# Acceptance Test: Single Image Processing Performance (<5 seconds)

## Summary
Validate AC-7 requirement that each individual image processes in less than 5 seconds on target hardware (NVIDIA RTX 2060/3070).

## Linked Acceptance Criteria
**AC-7**: Less than 5 seconds for processing one image.

## Preconditions
- ASTRAL-Next system deployed on target hardware
- Hardware: NVIDIA RTX 2060 (minimum) or RTX 3070 (recommended)
- TensorRT FP16 optimized models loaded
- System warmed up (first frame excluded from timing)
- No other GPU-intensive processes running

## Test Data
- **Test Images**: AD000001-AD000010 (representative sample)
- **Scenarios**:
  - Easy: Normal overlap, clear features (AD000001-002)
  - Medium: Agricultural texture (AD000005-006)
  - Hard: Minimal overlap (AD000032-033)

## Performance Breakdown Target
```
L1 SuperPoint+LightGlue: 50-150ms
L2 AnyLoc (keyframes only): 150-200ms
L3 LiteSAM (keyframes only): 60-100ms
Factor Graph Update: 50-100ms
Overhead (I/O, coordination): 50-100ms
-----------------------------------
Total (L1 only): <500ms
Total (L1+L2+L3): <700ms
Safety Margin: 4300ms
AC-7 Limit: 5000ms
```

## Test Steps

### Step 1: Measure Easy Scenario Performance
**Action**: Process AD000001 → AD000002 (normal overlap, clear features)
**Expected Result**:
```
Image Load: <50ms
L1 Processing: 80-120ms
Factor Graph: 30-50ms
Result Output: <20ms
---
Total: <240ms
Status: WELL_UNDER_LIMIT (4.8% of budget)
```

### Step 2: Measure Medium Scenario Performance
**Action**: Process AD000005 → AD000006 (agricultural texture)
**Expected Result**:
```
Image Load: <50ms
L1 Processing: 100-150ms (more features)
Factor Graph: 40-60ms
Result Output: <20ms
---
Total: <280ms
Status: UNDER_LIMIT (5.6% of budget)
```

### Step 3: Measure Hard Scenario Performance
**Action**: Process AD000032 → AD000033 (220.6m jump, minimal overlap)
**Expected Result**:
```
Image Load: <50ms
L1 Processing: 150-200ms (adaptive depth)
L1 Confidence: LOW → Triggers L2
L2 Processing: 150-200ms
L3 Refinement: 80-120ms
Factor Graph: 80-120ms (more complex)
Result Output: <30ms
---
Total: <720ms
Status: UNDER_LIMIT (14.4% of budget)
```

### Step 4: Measure Worst-Case Performance
**Action**: Process with all layers active + large factor graph
**Expected Result**:
```
Image Load: 80ms
L1 Processing: 200ms
L2 Processing: 200ms
L3 Refinement: 120ms
Factor Graph: 150ms (200+ nodes)
Result Output: 50ms
---
Total: <800ms
Status: UNDER_LIMIT (16% of budget)
```

### Step 5: Statistical Performance Analysis
**Action**: Process 10 representative images, calculate statistics
**Expected Result**:
```
Mean processing time: 350ms
Median processing time: 280ms
90th percentile: 500ms
95th percentile: 650ms
99th percentile: 800ms
Max: <900ms
All: <5000ms (AC-7 requirement)
Status: PASS
```

### Step 6: Verify TensorRT Optimization Impact
**Action**: Compare TensorRT FP16 vs PyTorch FP32 performance
**Expected Result**:
```
PyTorch FP32 (baseline): 800-1200ms per image
TensorRT FP16 (optimized): 250-400ms per image
Speedup: 2.5-3.5x
Without TensorRT: Would fail AC-7
With TensorRT: Comfortably passes AC-7
```

## Pass/Fail Criteria

**PASS if**:
- 100% of images process in <5000ms
- Mean processing time <1000ms (20% of budget)
- 99th percentile <2000ms (40% of budget)
- TensorRT FP16 optimization active and verified
- Performance consistent across easy/medium/hard scenarios

**FAIL if**:
- ANY image takes ≥5000ms
- Mean processing time >2000ms
- System cannot maintain <5s with TensorRT optimization
- Performance degrades over time (memory leak)

## Hardware Requirements Validation

### RTX 2060 (Minimum)
- VRAM: 6GB
- Expected performance: 90th percentile <1000ms
- Status: Meets AC-7 with optimization

### RTX 3070 (Recommended)
- VRAM: 8GB  
- Expected performance: 90th percentile <700ms
- Status: Comfortably exceeds AC-7

## Performance Optimization Checklist
- TensorRT FP16 models compiled and loaded
- CUDA graphs enabled for inference
- Batch size = 1 (real-time constraint)
- Asynchronous GPU operations where possible
- Memory pre-allocated (no runtime allocation)
- Factor graph incremental updates (iSAM2)

## Monitoring and Profiling
- **NVIDIA Nsight**: GPU utilization >80% during processing
- **CPU Usage**: <50% (GPU-bound workload)
- **Memory**: Stable (no leaks over 100+ images)
- **Thermal**: GPU <85°C sustained

## Notes
- AC-7 specifies "processing one image", interpreted as latency per image
- 5-second budget is generous given target ~500ms actual performance
- Margin allows for:
  - Older hardware (RTX 2060)
  - Complex scenarios (multiple layers active)
  - Factor graph growth over long flights
  - System overhead
- Real-time (<100ms) not required; <5s is operational target