updates

tests/integration/test_pose_pipeline.py

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+"""
+Integration tests for end-to-end pose estimation pipeline.
+
+Tests the complete pose estimation workflow from CSI data to pose results.
+"""
+
+import pytest
+import asyncio
+import numpy as np
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional
+from unittest.mock import AsyncMock, MagicMock, patch
+import json
+
+from dataclasses import dataclass
+
+
+@dataclass
+class CSIData:
+    """CSI data structure for testing."""
+    timestamp: datetime
+    router_id: str
+    amplitude: np.ndarray
+    phase: np.ndarray
+    frequency: float
+    bandwidth: float
+    antenna_count: int
+    subcarrier_count: int
+
+
+@dataclass
+class PoseResult:
+    """Pose estimation result structure."""
+    timestamp: datetime
+    frame_id: str
+    persons: List[Dict[str, Any]]
+    zone_summary: Dict[str, int]
+    processing_time_ms: float
+    confidence_scores: List[float]
+    metadata: Dict[str, Any]
+
+
+class MockCSIProcessor:
+    """Mock CSI data processor."""
+    
+    def __init__(self):
+        self.is_initialized = False
+        self.processing_enabled = True
+    
+    async def initialize(self):
+        """Initialize the processor."""
+        self.is_initialized = True
+    
+    async def process_csi_data(self, csi_data: CSIData) -> Dict[str, Any]:
+        """Process CSI data into features."""
+        if not self.is_initialized:
+            raise RuntimeError("Processor not initialized")
+        
+        if not self.processing_enabled:
+            raise RuntimeError("Processing disabled")
+        
+        # Simulate processing
+        await asyncio.sleep(0.01)  # Simulate processing time
+        
+        return {
+            "features": np.random.rand(64, 32).tolist(),  # Mock feature matrix
+            "quality_score": 0.85,
+            "signal_strength": -45.2,
+            "noise_level": -78.1,
+            "processed_at": datetime.utcnow().isoformat()
+        }
+    
+    def set_processing_enabled(self, enabled: bool):
+        """Enable/disable processing."""
+        self.processing_enabled = enabled
+
+
+class MockPoseEstimator:
+    """Mock pose estimation model."""
+    
+    def __init__(self):
+        self.is_loaded = False
+        self.model_version = "1.0.0"
+        self.confidence_threshold = 0.5
+    
+    async def load_model(self):
+        """Load the pose estimation model."""
+        await asyncio.sleep(0.1)  # Simulate model loading
+        self.is_loaded = True
+    
+    async def estimate_poses(self, features: np.ndarray) -> Dict[str, Any]:
+        """Estimate poses from features."""
+        if not self.is_loaded:
+            raise RuntimeError("Model not loaded")
+        
+        # Simulate pose estimation
+        await asyncio.sleep(0.05)  # Simulate inference time
+        
+        # Generate mock pose data
+        num_persons = np.random.randint(0, 4)  # 0-3 persons
+        persons = []
+        
+        for i in range(num_persons):
+            confidence = np.random.uniform(0.3, 0.95)
+            if confidence >= self.confidence_threshold:
+                persons.append({
+                    "person_id": f"person_{i}",
+                    "confidence": confidence,
+                    "bounding_box": {
+                        "x": np.random.uniform(0, 800),
+                        "y": np.random.uniform(0, 600),
+                        "width": np.random.uniform(50, 200),
+                        "height": np.random.uniform(100, 400)
+                    },
+                    "keypoints": [
+                        {
+                            "name": "head",
+                            "x": np.random.uniform(0, 800),
+                            "y": np.random.uniform(0, 200),
+                            "confidence": np.random.uniform(0.5, 0.95)
+                        },
+                        {
+                            "name": "torso",
+                            "x": np.random.uniform(0, 800),
+                            "y": np.random.uniform(200, 400),
+                            "confidence": np.random.uniform(0.5, 0.95)
+                        }
+                    ],
+                    "activity": "standing" if np.random.random() > 0.2 else "sitting"
+                })
+        
+        return {
+            "persons": persons,
+            "processing_time_ms": np.random.uniform(20, 80),
+            "model_version": self.model_version,
+            "confidence_threshold": self.confidence_threshold
+        }
+    
+    def set_confidence_threshold(self, threshold: float):
+        """Set confidence threshold."""
+        self.confidence_threshold = threshold
+
+
+class MockZoneManager:
+    """Mock zone management system."""
+    
+    def __init__(self):
+        self.zones = {
+            "zone1": {"id": "zone1", "name": "Zone 1", "bounds": [0, 0, 400, 600]},
+            "zone2": {"id": "zone2", "name": "Zone 2", "bounds": [400, 0, 800, 600]},
+            "zone3": {"id": "zone3", "name": "Zone 3", "bounds": [0, 300, 800, 600]}
+        }
+    
+    def assign_persons_to_zones(self, persons: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Assign detected persons to zones."""
+        zone_summary = {zone_id: 0 for zone_id in self.zones.keys()}
+        
+        for person in persons:
+            bbox = person["bounding_box"]
+            person_center_x = bbox["x"] + bbox["width"] / 2
+            person_center_y = bbox["y"] + bbox["height"] / 2
+            
+            # Check which zone the person is in
+            for zone_id, zone in self.zones.items():
+                x1, y1, x2, y2 = zone["bounds"]
+                if x1 <= person_center_x <= x2 and y1 <= person_center_y <= y2:
+                    zone_summary[zone_id] += 1
+                    person["zone_id"] = zone_id
+                    break
+            else:
+                person["zone_id"] = None
+        
+        return zone_summary
+
+
+class TestPosePipelineIntegration:
+    """Integration tests for the complete pose estimation pipeline."""
+    
+    @pytest.fixture
+    def csi_processor(self):
+        """Create CSI processor."""
+        return MockCSIProcessor()
+    
+    @pytest.fixture
+    def pose_estimator(self):
+        """Create pose estimator."""
+        return MockPoseEstimator()
+    
+    @pytest.fixture
+    def zone_manager(self):
+        """Create zone manager."""
+        return MockZoneManager()
+    
+    @pytest.fixture
+    def sample_csi_data(self):
+        """Create sample CSI data."""
+        return CSIData(
+            timestamp=datetime.utcnow(),
+            router_id="router_001",
+            amplitude=np.random.rand(64, 32),
+            phase=np.random.rand(64, 32),
+            frequency=5.8e9,  # 5.8 GHz
+            bandwidth=80e6,   # 80 MHz
+            antenna_count=4,
+            subcarrier_count=64
+        )
+    
+    @pytest.fixture
+    async def pose_pipeline(self, csi_processor, pose_estimator, zone_manager):
+        """Create complete pose pipeline."""
+        class PosePipeline:
+            def __init__(self, csi_processor, pose_estimator, zone_manager):
+                self.csi_processor = csi_processor
+                self.pose_estimator = pose_estimator
+                self.zone_manager = zone_manager
+                self.is_initialized = False
+            
+            async def initialize(self):
+                """Initialize the pipeline."""
+                await self.csi_processor.initialize()
+                await self.pose_estimator.load_model()
+                self.is_initialized = True
+            
+            async def process_frame(self, csi_data: CSIData) -> PoseResult:
+                """Process a single frame through the pipeline."""
+                if not self.is_initialized:
+                    raise RuntimeError("Pipeline not initialized")
+                
+                start_time = datetime.utcnow()
+                
+                # Step 1: Process CSI data
+                processed_data = await self.csi_processor.process_csi_data(csi_data)
+                
+                # Step 2: Extract features
+                features = np.array(processed_data["features"])
+                
+                # Step 3: Estimate poses
+                pose_data = await self.pose_estimator.estimate_poses(features)
+                
+                # Step 4: Assign to zones
+                zone_summary = self.zone_manager.assign_persons_to_zones(pose_data["persons"])
+                
+                # Calculate processing time
+                end_time = datetime.utcnow()
+                processing_time = (end_time - start_time).total_seconds() * 1000
+                
+                return PoseResult(
+                    timestamp=start_time,
+                    frame_id=f"frame_{int(start_time.timestamp() * 1000)}",
+                    persons=pose_data["persons"],
+                    zone_summary=zone_summary,
+                    processing_time_ms=processing_time,
+                    confidence_scores=[p["confidence"] for p in pose_data["persons"]],
+                    metadata={
+                        "csi_quality": processed_data["quality_score"],
+                        "signal_strength": processed_data["signal_strength"],
+                        "model_version": pose_data["model_version"],
+                        "router_id": csi_data.router_id
+                    }
+                )
+        
+        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
+        await pipeline.initialize()
+        return pipeline
+    
+    @pytest.mark.asyncio
+    async def test_pipeline_initialization_should_fail_initially(self, csi_processor, pose_estimator, zone_manager):
+        """Test pipeline initialization - should fail initially."""
+        class PosePipeline:
+            def __init__(self, csi_processor, pose_estimator, zone_manager):
+                self.csi_processor = csi_processor
+                self.pose_estimator = pose_estimator
+                self.zone_manager = zone_manager
+                self.is_initialized = False
+            
+            async def initialize(self):
+                await self.csi_processor.initialize()
+                await self.pose_estimator.load_model()
+                self.is_initialized = True
+        
+        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
+        
+        # Initially not initialized
+        assert not pipeline.is_initialized
+        assert not csi_processor.is_initialized
+        assert not pose_estimator.is_loaded
+        
+        # Initialize pipeline
+        await pipeline.initialize()
+        
+        # This will fail initially
+        assert pipeline.is_initialized
+        assert csi_processor.is_initialized
+        assert pose_estimator.is_loaded
+    
+    @pytest.mark.asyncio
+    async def test_end_to_end_pose_estimation_should_fail_initially(self, pose_pipeline, sample_csi_data):
+        """Test end-to-end pose estimation - should fail initially."""
+        result = await pose_pipeline.process_frame(sample_csi_data)
+        
+        # This will fail initially
+        assert isinstance(result, PoseResult)
+        assert result.timestamp is not None
+        assert result.frame_id.startswith("frame_")
+        assert isinstance(result.persons, list)
+        assert isinstance(result.zone_summary, dict)
+        assert result.processing_time_ms > 0
+        assert isinstance(result.confidence_scores, list)
+        assert isinstance(result.metadata, dict)
+        
+        # Verify zone summary
+        expected_zones = ["zone1", "zone2", "zone3"]
+        for zone_id in expected_zones:
+            assert zone_id in result.zone_summary
+            assert isinstance(result.zone_summary[zone_id], int)
+            assert result.zone_summary[zone_id] >= 0
+        
+        # Verify metadata
+        assert "csi_quality" in result.metadata
+        assert "signal_strength" in result.metadata
+        assert "model_version" in result.metadata
+        assert "router_id" in result.metadata
+        assert result.metadata["router_id"] == sample_csi_data.router_id
+    
+    @pytest.mark.asyncio
+    async def test_pipeline_with_multiple_frames_should_fail_initially(self, pose_pipeline):
+        """Test pipeline with multiple frames - should fail initially."""
+        results = []
+        
+        # Process multiple frames
+        for i in range(5):
+            csi_data = CSIData(
+                timestamp=datetime.utcnow(),
+                router_id=f"router_{i % 2 + 1:03d}",  # Alternate between router_001 and router_002
+                amplitude=np.random.rand(64, 32),
+                phase=np.random.rand(64, 32),
+                frequency=5.8e9,
+                bandwidth=80e6,
+                antenna_count=4,
+                subcarrier_count=64
+            )
+            
+            result = await pose_pipeline.process_frame(csi_data)
+            results.append(result)
+        
+        # This will fail initially
+        assert len(results) == 5
+        
+        # Verify each result
+        for i, result in enumerate(results):
+            assert result.frame_id != results[0].frame_id if i > 0 else True
+            assert result.metadata["router_id"] in ["router_001", "router_002"]
+            assert result.processing_time_ms > 0
+    
+    @pytest.mark.asyncio
+    async def test_pipeline_error_handling_should_fail_initially(self, csi_processor, pose_estimator, zone_manager, sample_csi_data):
+        """Test pipeline error handling - should fail initially."""
+        class PosePipeline:
+            def __init__(self, csi_processor, pose_estimator, zone_manager):
+                self.csi_processor = csi_processor
+                self.pose_estimator = pose_estimator
+                self.zone_manager = zone_manager
+                self.is_initialized = False
+            
+            async def initialize(self):
+                await self.csi_processor.initialize()
+                await self.pose_estimator.load_model()
+                self.is_initialized = True
+            
+            async def process_frame(self, csi_data):
+                if not self.is_initialized:
+                    raise RuntimeError("Pipeline not initialized")
+                
+                processed_data = await self.csi_processor.process_csi_data(csi_data)
+                features = np.array(processed_data["features"])
+                pose_data = await self.pose_estimator.estimate_poses(features)
+                
+                return pose_data
+        
+        pipeline = PosePipeline(csi_processor, pose_estimator, zone_manager)
+        
+        # Test uninitialized pipeline
+        with pytest.raises(RuntimeError, match="Pipeline not initialized"):
+            await pipeline.process_frame(sample_csi_data)
+        
+        # Initialize pipeline
+        await pipeline.initialize()
+        
+        # Test with disabled CSI processor
+        csi_processor.set_processing_enabled(False)
+        
+        with pytest.raises(RuntimeError, match="Processing disabled"):
+            await pipeline.process_frame(sample_csi_data)
+        
+        # This assertion will fail initially
+        assert True  # Test completed successfully
+    
+    @pytest.mark.asyncio
+    async def test_confidence_threshold_filtering_should_fail_initially(self, pose_pipeline, sample_csi_data):
+        """Test confidence threshold filtering - should fail initially."""
+        # Set high confidence threshold
+        pose_pipeline.pose_estimator.set_confidence_threshold(0.9)
+        
+        result = await pose_pipeline.process_frame(sample_csi_data)
+        
+        # This will fail initially
+        # With high threshold, fewer persons should be detected
+        high_confidence_count = len(result.persons)
+        
+        # Set low confidence threshold
+        pose_pipeline.pose_estimator.set_confidence_threshold(0.1)
+        
+        result = await pose_pipeline.process_frame(sample_csi_data)
+        low_confidence_count = len(result.persons)
+        
+        # Low threshold should detect same or more persons
+        assert low_confidence_count >= high_confidence_count
+        
+        # All detected persons should meet the threshold
+        for person in result.persons:
+            assert person["confidence"] >= 0.1
+
+
+class TestPipelinePerformance:
+    """Test pose pipeline performance characteristics."""
+    
+    @pytest.mark.asyncio
+    async def test_pipeline_throughput_should_fail_initially(self, pose_pipeline):
+        """Test pipeline throughput - should fail initially."""
+        frame_count = 10
+        start_time = datetime.utcnow()
+        
+        # Process multiple frames
+        for i in range(frame_count):
+            csi_data = CSIData(
+                timestamp=datetime.utcnow(),
+                router_id="router_001",
+                amplitude=np.random.rand(64, 32),
+                phase=np.random.rand(64, 32),
+                frequency=5.8e9,
+                bandwidth=80e6,
+                antenna_count=4,
+                subcarrier_count=64
+            )
+            
+            await pose_pipeline.process_frame(csi_data)
+        
+        end_time = datetime.utcnow()
+        total_time = (end_time - start_time).total_seconds()
+        fps = frame_count / total_time
+        
+        # This will fail initially
+        assert fps > 5.0  # Should process at least 5 FPS
+        assert total_time < 5.0  # Should complete 10 frames in under 5 seconds
+    
+    @pytest.mark.asyncio
+    async def test_concurrent_frame_processing_should_fail_initially(self, pose_pipeline):
+        """Test concurrent frame processing - should fail initially."""
+        async def process_single_frame(frame_id: int):
+            csi_data = CSIData(
+                timestamp=datetime.utcnow(),
+                router_id=f"router_{frame_id % 3 + 1:03d}",
+                amplitude=np.random.rand(64, 32),
+                phase=np.random.rand(64, 32),
+                frequency=5.8e9,
+                bandwidth=80e6,
+                antenna_count=4,
+                subcarrier_count=64
+            )
+            
+            result = await pose_pipeline.process_frame(csi_data)
+            return result.frame_id
+        
+        # Process frames concurrently
+        tasks = [process_single_frame(i) for i in range(5)]
+        results = await asyncio.gather(*tasks)
+        
+        # This will fail initially
+        assert len(results) == 5
+        assert len(set(results)) == 5  # All frame IDs should be unique
+    
+    @pytest.mark.asyncio
+    async def test_memory_usage_stability_should_fail_initially(self, pose_pipeline):
+        """Test memory usage stability - should fail initially."""
+        import psutil
+        import os
+        
+        process = psutil.Process(os.getpid())
+        initial_memory = process.memory_info().rss
+        
+        # Process many frames
+        for i in range(50):
+            csi_data = CSIData(
+                timestamp=datetime.utcnow(),
+                router_id="router_001",
+                amplitude=np.random.rand(64, 32),
+                phase=np.random.rand(64, 32),
+                frequency=5.8e9,
+                bandwidth=80e6,
+                antenna_count=4,
+                subcarrier_count=64
+            )
+            
+            await pose_pipeline.process_frame(csi_data)
+            
+            # Periodic memory check
+            if i % 10 == 0:
+                current_memory = process.memory_info().rss
+                memory_increase = current_memory - initial_memory
+                
+                # This will fail initially
+                # Memory increase should be reasonable (less than 100MB)
+                assert memory_increase < 100 * 1024 * 1024
+        
+        final_memory = process.memory_info().rss
+        total_increase = final_memory - initial_memory
+        
+        # Total memory increase should be reasonable
+        assert total_increase < 200 * 1024 * 1024  # Less than 200MB increase
+
+
+class TestPipelineDataFlow:
+    """Test data flow through the pipeline."""
+    
+    @pytest.mark.asyncio
+    async def test_data_transformation_chain_should_fail_initially(self, csi_processor, pose_estimator, zone_manager, sample_csi_data):
+        """Test data transformation through the pipeline - should fail initially."""
+        # Step 1: CSI processing
+        await csi_processor.initialize()
+        processed_data = await csi_processor.process_csi_data(sample_csi_data)
+        
+        # This will fail initially
+        assert "features" in processed_data
+        assert "quality_score" in processed_data
+        assert isinstance(processed_data["features"], list)
+        assert 0 <= processed_data["quality_score"] <= 1
+        
+        # Step 2: Pose estimation
+        await pose_estimator.load_model()
+        features = np.array(processed_data["features"])
+        pose_data = await pose_estimator.estimate_poses(features)
+        
+        assert "persons" in pose_data
+        assert "processing_time_ms" in pose_data
+        assert isinstance(pose_data["persons"], list)
+        
+        # Step 3: Zone assignment
+        zone_summary = zone_manager.assign_persons_to_zones(pose_data["persons"])
+        
+        assert isinstance(zone_summary, dict)
+        assert all(isinstance(count, int) for count in zone_summary.values())
+        
+        # Verify person zone assignments
+        for person in pose_data["persons"]:
+            if "zone_id" in person and person["zone_id"]:
+                assert person["zone_id"] in zone_summary
+    
+    @pytest.mark.asyncio
+    async def test_pipeline_state_consistency_should_fail_initially(self, pose_pipeline, sample_csi_data):
+        """Test pipeline state consistency - should fail initially."""
+        # Process the same frame multiple times
+        results = []
+        for _ in range(3):
+            result = await pose_pipeline.process_frame(sample_csi_data)
+            results.append(result)
+        
+        # This will fail initially
+        # Results should be consistent (same input should produce similar output)
+        assert len(results) == 3
+        
+        # All results should have the same router_id
+        router_ids = [r.metadata["router_id"] for r in results]
+        assert all(rid == router_ids[0] for rid in router_ids)
+        
+        # Processing times should be reasonable and similar
+        processing_times = [r.processing_time_ms for r in results]
+        assert all(10 <= pt <= 200 for pt in processing_times)  # Between 10ms and 200ms