Add unit tests for visualization.py

[Copilot]

No test coverage existed for src/animl/utils/visualization.py. Adds tests/test_visualization.py with 28 tests covering all three public functions.

Test coverage

TestPlotBox (15 tests)

• Series and DataFrame inputs, single and multi-row
• Pixel-level assertion that boxes are actually drawn
• min_conf filtering, NaN bbox skipping
• All classifier_label_col modes: None, "category", custom column
• show_confidence, custom colors/detector_labels, None fallback to defaults
• ValueError on missing required columns; FileNotFoundError on bad path

TestPlotAllBoundingBoxes (9 tests)

• Output dir auto-created; _box.jpg written and readable
• Multiple source images → multiple output files
• classifier_label_col, confidence threshold, custom colors/labels
• ValueError on wrong file_col or mismatched colors/detector_labels lengths

TestPlotFromFile (4 tests)

• CSV → output files with {stem}_{i}_{ext} naming
• Output is a valid image
• Multiple rows produce distinct indexed files
• Non-image extension falls back to .jpg

Uses real sample images from examples/Southwest/, temporary directories with teardown cleanup, and return_img=True throughout to avoid display windows.

Warning

Firewall rules blocked me from connecting to one or more addresses (expand for details)

I tried to connect to the following addresses, but was blocked by firewall rules:

• dns.google
  • Triggering command: /usr/bin/python python -c from animl.utils import visualization; print('OK') (dns block)
  • Triggering command: /usr/bin/python python -m pytest tests/test_visualization.py -v (dns block)
  • Triggering command: `/usr/bin/python python -c
    from pathlib import Path
    import csv, tempfile, shutil
    from animl.utils import visualization

img = list((Path.cwd() / 'examples' / 'Southwest').glob('*.JPG'))[0]
tmp = tempfile.mkdtemp()
out_dir = Path(tmp) / 'out'
out_dir.mkdir()

rows = [{'filepath': s` (dns block)

• one.one.one.one
  • Triggering command: /usr/bin/python python -c from animl.utils import visualization; print('OK') (dns block)
  • Triggering command: /usr/bin/python python -m pytest tests/test_visualization.py -v (dns block)
  • Triggering command: `/usr/bin/python python -c
    from pathlib import Path
    import csv, tempfile, shutil
    from animl.utils import visualization

img = list((Path.cwd() / 'examples' / 'Southwest').glob('*.JPG'))[0]
tmp = tempfile.mkdtemp()
out_dir = Path(tmp) / 'out'
out_dir.mkdir()

rows = [{'filepath': s` (dns block)

If you need me to access, download, or install something from one of these locations, you can either:

• Configure Actions setup steps to set up my environment, which run before the firewall is enabled
• Add the appropriate URLs or hosts to the custom allowlist in this repository's Copilot coding agent settings (admins only)

Original prompt

Add unittest for visualization.py

Create a comprehensive unittest for src/animl/utils/visualization.py following the existing test patterns in the repository.

Context

The visualization.py module contains three main functions that need testing:

1. plot_box() - Plots bounding boxes on images
2. plot_all_bounding_boxes() - Processes a manifest and saves images with boxes
3. plot_from_file() - Reads CSV and plots boxes

Reference the existing visualization.py code here: https://github.com/conservationtechlab/animl-py/blob/371db736daed9b380f33960e5ef32e4c5273907d/src/animl/utils/visualization.py

Requirements

Test File Location

• Create the test file at: tests/test_visualization.py

Test Structure

Follow the existing test patterns from the repository:

• Use unittest framework (like tests/buow_test.py)
• Include @unittest.skip decorator where appropriate for tests that require external resources
• Use proper imports from animl package

Tests to Include

1. Test plot_box() function:

   • Test with single row (pandas Series)
   • Test with multiple rows (pandas DataFrame)
   • Test with minimum confidence threshold filtering
   • Test with different label_col options (None, "category", custom column)
   • Test show_confidence parameter (True/False)
   • Test return_img parameter (returns image vs displays)
   • Test error handling for missing required columns
   • Test error handling for non-existent file paths
   • Test video frame extraction (if 'frame' column exists)

2. Test plot_all_bounding_boxes() function:

   • Test with image files
   • Test with video files (different frames)
   • Test output directory creation
   • Test file naming (with _box suffix)
   • Test confidence threshold filtering
   • Test custom colors and detector labels
   • Test error handling for missing required columns

3. Test plot_from_file() function:

   • Test CSV file reading and processing
   • Test output file creation
   • Test file naming with index suffix

Test Data

• Use sample images from examples/Southwest/ directory (referenced in other tests)
• Create minimal test DataFrames with required columns:
  • filepath, conf, bbox_x, bbox_y, bbox_w, bbox_h, category
• Use temporary directories for output (with cleanup)

Expected Test Pattern

"""
Test visualization functions

"""
import unittest
import tempfile
import shutil
from pathlib import Path
import pandas as pd
import numpy as np
import cv2

from animl.utils import visualization


class TestVisualization(unittest.TestCase):
    
    @classmethod
    def setUpClass(cls):
        """Set up test fixtures that are reused across tests"""
        cls.image_dir = Path.cwd() / 'examples' / 'Southwest'
        cls.test_images = list(cls.image_dir.glob('*.JPG'))[:2]  # Use first 2 images
        
    def setUp(self):
        """Set up test fixtures for each test"""
        self.temp_dir = tempfile.mkdtemp()
        
    def tearDown(self):
        """Clean up after each test"""
        shutil.rmtree(self.temp_dir, ignore_errors=True)
    
    def test_plot_box_single_row(self):
        """Test plot_box with a single pandas Series"""
        # Create test data
        # Call function
        # Assert results
        
    def test_plot_box_multiple_rows(self):
        """Test plot_box with pandas DataFrame"""
        # Test implementation
        
    # Add more tests...


if __name__ == '__main__':
    unittest.main()

Code Quality

• Include docstrings for all test methods
• Use descriptive test names
• Add comments explaining test setup and assertions
• Handle cleanup properly (temp directories, files)
• Include both positive and negative test cases (error conditions)

Additional Notes

• Don't display images during tests (use return_img=True)
• Consider mocking cv2.imshow for tests that would otherwise display windows
• Test both image and video file handling
• Verify generated image files exist and have expected properties
• Check that bounding boxes are drawn correctly (verify image is modified)

Deliverables

1. Create tests/test_visualization.py with comprehensive unittest coverage
2. Follow existing test patterns in the repository
3. Ensure tests can run without external dependencies (use existing example images)
4. Include proper cleanup and error handling

The following is the prior conversation context from the user's chat exploration (may be truncated):

Assistant: To test a GUI-based app like MatchyPatchy, you'd typically use a combination of testing strategies. Let me first look at the MatchyPatchy repository to understand its structure and see what testing approach would work best.

Assistant: I found MatchyPatchy! It's a GUI tool for human validation of AI-powered animal re-identification developed by the San Diego Zoo Wildlife Alliance Conservation Technology Lab. Based on the codebase, here's how you can approach testing this GUI application:

Testi...

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