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"""
Tests for the processing pipeline.
"""
import pytest
import numpy as np
import cv2
from unittest.mock import Mock, patch, MagicMock
from pathlib import Path
from api.pipeline import (
ProcessingPipeline,
PipelineConfig,
PipelineResult,
ProcessingMode,
PipelineStage
)
class TestPipelineConfig:
"""Test pipeline configuration."""
def test_default_config(self):
"""Test default configuration values."""
config = PipelineConfig()
assert config.mode == ProcessingMode.PHOTO
assert config.quality_preset == "high"
assert config.use_gpu == True
assert config.enable_cache == True
def test_custom_config(self):
"""Test custom configuration."""
config = PipelineConfig(
mode=ProcessingMode.VIDEO,
quality_preset="ultra",
use_gpu=False,
batch_size=4
)
assert config.mode == ProcessingMode.VIDEO
assert config.quality_preset == "ultra"
assert config.use_gpu == False
assert config.batch_size == 4
class TestProcessingPipeline:
"""Test the main processing pipeline."""
@pytest.fixture
def mock_pipeline(self, pipeline_config):
"""Create a pipeline with mocked components."""
with patch('api.pipeline.ModelFactory') as mock_factory:
with patch('api.pipeline.DeviceManager') as mock_device:
mock_device.return_value.get_device.return_value = 'cpu'
mock_factory.return_value.load_model.return_value = Mock()
pipeline = ProcessingPipeline(pipeline_config)
return pipeline
def test_pipeline_initialization(self, mock_pipeline):
"""Test pipeline initialization."""
assert mock_pipeline is not None
assert mock_pipeline.config is not None
assert mock_pipeline.current_stage == PipelineStage.INITIALIZATION
def test_process_image_success(self, mock_pipeline, sample_image, sample_background):
"""Test successful image processing."""
# Mock the processing methods
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
result = mock_pipeline.process_image(sample_image, sample_background)
assert result is not None
assert isinstance(result, PipelineResult)
assert result.success == True
assert result.output_image is not None
def test_process_image_with_effects(self, mock_pipeline, sample_image):
"""Test image processing with effects."""
mock_pipeline.config.apply_effects = ['bokeh', 'vignette']
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
result = mock_pipeline.process_image(sample_image, None)
assert result is not None
assert result.success == True
def test_process_image_failure(self, mock_pipeline, sample_image):
"""Test image processing failure handling."""
# Mock segmentation to fail
mock_pipeline._segment_image = Mock(side_effect=Exception("Segmentation failed"))
result = mock_pipeline.process_image(sample_image, None)
assert result is not None
assert result.success == False
assert len(result.errors) > 0
@pytest.mark.parametrize("quality", ["low", "medium", "high", "ultra"])
def test_quality_presets(self, mock_pipeline, sample_image, quality):
"""Test different quality presets."""
mock_pipeline.config.quality_preset = quality
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
result = mock_pipeline.process_image(sample_image, None)
assert result is not None
assert result.success == True
def test_batch_processing(self, mock_pipeline, sample_image):
"""Test batch processing of multiple images."""
images = [sample_image] * 3
# Mock processing
mock_pipeline.process_image = Mock(return_value=PipelineResult(
success=True,
output_image=sample_image,
quality_score=0.9
))
results = mock_pipeline.process_batch(images)
assert len(results) == 3
assert all(r.success for r in results)
def test_progress_callback(self, mock_pipeline, sample_image):
"""Test progress callback functionality."""
progress_values = []
def progress_callback(value, message):
progress_values.append(value)
mock_pipeline.config.progress_callback = progress_callback
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
result = mock_pipeline.process_image(sample_image, None)
assert len(progress_values) > 0
assert 0.0 <= max(progress_values) <= 1.0
def test_cache_functionality(self, mock_pipeline, sample_image):
"""Test caching functionality."""
mock_pipeline.config.enable_cache = True
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
# First call
result1 = mock_pipeline.process_image(sample_image, None)
# Second call (should use cache)
result2 = mock_pipeline.process_image(sample_image, None)
assert result1.success == result2.success
# Verify segmentation was only called once (cache hit on second call)
assert mock_pipeline._segment_image.call_count == 1
def test_memory_management(self, mock_pipeline):
"""Test memory management and cleanup."""
initial_cache_size = len(mock_pipeline.cache)
# Process multiple images to fill cache
for i in range(10):
image = np.random.randint(0, 255, (512, 512, 3), dtype=np.uint8)
mock_pipeline.cache[f"test_{i}"] = PipelineResult(success=True)
# Clear cache
mock_pipeline.clear_cache()
assert len(mock_pipeline.cache) == 0
def test_statistics_tracking(self, mock_pipeline, sample_image):
"""Test statistics tracking."""
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
# Process image
result = mock_pipeline.process_image(sample_image, None)
# Get statistics
stats = mock_pipeline.get_statistics()
assert 'total_processed' in stats
assert stats['total_processed'] > 0
assert 'avg_time' in stats
class TestPipelineIntegration:
"""Integration tests for the pipeline."""
@pytest.mark.integration
@pytest.mark.slow
def test_end_to_end_processing(self, sample_image, sample_background, temp_dir):
"""Test end-to-end processing pipeline."""
config = PipelineConfig(
use_gpu=False,
quality_preset="medium",
enable_cache=False
)
# Create pipeline (will use real components if available)
try:
pipeline = ProcessingPipeline(config)
except Exception:
pytest.skip("Models not available for integration test")
# Process image
result = pipeline.process_image(sample_image, sample_background)
if result.success:
assert result.output_image is not None
assert result.output_image.shape == sample_image.shape
assert result.quality_score > 0
# Save output
output_path = temp_dir / "test_output.png"
cv2.imwrite(str(output_path), result.output_image)
assert output_path.exists()
@pytest.mark.integration
@pytest.mark.slow
def test_video_frame_processing(self, sample_video, temp_dir):
"""Test processing video frames."""
config = PipelineConfig(
mode=ProcessingMode.VIDEO,
use_gpu=False,
quality_preset="low"
)
try:
pipeline = ProcessingPipeline(config)
except Exception:
pytest.skip("Models not available for integration test")
# Open video
cap = cv2.VideoCapture(sample_video)
processed_frames = []
# Process first 5 frames
for i in range(5):
ret, frame = cap.read()
if not ret:
break
result = pipeline.process_image(frame, None)
if result.success:
processed_frames.append(result.output_image)
cap.release()
assert len(processed_frames) > 0
# Save as video
if processed_frames:
output_path = temp_dir / "test_video_out.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(str(output_path), fourcc, 30.0,
(processed_frames[0].shape[1], processed_frames[0].shape[0]))
for frame in processed_frames:
out.write(frame)
out.release()
assert output_path.exists()
class TestPipelinePerformance:
"""Performance tests for the pipeline."""
@pytest.mark.slow
def test_processing_speed(self, mock_pipeline, sample_image, performance_timer):
"""Test processing speed."""
# Mock processing
mock_pipeline._segment_image = Mock(return_value=np.ones((512, 512), dtype=np.uint8) * 255)
mock_pipeline.alpha_matting.process = Mock(return_value={
'alpha': np.ones((512, 512), dtype=np.float32),
'confidence': 0.95
})
with performance_timer as timer:
result = mock_pipeline.process_image(sample_image, None)
assert result.success == True
assert timer.elapsed < 1.0 # Should process in under 1 second
@pytest.mark.slow
def test_batch_processing_speed(self, mock_pipeline, sample_image, performance_timer):
"""Test batch processing speed."""
images = [sample_image] * 10
# Mock processing
mock_pipeline.process_image = Mock(return_value=PipelineResult(
success=True,
output_image=sample_image,
quality_score=0.9
))
with performance_timer as timer:
results = mock_pipeline.process_batch(images)
assert len(results) == 10
assert timer.elapsed < 5.0 # Should process 10 images in under 5 seconds
def test_memory_usage(self, mock_pipeline, sample_image):
"""Test memory usage during processing."""
import psutil
import os
process = psutil.Process(os.getpid())
initial_memory = process.memory_info().rss / 1024 / 1024 # MB
# Process multiple images
for _ in range(10):
mock_pipeline.process_image(sample_image, None)
final_memory = process.memory_info().rss / 1024 / 1024 # MB
memory_increase = final_memory - initial_memory
# Memory increase should be reasonable (less than 500MB for 10 images)
assert memory_increase < 500 |