# 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.")