941e17fe6e
✅ Phase 3: Decision Framework Tests - Decision engine functionality tests - Voting system tests (majority, weighted, unanimous) - Consensus algorithm tests - Agent lifecycle management tests - Integration tests for decision processes ✅ Phase 4: Autonomous Decision Making Tests - Autonomous decision engine tests - Learning system tests (experience-based learning) - Policy engine tests (compliance evaluation) - Self-correction mechanism tests - Goal-oriented behavior tests - Full autonomous cycle integration tests ✅ Phase 5: Computer Vision Integration Tests - Vision processor tests (object detection, scene analysis, OCR) - Multi-modal integration tests - Context integration tests - Visual reasoning tests (spatial, temporal) - Performance metrics tests - End-to-end vision pipeline tests ✅ Test Infrastructure - Comprehensive test runner for all phases - Mock implementations for testing - Performance testing capabilities - Integration test coverage - Phase-based test organization 🚀 All Phase Tests Now Implemented and Ready for Execution!
642 lines
24 KiB
Python
642 lines
24 KiB
Python
"""
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Phase 5: Computer Vision Integration Tests
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Tests for visual intelligence, image processing, and multi-modal integration
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"""
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import pytest
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import asyncio
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import json
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import base64
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from datetime import datetime, timedelta
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from unittest.mock import Mock, AsyncMock
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from typing import Dict, List, Any, Optional, Tuple
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# Mock imports for testing
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class MockVisionProcessor:
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def __init__(self):
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self.processed_images = {}
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self.detection_results = {}
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self.analysis_results = {}
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async def process_image(self, image_data: bytes, processing_type: str = 'general') -> Dict[str, Any]:
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"""Process image data"""
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image_id = f"img_{len(self.processed_images)}"
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result = {
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'image_id': image_id,
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'processing_type': processing_type,
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'size': len(image_data),
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'format': 'processed',
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'timestamp': datetime.utcnow().isoformat(),
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'analysis': await self._analyze_image(image_data, processing_type)
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}
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self.processed_images[image_id] = result
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return result
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async def _analyze_image(self, image_data: bytes, processing_type: str) -> Dict[str, Any]:
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"""Analyze image based on processing type"""
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if processing_type == 'object_detection':
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return await self._detect_objects(image_data)
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elif processing_type == 'scene_analysis':
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return await self._analyze_scene(image_data)
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elif processing_type == 'text_extraction':
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return await self._extract_text(image_data)
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else:
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return await self._general_analysis(image_data)
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async def _detect_objects(self, image_data: bytes) -> Dict[str, Any]:
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"""Detect objects in image"""
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# Mock object detection
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objects = [
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{'class': 'person', 'confidence': 0.92, 'bbox': [100, 150, 200, 300]},
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{'class': 'car', 'confidence': 0.87, 'bbox': [300, 200, 500, 350]},
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{'class': 'building', 'confidence': 0.95, 'bbox': [0, 0, 600, 400]}
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]
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self.detection_results[f"detection_{len(self.detection_results)}"] = objects
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return {
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'objects_detected': len(objects),
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'objects': objects,
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'detection_confidence': sum(obj['confidence'] for obj in objects) / len(objects)
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}
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async def _analyze_scene(self, image_data: bytes) -> Dict[str, Any]:
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"""Analyze scene context"""
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# Mock scene analysis
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scene_info = {
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'scene_type': 'urban_street',
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'confidence': 0.88,
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'elements': ['vehicles', 'pedestrians', 'buildings'],
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'weather': 'clear',
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'time_of_day': 'daytime',
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'complexity': 'medium'
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}
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return scene_info
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async def _extract_text(self, image_data: bytes) -> Dict[str, Any]:
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"""Extract text from image"""
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# Mock OCR
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text_data = {
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'text_found': True,
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'extracted_text': ['STOP', 'MAIN ST', 'NO PARKING'],
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'confidence': 0.91,
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'language': 'en',
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'text_regions': [
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{'text': 'STOP', 'bbox': [50, 100, 150, 150]},
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{'text': 'MAIN ST', 'bbox': [200, 100, 350, 150]}
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]
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}
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return text_data
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async def _general_analysis(self, image_data: bytes) -> Dict[str, Any]:
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"""General image analysis"""
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return {
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'brightness': 0.7,
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'contrast': 0.8,
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'sharpness': 0.75,
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'color_distribution': {'red': 0.3, 'green': 0.4, 'blue': 0.3},
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'dominant_colors': ['blue', 'green', 'white'],
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'image_quality': 'good'
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}
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class MockMultiModalAgent:
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def __init__(self):
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self.vision_processor = MockVisionProcessor()
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self.integrated_results = {}
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async def process_multi_modal(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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"""Process multi-modal inputs"""
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result_id = f"multi_{len(self.integrated_results)}"
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# Process different modalities
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results = {}
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if 'image' in inputs:
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results['vision'] = await self.vision_processor.process_image(
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inputs['image'],
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inputs.get('vision_processing_type', 'general')
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)
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if 'text' in inputs:
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results['text'] = await self._process_text(inputs['text'])
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if 'sensor_data' in inputs:
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results['sensor'] = await self._process_sensor_data(inputs['sensor_data'])
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# Integrate results
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integrated_result = {
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'result_id': result_id,
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'modalities_processed': list(results.keys()),
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'integration': await self._integrate_modalities(results),
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'timestamp': datetime.utcnow().isoformat()
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}
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self.integrated_results[result_id] = integrated_result
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return integrated_result
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async def _process_text(self, text: str) -> Dict[str, Any]:
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"""Process text input"""
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return {
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'text_length': len(text),
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'language': 'en',
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'sentiment': 'neutral',
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'entities': [],
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'keywords': text.split()[:5]
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}
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async def _process_sensor_data(self, sensor_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Process sensor data"""
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return {
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'sensor_type': sensor_data.get('type', 'unknown'),
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'readings': sensor_data.get('readings', {}),
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'timestamp': sensor_data.get('timestamp', datetime.utcnow().isoformat()),
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'quality': 'good'
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}
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async def _integrate_modalities(self, results: Dict[str, Any]) -> Dict[str, Any]:
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"""Integrate results from different modalities"""
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integration = {
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'confidence': 0.85,
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'completeness': len(results) / 3.0, # Assuming 3 modalities max
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'cross_modal_insights': []
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}
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# Add cross-modal insights
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if 'vision' in results and 'text' in results:
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if 'objects' in results['vision'].get('analysis', {}):
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integration['cross_modal_insights'].append(
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f"Visual context: {len(results['vision']['analysis']['objects'])} objects detected"
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)
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return integration
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class MockContextIntegration:
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def __init__(self):
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self.context_history = []
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self.context_models = {}
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async def integrate_context(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Integrate vision results with context"""
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context_id = f"ctx_{len(self.context_history)}"
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integration = {
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'context_id': context_id,
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'vision_result': vision_result,
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'context_data': context_data,
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'enhanced_understanding': await self._enhance_understanding(vision_result, context_data),
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'timestamp': datetime.utcnow().isoformat()
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}
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self.context_history.append(integration)
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return integration
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async def _enhance_understanding(self, vision_result: Dict[str, Any], context_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Enhance understanding with context"""
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enhanced = {
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'scene_understanding': vision_result.get('analysis', {}),
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'contextual_insights': [],
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'confidence_boost': 0.0
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}
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# Add contextual insights
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if context_data.get('location') == 'intersection':
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enhanced['contextual_insights'].append("Traffic monitoring context")
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enhanced['confidence_boost'] += 0.1
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if context_data.get('time_of_day') == 'night':
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enhanced['contextual_insights'].append("Low light conditions detected")
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enhanced['confidence_boost'] -= 0.05
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return enhanced
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class TestVisionProcessor:
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"""Test vision processing functionality"""
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def setup_method(self):
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self.vision_processor = MockVisionProcessor()
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self.sample_image = b'sample_image_data_for_testing'
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@pytest.mark.asyncio
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async def test_image_processing(self):
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"""Test basic image processing"""
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result = await self.vision_processor.process_image(self.sample_image)
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assert result['image_id'].startswith('img_')
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assert result['size'] == len(self.sample_image)
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assert result['format'] == 'processed'
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assert 'analysis' in result
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assert 'timestamp' in result
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@pytest.mark.asyncio
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async def test_object_detection(self):
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"""Test object detection functionality"""
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result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
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assert 'analysis' in result
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assert 'objects_detected' in result['analysis']
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assert 'objects' in result['analysis']
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assert len(result['analysis']['objects']) > 0
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# Check object structure
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for obj in result['analysis']['objects']:
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assert 'class' in obj
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assert 'confidence' in obj
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assert 'bbox' in obj
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assert 0 <= obj['confidence'] <= 1
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@pytest.mark.asyncio
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async def test_scene_analysis(self):
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"""Test scene analysis functionality"""
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result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis')
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assert 'analysis' in result
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assert 'scene_type' in result['analysis']
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assert 'confidence' in result['analysis']
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assert 'elements' in result['analysis']
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assert result['analysis']['scene_type'] == 'urban_street'
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assert 0 <= result['analysis']['confidence'] <= 1
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@pytest.mark.asyncio
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async def test_text_extraction(self):
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"""Test text extraction (OCR) functionality"""
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result = await self.vision_processor.process_image(self.sample_image, 'text_extraction')
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assert 'analysis' in result
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assert 'text_found' in result['analysis']
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assert 'extracted_text' in result['analysis']
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if result['analysis']['text_found']:
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assert len(result['analysis']['extracted_text']) > 0
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assert 'confidence' in result['analysis']
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@pytest.mark.asyncio
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async def test_general_analysis(self):
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"""Test general image analysis"""
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result = await self.vision_processor.process_image(self.sample_image, 'general')
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assert 'analysis' in result
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assert 'brightness' in result['analysis']
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assert 'contrast' in result['analysis']
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assert 'sharpness' in result['analysis']
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assert 'color_distribution' in result['analysis']
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# Check value ranges
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assert 0 <= result['analysis']['brightness'] <= 1
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assert 0 <= result['analysis']['contrast'] <= 1
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assert 0 <= result['analysis']['sharpness'] <= 1
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class TestMultiModalIntegration:
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"""Test multi-modal integration"""
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def setup_method(self):
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self.multi_modal_agent = MockMultiModalAgent()
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self.sample_image = b'sample_image_data'
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self.sample_text = "This is a sample text for testing"
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self.sample_sensor_data = {
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'type': 'temperature',
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'readings': {'value': 25.5, 'unit': 'celsius'},
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'timestamp': datetime.utcnow().isoformat()
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}
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@pytest.mark.asyncio
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async def test_vision_only_processing(self):
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"""Test processing with only vision input"""
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inputs = {'image': self.sample_image}
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result = await self.multi_modal_agent.process_multi_modal(inputs)
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assert result['result_id'].startswith('multi_')
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assert 'vision' in result['modalities_processed']
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assert 'integration' in result
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assert 'confidence' in result['integration']
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@pytest.mark.asyncio
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async def test_text_only_processing(self):
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"""Test processing with only text input"""
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inputs = {'text': self.sample_text}
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result = await self.multi_modal_agent.process_multi_modal(inputs)
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assert result['result_id'].startswith('multi_')
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assert 'text' in result['modalities_processed']
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assert 'integration' in result
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@pytest.mark.asyncio
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async def test_sensor_only_processing(self):
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"""Test processing with only sensor input"""
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inputs = {'sensor_data': self.sample_sensor_data}
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result = await self.multi_modal_agent.process_multi_modal(inputs)
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assert result['result_id'].startswith('multi_')
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assert 'sensor' in result['modalities_processed']
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assert 'integration' in result
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@pytest.mark.asyncio
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async def test_full_multi_modal_processing(self):
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"""Test processing with all modalities"""
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inputs = {
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'image': self.sample_image,
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'text': self.sample_text,
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'sensor_data': self.sample_sensor_data
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}
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result = await self.multi_modal_agent.process_multi_modal(inputs)
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assert result['result_id'].startswith('multi_')
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assert len(result['modalities_processed']) == 3
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assert 'vision' in result['modalities_processed']
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assert 'text' in result['modalities_processed']
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assert 'sensor' in result['modalities_processed']
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assert 'integration' in result
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assert 'cross_modal_insights' in result['integration']
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@pytest.mark.asyncio
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async def test_cross_modal_insights(self):
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"""Test cross-modal insight generation"""
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inputs = {
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'image': self.sample_image,
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'text': self.sample_text,
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'vision_processing_type': 'object_detection'
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}
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result = await self.multi_modal_agent.process_multi_modal(inputs)
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assert 'cross_modal_insights' in result['integration']
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assert len(result['integration']['cross_modal_insights']) > 0
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class TestContextIntegration:
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"""Test context integration with vision"""
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def setup_method(self):
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self.context_integration = MockContextIntegration()
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self.vision_processor = MockVisionProcessor()
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self.sample_image = b'sample_image_data'
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@pytest.mark.asyncio
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async def test_basic_context_integration(self):
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"""Test basic context integration"""
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vision_result = await self.vision_processor.process_image(self.sample_image)
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context_data = {
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'location': 'intersection',
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'time_of_day': 'daytime',
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'weather': 'clear'
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}
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result = await self.context_integration.integrate_context(vision_result, context_data)
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assert result['context_id'].startswith('ctx_')
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assert 'vision_result' in result
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assert 'context_data' in result
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assert 'enhanced_understanding' in result
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@pytest.mark.asyncio
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async def test_location_context(self):
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"""Test location-based context integration"""
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vision_result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
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context_data = {
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'location': 'intersection',
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'traffic_flow': 'moderate'
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}
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result = await self.context_integration.integrate_context(vision_result, context_data)
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assert 'enhanced_understanding' in result
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assert 'contextual_insights' in result['enhanced_understanding']
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assert any('traffic' in insight for insight in result['enhanced_understanding']['contextual_insights'])
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@pytest.mark.asyncio
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async def test_time_context(self):
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"""Test time-based context integration"""
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vision_result = await self.vision_processor.process_image(self.sample_image)
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context_data = {
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'time_of_day': 'night',
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'lighting_conditions': 'low'
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}
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result = await self.context_integration.integrate_context(vision_result, context_data)
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assert 'enhanced_understanding' in result
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assert 'confidence_boost' in result['enhanced_understanding']
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assert result['enhanced_understanding']['confidence_boost'] < 0 # Night time penalty
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@pytest.mark.asyncio
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async def test_context_history_tracking(self):
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"""Test context history tracking"""
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for i in range(3):
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vision_result = await self.vision_processor.process_image(self.sample_image)
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context_data = {
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'location': f'location_{i}',
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'timestamp': datetime.utcnow().isoformat()
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}
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await self.context_integration.integrate_context(vision_result, context_data)
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assert len(self.context_integration.context_history) == 3
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for context in self.context_integration.context_history:
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assert context['context_id'].startswith('ctx_')
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class TestVisualReasoning:
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"""Test visual reasoning capabilities"""
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def setup_method(self):
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self.vision_processor = MockVisionProcessor()
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self.multi_modal_agent = MockMultiModalAgent()
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self.sample_image = b'sample_image_data'
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@pytest.mark.asyncio
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async def test_visual_scene_understanding(self):
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"""Test visual scene understanding"""
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result = await self.vision_processor.process_image(self.sample_image, 'scene_analysis')
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assert 'analysis' in result
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assert 'scene_type' in result['analysis']
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assert 'elements' in result['analysis']
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assert 'complexity' in result['analysis']
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# Verify scene understanding
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scene = result['analysis']
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assert len(scene['elements']) > 0
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assert scene['complexity'] in ['low', 'medium', 'high']
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@pytest.mark.asyncio
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async def test_object_relationships(self):
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"""Test understanding object relationships"""
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result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
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assert 'analysis' in result
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assert 'objects' in result['analysis']
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objects = result['analysis']['objects']
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if len(objects) > 1:
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# Mock relationship analysis
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relationships = []
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for i, obj1 in enumerate(objects):
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for obj2 in objects[i+1:]:
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if obj1['class'] == 'person' and obj2['class'] == 'car':
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relationships.append('person_near_car')
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assert len(relationships) >= 0
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@pytest.mark.asyncio
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async def test_spatial_reasoning(self):
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"""Test spatial reasoning"""
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result = await self.vision_processor.process_image(self.sample_image, 'object_detection')
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assert 'analysis' in result
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assert 'objects' in result['analysis']
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objects = result['analysis']['objects']
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for obj in objects:
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assert 'bbox' in obj
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assert len(obj['bbox']) == 4 # [x1, y1, x2, y2]
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# Verify bbox coordinates
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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__])
|