| """ |
| Tests for the Visualization Module — ActionVisualizer. |
| """ |
|
|
| import numpy as np |
| import pytest |
|
|
| from src.visualization.visualizer import ActionVisualizer |
|
|
|
|
| class TestActionVisualizer: |
| """Tests for ActionVisualizer methods.""" |
|
|
| @pytest.fixture |
| def visualizer(self) -> ActionVisualizer: |
| return ActionVisualizer() |
|
|
| @pytest.fixture |
| def test_image(self) -> np.ndarray: |
| """A 480x640 synthetic test image.""" |
| return np.zeros((480, 640, 3), dtype=np.uint8) |
|
|
| @pytest.fixture |
| def sample_detections(self) -> list[dict]: |
| return [ |
| {"label": "bottle", "confidence": 0.92, "bbox": [100, 150, 200, 350]}, |
| {"label": "box", "confidence": 0.85, "bbox": [400, 300, 550, 500]}, |
| ] |
|
|
| @pytest.fixture |
| def sample_action_plan(self) -> dict: |
| return { |
| "action": "move", |
| "steps": [ |
| "Step 1: Locate 'bottle'", |
| "Step 2: Locate 'box'", |
| "Step 3: Move 'bottle' → 'box'", |
| "Step 4: Verify action completion", |
| ], |
| "source_bbox": [100, 150, 200, 350], |
| "dest_bbox": [400, 300, 550, 500], |
| "status": "success", |
| } |
|
|
| def test_draw_detections_returns_image( |
| self, visualizer, test_image, sample_detections |
| ): |
| """draw_detections should return an image of the same shape.""" |
| result = visualizer.draw_detections(test_image, sample_detections) |
|
|
| assert isinstance(result, np.ndarray) |
| assert result.shape == test_image.shape |
|
|
| def test_draw_detections_modifies_image( |
| self, visualizer, test_image, sample_detections |
| ): |
| """draw_detections should modify the image (not return a blank copy).""" |
| result = visualizer.draw_detections(test_image, sample_detections) |
|
|
| |
| assert not np.array_equal(result, test_image) |
|
|
| def test_draw_detections_empty_list(self, visualizer, test_image): |
| """draw_detections with empty list should still return an image.""" |
| result = visualizer.draw_detections(test_image, []) |
|
|
| assert isinstance(result, np.ndarray) |
| assert result.shape == test_image.shape |
|
|
| def test_visualize_returns_image( |
| self, visualizer, test_image, sample_action_plan |
| ): |
| """visualize should return an image of the same shape.""" |
| result = visualizer.visualize(test_image, sample_action_plan) |
|
|
| assert isinstance(result, np.ndarray) |
| assert result.shape == test_image.shape |
|
|
| def test_visualize_modifies_image( |
| self, visualizer, test_image, sample_action_plan |
| ): |
| """visualize should modify the image.""" |
| result = visualizer.visualize(test_image, sample_action_plan) |
|
|
| assert not np.array_equal(result, test_image) |
|
|
| def test_visualize_with_no_bboxes(self, visualizer, test_image): |
| """visualize should handle missing bounding boxes gracefully.""" |
| plan = { |
| "action": "inspect", |
| "steps": ["Step 1: Inspect object"], |
| "source_bbox": None, |
| "dest_bbox": None, |
| "status": "failed", |
| } |
|
|
| result = visualizer.visualize(test_image, plan) |
|
|
| assert isinstance(result, np.ndarray) |
| assert result.shape == test_image.shape |
|
|
| def test_draw_detections_invalid_image(self, visualizer): |
| """draw_detections with None should return a fallback image.""" |
| result = visualizer.draw_detections(None, []) |
|
|
| assert isinstance(result, np.ndarray) |
|
|
