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"""
Unit tests for MovementClassifier
Tests movement classification, intensity calculation, and rhythm detection
"""
import pytest
import numpy as np
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).parent.parent))
from app.movement_classifier import MovementClassifier, MovementType, MovementMetrics
from app.pose_analyzer import PoseKeypoints
from app.config import Config
class TestMovementClassifier:
"""Test suite for MovementClassifier functionality"""
@pytest.fixture
def classifier(self):
"""Create MovementClassifier instance for testing"""
return MovementClassifier()
@pytest.fixture
def standing_sequence(self):
"""Create a sequence representing standing still"""
sequence = []
base_landmarks = np.random.rand(33, 3)
for i in range(10):
# Very small random noise to simulate standing
landmarks = base_landmarks + np.random.normal(0, 0.001, (33, 3))
landmarks[:, 2] = 0.9 # High confidence
pose = PoseKeypoints(
landmarks=landmarks,
frame_number=i,
timestamp=i/30.0,
confidence=0.9
)
sequence.append(pose)
return sequence
@pytest.fixture
def dancing_sequence(self):
"""Create a sequence representing dancing (high movement)"""
sequence = []
for i in range(20):
# Create varied movement
landmarks = np.random.rand(33, 3)
landmarks[:, 2] = 0.9
# Add more variation to simulate dancing
landmarks[:, 0] += np.sin(i * 0.5) * 0.1
landmarks[:, 1] += np.cos(i * 0.5) * 0.1
pose = PoseKeypoints(
landmarks=landmarks,
frame_number=i,
timestamp=i/30.0,
confidence=0.9
)
sequence.append(pose)
return sequence
@pytest.fixture
def jumping_sequence(self):
"""Create a sequence representing jumping (vertical movement)"""
sequence = []
base_landmarks = np.random.rand(33, 3)
for i in range(15):
landmarks = base_landmarks.copy()
# Simulate vertical jump (modify hip positions)
jump_height = 0.1 * np.sin(i * np.pi / 7) # Jump cycle
landmarks[23, 1] -= jump_height # Left hip
landmarks[24, 1] -= jump_height # Right hip
landmarks[:, 2] = 0.9
pose = PoseKeypoints(
landmarks=landmarks,
frame_number=i,
timestamp=i/30.0,
confidence=0.9
)
sequence.append(pose)
return sequence
def test_classifier_initialization(self, classifier):
"""Test MovementClassifier initializes correctly"""
assert classifier is not None
assert len(classifier.movement_history) == 0
assert classifier.smoothing_window > 0
def test_analyze_empty_sequence(self, classifier):
"""Test analyzing empty sequence"""
metrics = classifier.analyze_sequence([])
assert metrics.movement_type == MovementType.UNKNOWN
assert metrics.intensity == 0.0
assert metrics.velocity == 0.0
def test_analyze_standing_sequence(self, classifier, standing_sequence):
"""Test classification of standing movement"""
metrics = classifier.analyze_sequence(standing_sequence)
assert metrics is not None
# Standing should have low velocity
assert metrics.velocity < Config.VELOCITY_WALKING
# Should have low intensity
assert metrics.intensity < 30.0
def test_analyze_dancing_sequence(self, classifier, dancing_sequence):
"""Test classification of dancing movement"""
metrics = classifier.analyze_sequence(dancing_sequence)
assert metrics is not None
# Dancing should have higher velocity
assert metrics.velocity > Config.VELOCITY_STANDING
# Should have higher intensity
assert metrics.intensity > 20.0
def test_velocity_calculation(self, classifier, dancing_sequence):
"""Test velocity calculation between frames"""
velocities = classifier._calculate_velocities(dancing_sequence)
assert len(velocities) == len(dancing_sequence) - 1
assert np.all(velocities >= 0) # Velocities should be non-negative
def test_jumping_detection(self, classifier, jumping_sequence):
"""Test jumping detection algorithm"""
is_jumping = classifier._detect_jumping(jumping_sequence)
# Jumping sequence should be detected
# Note: This may be True or False depending on threshold sensitivity
assert isinstance(is_jumping, bool)
def test_crouching_detection(self, classifier):
"""Test crouching detection"""
# Create crouching pose (compressed torso)
landmarks = np.random.rand(33, 3)
landmarks[11, 1] = 0.3 # Shoulder
landmarks[12, 1] = 0.3
landmarks[23, 1] = 0.35 # Hip (very close to shoulder)
landmarks[24, 1] = 0.35
landmarks[:, 2] = 0.9
crouch_pose = PoseKeypoints(
landmarks=landmarks,
frame_number=0,
timestamp=0.0,
confidence=0.9
)
is_crouching = classifier._detect_crouching([crouch_pose])
assert isinstance(is_crouching, bool)
def test_intensity_calculation(self, classifier, dancing_sequence):
"""Test movement intensity calculation"""
velocities = classifier._calculate_velocities(dancing_sequence)
movement_type = MovementType.DANCING
intensity = classifier._calculate_intensity(velocities, movement_type)
assert 0 <= intensity <= 100
assert isinstance(intensity, (int, float))
def test_body_part_activity(self, classifier, dancing_sequence):
"""Test body part activity calculation"""
activity = classifier._calculate_body_part_activity(dancing_sequence)
# Should have activity scores for all body parts
expected_parts = ["head", "torso", "left_arm", "right_arm", "left_leg", "right_leg"]
for part in expected_parts:
assert part in activity
assert 0 <= activity[part] <= 100
def test_movement_summary_empty(self, classifier):
"""Test movement summary with no data"""
summary = classifier.get_movement_summary()
assert summary['total_sequences'] == 0
assert summary['average_intensity'] == 0.0
assert summary['most_active_body_part'] == "none"
def test_movement_summary_with_data(self, classifier, dancing_sequence):
"""Test movement summary with analyzed data"""
classifier.analyze_sequence(dancing_sequence)
summary = classifier.get_movement_summary()
assert summary['total_sequences'] == 1
assert summary['average_intensity'] > 0
assert 'movement_distribution' in summary
assert 'most_active_body_part' in summary
def test_rhythm_detection_short_sequence(self, classifier, standing_sequence):
"""Test rhythm detection with short sequence"""
rhythm = classifier.detect_rhythm_patterns(standing_sequence[:5], fps=30.0)
assert 'has_rhythm' in rhythm
assert 'estimated_bpm' in rhythm
assert rhythm['estimated_bpm'] >= 0
def test_rhythm_detection_long_sequence(self, classifier, dancing_sequence):
"""Test rhythm detection with adequate sequence"""
rhythm = classifier.detect_rhythm_patterns(dancing_sequence, fps=30.0)
assert 'has_rhythm' in rhythm
assert 'estimated_bpm' in rhythm
assert 'peak_count' in rhythm
assert 'rhythm_consistency' in rhythm
def test_find_peaks(self, classifier):
"""Test peak detection in signal"""
# Create signal with obvious peaks
signal = np.array([0, 1, 0, 1, 0, 1, 0, 5, 0, 1, 0])
peaks = classifier._find_peaks(signal, threshold_percentile=50)
assert len(peaks) > 0
# Peak at index 7 (value=5) should be detected
assert 7 in peaks
def test_movement_smoothness_empty(self, classifier):
"""Test smoothness calculation with minimal data"""
sequence = []
smoothness = classifier.calculate_movement_smoothness(sequence)
assert smoothness == 100.0 # Default for no data
def test_movement_smoothness_smooth_motion(self, classifier, standing_sequence):
"""Test smoothness with smooth motion"""
smoothness = classifier.calculate_movement_smoothness(standing_sequence)
assert 0 <= smoothness <= 100
# Standing should be very smooth
assert smoothness > 80
def test_movement_smoothness_jerky_motion(self, classifier):
"""Test smoothness with jerky motion"""
sequence = []
for i in range(10):
# Create jerky movement (alternating positions)
landmarks = np.random.rand(33, 3)
if i % 2 == 0:
landmarks[:, 0] += 0.2
landmarks[:, 2] = 0.9
pose = PoseKeypoints(
landmarks=landmarks,
frame_number=i,
timestamp=i/30.0,
confidence=0.9
)
sequence.append(pose)
smoothness = classifier.calculate_movement_smoothness(sequence)
assert 0 <= smoothness <= 100
def test_movement_type_enum(self):
"""Test MovementType enum values"""
assert MovementType.STANDING.value == "Standing"
assert MovementType.WALKING.value == "Walking"
assert MovementType.DANCING.value == "Dancing"
assert MovementType.JUMPING.value == "Jumping"
assert MovementType.CROUCHING.value == "Crouching"
assert MovementType.UNKNOWN.value == "Unknown"
def test_reset_classifier(self, classifier, dancing_sequence):
"""Test resetting classifier clears history"""
classifier.analyze_sequence(dancing_sequence)
assert len(classifier.movement_history) > 0
classifier.reset()
assert len(classifier.movement_history) == 0
def test_multiple_sequence_analysis(self, classifier, standing_sequence, dancing_sequence):
"""Test analyzing multiple sequences"""
metrics1 = classifier.analyze_sequence(standing_sequence)
metrics2 = classifier.analyze_sequence(dancing_sequence)
assert len(classifier.movement_history) == 2
assert metrics1.intensity != metrics2.intensity
def test_body_parts_defined(self, classifier):
"""Test that all body parts are properly defined"""
assert 'head' in classifier.BODY_PARTS
assert 'torso' in classifier.BODY_PARTS
assert 'left_arm' in classifier.BODY_PARTS
assert 'right_arm' in classifier.BODY_PARTS
assert 'left_leg' in classifier.BODY_PARTS
assert 'right_leg' in classifier.BODY_PARTS
# Each body part should have landmark indices
for part, indices in classifier.BODY_PARTS.items():
assert len(indices) > 0
assert all(0 <= idx < 33 for idx in indices)
if __name__ == "__main__":
pytest.main([__file__, "-v"]) |