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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)
141 lines
5.0 KiB
Python
141 lines
5.0 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 AgeGender attribute predictor."""
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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.attribute import AgeGender, AttributeResult
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@pytest.fixture
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def age_gender_model():
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return AgeGender()
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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(age_gender_model):
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assert age_gender_model is not None, 'AgeGender model initialization failed.'
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def test_prediction_output_format(age_gender_model, mock_image, mock_bbox):
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result = age_gender_model.predict(mock_image, mock_bbox)
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assert isinstance(result, AttributeResult), f'Result should be AttributeResult, got {type(result)}'
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assert isinstance(result.gender, int), f'Gender should be int, got {type(result.gender)}'
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assert isinstance(result.age, int), f'Age should be int, got {type(result.age)}'
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assert isinstance(result.sex, str), f'Sex should be str, got {type(result.sex)}'
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def test_gender_values(age_gender_model, mock_image, mock_bbox):
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result = age_gender_model.predict(mock_image, mock_bbox)
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assert result.gender in [0, 1], f'Gender should be 0 (Female) or 1 (Male), got {result.gender}'
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assert result.sex in ['Female', 'Male'], f'Sex should be Female or Male, got {result.sex}'
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def test_age_range(age_gender_model, mock_image, mock_bbox):
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result = age_gender_model.predict(mock_image, mock_bbox)
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assert 0 <= result.age <= 120, f'Age should be between 0 and 120, got {result.age}'
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def test_different_bbox_sizes(age_gender_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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result = age_gender_model.predict(mock_image, bbox)
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assert result.gender in [0, 1], f'Failed for bbox {bbox}'
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assert 0 <= result.age <= 120, f'Age out of range for bbox {bbox}'
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def test_different_image_sizes(age_gender_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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result = age_gender_model.predict(mock_image, mock_bbox)
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assert result.gender in [0, 1], f'Failed for image size {size}'
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assert 0 <= result.age <= 120, f'Age out of range for image size {size}'
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def test_consistency(age_gender_model, mock_image, mock_bbox):
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result1 = age_gender_model.predict(mock_image, mock_bbox)
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result2 = age_gender_model.predict(mock_image, mock_bbox)
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assert result1.gender == result2.gender, 'Same input should produce same gender prediction'
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assert result1.age == result2.age, 'Same input should produce same age prediction'
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def test_bbox_list_format(age_gender_model, mock_image):
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bbox_list = [100, 100, 300, 300]
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result = age_gender_model.predict(mock_image, bbox_list)
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assert result.gender in [0, 1], 'Should work with bbox as list'
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assert 0 <= result.age <= 120, 'Age should be in valid range'
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def test_bbox_array_format(age_gender_model, mock_image):
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bbox_array = np.array([100, 100, 300, 300])
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result = age_gender_model.predict(mock_image, bbox_array)
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assert result.gender in [0, 1], 'Should work with bbox as numpy array'
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assert 0 <= result.age <= 120, 'Age should be in valid range'
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def test_multiple_predictions(age_gender_model, mock_image):
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bboxes = [
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[50, 50, 150, 150],
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[200, 200, 350, 350],
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[400, 400, 550, 550],
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]
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results = []
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for bbox in bboxes:
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result = age_gender_model.predict(mock_image, bbox)
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results.append(result)
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assert len(results) == 3, 'Should have 3 predictions'
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for result in results:
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assert result.gender in [0, 1]
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assert 0 <= result.age <= 120
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def test_age_is_positive(age_gender_model, mock_image, mock_bbox):
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for _ in range(5):
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result = age_gender_model.predict(mock_image, mock_bbox)
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assert result.age >= 0, f'Age should be non-negative, got {result.age}'
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def test_output_format_for_visualization(age_gender_model, mock_image, mock_bbox):
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result = age_gender_model.predict(mock_image, mock_bbox)
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text = f'{result.sex}, {result.age}y'
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assert isinstance(text, str), 'Should be able to format as string'
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assert 'Male' in text or 'Female' in text, 'Text should contain gender'
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assert 'y' in text, "Text should contain 'y' for years"
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def test_attribute_result_fields(age_gender_model, mock_image, mock_bbox):
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"""Test that AttributeResult has correct fields for AgeGender model."""
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result = age_gender_model.predict(mock_image, mock_bbox)
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# AgeGender should set gender and age
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assert result.gender is not None
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assert result.age is not None
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# AgeGender should NOT set race and age_group (FairFace only)
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assert result.race is None
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assert result.age_group is None
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