mirror of
https://github.com/yakhyo/uniface.git
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0c93598007
* feat: Update linting and type annotations, return types in detect * feat: add face analyzer and face classes * chore: Update the format and clean up some docstrings * docs: Update usage documentation * feat: Change AgeGender model output to 0, 1 instead of string (Female, Male) * test: Update testing code * feat: Add Apple silicon backend for torchscript inference * feat: Add face analyzer example and add run emotion for testing
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import numpy as np
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import pytest
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from uniface.constants import RetinaFaceWeights
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from uniface.detection import RetinaFace
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@pytest.fixture
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def retinaface_model():
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return RetinaFace(
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model_name=RetinaFaceWeights.MNET_V2,
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conf_thresh=0.5,
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pre_nms_topk=5000,
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nms_thresh=0.4,
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post_nms_topk=750,
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)
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def test_model_initialization(retinaface_model):
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assert retinaface_model is not None, 'Model initialization failed.'
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def test_inference_on_640x640_image(retinaface_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = retinaface_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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assert isinstance(face, dict), 'Each detection should be a dictionary.'
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assert 'bbox' in face, "Each detection should have a 'bbox' key."
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assert 'confidence' in face, "Each detection should have a 'confidence' key."
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assert 'landmarks' in face, "Each detection should have a 'landmarks' key."
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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(retinaface_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = retinaface_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(retinaface_model):
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empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
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faces = retinaface_model.detect(empty_image)
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assert len(faces) == 0, 'Should detect no faces in a blank image.'
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