| """Provider tests for HaarDetector.""" |
|
|
| from __future__ import annotations |
|
|
| import cv2 |
| import numpy as np |
| import pytest |
|
|
| from config.settings import Settings |
| from pipeline.feature_extraction import PipelineOutput |
| from providers.detection.haar import HaarDetector |
|
|
|
|
| @pytest.fixture |
| def haar_detector(): |
| return HaarDetector(settings=Settings(environment="test", db_path=":memory:")) |
|
|
|
|
| @pytest.fixture |
| def pipeline_output(sample_image_bytes): |
| """A PipelineOutput wrapping a synthetic image.""" |
| img = cv2.imdecode(np.frombuffer(sample_image_bytes, np.uint8), cv2.IMREAD_COLOR) |
| return PipelineOutput( |
| image=img, |
| image_hash="test-hash", |
| width=img.shape[1], |
| height=img.shape[0], |
| source="bytes", |
| ) |
|
|
|
|
| class TestHaarDetector: |
| def test_provider_name(self, haar_detector): |
| assert haar_detector.name == "haar" |
|
|
| def test_capability(self, haar_detector): |
| from models.providers import ProviderCapability |
| assert haar_detector.capability == ProviderCapability.DETECTION |
|
|
| def test_is_available(self, haar_detector): |
| assert haar_detector.is_available() is True |
|
|
| def test_execute_returns_provider_result(self, haar_detector, pipeline_output): |
| from providers.base import ProviderResult |
| result = haar_detector.execute(pipeline_output) |
| assert isinstance(result, ProviderResult) |
| assert result.provider == "haar" |
| assert result.success is True |
| assert result.elapsed_ms > 0 |
|
|
| def test_execute_normalized_has_expected_keys(self, haar_detector, pipeline_output): |
| result = haar_detector.execute(pipeline_output) |
| assert "boxes" in result.normalized |
| assert "num_faces" in result.normalized |
| assert "confidences" in result.normalized |
| assert "landmarks" in result.normalized |
|
|
| def test_execute_raw_has_expected_keys(self, haar_detector, pipeline_output): |
| result = haar_detector.execute(pipeline_output) |
| assert "rectangles" in result.raw |
| assert "num_faces" in result.raw |
|
|
| def test_execute_on_black_image_finds_no_faces(self, haar_detector): |
| img = np.zeros((200, 200, 3), dtype=np.uint8) |
| po = PipelineOutput(image=img, image_hash="x", width=200, height=200, source="bytes") |
| result = haar_detector.execute(po) |
| assert result.success is True |
| assert result.normalized["num_faces"] == 0 |
|
|
| def test_execute_handles_empty_image_gracefully(self, haar_detector): |
| |
| img = np.zeros((1, 1, 3), dtype=np.uint8) |
| po = PipelineOutput(image=img, image_hash="x", width=1, height=1, source="bytes") |
| result = haar_detector.execute(po) |
| assert result.success is True |
|
|
