.fixture(autouse=True)
+def reset_environment():
+    """Reset environment variables before each test."""
+    original_env = os.environ.copy()
+    yield
+    os.environ.clear()
+    os.environ.update(original_env)
+
+
+@pytest.fixture
+def capture_logs(caplog):
+    """Fixture to capture log messages during tests."""
+    with caplog.at_level("DEBUG"):
+        yield caplog
+
+
+def pytest_configure(config):
+    """Configure pytest with custom settings."""
+    # Set random seeds for reproducibility
+    np.random.seed(42)
+    torch.manual_seed(42)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(42)
+
+    # Disable GPU for tests by default
+    os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+
+def pytest_collection_modifyitems(config, items):
+    """Modify test collection to add markers automatically."""
+    for item in items:
+        # Add unit marker to tests in unit directory
+        if "unit" in str(item.fspath):
+            item.add_marker(pytest.mark.unit)
+        # Add integration marker to tests in integration directory
+        elif "integration" in str(item.fspath):
+            item.add_marker(pytest.mark.integration)
+
+        # Add slow marker to tests with "slow" in their name
+        if "slow" in item.nodeid:
+            item.add_marker(pytest.mark.slow)