mirror of
https://github.com/ruvnet/RuView.git
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f3c77b1750
- 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.
354 lines
No EOL
13 KiB
Python
354 lines
No EOL
13 KiB
Python
# 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: ✓") |