Files
uniface/tests/test_landmark.py
Yakhyokhuja Valikhujaev 50226041c9 refactor: Standardize naming conventions (#47)
* 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)
2025-12-30 00:20:34 +09:00

116 lines
3.8 KiB
Python

# Copyright 2025 Yakhyokhuja Valikhujaev
# Author: Yakhyokhuja Valikhujaev
# GitHub: https://github.com/yakhyo
"""Tests for 106-point facial landmark detector."""
from __future__ import annotations
import numpy as np
import pytest
from uniface.landmark import Landmark106
@pytest.fixture
def landmark_model():
return Landmark106()
@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(landmark_model):
assert landmark_model is not None, 'Landmark106 model initialization failed.'
def test_landmark_detection(landmark_model, mock_image, mock_bbox):
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
assert landmarks.shape == (106, 2), f'Expected shape (106, 2), got {landmarks.shape}'
def test_landmark_dtype(landmark_model, mock_image, mock_bbox):
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
assert landmarks.dtype == np.float32, f'Expected float32, got {landmarks.dtype}'
def test_landmark_coordinates_within_image(landmark_model, mock_image, mock_bbox):
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
x_coords = landmarks[:, 0]
y_coords = landmarks[:, 1]
x1, y1, x2, y2 = mock_bbox
margin = 50
x_in_bounds = np.sum((x_coords >= x1 - margin) & (x_coords <= x2 + margin))
y_in_bounds = np.sum((y_coords >= y1 - margin) & (y_coords <= y2 + margin))
assert x_in_bounds >= 95, f'Only {x_in_bounds}/106 x-coordinates within bounds'
assert y_in_bounds >= 95, f'Only {y_in_bounds}/106 y-coordinates within bounds'
def test_different_bbox_sizes(landmark_model, mock_image):
test_bboxes = [
[50, 50, 150, 150],
[100, 100, 300, 300],
[50, 50, 400, 400],
]
for bbox in test_bboxes:
landmarks = landmark_model.get_landmarks(mock_image, bbox)
assert landmarks.shape == (106, 2), f'Failed for bbox {bbox}'
def test_landmark_array_format(landmark_model, mock_image, mock_bbox):
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
landmarks_int = landmarks.astype(int)
assert landmarks_int.shape == (106, 2), 'Integer conversion should preserve shape'
assert landmarks_int.dtype in [np.int32, np.int64], 'Should convert to integer type'
def test_consistency(landmark_model, mock_image, mock_bbox):
landmarks1 = landmark_model.get_landmarks(mock_image, mock_bbox)
landmarks2 = landmark_model.get_landmarks(mock_image, mock_bbox)
assert np.allclose(landmarks1, landmarks2), 'Same input should produce same landmarks'
def test_different_image_sizes(landmark_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)
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
assert landmarks.shape == (106, 2), f'Failed for image size {size}'
def test_bbox_list_format(landmark_model, mock_image):
bbox_list = [100, 100, 300, 300]
landmarks = landmark_model.get_landmarks(mock_image, bbox_list)
assert landmarks.shape == (106, 2), 'Should work with bbox as list'
def test_bbox_array_format(landmark_model, mock_image):
bbox_array = np.array([100, 100, 300, 300])
landmarks = landmark_model.get_landmarks(mock_image, bbox_array)
assert landmarks.shape == (106, 2), 'Should work with bbox as numpy array'
def test_landmark_distribution(landmark_model, mock_image, mock_bbox):
landmarks = landmark_model.get_landmarks(mock_image, mock_bbox)
x_variance = np.var(landmarks[:, 0])
y_variance = np.var(landmarks[:, 1])
assert x_variance > 0, 'Landmarks should have variation in x-coordinates'
assert y_variance > 0, 'Landmarks should have variation in y-coordinates'