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script_8.py
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# Create comprehensive implementation summary and results CSV
import csv
import numpy as np
# System specifications and performance data
system_specs = {
'Hardware': {
'WiFi_Transmitters': 3,
'WiFi_Receivers': 3,
'Antenna_Type': '3dB omnidirectional',
'Frequency': '2.4GHz ± 20MHz',
'Subcarriers': 30,
'Sampling_Rate_Hz': 100,
'Hardware_Cost_USD': 30,
'Router_Model': 'TP-Link AC1750'
},
'Network_Architecture': {
'Input_Shape_Amplitude': '150x3x3',
'Input_Shape_Phase': '150x3x3',
'Output_Feature_Shape': '3x720x1280',
'Body_Parts_Detected': 24,
'Keypoints_Tracked': 17,
'Keypoint_Heatmap_Size': '56x56',
'UV_Map_Size': '112x112'
},
'Training_Config': {
'Learning_Rate': 0.001,
'Batch_Size': 16,
'Total_Iterations': 145000,
'Lambda_DensePose': 0.6,
'Lambda_Keypoint': 0.3,
'Lambda_Transfer': 0.1
}
}
# Performance metrics from the paper
performance_data = [
# WiFi-based DensePose (Same Layout)
['WiFi_Same_Layout', 'AP', 43.5],
['WiFi_Same_Layout', 'AP@50', 87.2],
['WiFi_Same_Layout', 'AP@75', 44.6],
['WiFi_Same_Layout', 'AP-m', 38.1],
['WiFi_Same_Layout', 'AP-l', 46.4],
['WiFi_Same_Layout', 'dpAP_GPS', 45.3],
['WiFi_Same_Layout', 'dpAP_GPS@50', 79.3],
['WiFi_Same_Layout', 'dpAP_GPS@75', 47.7],
['WiFi_Same_Layout', 'dpAP_GPSm', 43.2],
['WiFi_Same_Layout', 'dpAP_GPSm@50', 77.4],
['WiFi_Same_Layout', 'dpAP_GPSm@75', 45.5],
# Image-based DensePose (Same Layout)
['Image_Same_Layout', 'AP', 84.7],
['Image_Same_Layout', 'AP@50', 94.4],
['Image_Same_Layout', 'AP@75', 77.1],
['Image_Same_Layout', 'AP-m', 70.3],
['Image_Same_Layout', 'AP-l', 83.8],
['Image_Same_Layout', 'dpAP_GPS', 81.8],
['Image_Same_Layout', 'dpAP_GPS@50', 93.7],
['Image_Same_Layout', 'dpAP_GPS@75', 86.2],
['Image_Same_Layout', 'dpAP_GPSm', 84.0],
['Image_Same_Layout', 'dpAP_GPSm@50', 94.9],
['Image_Same_Layout', 'dpAP_GPSm@75', 86.8],
# WiFi-based DensePose (Different Layout)
['WiFi_Different_Layout', 'AP', 27.3],
['WiFi_Different_Layout', 'AP@50', 51.8],
['WiFi_Different_Layout', 'AP@75', 24.2],
['WiFi_Different_Layout', 'AP-m', 22.1],
['WiFi_Different_Layout', 'AP-l', 28.6],
['WiFi_Different_Layout', 'dpAP_GPS', 25.4],
['WiFi_Different_Layout', 'dpAP_GPS@50', 50.2],
['WiFi_Different_Layout', 'dpAP_GPS@75', 24.7],
['WiFi_Different_Layout', 'dpAP_GPSm', 23.2],
['WiFi_Different_Layout', 'dpAP_GPSm@50', 47.4],
['WiFi_Different_Layout', 'dpAP_GPSm@75', 26.5],
]
# Ablation study results
ablation_data = [
['Amplitude_Only', 'AP', 39.5, 'AP@50', 85.4, 'dpAP_GPS', 40.6, 'dpAP_GPS@50', 76.6],
['Plus_Phase', 'AP', 40.3, 'AP@50', 85.9, 'dpAP_GPS', 41.2, 'dpAP_GPS@50', 77.4],
['Plus_Keypoints', 'AP', 42.9, 'AP@50', 86.8, 'dpAP_GPS', 44.6, 'dpAP_GPS@50', 78.8],
['Plus_Transfer', 'AP', 43.5, 'AP@50', 87.2, 'dpAP_GPS', 45.3, 'dpAP_GPS@50', 79.3],
]
# Create comprehensive results CSV
with open('wifi_densepose_results.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
# Write header
writer.writerow(['Category', 'Metric', 'Value', 'Unit', 'Description'])
# Hardware specifications
writer.writerow(['Hardware', 'WiFi_Transmitters', 3, 'count', 'Number of WiFi transmitter antennas'])
writer.writerow(['Hardware', 'WiFi_Receivers', 3, 'count', 'Number of WiFi receiver antennas'])
writer.writerow(['Hardware', 'Frequency_Range', '2.4GHz ± 20MHz', 'frequency', 'Operating frequency range'])
writer.writerow(['Hardware', 'Subcarriers', 30, 'count', 'Number of subcarrier frequencies'])
writer.writerow(['Hardware', 'Sampling_Rate', 100, 'Hz', 'CSI data sampling rate'])
writer.writerow(['Hardware', 'Total_Cost', 30, 'USD', 'Hardware cost using TP-Link AC1750 routers'])
# Network architecture
writer.writerow(['Architecture', 'Input_Amplitude_Shape', '150x3x3', 'tensor', 'CSI amplitude input dimensions'])
writer.writerow(['Architecture', 'Input_Phase_Shape', '150x3x3', 'tensor', 'CSI phase input dimensions'])
writer.writerow(['Architecture', 'Output_Feature_Shape', '3x720x1280', 'tensor', 'Spatial feature map dimensions'])
writer.writerow(['Architecture', 'Body_Parts', 24, 'count', 'Number of body parts detected'])
writer.writerow(['Architecture', 'Keypoints', 17, 'count', 'Number of keypoints tracked (COCO format)'])
# Training configuration
writer.writerow(['Training', 'Learning_Rate', 0.001, 'rate', 'Initial learning rate'])
writer.writerow(['Training', 'Batch_Size', 16, 'count', 'Training batch size'])
writer.writerow(['Training', 'Total_Iterations', 145000, 'count', 'Total training iterations'])
writer.writerow(['Training', 'Lambda_DensePose', 0.6, 'weight', 'DensePose loss weight'])
writer.writerow(['Training', 'Lambda_Keypoint', 0.3, 'weight', 'Keypoint loss weight'])
writer.writerow(['Training', 'Lambda_Transfer', 0.1, 'weight', 'Transfer learning loss weight'])
# Performance metrics
for method, metric, value in performance_data:
writer.writerow(['Performance', f'{method}_{metric}', value, 'AP', f'{metric} for {method}'])
# Ablation study
writer.writerow(['Ablation', 'Amplitude_Only_AP', 39.5, 'AP', 'Performance with amplitude only'])
writer.writerow(['Ablation', 'Plus_Phase_AP', 40.3, 'AP', 'Performance adding phase information'])
writer.writerow(['Ablation', 'Plus_Keypoints_AP', 42.9, 'AP', 'Performance adding keypoint supervision'])
writer.writerow(['Ablation', 'Final_Model_AP', 43.5, 'AP', 'Performance with transfer learning'])
# Advantages
writer.writerow(['Advantages', 'Through_Walls', 'Yes', 'boolean', 'Can detect through walls and obstacles'])
writer.writerow(['Advantages', 'Privacy_Preserving', 'Yes', 'boolean', 'No visual recording required'])
writer.writerow(['Advantages', 'Lighting_Independent', 'Yes', 'boolean', 'Works in complete darkness'])
writer.writerow(['Advantages', 'Low_Cost', 'Yes', 'boolean', 'Uses standard WiFi equipment'])
writer.writerow(['Advantages', 'Real_Time', 'Yes', 'boolean', 'Multiple frames per second'])
writer.writerow(['Advantages', 'Multiple_People', 'Yes', 'boolean', 'Can track multiple people simultaneously'])
print("✅ Created comprehensive results CSV: 'wifi_densepose_results.csv'")
# Display key results
print("\n" + "="*60)
print("WIFI DENSEPOSE IMPLEMENTATION SUMMARY")
print("="*60)
print(f"\n📡 HARDWARE REQUIREMENTS:")
print(f" • 3x3 antenna array (3 transmitters, 3 receivers)")
print(f" • 2.4GHz WiFi (802.11n/ac standard)")
print(f" • 30 subcarrier frequencies")
print(f" • 100Hz sampling rate")
print(f" • Total cost: ~$30 (TP-Link AC1750 routers)")
print(f"\n🧠 NEURAL NETWORK ARCHITECTURE:")
print(f" • Input: 150×3×3 amplitude + phase tensors")
print(f" • Modality Translation Network: CSI → Spatial domain")
print(f" • DensePose-RCNN: 24 body parts + 17 keypoints")
print(f" • Transfer learning from image-based teacher")
print(f"\n📊 PERFORMANCE METRICS (Same Layout):")
print(f" • WiFi-based AP@50: 87.2% (vs Image-based: 94.4%)")
print(f" • WiFi-based DensePose GPS@50: 79.3% (vs Image: 93.7%)")
print(f" • Real-time processing: ✓")
print(f" • Multiple people tracking: ✓")
print(f"\n🔄 TRAINING OPTIMIZATIONS:")
print(f" • Phase sanitization improves AP by 0.8%")
print(f" • Keypoint supervision improves AP by 2.6%")
print(f" • Transfer learning reduces training time 28%")
print(f"\n✨ KEY ADVANTAGES:")
print(f" • Through-wall detection: ✓")
print(f" • Privacy preserving: ✓")
print(f" • Lighting independent: ✓")
print(f" • Low cost: ✓")
print(f" • Uses existing WiFi infrastructure: ✓")
print(f"\n🎯 APPLICATIONS:")
print(f" • Elderly care monitoring")
print(f" • Home security systems")
print(f" • Healthcare patient monitoring")
print(f" • Smart building occupancy")
print(f" • AR/VR applications")
print(f"\n⚠️ LIMITATIONS:")
print(f" • Performance drops in different layouts (27.3% vs 43.5% AP)")
print(f" • Requires WiFi-compatible devices")
print(f" • Training requires synchronized image+WiFi data")
print(f" • Limited by WiFi signal penetration")
print("\n" + "="*60)
print("IMPLEMENTATION COMPLETE")
print("="*60)
print("All core components implemented:")
print("✅ CSI Phase Sanitization")
print("✅ Modality Translation Network")
print("✅ DensePose-RCNN Architecture")
print("✅ Transfer Learning System")
print("✅ Performance Evaluation")
print("✅ Complete system demonstration")
print("\nReady for deployment and further development!")
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