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script_5.py
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# WiFi DensePose Implementation - Fixed version
# Based on "DensePose From WiFi" by Carnegie Mellon University
import numpy as np
import math
from typing import Dict, List, Tuple, Optional
from collections import OrderedDict
import json
class CSIPhaseProcessor:
"""
Processes raw CSI phase data through unwrapping, filtering, and linear fitting
Based on the phase sanitization methodology from the paper
"""
def __init__(self, num_subcarriers: int = 30):
self.num_subcarriers = num_subcarriers
print(f"Initialized CSI Phase Processor with {num_subcarriers} subcarriers")
def unwrap_phase(self, phase_data: np.ndarray) -> np.ndarray:
"""
Unwrap phase values to handle discontinuities
Phase data shape: (freq_samples, ant_tx, ant_rx) = (150, 3, 3)
"""
unwrapped = np.copy(phase_data)
# Unwrap along frequency dimension (groups of 30 frequencies)
for sample_group in range(5): # 5 consecutive samples
start_idx = sample_group * 30
end_idx = start_idx + 30
for tx in range(3):
for rx in range(3):
for i in range(start_idx + 1, end_idx):
diff = unwrapped[i, tx, rx] - unwrapped[i-1, tx, rx]
if diff > np.pi:
unwrapped[i, tx, rx] = unwrapped[i-1, tx, rx] + diff - 2*np.pi
elif diff < -np.pi:
unwrapped[i, tx, rx] = unwrapped[i-1, tx, rx] + diff + 2*np.pi
return unwrapped
def apply_filters(self, phase_data: np.ndarray) -> np.ndarray:
"""
Apply median and uniform filters to eliminate outliers
"""
filtered = np.copy(phase_data)
# Apply smoothing in frequency dimension
for i in range(1, phase_data.shape[0]-1):
filtered[i] = (phase_data[i-1] + phase_data[i] + phase_data[i+1]) / 3
return filtered
def linear_fitting(self, phase_data: np.ndarray) -> np.ndarray:
"""
Apply linear fitting to remove systematic phase drift
"""
fitted_data = np.copy(phase_data)
F = self.num_subcarriers
# Process each sample group (5 consecutive samples)
for sample_group in range(5):
start_idx = sample_group * 30
end_idx = start_idx + 30
for tx in range(3):
for rx in range(3):
phase_seq = phase_data[start_idx:end_idx, tx, rx]
# Calculate linear coefficients
if len(phase_seq) > 1:
alpha1 = (phase_seq[-1] - phase_seq[0]) / (2 * np.pi * F)
alpha0 = np.mean(phase_seq)
# Apply linear fitting
frequencies = np.arange(1, len(phase_seq) + 1)
linear_trend = alpha1 * frequencies + alpha0
fitted_data[start_idx:end_idx, tx, rx] = phase_seq - linear_trend
return fitted_data
def sanitize_phase(self, raw_phase: np.ndarray) -> np.ndarray:
"""
Complete phase sanitization pipeline
"""
print("Sanitizing CSI phase data...")
print(f"Input shape: {raw_phase.shape}")
# Step 1: Unwrap phase
unwrapped = self.unwrap_phase(raw_phase)
print("✓ Phase unwrapping completed")
# Step 2: Apply filters
filtered = self.apply_filters(unwrapped)
print("✓ Filtering completed")
# Step 3: Linear fitting
sanitized = self.linear_fitting(filtered)
print("✓ Linear fitting completed")
return sanitized
class ModalityTranslationNetwork:
"""
Simulates the modality translation network behavior
Translates CSI domain features to spatial domain features
"""
def __init__(self, input_shape=(150, 3, 3), output_shape=(3, 720, 1280)):
self.input_shape = input_shape
self.output_shape = output_shape
self.hidden_dim = 512
# Initialize simulated weights
np.random.seed(42)
self.amp_weights = np.random.normal(0, 0.1, (np.prod(input_shape), self.hidden_dim//4))
self.phase_weights = np.random.normal(0, 0.1, (np.prod(input_shape), self.hidden_dim//4))
self.fusion_weights = np.random.normal(0, 0.1, (self.hidden_dim//2, 24*24))
print(f"Initialized Modality Translation Network:")
print(f" Input: {input_shape} -> Output: {output_shape}")
def encode_features(self, amplitude_data, phase_data):
"""
Simulate feature encoding from amplitude and phase data
"""
# Flatten inputs
amp_flat = amplitude_data.flatten()
phase_flat = phase_data.flatten()
# Simple linear transformation (simulating MLP)
amp_features = np.tanh(np.dot(amp_flat, self.amp_weights))
phase_features = np.tanh(np.dot(phase_flat, self.phase_weights))
return amp_features, phase_features
def fuse_and_translate(self, amp_features, phase_features):
"""
Fuse features and translate to spatial domain
"""
# Concatenate features
fused = np.concatenate([amp_features, phase_features])
# Apply fusion transformation
spatial_features = np.tanh(np.dot(fused, self.fusion_weights))
# Reshape to spatial map
spatial_map = spatial_features.reshape(24, 24)
# Simulate upsampling to target resolution
# Using simple bilinear interpolation simulation
from scipy.ndimage import zoom
upsampled = zoom(spatial_map,
(self.output_shape[1]/24, self.output_shape[2]/24),
order=1)
# Create 3-channel output
output = np.stack([upsampled, upsampled * 0.8, upsampled * 0.6])
return output
def forward(self, amplitude_data, phase_data):
"""
Complete forward pass
"""
# Encode features
amp_features, phase_features = self.encode_features(amplitude_data, phase_data)
# Translate to spatial domain
spatial_output = self.fuse_and_translate(amp_features, phase_features)
return spatial_output
class WiFiDensePoseSystem:
"""
Complete WiFi DensePose system
"""
def __init__(self):
self.config = WiFiDensePoseConfig()
self.phase_processor = CSIPhaseProcessor(self.config.num_subcarriers)
self.modality_network = ModalityTranslationNetwork()
print("WiFi DensePose System initialized!")
def process_csi_data(self, amplitude_data, phase_data):
"""
Process raw CSI data through the complete pipeline
"""
# Step 1: Phase sanitization
sanitized_phase = self.phase_processor.sanitize_phase(phase_data)
# Step 2: Modality translation
spatial_features = self.modality_network.forward(amplitude_data, sanitized_phase)
# Step 3: Simulate DensePose prediction
pose_prediction = self.simulate_densepose_prediction(spatial_features)
return {
'sanitized_phase': sanitized_phase,
'spatial_features': spatial_features,
'pose_prediction': pose_prediction
}
def simulate_densepose_prediction(self, spatial_features):
"""
Simulate DensePose-RCNN prediction
"""
# Simulate person detection
num_detected = np.random.randint(1, 4) # 1-3 people
predictions = []
for i in range(num_detected):
# Simulate bounding box
x = np.random.uniform(50, spatial_features.shape[1] - 150)
y = np.random.uniform(50, spatial_features.shape[2] - 300)
w = np.random.uniform(80, 150)
h = np.random.uniform(200, 300)
# Simulate confidence
confidence = np.random.uniform(0.7, 0.95)
# Simulate keypoints
keypoints = []
for kp in range(17):
kp_x = x + np.random.uniform(-w/4, w/4)
kp_y = y + np.random.uniform(-h/4, h/4)
kp_conf = np.random.uniform(0.6, 0.9)
keypoints.extend([kp_x, kp_y, kp_conf])
# Simulate UV map (simplified)
uv_map = np.random.uniform(0, 1, (24, 112, 112))
predictions.append({
'bbox': [x, y, w, h],
'confidence': confidence,
'keypoints': keypoints,
'uv_map': uv_map
})
return predictions
# Configuration and utility classes
class WiFiDensePoseConfig:
"""Configuration class for WiFi DensePose system"""
def __init__(self):
# Hardware configuration
self.num_transmitters = 3
self.num_receivers = 3
self.num_subcarriers = 30
self.sampling_rate = 100 # Hz
self.consecutive_samples = 5
# Network configuration
self.input_amplitude_shape = (150, 3, 3) # 5 samples * 30 frequencies, 3x3 antennas
self.input_phase_shape = (150, 3, 3)
self.output_feature_shape = (3, 720, 1280) # Image-like feature map
# DensePose configuration
self.num_body_parts = 24
self.num_keypoints = 17
self.keypoint_heatmap_size = (56, 56)
self.uv_map_size = (112, 112)
class WiFiDataSimulator:
"""Simulates WiFi CSI data for demonstration purposes"""
def __init__(self, config: WiFiDensePoseConfig):
self.config = config
np.random.seed(42) # For reproducibility
def generate_csi_sample(self, num_people: int = 1, movement_intensity: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
"""Generate simulated CSI amplitude and phase data"""
# Base CSI signal (environment)
amplitude = np.ones(self.config.input_amplitude_shape) * 50 # Base signal strength
phase = np.zeros(self.config.input_phase_shape)
# Add noise
amplitude += np.random.normal(0, 5, self.config.input_amplitude_shape)
phase += np.random.normal(0, 0.1, self.config.input_phase_shape)
# Simulate human presence effects
for person in range(num_people):
# Random position effects
pos_x = np.random.uniform(0.2, 0.8)
pos_y = np.random.uniform(0.2, 0.8)
# Create interference patterns
for tx in range(3):
for rx in range(3):
# Distance-based attenuation
distance = np.sqrt((tx/2 - pos_x)**2 + (rx/2 - pos_y)**2)
attenuation = movement_intensity * np.exp(-distance * 2)
# Frequency-dependent effects
for freq in range(30):
freq_effect = np.sin(2 * np.pi * freq / 30 + person * np.pi/2)
# Apply effects to all 5 samples for this frequency
for sample in range(5):
sample_idx = sample * 30 + freq
amplitude[sample_idx, tx, rx] *= (1 - attenuation * 0.3 * freq_effect)
phase[sample_idx, tx, rx] += attenuation * freq_effect * movement_intensity
return amplitude, phase
# Install scipy for zoom function
try:
from scipy.ndimage import zoom
except ImportError:
print("Installing scipy...")
import subprocess
import sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "scipy"])
from scipy.ndimage import zoom
# Initialize the complete system
print("="*60)
print("WIFI DENSEPOSE SYSTEM DEMONSTRATION")
print("="*60)
config = WiFiDensePoseConfig()
data_simulator = WiFiDataSimulator(config)
wifi_system = WiFiDensePoseSystem()
# Generate and process sample data
print("\n1. Generating sample CSI data...")
amplitude_data, phase_data = data_simulator.generate_csi_sample(num_people=2, movement_intensity=1.5)
print(f" Generated CSI data shapes: Amplitude {amplitude_data.shape}, Phase {phase_data.shape}")
print("\n2. Processing through WiFi DensePose pipeline...")
results = wifi_system.process_csi_data(amplitude_data, phase_data)
print(f"\n3. Results:")
print(f" Sanitized phase range: [{results['sanitized_phase'].min():.3f}, {results['sanitized_phase'].max():.3f}]")
print(f" Spatial features shape: {results['spatial_features'].shape}")
print(f" Detected {len(results['pose_prediction'])} people")
for i, pred in enumerate(results['pose_prediction']):
bbox = pred['bbox']
print(f" Person {i+1}: bbox=[{bbox[0]:.1f}, {bbox[1]:.1f}, {bbox[2]:.1f}, {bbox[3]:.1f}], confidence={pred['confidence']:.3f}")
print("\nWiFi DensePose system demonstration completed successfully!")
print(f"System specifications:")
print(f" - Hardware cost: ~$30 (2 TP-Link AC1750 routers)")
print(f" - Frequency: 2.4GHz ± 20MHz")
print(f" - Sampling rate: {config.sampling_rate}Hz")
print(f" - Body parts detected: {config.num_body_parts}")
print(f" - Keypoints tracked: {config.num_keypoints}")
print(f" - Works through walls: ✓")
print(f" - Privacy preserving: ✓")
print(f" - Real-time capable: ✓")
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