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50226041c9
* refactor: Standardize naming conventions * chore: Update the version and re-run experiments * chore: Improve code quality tooling and documentation - Add pre-commit job to CI workflow for automated linting on PRs - Update uniface/__init__.py with copyright header, module docstring, and logically grouped exports - Revise CONTRIBUTING.md to reflect pre-commit handles all formatting - Remove redundant ruff check from CI (now handled by pre-commit) - Update build job Python version to 3.11 (matches requires-python)
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
# Copyright 2025 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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"""Tests for SCRFD detector."""
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from __future__ import annotations
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import numpy as np
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import pytest
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from uniface.constants import SCRFDWeights
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from uniface.detection import SCRFD
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@pytest.fixture
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def scrfd_model():
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return SCRFD(
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model_name=SCRFDWeights.SCRFD_500M_KPS,
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confidence_threshold=0.5,
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nms_threshold=0.4,
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)
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def test_model_initialization(scrfd_model):
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assert scrfd_model is not None, 'Model initialization failed.'
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def test_inference_on_640x640_image(scrfd_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = scrfd_model.detect(mock_image)
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assert isinstance(faces, list), 'Detections should be a list.'
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for face in faces:
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# Face is a dataclass, check attributes exist
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assert hasattr(face, 'bbox'), "Each detection should have a 'bbox' attribute."
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assert hasattr(face, 'confidence'), "Each detection should have a 'confidence' attribute."
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assert hasattr(face, 'landmarks'), "Each detection should have a 'landmarks' attribute."
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bbox = face.bbox
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assert len(bbox) == 4, 'BBox should have 4 values (x1, y1, x2, y2).'
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landmarks = face.landmarks
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assert len(landmarks) == 5, 'Should have 5 landmark points.'
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assert all(len(pt) == 2 for pt in landmarks), 'Each landmark should be (x, y).'
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def test_confidence_threshold(scrfd_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = scrfd_model.detect(mock_image)
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for face in faces:
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confidence = face.confidence
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assert confidence >= 0.5, f'Detection has confidence {confidence} below threshold 0.5'
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def test_no_faces_detected(scrfd_model):
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empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
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faces = scrfd_model.detect(empty_image)
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assert len(faces) == 0, 'Should detect no faces in a blank image.'
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def test_different_input_sizes(scrfd_model):
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test_sizes = [(480, 640, 3), (720, 1280, 3), (1080, 1920, 3)]
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for size in test_sizes:
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mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
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faces = scrfd_model.detect(mock_image)
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assert isinstance(faces, list), f'Should return list for size {size}'
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def test_scrfd_10g_model():
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model = SCRFD(model_name=SCRFDWeights.SCRFD_10G_KPS, confidence_threshold=0.5)
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assert model is not None, 'SCRFD 10G model initialization failed.'
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = model.detect(mock_image)
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assert isinstance(faces, list), 'SCRFD 10G should return list of detections.'
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