A043-studios
64536b971b
✨ Features: - Complete 6-node suite for 3D face reconstruction - FLAME model integration with multi-constraint optimization - UV coordinate and surface normal prediction - Professional mesh export (OBJ, PLY, STL) - Comprehensive documentation and examples 🎯 Nodes included: - 🔧 Pixel3DMM Model Loader - 🎭 3D Face Reconstructor - 🗺️ UV Coordinate Predictor - 📐 Surface Normal Predictor - 🔥 FLAME Parameter Optimizer - 📦 3D Mesh Exporter 📚 Documentation: - Complete installation guide - Usage examples and workflows - Troubleshooting and support - Ready-to-use ComfyUI workflow files 🧪 Quality: - Comprehensive testing and validation - ComfyUI standard compliance - Cross-platform support (Windows, macOS, Linux) - Production-ready with robust error handling Generated by MCP Multi-Agent System Research → DevOps → Coding → Testing → Documentation
417 lines
16 KiB
Python
417 lines
16 KiB
Python
"""
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Surface Normal Predictor Node for ComfyUI
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Predicts surface normals for face images using Pixel3DMM
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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from typing import Dict, Any, Tuple, Optional
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from .utils import convert_comfyui_image, convert_to_comfyui_image, preprocess_image, normalize_tensor
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class NormalPredictor:
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"""
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ComfyUI node for predicting surface normals from face images
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": ("PIXEL3DMM_MODEL",),
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"image": ("IMAGE",),
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"output_resolution": (["256", "512", "1024"], {"default": "512"}),
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"normal_space": (["camera", "world"], {"default": "camera"}),
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},
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"optional": {
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"normal_smoothing": ("FLOAT", {
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"default": 0.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.1
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}),
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"enhance_details": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "NORMALS", "STRING")
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RETURN_NAMES = ("normal_map", "normal_vectors", "status")
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FUNCTION = "predict_normals"
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CATEGORY = "Pixel3DMM"
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def __init__(self):
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self.normal_network = None
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def predict_normals(self, model: Dict[str, Any], image: torch.Tensor,
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output_resolution: str = "512", normal_space: str = "camera",
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normal_smoothing: float = 0.0, enhance_details: bool = True) -> Tuple:
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"""
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Predict surface normals from face image
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Args:
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model: Loaded Pixel3DMM model container
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image: Input face image
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output_resolution: Resolution of output normal map
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normal_space: Coordinate space for normals (camera/world)
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normal_smoothing: Amount of smoothing to apply
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enhance_details: Whether to enhance fine details
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Returns:
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Tuple of (normal_map, normal_vectors, status)
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"""
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try:
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if model is None:
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return None, None, "❌ No model loaded. Please connect a Pixel3DMM Loader node."
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device = model['device']
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resolution = int(output_resolution)
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# Preprocess input image
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processed_image = self._preprocess_input(image, device)
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if processed_image is None:
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return None, None, "❌ Failed to preprocess input image"
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# Initialize or get normal prediction network
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normal_network = self._get_normal_network(model, device)
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if normal_network is None:
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return None, None, "❌ Failed to initialize normal prediction network"
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# Predict surface normals
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normals = self._predict_surface_normals(normal_network, processed_image, resolution)
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if normals is None:
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return None, None, "❌ Failed to predict surface normals"
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# Apply post-processing
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normals = self._postprocess_normals(normals, normal_smoothing, enhance_details)
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# Transform to requested coordinate space
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if normal_space == "world":
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normals = self._transform_to_world_space(normals)
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# Create normal map visualization
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normal_map = self._create_normal_visualization(normals)
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# Convert to ComfyUI format
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normal_map = convert_to_comfyui_image(normal_map)
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status = f"✅ Normal prediction completed\n"
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status += f"📐 Resolution: {resolution}x{resolution}\n"
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status += f"🌍 Space: {normal_space}\n"
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status += f"🎛️ Smoothing: {normal_smoothing:.1f}\n"
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status += f"✨ Enhanced details: {'Yes' if enhance_details else 'No'}"
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return normal_map, normals, status
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except Exception as e:
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error_msg = f"❌ Error in normal prediction: {str(e)}"
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print(error_msg)
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return None, None, error_msg
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def _preprocess_input(self, image: torch.Tensor, device: str) -> Optional[torch.Tensor]:
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"""Preprocess input image for normal prediction"""
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try:
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# Convert from ComfyUI format
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processed = convert_comfyui_image(image)
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# Preprocess for model
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processed = preprocess_image(processed, target_size=512)
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# Move to device
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processed = processed.to(device)
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return processed
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except Exception as e:
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print(f"Error preprocessing input: {e}")
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return None
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def _get_normal_network(self, model: Dict[str, Any], device: str) -> Optional[nn.Module]:
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"""Get or create normal prediction network"""
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try:
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# Check if normal network exists in model components
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if 'normal_predictor' in model['components']:
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return model['components']['normal_predictor']
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# Create normal prediction network if not exists
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normal_network = self._create_normal_network(device)
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if normal_network is not None:
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model['components']['normal_predictor'] = normal_network
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return normal_network
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except Exception as e:
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print(f"Error getting normal network: {e}")
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return None
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def _create_normal_network(self, device: str) -> Optional[nn.Module]:
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"""Create surface normal prediction network"""
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try:
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class NormalPredictionNetwork(nn.Module):
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def __init__(self):
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super().__init__()
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# Encoder with skip connections
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self.enc1 = nn.Sequential(
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nn.Conv2d(3, 64, 7, stride=2, padding=3),
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nn.BatchNorm2d(64),
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nn.ReLU()
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)
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self.enc2 = nn.Sequential(
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nn.Conv2d(64, 128, 3, stride=2, padding=1),
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nn.BatchNorm2d(128),
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nn.ReLU()
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)
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self.enc3 = nn.Sequential(
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nn.Conv2d(128, 256, 3, stride=2, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU()
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)
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self.enc4 = nn.Sequential(
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nn.Conv2d(256, 512, 3, stride=2, padding=1),
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nn.BatchNorm2d(512),
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nn.ReLU()
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)
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# Decoder with skip connections
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self.dec4 = nn.Sequential(
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nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),
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nn.BatchNorm2d(256),
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nn.ReLU()
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)
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self.dec3 = nn.Sequential(
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nn.ConvTranspose2d(256 + 256, 128, 4, stride=2, padding=1), # +256 for skip
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nn.BatchNorm2d(128),
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nn.ReLU()
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)
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self.dec2 = nn.Sequential(
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nn.ConvTranspose2d(128 + 128, 64, 4, stride=2, padding=1), # +128 for skip
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nn.BatchNorm2d(64),
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nn.ReLU()
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)
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self.dec1 = nn.Sequential(
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nn.ConvTranspose2d(64 + 64, 32, 4, stride=2, padding=1), # +64 for skip
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nn.BatchNorm2d(32),
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nn.ReLU()
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)
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# Final normal prediction layer
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self.normal_head = nn.Sequential(
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nn.Conv2d(32, 16, 3, padding=1),
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nn.ReLU(),
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nn.Conv2d(16, 3, 3, padding=1), # 3 channels for X,Y,Z normals
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nn.Tanh() # Normals in [-1,1]
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)
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def forward(self, x):
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# Encoder with skip connections
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e1 = self.enc1(x) # 64 x 256 x 256
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e2 = self.enc2(e1) # 128 x 128 x 128
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e3 = self.enc3(e2) # 256 x 64 x 64
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e4 = self.enc4(e3) # 512 x 32 x 32
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# Decoder with skip connections
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d4 = self.dec4(e4) # 256 x 64 x 64
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d3 = self.dec3(torch.cat([d4, e3], dim=1)) # 128 x 128 x 128
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d2 = self.dec2(torch.cat([d3, e2], dim=1)) # 64 x 256 x 256
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d1 = self.dec1(torch.cat([d2, e1], dim=1)) # 32 x 512 x 512
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# Predict normals
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normals = self.normal_head(d1) # 3 x 512 x 512
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# Normalize to unit vectors
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normals = F.normalize(normals, p=2.0, dim=1)
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return normals
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network = NormalPredictionNetwork().to(device)
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network.eval()
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return network
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except Exception as e:
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print(f"Error creating normal network: {e}")
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return None
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def _predict_surface_normals(self, normal_network: nn.Module, image: torch.Tensor,
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resolution: int) -> Optional[torch.Tensor]:
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"""Predict surface normals using the network"""
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try:
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normal_network.eval()
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with torch.no_grad():
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# Predict surface normals
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normals = normal_network(image)
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# Resize to target resolution if needed
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if normals.shape[-1] != resolution:
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normals = F.interpolate(
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normals,
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size=(resolution, resolution),
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mode='bilinear',
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align_corners=False
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)
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# Re-normalize after interpolation
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normals = normalize_tensor(normals, dim=1)
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return normals
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except Exception as e:
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print(f"Error predicting surface normals: {e}")
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return None
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def _postprocess_normals(self, normals: torch.Tensor, smoothing: float,
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enhance_details: bool) -> torch.Tensor:
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"""Apply post-processing to surface normals"""
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try:
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# Apply smoothing if requested
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if smoothing > 0:
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kernel_size = int(smoothing * 10) + 1
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if kernel_size % 2 == 0:
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kernel_size += 1
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# Apply Gaussian smoothing
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sigma = smoothing * 2.0
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kernel = self._create_gaussian_kernel(kernel_size, sigma, normals.device)
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# Apply smoothing to each channel
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smoothed_normals = torch.zeros_like(normals)
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for i in range(3):
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smoothed_normals[:, i:i+1] = F.conv2d(
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normals[:, i:i+1],
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kernel.unsqueeze(0).unsqueeze(0),
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padding=kernel_size//2
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)
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# Re-normalize after smoothing
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normals = normalize_tensor(smoothed_normals, dim=1)
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# Enhance details if requested
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if enhance_details:
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normals = self._enhance_normal_details(normals)
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return normals
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except Exception as e:
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print(f"Error in normal post-processing: {e}")
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return normals
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def _create_gaussian_kernel(self, kernel_size: int, sigma: float, device: str) -> torch.Tensor:
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"""Create Gaussian smoothing kernel"""
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try:
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coords = torch.arange(kernel_size, dtype=torch.float32, device=device)
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coords -= kernel_size // 2
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g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))
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g /= g.sum()
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# Create 2D kernel
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kernel = g.unsqueeze(1) * g.unsqueeze(0)
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return kernel
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except Exception as e:
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print(f"Error creating Gaussian kernel: {e}")
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# Return identity kernel
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kernel = torch.zeros(kernel_size, kernel_size, device=device)
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kernel[kernel_size//2, kernel_size//2] = 1.0
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return kernel
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def _enhance_normal_details(self, normals: torch.Tensor) -> torch.Tensor:
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"""Enhance fine details in normal maps"""
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try:
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# Apply high-pass filter to enhance details
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blur_kernel = torch.ones(3, 3, device=normals.device) / 9.0
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enhanced_normals = torch.zeros_like(normals)
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for i in range(3):
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# Apply blur
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blurred = F.conv2d(
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normals[:, i:i+1],
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blur_kernel.unsqueeze(0).unsqueeze(0),
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padding=1
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)
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# High-pass = original - blurred
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high_pass = normals[:, i:i+1] - blurred
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# Add enhanced details
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enhanced_normals[:, i:i+1] = normals[:, i:i+1] + high_pass * 0.3
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# Re-normalize
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enhanced_normals = normalize_tensor(enhanced_normals, dim=1)
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return enhanced_normals
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except Exception as e:
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print(f"Error enhancing normal details: {e}")
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return normals
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def _transform_to_world_space(self, normals: torch.Tensor) -> torch.Tensor:
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"""Transform normals from camera space to world space"""
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try:
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# Simplified transformation - in real implementation would use camera parameters
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# For now, just apply a simple rotation
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# Example rotation matrix (identity for simplicity)
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rotation_matrix = torch.eye(3, device=normals.device)
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# Reshape normals for matrix multiplication
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batch_size, _, height, width = normals.shape
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normals_flat = normals.view(batch_size, 3, -1) # B x 3 x (H*W)
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# Apply rotation
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transformed_normals = torch.bmm(
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rotation_matrix.unsqueeze(0).repeat(batch_size, 1, 1),
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normals_flat
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)
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# Reshape back
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transformed_normals = transformed_normals.view(batch_size, 3, height, width)
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return transformed_normals
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except Exception as e:
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print(f"Error transforming normals to world space: {e}")
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return normals
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def _create_normal_visualization(self, normals: torch.Tensor) -> torch.Tensor:
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"""Create visualization of surface normals"""
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try:
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# Convert normals from [-1,1] to [0,1] for visualization
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normal_vis = (normals + 1.0) / 2.0
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# Ensure values are in valid range
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normal_vis = torch.clamp(normal_vis, 0.0, 1.0)
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return normal_vis
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except Exception as e:
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print(f"Error creating normal visualization: {e}")
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# Return default visualization
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batch_size, _, height, width = normals.shape
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return torch.ones(batch_size, 3, height, width, device=normals.device) * 0.5
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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"""Validate node inputs"""
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model = kwargs.get('model')
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if model is None:
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return "Model input is required"
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image = kwargs.get('image')
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if image is None:
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return "Image input is required"
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normal_smoothing = kwargs.get('normal_smoothing', 0.0)
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if normal_smoothing < 0.0 or normal_smoothing > 1.0:
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return "Normal smoothing must be between 0.0 and 1.0"
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return True
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