""" Phase 5: Computer Vision Integration Tests Tests for visual intelligence, image processing, and multi-modal integration """ import pytest import asyncio import json import base64 from datetime import datetime, timedelta from unittest.mock import Mock, AsyncMock from typing import Dict, List, Any, Optional, Tuple # Mock imports for testing class MockVisionProcessor: def __init__(self): self.processed_images = {} self.detection_results = {} self.analysis_results = {} async def process_image(self, image_data: bytes, processing_type: str = 'general') -> Dict[str, Any]: """Process image data""" image_id = f"img_{len(self.processed_images)}" result = { 'image_id': image_id, 'processing_type': processing_type, 'size': len(image_data), 'format': 'processed', 'timestamp': datetime.utcnow().isoformat(), 'analysis': await self._analyze_image(image_data, processing_type) } self.processed_images[image_id] = result return result async def _analyze_image(self, image_data: bytes, processing_type: str) -> Dict[str, Any]: """Analyze image based on processing type""" if processing_type == 'object_detection': return await self._detect_objects(image_data) elif processing_type == 'scene_analysis': return await self._analyze_scene(image_data) elif processing_type == 'text_extraction': return await self._extract_text(image_data) else: return await self._general_analysis(image_data) async def _detect_objects(self, image_data: bytes) -> Dict[str, Any]: """Detect objects in image""" # Mock object detection objects = [ {'class': 'person', 'confidence': 0.92, 'bbox': [100, 150, 200, 300]}, {'class': 'car', 'confidence': 0.87, 'bbox': [300, 200, 500, 350]}, {'class': 'building', 'confidence': 0.95, 'bbox': [0, 0, 600, 400]} ] self.detection_results[f"detection_{len(self.detection_results)}"] = objects return { 'objects_detected': len(objects), 'objects': objects, 'detection_confidence': sum(obj['confidence'] for obj in objects) / len(objects) } async def _analyze_scene(self, image_data: bytes) -> Dict[str, Any]: """Analyze scene context""" # Mock scene analysis scene_info = { 'scene_type': 'urban_street', 'confidence': 0.88, 'elements': ['vehicles', 'pedestrians', 'buildings'], 'weather': 'clear', 'time_of_day': 'daytime', 'complexity': 'medium' } return scene_info async def _extract_text(self, image_data: bytes) -> Dict[str, Any]: """Extract text from image""" # Mock OCR text_data = { 'text_found': True, 'extracted_text': ['STOP', 'MAIN ST', 'NO PARKING'], 'confidence': 0.91, 'language': 'en', 'text_regions': [ {'text': 'STOP', 'bbox': [50, 100, 150, 150]}, {'text': 'MAIN ST', 'bbox': [200, 100, 350, 150]} ] } return text_data async def _general_analysis(self, image_data: bytes) -> Dict[str, Any]: """General image analysis""" return { 'brightness': 0.7, 'contrast': 0.8, 'sharpness': 0.75, 'color_distribution': {'red': 0.3, 'green': 0.4, 'blue': 0.3}, 'dominant_colors': ['blue', 'green', 'white'], 'image_quality': 'good' } class MockMultiModalAgent: def __init__(self): self.vision_processor = MockVisionProcessor() self.integrated_results = {} async def process_multi_modal(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Process multi-modal inputs""" result_id = f"multi_{len(self.integrated_results)}" # Process different modalities results = {} if 'image' in inputs: results['vision'] = await self.vision_processor.process_image( inputs['image'], inputs.get('vision_processing_type', 'general') ) if 'text' in inputs: results['text'] = await self._process_text(inputs['text']) if 'sensor_data' in inputs: results['sensor'] = await self._process_sensor_data(inputs['sensor_data']) # Integrate results integrated_result = { 'result_id': result_id, 'modalities_processed': list(results.keys()), 'integration': await self._integrate_modalities(results), 'timestamp': datetime.utcnow().isoformat() } self.integrated_results[result_id] = integrated_result return integrated_result async def _process_text(self, text: str) -> Dict[str, Any]: """Process text input""" return { 'text_length': len(text), 'language': 'en', 'sentiment': 'neutral', 'entities': [], 'keywords': text.split()[:5] } async def _process_sensor_data(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]: """Process sensor data""" return { 'sensor_type': sensor_data.get('type', 'unknown'), 'readings': sensor_data.get('readings', {}), 'timestamp': sensor_data.get('timestamp', datetime.utcnow().isoformat()), 'quality': 'good' } async def _integrate_modalities(self, results: Dict[str, Any]) -> Dict[str, Any]: """Integrate results from different modalities""" integration = { 'confidence': 0.85, 'completeness': len(results) / 3.0, # Assuming 3 modalities max 'cross_modal_insights': [] } # Add cross-modal insights if 'vision' in results and 'text' in results: if 'objects' in results['vision'].get('analysis', {}): integration['cross_modal_insights'].append( f"Visual context: {len(results['vision']['analysis']['objects'])} objects detected" ) return integration class MockContextIntegration: def __init__(self): self.context_history = [] self.context_models = {} async def integrate_context(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]: """Integrate vision results with context""" context_id = f"ctx_{len(self.context_history)}" integration = { 'context_id': context_id, 'vision_result': vision_result, 'context_data': context_data, 'enhanced_understanding': await self._enhance_understanding(vision_result, context_data), 'timestamp': datetime.utcnow().isoformat() } self.context_history.append(integration) return integration async def _enhance_understanding(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]: """Enhance understanding with context""" enhanced = { 'scene_understanding': vision_result.get('analysis', {}), 'contextual_insights': [], 'confidence_boost': 0.0 } # Add contextual insights if context_data.get('location') == 'intersection': enhanced['contextual_insights'].append("Traffic monitoring context") enhanced['confidence_boost'] += 0.1 if context_data.get('time_of_day') == 'night': enhanced['contextual_insights'].append("Low light conditions detected") enhanced['confidence_boost'] -= 0.05 return enhanced class TestVisionProcessor: """Test vision processing functionality""" def setup_method(self): self.vision_processor = MockVisionProcessor() self.sample_image = b'sample_image_data_for_testing' @pytest.mark.asyncio async def test_image_processing(self): """Test basic image processing""" result = await self.vision_processor.process_image(self.sample_image) assert result['image_id'].startswith('img_') assert result['size'] == len(self.sample_image) assert result['format'] == 'processed' assert 'analysis' in result assert 'timestamp' in result @pytest.mark.asyncio async def test_object_detection(self): """Test object detection functionality""" result = await self.vision_processor.process_image(self.sample_image, 'object_detection') assert 'analysis' in result assert 'objects_detected' in result['analysis'] assert 'objects' in result['analysis'] assert len(result['analysis']['objects']) > 0 # Check object structure for obj in result['analysis']['objects']: assert 'class' in obj assert 'confidence' in obj assert 'bbox' in obj assert 0 <= obj['confidence'] <= 1 @pytest.mark.asyncio async def test_scene_analysis(self): """Test scene analysis functionality""" result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis') assert 'analysis' in result assert 'scene_type' in result['analysis'] assert 'confidence' in result['analysis'] assert 'elements' in result['analysis'] assert result['analysis']['scene_type'] == 'urban_street' assert 0 <= result['analysis']['confidence'] <= 1 @pytest.mark.asyncio async def test_text_extraction(self): """Test text extraction (OCR) functionality""" result = await self.vision_processor.process_image(self.sample_image, 'text_extraction') assert 'analysis' in result assert 'text_found' in result['analysis'] assert 'extracted_text' in result['analysis'] if result['analysis']['text_found']: assert len(result['analysis']['extracted_text']) > 0 assert 'confidence' in result['analysis'] @pytest.mark.asyncio async def test_general_analysis(self): """Test general image analysis""" result = await self.vision_processor.process_image(self.sample_image, 'general') assert 'analysis' in result assert 'brightness' in result['analysis'] assert 'contrast' in result['analysis'] assert 'sharpness' in result['analysis'] assert 'color_distribution' in result['analysis'] # Check value ranges assert 0 <= result['analysis']['brightness'] <= 1 assert 0 <= result['analysis']['contrast'] <= 1 assert 0 <= result['analysis']['sharpness'] <= 1 class TestMultiModalIntegration: """Test multi-modal integration""" def setup_method(self): self.multi_modal_agent = MockMultiModalAgent() self.sample_image = b'sample_image_data' self.sample_text = "This is a sample text for testing" self.sample_sensor_data = { 'type': 'temperature', 'readings': {'value': 25.5, 'unit': 'celsius'}, 'timestamp': datetime.utcnow().isoformat() } @pytest.mark.asyncio async def test_vision_only_processing(self): """Test processing with only vision input""" inputs = {'image': self.sample_image} result = await self.multi_modal_agent.process_multi_modal(inputs) assert result['result_id'].startswith('multi_') assert 'vision' in result['modalities_processed'] assert 'integration' in result assert 'confidence' in result['integration'] @pytest.mark.asyncio async def test_text_only_processing(self): """Test processing with only text input""" inputs = {'text': self.sample_text} result = await self.multi_modal_agent.process_multi_modal(inputs) assert result['result_id'].startswith('multi_') assert 'text' in result['modalities_processed'] assert 'integration' in result @pytest.mark.asyncio async def test_sensor_only_processing(self): """Test processing with only sensor input""" inputs = {'sensor_data': self.sample_sensor_data} result = await self.multi_modal_agent.process_multi_modal(inputs) assert result['result_id'].startswith('multi_') assert 'sensor' in result['modalities_processed'] assert 'integration' in result @pytest.mark.asyncio async def test_full_multi_modal_processing(self): """Test processing with all modalities""" inputs = { 'image': self.sample_image, 'text': self.sample_text, 'sensor_data': self.sample_sensor_data } result = await self.multi_modal_agent.process_multi_modal(inputs) assert result['result_id'].startswith('multi_') assert len(result['modalities_processed']) == 3 assert 'vision' in result['modalities_processed'] assert 'text' in result['modalities_processed'] assert 'sensor' in result['modalities_processed'] assert 'integration' in result assert 'cross_modal_insights' in result['integration'] @pytest.mark.asyncio async def test_cross_modal_insights(self): """Test cross-modal insight generation""" inputs = { 'image': self.sample_image, 'text': self.sample_text, 'vision_processing_type': 'object_detection' } result = await self.multi_modal_agent.process_multi_modal(inputs) assert 'cross_modal_insights' in result['integration'] assert len(result['integration']['cross_modal_insights']) > 0 class TestContextIntegration: """Test context integration with vision""" def setup_method(self): self.context_integration = MockContextIntegration() self.vision_processor = MockVisionProcessor() self.sample_image = b'sample_image_data' @pytest.mark.asyncio async def test_basic_context_integration(self): """Test basic context integration""" vision_result = await self.vision_processor.process_image(self.sample_image) context_data = { 'location': 'intersection', 'time_of_day': 'daytime', 'weather': 'clear' } result = await self.context_integration.integrate_context(vision_result, context_data) assert result['context_id'].startswith('ctx_') assert 'vision_result' in result assert 'context_data' in result assert 'enhanced_understanding' in result @pytest.mark.asyncio async def test_location_context(self): """Test location-based context integration""" vision_result = await self.vision_processor.process_image(self.sample_image, 'object_detection') context_data = { 'location': 'intersection', 'traffic_flow': 'moderate' } result = await self.context_integration.integrate_context(vision_result, context_data) assert 'enhanced_understanding' in result assert 'contextual_insights' in result['enhanced_understanding'] assert any('traffic' in insight for insight in result['enhanced_understanding']['contextual_insights']) @pytest.mark.asyncio async def test_time_context(self): """Test time-based context integration""" vision_result = await self.vision_processor.process_image(self.sample_image) context_data = { 'time_of_day': 'night', 'lighting_conditions': 'low' } result = await self.context_integration.integrate_context(vision_result, context_data) assert 'enhanced_understanding' in result assert 'confidence_boost' in result['enhanced_understanding'] assert result['enhanced_understanding']['confidence_boost'] < 0 # Night time penalty @pytest.mark.asyncio async def test_context_history_tracking(self): """Test context history tracking""" for i in range(3): vision_result = await self.vision_processor.process_image(self.sample_image) context_data = { 'location': f'location_{i}', 'timestamp': datetime.utcnow().isoformat() } await self.context_integration.integrate_context(vision_result, context_data) assert len(self.context_integration.context_history) == 3 for context in self.context_integration.context_history: assert context['context_id'].startswith('ctx_') class TestVisualReasoning: """Test visual reasoning capabilities""" def setup_method(self): self.vision_processor = MockVisionProcessor() self.multi_modal_agent = MockMultiModalAgent() self.sample_image = b'sample_image_data' @pytest.mark.asyncio async def test_visual_scene_understanding(self): """Test visual scene understanding""" result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis') assert 'analysis' in result assert 'scene_type' in result['analysis'] assert 'elements' in result['analysis'] assert 'complexity' in result['analysis'] # Verify scene understanding scene = result['analysis'] assert len(scene['elements']) > 0 assert scene['complexity'] in ['low', 'medium', 'high'] @pytest.mark.asyncio async def test_object_relationships(self): """Test understanding object relationships""" result = await self.vision_processor.process_image(self.sample_image, 'object_detection') assert 'analysis' in result assert 'objects' in result['analysis'] objects = result['analysis']['objects'] if len(objects) > 1: # Mock relationship analysis relationships = [] for i, obj1 in enumerate(objects): for obj2 in objects[i+1:]: if obj1['class'] == 'person' and obj2['class'] == 'car': relationships.append('person_near_car') assert len(relationships) >= 0 @pytest.mark.asyncio async def test_spatial_reasoning(self): """Test spatial reasoning""" result = await self.vision_processor.process_image(self.sample_image, 'object_detection') assert 'analysis' in result assert 'objects' in result['analysis'] objects = result['analysis']['objects'] for obj in objects: assert 'bbox' in obj assert len(obj['bbox']) == 4 # [x1, y1, x2, y2] # Verify bbox coordinates x1, y1, x2, y2 = obj['bbox'] assert x2 > x1 assert y2 > y1 @pytest.mark.asyncio async def test_temporal_reasoning(self): """Test temporal reasoning (changes over time)""" # Simulate processing multiple images over time results = [] for i in range(3): result = await self.vision_processor.process_image(self.sample_image) results.append(result) await asyncio.sleep(0.01) # Small delay # Analyze temporal changes if len(results) > 1: # Mock temporal analysis changes = [] for i in range(1, len(results)): if results[i]['analysis'] != results[i-1]['analysis']: changes.append(f"Change detected at step {i}") # Should have some analysis of changes assert len(results) == 3 class TestPerformanceMetrics: """Test performance metrics for vision processing""" def setup_method(self): self.vision_processor = MockVisionProcessor() self.sample_image = b'sample_image_data' @pytest.mark.asyncio async def test_processing_speed(self): """Test image processing speed""" start_time = datetime.utcnow() result = await self.vision_processor.process_image(self.sample_image) end_time = datetime.utcnow() processing_time = (end_time - start_time).total_seconds() assert processing_time < 2.0 # Should process within 2 seconds assert result['image_id'].startswith('img_') @pytest.mark.asyncio async def test_batch_processing(self): """Test batch image processing""" images = [self.sample_image] * 5 start_time = datetime.utcnow() results = [] for image in images: result = await self.vision_processor.process_image(image) results.append(result) end_time = datetime.utcnow() total_time = (end_time - start_time).total_seconds() avg_time = total_time / len(images) assert len(results) == 5 assert avg_time < 1.0 # Average should be under 1 second per image @pytest.mark.asyncio async def test_memory_usage(self): """Test memory usage during processing""" import psutil import os process = psutil.Process(os.getpid()) memory_before = process.memory_info().rss # Process multiple images for i in range(10): await self.vision_processor.process_image(self.sample_image) memory_after = process.memory_info().rss memory_increase = memory_after - memory_before # Memory increase should be reasonable (less than 100MB) assert memory_increase < 100 * 1024 * 1024 # 100MB in bytes # Integration tests class TestVisionIntegration: """Integration tests for vision system""" @pytest.mark.asyncio async def test_end_to_end_vision_pipeline(self): """Test complete vision processing pipeline""" vision_processor = MockVisionProcessor() multi_modal_agent = MockMultiModalAgent() context_integration = MockContextIntegration() # Step 1: Process image with object detection image_result = await vision_processor.process_image(b'test_image', 'object_detection') # Step 2: Integrate with context context_data = { 'location': 'urban_intersection', 'time': 'daytime', 'purpose': 'traffic_monitoring' } context_result = await context_integration.integrate_context(image_result, context_data) # Step 3: Multi-modal processing multi_modal_inputs = { 'image': b'test_image', 'text': 'Traffic monitoring report', 'sensor_data': {'type': 'camera', 'status': 'active'} } multi_modal_result = await multi_modal_agent.process_multi_modal(multi_modal_inputs) # Verify pipeline assert image_result['image_id'].startswith('img_') assert context_result['context_id'].startswith('ctx_') assert multi_modal_result['result_id'].startswith('multi_') assert 'objects' in image_result['analysis'] assert 'enhanced_understanding' in context_result assert len(multi_modal_result['modalities_processed']) == 3 @pytest.mark.asyncio async def test_real_time_vision_processing(self): """Test real-time vision processing capabilities""" vision_processor = MockVisionProcessor() # Simulate real-time processing processing_times = [] for i in range(10): start_time = datetime.utcnow() await vision_processor.process_image(f'frame_{i}'.encode()) end_time = datetime.utcnow() processing_times.append((end_time - start_time).total_seconds()) avg_time = sum(processing_times) / len(processing_times) max_time = max(processing_times) # Real-time constraints assert avg_time < 0.5 # Average under 500ms assert max_time < 1.0 # Max under 1 second assert len(processing_times) == 10 if __name__ == '__main__': pytest.main([__file__])