mirror of
https://github.com/ruvnet/RuView.git
synced 2026-04-28 05:59:32 +00:00
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.
261 lines
No EOL
9.5 KiB
Python
261 lines
No EOL
9.5 KiB
Python
# DensePose-RCNN Architecture for WiFi-based Human Pose Estimation
|
|
# Based on the DensePose paper and WiFi-DensePose implementation
|
|
|
|
import numpy as np
|
|
from typing import Dict, List, Tuple, Optional
|
|
from collections import OrderedDict
|
|
|
|
class ResNetFPN:
|
|
"""
|
|
Simulated ResNet-FPN backbone for feature extraction
|
|
"""
|
|
def __init__(self, input_channels=3, output_channels=256):
|
|
self.input_channels = input_channels
|
|
self.output_channels = output_channels
|
|
|
|
print(f"Initialized ResNet-FPN backbone:")
|
|
print(f" Input channels: {input_channels}")
|
|
print(f" Output channels: {output_channels}")
|
|
|
|
def extract_features(self, input_tensor):
|
|
"""
|
|
Simulates feature extraction through ResNet-FPN
|
|
Returns a dict of feature maps at different levels (P2-P5)
|
|
"""
|
|
input_shape = input_tensor.shape
|
|
print(f"Extracting features from input shape: {input_shape}")
|
|
|
|
# Simulate FPN feature maps at different scales
|
|
P2 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//4, input_shape[2]//4)
|
|
P3 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//8, input_shape[2]//8)
|
|
P4 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//16, input_shape[2]//16)
|
|
P5 = np.random.rand(input_shape[0], self.output_channels, input_shape[1]//32, input_shape[2]//32)
|
|
|
|
return {
|
|
'P2': P2,
|
|
'P3': P3,
|
|
'P4': P4,
|
|
'P5': P5
|
|
}
|
|
|
|
class RegionProposalNetwork:
|
|
"""
|
|
Simulated Region Proposal Network (RPN)
|
|
"""
|
|
def __init__(self, feature_channels=256, anchor_scales=[8, 16, 32], anchor_ratios=[0.5, 1, 2]):
|
|
self.feature_channels = feature_channels
|
|
self.anchor_scales = anchor_scales
|
|
self.anchor_ratios = anchor_ratios
|
|
|
|
print(f"Initialized Region Proposal Network:")
|
|
print(f" Feature channels: {feature_channels}")
|
|
print(f" Anchor scales: {anchor_scales}")
|
|
print(f" Anchor ratios: {anchor_ratios}")
|
|
|
|
def propose_regions(self, feature_maps, num_proposals=100):
|
|
"""
|
|
Simulates proposing regions of interest from feature maps
|
|
"""
|
|
proposals = []
|
|
|
|
# Generate proposals with varying confidence
|
|
for i in range(num_proposals):
|
|
# Create random bounding box
|
|
x = np.random.uniform(0, 1)
|
|
y = np.random.uniform(0, 1)
|
|
w = np.random.uniform(0.05, 0.3)
|
|
h = np.random.uniform(0.1, 0.5)
|
|
|
|
# Add confidence score
|
|
confidence = np.random.beta(5, 2) # Biased toward higher confidence
|
|
|
|
proposals.append({
|
|
'bbox': [x, y, w, h],
|
|
'confidence': confidence
|
|
})
|
|
|
|
# Sort by confidence
|
|
proposals.sort(key=lambda x: x['confidence'], reverse=True)
|
|
|
|
return proposals
|
|
|
|
class ROIAlign:
|
|
"""
|
|
Simulated ROI Align operation
|
|
"""
|
|
def __init__(self, output_size=(7, 7)):
|
|
self.output_size = output_size
|
|
print(f"Initialized ROI Align with output size: {output_size}")
|
|
|
|
def extract_features(self, feature_maps, proposals):
|
|
"""
|
|
Simulates ROI Align to extract fixed-size features for each proposal
|
|
"""
|
|
roi_features = []
|
|
|
|
for proposal in proposals:
|
|
# Create a random feature map for each proposal
|
|
features = np.random.rand(feature_maps['P2'].shape[1], self.output_size[0], self.output_size[1])
|
|
roi_features.append(features)
|
|
|
|
return np.array(roi_features)
|
|
|
|
class DensePoseHead:
|
|
"""
|
|
DensePose prediction head for estimating UV coordinates
|
|
"""
|
|
def __init__(self, input_channels=256, num_parts=24, output_size=(112, 112)):
|
|
self.input_channels = input_channels
|
|
self.num_parts = num_parts
|
|
self.output_size = output_size
|
|
|
|
print(f"Initialized DensePose Head:")
|
|
print(f" Input channels: {input_channels}")
|
|
print(f" Body parts: {num_parts}")
|
|
print(f" Output size: {output_size}")
|
|
|
|
def predict(self, roi_features):
|
|
"""
|
|
Predict body part labels and UV coordinates
|
|
"""
|
|
batch_size = roi_features.shape[0]
|
|
|
|
# Predict part classification (24 parts + background)
|
|
part_pred = np.random.rand(batch_size, self.num_parts + 1, self.output_size[0], self.output_size[1])
|
|
part_pred = np.exp(part_pred) / np.sum(np.exp(part_pred), axis=1, keepdims=True) # Apply softmax
|
|
|
|
# Predict UV coordinates for each part
|
|
u_pred = np.random.rand(batch_size, self.num_parts, self.output_size[0], self.output_size[1])
|
|
v_pred = np.random.rand(batch_size, self.num_parts, self.output_size[0], self.output_size[1])
|
|
|
|
return {
|
|
'part_pred': part_pred,
|
|
'u_pred': u_pred,
|
|
'v_pred': v_pred
|
|
}
|
|
|
|
class KeypointHead:
|
|
"""
|
|
Keypoint prediction head for estimating body keypoints
|
|
"""
|
|
def __init__(self, input_channels=256, num_keypoints=17, output_size=(56, 56)):
|
|
self.input_channels = input_channels
|
|
self.num_keypoints = num_keypoints
|
|
self.output_size = output_size
|
|
|
|
print(f"Initialized Keypoint Head:")
|
|
print(f" Input channels: {input_channels}")
|
|
print(f" Keypoints: {num_keypoints}")
|
|
print(f" Output size: {output_size}")
|
|
|
|
def predict(self, roi_features):
|
|
"""
|
|
Predict keypoint heatmaps
|
|
"""
|
|
batch_size = roi_features.shape[0]
|
|
|
|
# Predict keypoint heatmaps
|
|
keypoint_heatmaps = np.random.rand(batch_size, self.num_keypoints, self.output_size[0], self.output_size[1])
|
|
|
|
# Apply softmax to get probability distributions
|
|
keypoint_heatmaps = np.exp(keypoint_heatmaps) / np.sum(np.exp(keypoint_heatmaps), axis=(2, 3), keepdims=True)
|
|
|
|
return keypoint_heatmaps
|
|
|
|
class DensePoseRCNN:
|
|
"""
|
|
Complete DensePose-RCNN architecture
|
|
"""
|
|
def __init__(self):
|
|
self.backbone = ResNetFPN(input_channels=3, output_channels=256)
|
|
self.rpn = RegionProposalNetwork()
|
|
self.roi_align = ROIAlign(output_size=(7, 7))
|
|
self.densepose_head = DensePoseHead()
|
|
self.keypoint_head = KeypointHead()
|
|
|
|
print("Initialized DensePose-RCNN architecture")
|
|
|
|
def forward(self, input_tensor):
|
|
"""
|
|
Forward pass through the DensePose-RCNN network
|
|
"""
|
|
# Extract features from backbone
|
|
feature_maps = self.backbone.extract_features(input_tensor)
|
|
|
|
# Generate region proposals
|
|
proposals = self.rpn.propose_regions(feature_maps)
|
|
|
|
# Keep only top proposals
|
|
top_proposals = proposals[:10]
|
|
|
|
# Extract ROI features
|
|
roi_features = self.roi_align.extract_features(feature_maps, top_proposals)
|
|
|
|
# Predict DensePose outputs
|
|
densepose_outputs = self.densepose_head.predict(roi_features)
|
|
|
|
# Predict keypoints
|
|
keypoint_heatmaps = self.keypoint_head.predict(roi_features)
|
|
|
|
# Process results into a structured format
|
|
results = []
|
|
for i, proposal in enumerate(top_proposals):
|
|
# Get most likely part label for each pixel
|
|
part_probs = densepose_outputs['part_pred'][i]
|
|
part_labels = np.argmax(part_probs, axis=0)
|
|
|
|
# Extract UV coordinates for the predicted parts
|
|
u_coords = densepose_outputs['u_pred'][i]
|
|
v_coords = densepose_outputs['v_pred'][i]
|
|
|
|
# Extract keypoint coordinates from heatmaps
|
|
keypoints = []
|
|
for k in range(self.keypoint_head.num_keypoints):
|
|
heatmap = keypoint_heatmaps[i, k]
|
|
max_idx = np.argmax(heatmap)
|
|
y, x = np.unravel_index(max_idx, heatmap.shape)
|
|
confidence = np.max(heatmap)
|
|
keypoints.append([x, y, confidence])
|
|
|
|
results.append({
|
|
'bbox': proposal['bbox'],
|
|
'confidence': proposal['confidence'],
|
|
'part_labels': part_labels,
|
|
'u_coords': u_coords,
|
|
'v_coords': v_coords,
|
|
'keypoints': keypoints
|
|
})
|
|
|
|
return results
|
|
|
|
# Demonstrate the DensePose-RCNN architecture
|
|
print("="*60)
|
|
print("DENSEPOSE-RCNN ARCHITECTURE DEMONSTRATION")
|
|
print("="*60)
|
|
|
|
# Create model
|
|
model = DensePoseRCNN()
|
|
|
|
# Create a dummy input tensor
|
|
input_tensor = np.random.rand(1, 3, 720, 1280)
|
|
print(f"\nPassing input tensor with shape {input_tensor.shape} through model...")
|
|
|
|
# Forward pass
|
|
results = model.forward(input_tensor)
|
|
|
|
# Display results
|
|
print(f"\nDensePose-RCNN Results:")
|
|
print(f" Detected {len(results)} people")
|
|
|
|
for i, person in enumerate(results):
|
|
bbox = person['bbox']
|
|
print(f" Person {i+1}:")
|
|
print(f" Bounding box: [{bbox[0]:.3f}, {bbox[1]:.3f}, {bbox[2]:.3f}, {bbox[3]:.3f}]")
|
|
print(f" Confidence: {person['confidence']:.3f}")
|
|
print(f" Part labels shape: {person['part_labels'].shape}")
|
|
print(f" UV coordinates shape: ({person['u_coords'].shape}, {person['v_coords'].shape})")
|
|
print(f" Keypoints: {len(person['keypoints'])}")
|
|
|
|
print("\nDensePose-RCNN demonstration completed!")
|
|
print("This architecture forms the core of the WiFi-DensePose system")
|
|
print("when combined with the CSI processing and modality translation components.") |