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