mirror of
https://github.com/yakhyo/uniface.git
synced 2025-12-30 09:02:25 +00:00
64ad0d2f53
* feat: Add FairFace model and unified AttributeResult return type - Update FaceAnalyzer to support FairFace - Update documentation (README.md, QUICKSTART.md, MODELS.md) * docs: Change python3.10 to python3.11 in python badge * chore: Remove unused import * fix: Fix test for age gender to reflect AttributeResult type
133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from uniface.attribute import AgeGender, AttributeResult
|
|
|
|
|
|
@pytest.fixture
|
|
def age_gender_model():
|
|
return AgeGender()
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_image():
|
|
return np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_bbox():
|
|
return [100, 100, 300, 300]
|
|
|
|
|
|
def test_model_initialization(age_gender_model):
|
|
assert age_gender_model is not None, 'AgeGender model initialization failed.'
|
|
|
|
|
|
def test_prediction_output_format(age_gender_model, mock_image, mock_bbox):
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
assert isinstance(result, AttributeResult), f'Result should be AttributeResult, got {type(result)}'
|
|
assert isinstance(result.gender, int), f'Gender should be int, got {type(result.gender)}'
|
|
assert isinstance(result.age, int), f'Age should be int, got {type(result.age)}'
|
|
assert isinstance(result.sex, str), f'Sex should be str, got {type(result.sex)}'
|
|
|
|
|
|
def test_gender_values(age_gender_model, mock_image, mock_bbox):
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
assert result.gender in [0, 1], f'Gender should be 0 (Female) or 1 (Male), got {result.gender}'
|
|
assert result.sex in ['Female', 'Male'], f'Sex should be Female or Male, got {result.sex}'
|
|
|
|
|
|
def test_age_range(age_gender_model, mock_image, mock_bbox):
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
assert 0 <= result.age <= 120, f'Age should be between 0 and 120, got {result.age}'
|
|
|
|
|
|
def test_different_bbox_sizes(age_gender_model, mock_image):
|
|
test_bboxes = [
|
|
[50, 50, 150, 150],
|
|
[100, 100, 300, 300],
|
|
[50, 50, 400, 400],
|
|
]
|
|
|
|
for bbox in test_bboxes:
|
|
result = age_gender_model.predict(mock_image, bbox)
|
|
assert result.gender in [0, 1], f'Failed for bbox {bbox}'
|
|
assert 0 <= result.age <= 120, f'Age out of range for bbox {bbox}'
|
|
|
|
|
|
def test_different_image_sizes(age_gender_model, mock_bbox):
|
|
test_sizes = [(480, 640, 3), (720, 1280, 3), (1080, 1920, 3)]
|
|
|
|
for size in test_sizes:
|
|
mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
assert result.gender in [0, 1], f'Failed for image size {size}'
|
|
assert 0 <= result.age <= 120, f'Age out of range for image size {size}'
|
|
|
|
|
|
def test_consistency(age_gender_model, mock_image, mock_bbox):
|
|
result1 = age_gender_model.predict(mock_image, mock_bbox)
|
|
result2 = age_gender_model.predict(mock_image, mock_bbox)
|
|
|
|
assert result1.gender == result2.gender, 'Same input should produce same gender prediction'
|
|
assert result1.age == result2.age, 'Same input should produce same age prediction'
|
|
|
|
|
|
def test_bbox_list_format(age_gender_model, mock_image):
|
|
bbox_list = [100, 100, 300, 300]
|
|
result = age_gender_model.predict(mock_image, bbox_list)
|
|
assert result.gender in [0, 1], 'Should work with bbox as list'
|
|
assert 0 <= result.age <= 120, 'Age should be in valid range'
|
|
|
|
|
|
def test_bbox_array_format(age_gender_model, mock_image):
|
|
bbox_array = np.array([100, 100, 300, 300])
|
|
result = age_gender_model.predict(mock_image, bbox_array)
|
|
assert result.gender in [0, 1], 'Should work with bbox as numpy array'
|
|
assert 0 <= result.age <= 120, 'Age should be in valid range'
|
|
|
|
|
|
def test_multiple_predictions(age_gender_model, mock_image):
|
|
bboxes = [
|
|
[50, 50, 150, 150],
|
|
[200, 200, 350, 350],
|
|
[400, 400, 550, 550],
|
|
]
|
|
|
|
results = []
|
|
for bbox in bboxes:
|
|
result = age_gender_model.predict(mock_image, bbox)
|
|
results.append(result)
|
|
|
|
assert len(results) == 3, 'Should have 3 predictions'
|
|
for result in results:
|
|
assert result.gender in [0, 1]
|
|
assert 0 <= result.age <= 120
|
|
|
|
|
|
def test_age_is_positive(age_gender_model, mock_image, mock_bbox):
|
|
for _ in range(5):
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
assert result.age >= 0, f'Age should be non-negative, got {result.age}'
|
|
|
|
|
|
def test_output_format_for_visualization(age_gender_model, mock_image, mock_bbox):
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
text = f'{result.sex}, {result.age}y'
|
|
assert isinstance(text, str), 'Should be able to format as string'
|
|
assert 'Male' in text or 'Female' in text, 'Text should contain gender'
|
|
assert 'y' in text, "Text should contain 'y' for years"
|
|
|
|
|
|
def test_attribute_result_fields(age_gender_model, mock_image, mock_bbox):
|
|
"""Test that AttributeResult has correct fields for AgeGender model."""
|
|
result = age_gender_model.predict(mock_image, mock_bbox)
|
|
|
|
# AgeGender should set gender and age
|
|
assert result.gender is not None
|
|
assert result.age is not None
|
|
|
|
# AgeGender should NOT set race and age_group (FairFace only)
|
|
assert result.race is None
|
|
assert result.age_group is None
|