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
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
import numpy as np
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import pytest
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from uniface.landmark import Landmark106
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@pytest.fixture
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def landmark_model():
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return Landmark106()
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@pytest.fixture
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def mock_image():
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return np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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@pytest.fixture
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def mock_bbox():
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return [100, 100, 300, 300]
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def test_model_initialization(landmark_model):
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assert landmark_model is not None, 'Landmark106 model initialization failed.'
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def test_landmark_detection(landmark_model, mock_image, mock_bbox):
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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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assert landmarks.shape == (106, 2), f'Expected shape (106, 2), got {landmarks.shape}'
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def test_landmark_dtype(landmark_model, mock_image, mock_bbox):
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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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assert landmarks.dtype == np.float32, f'Expected float32, got {landmarks.dtype}'
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def test_landmark_coordinates_within_image(landmark_model, mock_image, mock_bbox):
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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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x_coords = landmarks[:, 0]
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y_coords = landmarks[:, 1]
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x1, y1, x2, y2 = mock_bbox
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margin = 50
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x_in_bounds = np.sum((x_coords >= x1 - margin) & (x_coords <= x2 + margin))
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y_in_bounds = np.sum((y_coords >= y1 - margin) & (y_coords <= y2 + margin))
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assert x_in_bounds >= 95, f'Only {x_in_bounds}/106 x-coordinates within bounds'
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assert y_in_bounds >= 95, f'Only {y_in_bounds}/106 y-coordinates within bounds'
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def test_different_bbox_sizes(landmark_model, mock_image):
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test_bboxes = [
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[50, 50, 150, 150],
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[100, 100, 300, 300],
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[50, 50, 400, 400],
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]
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for bbox in test_bboxes:
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landmarks = landmark_model.get_landmarks(mock_image, bbox)
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assert landmarks.shape == (106, 2), f'Failed for bbox {bbox}'
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def test_landmark_array_format(landmark_model, mock_image, mock_bbox):
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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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landmarks_int = landmarks.astype(int)
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assert landmarks_int.shape == (106, 2), 'Integer conversion should preserve shape'
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assert landmarks_int.dtype in [np.int32, np.int64], 'Should convert to integer type'
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def test_consistency(landmark_model, mock_image, mock_bbox):
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landmarks1 = landmark_model.get_landmarks(mock_image, mock_bbox)
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landmarks2 = landmark_model.get_landmarks(mock_image, mock_bbox)
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assert np.allclose(landmarks1, landmarks2), 'Same input should produce same landmarks'
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def test_different_image_sizes(landmark_model, mock_bbox):
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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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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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assert landmarks.shape == (106, 2), f'Failed for image size {size}'
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def test_bbox_list_format(landmark_model, mock_image):
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bbox_list = [100, 100, 300, 300]
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landmarks = landmark_model.get_landmarks(mock_image, bbox_list)
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assert landmarks.shape == (106, 2), 'Should work with bbox as list'
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def test_bbox_array_format(landmark_model, mock_image):
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bbox_array = np.array([100, 100, 300, 300])
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landmarks = landmark_model.get_landmarks(mock_image, bbox_array)
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assert landmarks.shape == (106, 2), 'Should work with bbox as numpy array'
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def test_landmark_distribution(landmark_model, mock_image, mock_bbox):
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landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
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x_variance = np.var(landmarks[:, 0])
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y_variance = np.var(landmarks[:, 1])
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assert x_variance > 0, 'Landmarks should have variation in x-coordinates'
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assert y_variance > 0, 'Landmarks should have variation in y-coordinates'
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