334 lines
13 KiB
Python
334 lines
13 KiB
Python
import pytest
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import numpy as np
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from unittest.mock import patch, MagicMock
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from bson import ObjectId
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from io import BytesIO
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from PIL import Image
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from src.services.image_processor import ImageProcessor
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from src.models.image import ImageModel
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class TestImageProcessor:
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"""Test image processing functionality"""
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@pytest.fixture
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def image_processor(self):
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"""Create image processor instance"""
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return ImageProcessor()
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@pytest.fixture
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def sample_image_data(self):
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"""Create sample image data"""
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# Create a simple test image using PIL
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img = Image.new('RGB', (800, 600), color='red')
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img_bytes = BytesIO()
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img.save(img_bytes, format='JPEG')
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img_bytes.seek(0)
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return img_bytes
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@pytest.fixture
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def sample_png_image(self):
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"""Create sample PNG image data"""
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img = Image.new('RGBA', (400, 300), color=(255, 0, 0, 128))
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img_bytes = BytesIO()
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img.save(img_bytes, format='PNG')
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img_bytes.seek(0)
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return img_bytes
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@pytest.fixture
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def sample_image_model(self):
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"""Create a sample image model"""
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return ImageModel(
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filename="test-image.jpg",
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original_filename="test_image.jpg",
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file_size=1024,
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content_type="image/jpeg",
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storage_path="images/test-image.jpg",
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team_id=ObjectId(),
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uploader_id=ObjectId()
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)
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def test_extract_image_metadata(self, image_processor, sample_image_data):
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"""Test extracting metadata from an image"""
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# Convert BytesIO to bytes if needed
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if hasattr(sample_image_data, 'read'):
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image_bytes = sample_image_data.read()
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sample_image_data.seek(0) # Reset for other tests
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else:
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image_bytes = sample_image_data
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metadata = image_processor.extract_metadata(image_bytes)
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# Should extract basic image properties
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assert 'width' in metadata
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assert 'height' in metadata
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assert 'format' in metadata
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assert 'mode' in metadata
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@pytest.mark.skip(reason="extract_exif_data method not implemented as separate method")
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def test_extract_exif_data(self, image_processor):
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"""Test extracting EXIF data from an image"""
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# This functionality is included in extract_metadata
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pass
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def test_resize_image(self, image_processor, sample_image_data):
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"""Test resizing an image"""
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# Convert BytesIO to bytes if needed
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if hasattr(sample_image_data, 'read'):
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image_bytes = sample_image_data.read()
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sample_image_data.seek(0) # Reset for other tests
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else:
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image_bytes = sample_image_data
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# Resize image
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resized_data, metadata = image_processor.resize_image(image_bytes, max_width=400, max_height=400)
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# Verify resize worked
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assert isinstance(resized_data, bytes)
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assert isinstance(metadata, dict)
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assert 'width' in metadata
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assert 'height' in metadata
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@pytest.mark.skip(reason="generate_thumbnail method not implemented")
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def test_generate_thumbnail(self, image_processor, sample_image_data):
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"""Test generating thumbnails"""
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pass
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@pytest.mark.skip(reason="optimize_image method not implemented")
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def test_optimize_image_quality(self, image_processor, sample_image_data):
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"""Test optimizing image quality and file size"""
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pass
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@pytest.mark.skip(reason="convert_format method not implemented")
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def test_convert_image_format(self, image_processor, sample_image_data):
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"""Test converting image formats"""
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pass
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@pytest.mark.skip(reason="detect_dominant_colors method not implemented")
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def test_detect_image_colors(self, image_processor, sample_image_data):
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"""Test detecting dominant colors in an image"""
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pass
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def test_validate_image_format(self, image_processor, sample_image_data):
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"""Test validating image formats"""
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# Convert BytesIO to bytes if needed
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if hasattr(sample_image_data, 'read'):
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image_bytes = sample_image_data.read()
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sample_image_data.seek(0) # Reset for other tests
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else:
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image_bytes = sample_image_data
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# Test with valid image
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is_valid, error = image_processor.validate_image(image_bytes, "image/jpeg")
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assert is_valid is True
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assert error is None
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# Test with invalid MIME type
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is_valid, error = image_processor.validate_image(image_bytes, "text/plain")
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assert is_valid is False
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assert error is not None
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@pytest.mark.skip(reason="calculate_perceptual_hash method not implemented")
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def test_calculate_image_hash(self, image_processor, sample_image_data):
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"""Test calculating perceptual hashes for duplicate detection"""
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pass
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@pytest.mark.skip(reason="detect_orientation method not implemented")
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def test_detect_image_orientation(self, image_processor, sample_image_data):
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"""Test detecting image orientation"""
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pass
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@pytest.mark.skip(reason="OCR functionality not implemented")
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def test_extract_text_from_image(self, image_processor):
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"""Test extracting text from images using OCR"""
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pass
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@pytest.mark.skip(reason="batch_process method not implemented")
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def test_batch_process_images(self, image_processor, sample_images):
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"""Test batch processing multiple images"""
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pass
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@pytest.mark.skip(reason="assess_quality method not implemented")
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def test_image_quality_assessment(self, image_processor, sample_image_data):
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"""Test assessing image quality metrics"""
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pass
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@pytest.mark.skip(reason="add_watermark method not implemented")
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def test_watermark_addition(self, image_processor, sample_image_data):
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"""Test adding watermarks to images"""
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pass
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@pytest.mark.skip(reason="compress_image method not implemented")
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def test_image_compression_levels(self, image_processor, sample_image_data):
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"""Test different compression levels"""
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pass
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def test_handle_corrupted_image(self, image_processor):
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"""Test handling corrupted image data"""
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# Create corrupted image data
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corrupted_data = b"corrupted image data"
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# Should handle gracefully without crashing
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metadata = image_processor.extract_metadata(corrupted_data)
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assert isinstance(metadata, dict) # Should return empty dict on error
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def test_large_image_processing(self, image_processor):
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"""Test processing large images"""
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# Create a large test image
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large_img = Image.new('RGB', (4000, 3000), color='green')
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img_bytes = BytesIO()
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large_img.save(img_bytes, format='JPEG')
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img_bytes.seek(0)
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# Extract metadata from large image
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metadata = image_processor.extract_metadata(img_bytes.getvalue())
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# Should handle large images
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if metadata: # Only check if metadata extraction succeeded
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assert metadata['width'] == 4000
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assert metadata['height'] == 3000
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@pytest.mark.skip(reason="convert_to_progressive_jpeg method not implemented")
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def test_progressive_jpeg_support(self, image_processor, sample_image_data):
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"""Test progressive JPEG creation"""
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pass
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class TestImageProcessorIntegration:
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"""Integration tests for image processor with other services"""
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def test_integration_with_storage_service(self, image_processor, sample_image_data):
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"""Test integration with storage service"""
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with patch('src.services.storage.StorageService') as mock_storage:
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mock_storage_instance = mock_storage.return_value
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mock_storage_instance.upload_file.return_value = (
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'images/processed.jpg', 'image/jpeg', 1024, {}
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)
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# Process and upload image
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result = image_processor.process_and_upload(
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sample_image_data,
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operations=['resize', 'optimize'],
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team_id=str(ObjectId())
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)
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# Verify integration
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assert 'storage_path' in result
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mock_storage_instance.upload_file.assert_called_once()
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def test_integration_with_embedding_service(self, image_processor, sample_image_data):
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"""Test integration with embedding service"""
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with patch('src.services.embedding_service.EmbeddingService') as mock_embedding:
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mock_embedding_instance = mock_embedding.return_value
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mock_embedding_instance.generate_embedding.return_value = [0.1] * 512
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# Process image and generate embedding
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result = image_processor.process_for_embedding(sample_image_data)
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# Verify integration
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assert 'processed_image' in result
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assert 'embedding' in result
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mock_embedding_instance.generate_embedding.assert_called_once()
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def test_pubsub_message_processing(self, image_processor):
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"""Test processing images from Pub/Sub messages"""
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# Mock Pub/Sub message
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message_data = {
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'image_id': str(ObjectId()),
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'storage_path': 'images/raw/test.jpg',
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'operations': ['resize', 'thumbnail', 'optimize']
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}
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with patch.object(image_processor, 'process_from_storage') as mock_process:
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mock_process.return_value = {
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'processed_path': 'images/processed/test.jpg',
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'thumbnail_path': 'images/thumbnails/test.jpg'
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}
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# Process message
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result = image_processor.handle_processing_message(message_data)
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# Verify message processing
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assert 'processed_path' in result
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mock_process.assert_called_once()
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def test_error_handling_and_retry(self, image_processor, sample_image_data):
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"""Test error handling and retry mechanisms"""
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# Mock transient error followed by success
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with patch.object(image_processor, 'extract_metadata') as mock_extract:
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# First call fails, second succeeds
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mock_extract.side_effect = [
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Exception("Transient error"),
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{'width': 800, 'height': 600, 'format': 'JPEG'}
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]
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# Should retry and succeed
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metadata = image_processor.extract_metadata_with_retry(
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sample_image_data,
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max_retries=2
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)
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assert metadata['width'] == 800
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assert mock_extract.call_count == 2
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class TestImageProcessorPerformance:
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"""Performance tests for image processing"""
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def test_processing_speed_benchmarks(self, image_processor):
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"""Test processing speed for different image sizes"""
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import time
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sizes = [(100, 100), (500, 500), (1000, 1000)]
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for width, height in sizes:
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# Create test image
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img = Image.new('RGB', (width, height), color='blue')
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img_bytes = BytesIO()
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img.save(img_bytes, format='JPEG')
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img_bytes.seek(0)
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# Measure processing time
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start_time = time.time()
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metadata = image_processor.extract_metadata(img_bytes)
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processing_time = time.time() - start_time
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# Verify reasonable processing time (adjust thresholds as needed)
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assert processing_time < 5.0 # Should process within 5 seconds
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assert metadata['width'] == width
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assert metadata['height'] == height
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def test_memory_usage_optimization(self, image_processor):
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"""Test memory usage during image processing"""
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# This would test memory usage patterns
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# Implementation depends on memory profiling tools
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pass
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def test_concurrent_processing(self, image_processor):
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"""Test concurrent image processing"""
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import concurrent.futures
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# Create multiple test images
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images = []
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for i in range(5):
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img = Image.new('RGB', (200, 200), color=(i*50, 0, 0))
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img_bytes = BytesIO()
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img.save(img_bytes, format='JPEG')
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img_bytes.seek(0)
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images.append(img_bytes)
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# Process concurrently
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with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
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futures = [
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executor.submit(image_processor.extract_metadata, img)
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for img in images
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]
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results = [future.result() for future in futures]
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# Verify all processed successfully
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assert len(results) == 5
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for result in results:
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assert 'width' in result
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assert 'height' in result |