update
This commit is contained in:
wTechArtist
+3
-1
@@ -5,13 +5,14 @@ ComfyUI VVL Video Camera Advanced
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from .comfyui_vggt_nodes import VGGTMultiInputNode
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from .vggt_model_loader import VVLVGGTLoader
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from .glb_point_cloud_processor import GLBPointCloudProcessor
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from .glb_point_cloud_processor import *
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# 节点类映射
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NODE_CLASS_MAPPINGS = {
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"VVLVGGTLoader": VVLVGGTLoader,
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"VGGTMultiInputNode": VGGTMultiInputNode,
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"GLBPointCloudProcessor": GLBPointCloudProcessor,
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"GLBPointCloudBounds": GLBPointCloudBounds,
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}
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# 节点显示名称映射
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@@ -19,6 +20,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"VVLVGGTLoader": "VVL VGGT 模型加载器",
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"VGGTMultiInputNode": "VVL VGGT 多输入重建",
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"GLBPointCloudProcessor": "VVL GLB 点云黑色点清理",
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"GLBPointCloudBounds": "VVL GLB 点云包围盒计算",
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}
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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@@ -3,6 +3,7 @@ import json
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import tempfile
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import logging
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import struct
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import time
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from typing import List, Any, Dict, Tuple
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import numpy as np
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@@ -302,8 +303,6 @@ class GLBPointCloudProcessor:
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# 计算RGB总和
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rgb_sum = rgb_colors.sum(axis=1)
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# 应用黑色阈值过滤
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if black_threshold > 0:
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dark_mask = rgb_sum >= black_threshold
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@@ -317,18 +316,711 @@ class GLBPointCloudProcessor:
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return removed_count
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class GLBPointCloudBounds:
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"""GLB点云包围盒计算器 - 计算包围盒并生成带可视化的点云文件"""
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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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"glb_file_path": ("STRING", {
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"default": "",
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"tooltip": "GLB点云文件路径：输入需要计算包围盒的GLB格式点云文件。支持绝对路径或相对于ComfyUI output目录的相对路径"
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}),
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},
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"optional": {
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"bounding_box_type": (["axis_aligned", "oriented"], {
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"default": "axis_aligned",
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"tooltip": "包围盒类型：axis_aligned=轴对齐包围盒(AABB)，计算快速；oriented=有向包围盒(OBB)，体积最小"
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}),
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"units": (["meters", "centimeters", "millimeters"], {
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"default": "meters",
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"tooltip": "输出单位：指定包围盒尺寸的输出单位"
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}),
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"add_bounding_box_visualization": ("BOOLEAN", {
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"default": True,
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"tooltip": "添加包围盒可视化：是否在输出的点云文件中添加红色包围盒线框点云，便于直接预览验证"
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}),
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"add_coordinate_axes": ("BOOLEAN", {
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"default": True,
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"tooltip": "添加坐标轴：是否添加RGB坐标轴(X=红色，Y=绿色，Z=蓝色)到输出点云中"
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}),
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"wireframe_density": ("INT", {
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"default": 150, "min": 20, "max": 200, "step": 10,
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"tooltip": "线框密度：每条包围盒边的点数，越高线框越清晰但点数越多。推荐50-100获得清晰效果"
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}),
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"enhance_visibility": ("BOOLEAN", {
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"default": True,
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"tooltip": "增强可见性：添加顶点高亮和面中心标记，让包围盒更容易识别"
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}),
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"output_filename": ("STRING", {
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"default": "pointcloud_with_bounds.glb",
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"tooltip": "输出文件名：带包围盒可视化的点云文件名"
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}),
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}
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}
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RETURN_TYPES = (
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"STRING", # JSON格式的scale数组
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"STRING", # 包围盒详细信息
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"STRING", # 输出的GLB文件路径
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"STRING", # 处理日志
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)
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RETURN_NAMES = (
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"scale_json",
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"bounds_info",
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"output_glb_path",
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"processing_log",
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)
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OUTPUT_TOOLTIPS = [
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"JSON格式的scale数组: {\"scale\": [长, 宽, 高]} - 可直接用于3D引擎缩放",
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"包围盒详细信息(JSON格式) - 包含中心点、最小点、最大点、体积等",
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"输出GLB文件路径 - 包含原始点云+包围盒可视化+坐标轴的完整点云文件",
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"详细的处理日志信息"
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]
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OUTPUT_NODE = True
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FUNCTION = "calculate_bounds_and_visualize"
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CATEGORY = "💃VVL/Point Cloud Analysis"
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def calculate_bounds_and_visualize(self,
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glb_file_path: str,
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bounding_box_type: str = "axis_aligned",
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units: str = "meters",
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add_bounding_box_visualization: bool = True,
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add_coordinate_axes: bool = True,
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wireframe_density: int = 50,
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enhance_visibility: bool = True,
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output_filename: str = "pointcloud_with_bounds.glb"):
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"""
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计算GLB点云文件的包围盒尺寸并生成带可视化的点云文件
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"""
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processing_log = []
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processing_log.append("开始GLB点云包围盒计算和可视化生成...")
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# 检查依赖
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if not TRIMESH_AVAILABLE:
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error_msg = "trimesh库不可用，无法处理GLB文件"
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logger.error(error_msg)
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processing_log.append(f"错误: {error_msg}")
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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# 验证输入文件路径
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if not glb_file_path or not glb_file_path.strip():
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error_msg = "GLB文件路径为空"
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logger.error(error_msg)
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processing_log.append(f"错误: {error_msg}")
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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# 处理文件路径
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input_path = self._resolve_file_path(glb_file_path.strip())
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self._current_input_path = input_path # 保存当前输入路径供后续使用
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processing_log.append(f"输入文件路径: {input_path}")
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if not os.path.exists(input_path):
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error_msg = f"GLB文件不存在: {input_path}"
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logger.error(error_msg)
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processing_log.append(f"错误: {error_msg}")
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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try:
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# 加载GLB文件
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processing_log.append("正在加载GLB文件...")
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scene = trimesh.load(input_path)
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# 收集所有点云数据
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all_vertices = []
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point_cloud_count = 0
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if isinstance(scene, trimesh.Scene):
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for name, geometry in scene.geometry.items():
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if isinstance(geometry, trimesh.PointCloud):
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all_vertices.append(geometry.vertices)
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point_cloud_count += 1
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processing_log.append(f"发现点云: {name}, 点数: {len(geometry.vertices)}")
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elif hasattr(geometry, 'vertices') and geometry.vertices is not None:
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# 处理可能被转换为Mesh的点云
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all_vertices.append(geometry.vertices)
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point_cloud_count += 1
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processing_log.append(f"发现几何体(作为点云处理): {name}, 点数: {len(geometry.vertices)}")
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elif isinstance(scene, trimesh.PointCloud):
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all_vertices.append(scene.vertices)
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point_cloud_count = 1
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processing_log.append(f"发现点云: main_pointcloud, 点数: {len(scene.vertices)}")
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elif hasattr(scene, 'vertices') and scene.vertices is not None:
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all_vertices.append(scene.vertices)
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point_cloud_count = 1
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processing_log.append(f"发现几何体(作为点云处理): main_geometry, 点数: {len(scene.vertices)}")
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else:
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error_msg = "GLB文件中未找到点云或几何体数据"
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processing_log.append(f"错误: {error_msg}")
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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if not all_vertices:
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error_msg = "GLB文件中没有可用的顶点数据"
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processing_log.append(f"错误: {error_msg}")
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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# 合并所有顶点
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combined_vertices = np.vstack(all_vertices)
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total_points = len(combined_vertices)
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processing_log.append(f"合并了 {point_cloud_count} 个点云，总点数: {total_points}")
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# 计算包围盒
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processing_log.append(f"计算包围盒类型: {bounding_box_type}")
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if bounding_box_type == "axis_aligned":
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# 轴对齐包围盒 (AABB)
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min_point = np.min(combined_vertices, axis=0)
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max_point = np.max(combined_vertices, axis=0)
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extents = max_point - min_point
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center = (min_point + max_point) / 2
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bounds_info = {
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"type": "axis_aligned_bounding_box",
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"min_point": min_point.tolist(),
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"max_point": max_point.tolist(),
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"center": center.tolist(),
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"extents": extents.tolist(),
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"volume": float(np.prod(extents)),
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"total_points": int(total_points),
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"point_clouds_count": int(point_cloud_count)
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}
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processing_log.append(f"AABB计算完成:")
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processing_log.append(f" 最小点: [{min_point[0]:.6f}, {min_point[1]:.6f}, {min_point[2]:.6f}]")
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processing_log.append(f" 最大点: [{max_point[0]:.6f}, {max_point[1]:.6f}, {max_point[2]:.6f}]")
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processing_log.append(f" 中心点: [{center[0]:.6f}, {center[1]:.6f}, {center[2]:.6f}]")
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processing_log.append(f" 尺寸: [{extents[0]:.6f}, {extents[1]:.6f}, {extents[2]:.6f}]")
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processing_log.append(f" 体积: {np.prod(extents):.6f}")
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else: # oriented
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# 有向包围盒 (OBB)
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try:
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to_origin, obb_extents = trimesh.bounds.oriented_bounds(combined_vertices)
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# 计算OBB的中心点和角点
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obb_center = -to_origin[:3, 3] # 变换矩阵的平移部分的负值
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bounds_info = {
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"type": "oriented_bounding_box",
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"center": obb_center.tolist(),
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"extents": obb_extents.tolist(),
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"transform_matrix": to_origin.tolist(),
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"volume": float(np.prod(obb_extents)),
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"total_points": int(total_points),
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"point_clouds_count": int(point_cloud_count)
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}
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extents = obb_extents
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processing_log.append(f"OBB计算完成:")
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processing_log.append(f" 中心点: [{obb_center[0]:.6f}, {obb_center[1]:.6f}, {obb_center[2]:.6f}]")
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processing_log.append(f" 尺寸: [{extents[0]:.6f}, {extents[1]:.6f}, {extents[2]:.6f}]")
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processing_log.append(f" 体积: {np.prod(extents):.6f}")
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except Exception as e:
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processing_log.append(f"OBB计算失败，回退到AABB: {str(e)}")
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# 回退到AABB
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min_point = np.min(combined_vertices, axis=0)
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max_point = np.max(combined_vertices, axis=0)
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extents = max_point - min_point
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center = (min_point + max_point) / 2
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bounds_info = {
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"type": "axis_aligned_bounding_box_fallback",
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"min_point": min_point.tolist(),
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"max_point": max_point.tolist(),
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"center": center.tolist(),
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"extents": extents.tolist(),
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"volume": float(np.prod(extents)),
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"total_points": int(total_points),
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"point_clouds_count": int(point_cloud_count)
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}
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# 应用单位转换
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unit_scale = {"meters": 1.0, "centimeters": 100.0, "millimeters": 1000.0}
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scale_factor = unit_scale.get(units, 1.0)
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scaled_extents = extents * scale_factor
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# 生成scale JSON (按照 [长, 宽, 高] 的顺序，通常是 [X, Y, Z])
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scale_array = [float(scaled_extents[0]), float(scaled_extents[1]), float(scaled_extents[2])]
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scale_json = {
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"scale": scale_array,
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"units": units,
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"original_extents": extents.tolist(),
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"scale_factor": scale_factor
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}
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# 添加单位信息到bounds_info
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bounds_info["units"] = units
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bounds_info["scale_factor"] = scale_factor
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bounds_info["scaled_extents"] = scaled_extents.tolist()
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processing_log.append(f"")
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processing_log.append(f"输出结果 (单位: {units}):")
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processing_log.append(f" Scale数组: [{scale_array[0]:.6f}, {scale_array[1]:.6f}, {scale_array[2]:.6f}]")
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processing_log.append(f" 长度(X): {scale_array[0]:.6f} {units}")
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processing_log.append(f" 宽度(Y): {scale_array[1]:.6f} {units}")
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processing_log.append(f" 高度(Z): {scale_array[2]:.6f} {units}")
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# 生成带可视化的点云文件
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output_glb_path = ""
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try:
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processing_log.append("正在生成带包围盒可视化的点云文件...")
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output_glb_path = self._generate_visualization_pointcloud(
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combined_vertices, extents, center, bounding_box_type,
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add_bounding_box_visualization, add_coordinate_axes,
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wireframe_density, enhance_visibility, output_filename, processing_log
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)
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processing_log.append("可视化点云文件生成完成!")
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except Exception as e:
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processing_log.append(f"可视化点云生成失败: {str(e)}")
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import traceback
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traceback.print_exc()
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processing_log.append("GLB点云包围盒计算和可视化完成!")
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return (
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json.dumps(scale_json, indent=2),
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json.dumps(bounds_info, indent=2),
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output_glb_path,
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"\n".join(processing_log)
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)
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except Exception as e:
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error_msg = f"计算包围盒时发生错误: {str(e)}"
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logger.error(error_msg)
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processing_log.append(f"错误: {error_msg}")
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import traceback
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traceback.print_exc()
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return "", json.dumps({"error": error_msg}), "", "\n".join(processing_log)
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def _resolve_file_path(self, file_path: str) -> str:
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"""解析文件路径，支持绝对路径和相对路径"""
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if os.path.isabs(file_path):
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return file_path
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# 尝试相对于ComfyUI输出目录
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if FOLDER_PATHS_AVAILABLE:
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output_dir = folder_paths.get_output_directory()
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candidate_path = os.path.join(output_dir, file_path)
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if os.path.exists(candidate_path):
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return candidate_path
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# 尝试相对于当前工作目录
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if os.path.exists(file_path):
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return os.path.abspath(file_path)
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# 返回原始路径（让后续检查处理错误）
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return file_path
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def _get_current_input_path(self):
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"""获取当前输入文件路径"""
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return getattr(self, '_current_input_path', None)
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def _normalize_colors(self, colors):
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"""标准化颜色数组为RGBA格式，0-255范围"""
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if colors is None:
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return None
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colors = np.array(colors)
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# 如果是0-1范围，转换为0-255
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if colors.max() <= 1.0:
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colors = (colors * 255).astype(np.uint8)
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else:
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colors = colors.astype(np.uint8)
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# 确保是RGBA格式
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if colors.shape[1] == 3: # RGB -> RGBA
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alpha_channel = np.full((colors.shape[0], 1), 255, dtype=np.uint8)
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colors = np.hstack([colors, alpha_channel])
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elif colors.shape[1] != 4:
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# 如果不是3或4通道，创建白色RGBA
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colors = np.tile([255, 255, 255, 255], (colors.shape[0], 1)).astype(np.uint8)
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return colors
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def _generate_visualization_pointcloud(self, combined_vertices, extents, center, bounds_type,
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add_bounding_box_visualization, add_coordinate_axes,
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wireframe_density, enhance_visibility, output_filename, processing_log):
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"""生成包含原始点云、包围盒线框和坐标轴的可视化点云文件"""
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try:
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# 重新加载原始GLB文件以保持颜色信息
|
||||
input_path = self._get_current_input_path()
|
||||
original_scene = trimesh.load(input_path)
|
||||
|
||||
# 提取原始点云数据（保持颜色）
|
||||
all_vertices = []
|
||||
all_colors = []
|
||||
original_point_count = 0
|
||||
|
||||
if isinstance(original_scene, trimesh.Scene):
|
||||
for name, geometry in original_scene.geometry.items():
|
||||
if isinstance(geometry, trimesh.PointCloud):
|
||||
all_vertices.append(geometry.vertices)
|
||||
# 保持原始颜色
|
||||
if hasattr(geometry.visual, 'vertex_colors') and geometry.visual.vertex_colors is not None:
|
||||
colors = geometry.visual.vertex_colors.copy()
|
||||
elif hasattr(geometry, 'colors') and geometry.colors is not None:
|
||||
colors = geometry.colors.copy()
|
||||
else:
|
||||
# 如果没有颜色信息，使用白色
|
||||
colors = np.tile([255, 255, 255, 255], (len(geometry.vertices), 1))
|
||||
|
||||
# 确保颜色格式一致 (RGBA, 0-255)
|
||||
colors = self._normalize_colors(colors)
|
||||
all_colors.append(colors)
|
||||
original_point_count += len(geometry.vertices)
|
||||
processing_log.append(f"保留原始点云: {name}, {len(geometry.vertices):,} 个点，颜色已保持")
|
||||
elif hasattr(geometry, 'vertices') and geometry.vertices is not None:
|
||||
all_vertices.append(geometry.vertices)
|
||||
# 处理可能被转换为Mesh的点云
|
||||
if hasattr(geometry.visual, 'vertex_colors') and geometry.visual.vertex_colors is not None:
|
||||
colors = geometry.visual.vertex_colors.copy()
|
||||
else:
|
||||
colors = np.tile([255, 255, 255, 255], (len(geometry.vertices), 1))
|
||||
|
||||
# 确保颜色格式一致 (RGBA, 0-255)
|
||||
colors = self._normalize_colors(colors)
|
||||
all_colors.append(colors)
|
||||
original_point_count += len(geometry.vertices)
|
||||
processing_log.append(f"保留原始几何体: {name}, {len(geometry.vertices):,} 个点，颜色已保持")
|
||||
elif isinstance(original_scene, trimesh.PointCloud):
|
||||
all_vertices.append(original_scene.vertices)
|
||||
if hasattr(original_scene.visual, 'vertex_colors') and original_scene.visual.vertex_colors is not None:
|
||||
colors = original_scene.visual.vertex_colors.copy()
|
||||
elif hasattr(original_scene, 'colors') and original_scene.colors is not None:
|
||||
colors = original_scene.colors.copy()
|
||||
else:
|
||||
colors = np.tile([255, 255, 255, 255], (len(original_scene.vertices), 1))
|
||||
|
||||
# 确保颜色格式一致 (RGBA, 0-255)
|
||||
colors = self._normalize_colors(colors)
|
||||
all_colors.append(colors)
|
||||
original_point_count = len(original_scene.vertices)
|
||||
processing_log.append(f"保留原始点云: {original_point_count:,} 个点，颜色已保持")
|
||||
elif hasattr(original_scene, 'vertices') and original_scene.vertices is not None:
|
||||
all_vertices.append(original_scene.vertices)
|
||||
if hasattr(original_scene.visual, 'vertex_colors') and original_scene.visual.vertex_colors is not None:
|
||||
colors = original_scene.visual.vertex_colors.copy()
|
||||
else:
|
||||
colors = np.tile([255, 255, 255, 255], (len(original_scene.vertices), 1))
|
||||
|
||||
# 确保颜色格式一致 (RGBA, 0-255)
|
||||
colors = self._normalize_colors(colors)
|
||||
all_colors.append(colors)
|
||||
original_point_count = len(original_scene.vertices)
|
||||
processing_log.append(f"保留原始几何体: {original_point_count:,} 个点，颜色已保持")
|
||||
|
||||
processing_log.append(f"原始点云总计: {original_point_count:,} 个点，颜色信息完整保留")
|
||||
|
||||
# 添加包围盒线框点云
|
||||
if add_bounding_box_visualization:
|
||||
box_vertices, box_colors = self._create_bounding_box_pointcloud(
|
||||
extents, center, bounds_type, wireframe_density, enhance_visibility, processing_log
|
||||
)
|
||||
if len(box_vertices) > 0:
|
||||
all_vertices.append(box_vertices)
|
||||
all_colors.append(box_colors)
|
||||
processing_log.append(f"包围盒线框: {len(box_vertices):,} 个点")
|
||||
|
||||
# 添加坐标轴点云
|
||||
if add_coordinate_axes:
|
||||
axes_vertices, axes_colors = self._create_coordinate_axes_pointcloud(
|
||||
extents, wireframe_density, processing_log
|
||||
)
|
||||
if len(axes_vertices) > 0:
|
||||
all_vertices.append(axes_vertices)
|
||||
all_colors.append(axes_colors)
|
||||
processing_log.append(f"坐标轴: {len(axes_vertices):,} 个点")
|
||||
|
||||
# 合并所有点云
|
||||
final_vertices = np.vstack(all_vertices)
|
||||
final_colors = np.vstack(all_colors)
|
||||
|
||||
processing_log.append(f"总点数: {len(final_vertices):,} 个点")
|
||||
|
||||
# 生成输出路径
|
||||
output_path = self._generate_output_path(output_filename)
|
||||
|
||||
# 创建trimesh点云对象
|
||||
visualization_pointcloud = trimesh.PointCloud(
|
||||
vertices=final_vertices,
|
||||
colors=final_colors
|
||||
)
|
||||
|
||||
# 保存为GLB文件
|
||||
visualization_pointcloud.export(output_path)
|
||||
|
||||
processing_log.append(f"可视化点云已保存: {output_path}")
|
||||
return output_path
|
||||
|
||||
except Exception as e:
|
||||
processing_log.append(f"可视化点云生成失败: {str(e)}")
|
||||
raise e
|
||||
|
||||
def _create_bounding_box_pointcloud(self, extents, center, bounds_type, wireframe_density, enhance_visibility, processing_log):
|
||||
"""创建包围盒线框的点云表示"""
|
||||
try:
|
||||
# 计算包围盒的8个顶点
|
||||
min_point = center - extents/2
|
||||
max_point = center + extents/2
|
||||
|
||||
box_vertices = np.array([
|
||||
[min_point[0], min_point[1], min_point[2]], # 0: min corner
|
||||
[max_point[0], min_point[1], min_point[2]], # 1
|
||||
[max_point[0], max_point[1], min_point[2]], # 2
|
||||
[min_point[0], max_point[1], min_point[2]], # 3
|
||||
[min_point[0], min_point[1], max_point[2]], # 4
|
||||
[max_point[0], min_point[1], max_point[2]], # 5
|
||||
[max_point[0], max_point[1], max_point[2]], # 6: max corner
|
||||
[min_point[0], max_point[1], max_point[2]], # 7
|
||||
])
|
||||
|
||||
# 定义包围盒的12条边
|
||||
edges = [
|
||||
(0, 1), (1, 2), (2, 3), (3, 0), # 底面
|
||||
(4, 5), (5, 6), (6, 7), (7, 4), # 顶面
|
||||
(0, 4), (1, 5), (2, 6), (3, 7), # 垂直边
|
||||
]
|
||||
|
||||
wireframe_points = []
|
||||
|
||||
# 1. 生成线框边上的点（更密集）
|
||||
for start_idx, end_idx in edges:
|
||||
start_point = box_vertices[start_idx]
|
||||
end_point = box_vertices[end_idx]
|
||||
|
||||
# 在每条边上生成密集的点
|
||||
for i in range(wireframe_density):
|
||||
t = i / (wireframe_density - 1) if wireframe_density > 1 else 0
|
||||
point = start_point + t * (end_point - start_point)
|
||||
wireframe_points.append(point)
|
||||
|
||||
# 2. 添加顶点高亮和面中心标记（可选增强可见性）
|
||||
if enhance_visibility:
|
||||
# 顶点高亮（每个顶点周围添加小点云）
|
||||
vertex_highlight_radius = np.min(extents) * 0.01 # 顶点高亮半径
|
||||
highlight_density = max(5, wireframe_density // 10) # 顶点周围的点数
|
||||
|
||||
for vertex in box_vertices:
|
||||
# 在每个顶点周围创建小的点云球
|
||||
for i in range(highlight_density):
|
||||
# 生成随机方向
|
||||
theta = np.random.uniform(0, 2 * np.pi)
|
||||
phi = np.random.uniform(0, np.pi)
|
||||
r = np.random.uniform(0, vertex_highlight_radius)
|
||||
|
||||
# 球坐标转换为笛卡尔坐标
|
||||
x = vertex[0] + r * np.sin(phi) * np.cos(theta)
|
||||
y = vertex[1] + r * np.sin(phi) * np.sin(theta)
|
||||
z = vertex[2] + r * np.cos(phi)
|
||||
|
||||
wireframe_points.append([x, y, z])
|
||||
|
||||
# 面中心点标记
|
||||
face_centers = [
|
||||
# 底面中心
|
||||
(box_vertices[0] + box_vertices[1] + box_vertices[2] + box_vertices[3]) / 4,
|
||||
# 顶面中心
|
||||
(box_vertices[4] + box_vertices[5] + box_vertices[6] + box_vertices[7]) / 4,
|
||||
# 前面中心
|
||||
(box_vertices[0] + box_vertices[1] + box_vertices[4] + box_vertices[5]) / 4,
|
||||
# 后面中心
|
||||
(box_vertices[2] + box_vertices[3] + box_vertices[6] + box_vertices[7]) / 4,
|
||||
# 左面中心
|
||||
(box_vertices[0] + box_vertices[3] + box_vertices[4] + box_vertices[7]) / 4,
|
||||
# 右面中心
|
||||
(box_vertices[1] + box_vertices[2] + box_vertices[5] + box_vertices[6]) / 4,
|
||||
]
|
||||
|
||||
# 在每个面中心添加标记点
|
||||
face_mark_density = max(3, wireframe_density // 20)
|
||||
face_mark_radius = np.min(extents) * 0.005
|
||||
|
||||
for face_center in face_centers:
|
||||
for i in range(face_mark_density):
|
||||
# 在面中心周围添加小的点云
|
||||
theta = np.random.uniform(0, 2 * np.pi)
|
||||
phi = np.random.uniform(0, np.pi)
|
||||
r = np.random.uniform(0, face_mark_radius)
|
||||
|
||||
x = face_center[0] + r * np.sin(phi) * np.cos(theta)
|
||||
y = face_center[1] + r * np.sin(phi) * np.sin(theta)
|
||||
z = face_center[2] + r * np.cos(phi)
|
||||
|
||||
wireframe_points.append([x, y, z])
|
||||
|
||||
wireframe_vertices = np.array(wireframe_points)
|
||||
|
||||
# 为包围盒线框设置红色
|
||||
wireframe_colors = np.tile([255, 0, 0, 255], (len(wireframe_vertices), 1))
|
||||
|
||||
processing_log.append(f"包围盒线框生成: {len(wireframe_vertices):,} 个点 (密度={wireframe_density})")
|
||||
|
||||
return wireframe_vertices, wireframe_colors
|
||||
|
||||
except Exception as e:
|
||||
processing_log.append(f"创建包围盒点云失败: {str(e)}")
|
||||
return np.array([]), np.array([])
|
||||
|
||||
def _create_coordinate_axes_pointcloud(self, extents, wireframe_density, processing_log):
|
||||
"""创建坐标轴的点云表示"""
|
||||
try:
|
||||
axis_length = np.max(extents) * 0.4
|
||||
axis_thickness = np.min(extents) * 0.005 # 坐标轴厚度
|
||||
axes_points = []
|
||||
axes_colors = []
|
||||
|
||||
# 计算坐标轴密度
|
||||
axis_line_density = wireframe_density
|
||||
axis_thickness_points = max(3, wireframe_density // 15) # 厚度方向的点数
|
||||
|
||||
# X轴 - 红色（粗线条）
|
||||
for i in range(axis_line_density):
|
||||
t = i / (axis_line_density - 1) if axis_line_density > 1 else 0
|
||||
base_point = [t * axis_length, 0, 0]
|
||||
|
||||
# 主轴线
|
||||
axes_points.append(base_point)
|
||||
axes_colors.append([255, 0, 0, 255])
|
||||
|
||||
# 增加厚度（在YZ平面上添加点）
|
||||
for j in range(axis_thickness_points):
|
||||
for k in range(axis_thickness_points):
|
||||
offset_y = (j - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
offset_z = (k - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
thick_point = [base_point[0], base_point[1] + offset_y, base_point[2] + offset_z]
|
||||
axes_points.append(thick_point)
|
||||
axes_colors.append([255, 0, 0, 255])
|
||||
|
||||
# X轴箭头头部
|
||||
arrow_length = axis_length * 0.1
|
||||
arrow_base = axis_length * 0.9
|
||||
for i in range(axis_line_density // 2):
|
||||
t = i / (axis_line_density // 2 - 1) if axis_line_density > 2 else 0
|
||||
# 箭头点
|
||||
arrow_x = arrow_base + t * arrow_length
|
||||
arrow_offset = (1 - t) * axis_thickness * 2
|
||||
|
||||
axes_points.append([arrow_x, arrow_offset, 0])
|
||||
axes_points.append([arrow_x, -arrow_offset, 0])
|
||||
axes_points.append([arrow_x, 0, arrow_offset])
|
||||
axes_points.append([arrow_x, 0, -arrow_offset])
|
||||
axes_colors.extend([[255, 0, 0, 255]] * 4)
|
||||
|
||||
# Y轴 - 绿色（粗线条）
|
||||
for i in range(axis_line_density):
|
||||
t = i / (axis_line_density - 1) if axis_line_density > 1 else 0
|
||||
base_point = [0, t * axis_length, 0]
|
||||
|
||||
# 主轴线
|
||||
axes_points.append(base_point)
|
||||
axes_colors.append([0, 255, 0, 255])
|
||||
|
||||
# 增加厚度（在XZ平面上添加点）
|
||||
for j in range(axis_thickness_points):
|
||||
for k in range(axis_thickness_points):
|
||||
offset_x = (j - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
offset_z = (k - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
thick_point = [base_point[0] + offset_x, base_point[1], base_point[2] + offset_z]
|
||||
axes_points.append(thick_point)
|
||||
axes_colors.append([0, 255, 0, 255])
|
||||
|
||||
# Y轴箭头头部
|
||||
for i in range(axis_line_density // 2):
|
||||
t = i / (axis_line_density // 2 - 1) if axis_line_density > 2 else 0
|
||||
arrow_y = arrow_base + t * arrow_length
|
||||
arrow_offset = (1 - t) * axis_thickness * 2
|
||||
|
||||
axes_points.append([arrow_offset, arrow_y, 0])
|
||||
axes_points.append([-arrow_offset, arrow_y, 0])
|
||||
axes_points.append([0, arrow_y, arrow_offset])
|
||||
axes_points.append([0, arrow_y, -arrow_offset])
|
||||
axes_colors.extend([[0, 255, 0, 255]] * 4)
|
||||
|
||||
# Z轴 - 蓝色（粗线条）
|
||||
for i in range(axis_line_density):
|
||||
t = i / (axis_line_density - 1) if axis_line_density > 1 else 0
|
||||
base_point = [0, 0, t * axis_length]
|
||||
|
||||
# 主轴线
|
||||
axes_points.append(base_point)
|
||||
axes_colors.append([0, 0, 255, 255])
|
||||
|
||||
# 增加厚度（在XY平面上添加点）
|
||||
for j in range(axis_thickness_points):
|
||||
for k in range(axis_thickness_points):
|
||||
offset_x = (j - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
offset_y = (k - axis_thickness_points//2) * axis_thickness / axis_thickness_points
|
||||
thick_point = [base_point[0] + offset_x, base_point[1] + offset_y, base_point[2]]
|
||||
axes_points.append(thick_point)
|
||||
axes_colors.append([0, 0, 255, 255])
|
||||
|
||||
# Z轴箭头头部
|
||||
for i in range(axis_line_density // 2):
|
||||
t = i / (axis_line_density // 2 - 1) if axis_line_density > 2 else 0
|
||||
arrow_z = arrow_base + t * arrow_length
|
||||
arrow_offset = (1 - t) * axis_thickness * 2
|
||||
|
||||
axes_points.append([arrow_offset, 0, arrow_z])
|
||||
axes_points.append([-arrow_offset, 0, arrow_z])
|
||||
axes_points.append([0, arrow_offset, arrow_z])
|
||||
axes_points.append([0, -arrow_offset, arrow_z])
|
||||
axes_colors.extend([[0, 0, 255, 255]] * 4)
|
||||
|
||||
axes_vertices = np.array(axes_points)
|
||||
axes_colors_array = np.array(axes_colors)
|
||||
|
||||
processing_log.append(f"坐标轴生成: {len(axes_vertices):,} 个点 (长度={axis_length:.3f})")
|
||||
|
||||
return axes_vertices, axes_colors_array
|
||||
|
||||
except Exception as e:
|
||||
processing_log.append(f"创建坐标轴点云失败: {str(e)}")
|
||||
return np.array([]), np.array([])
|
||||
|
||||
def _generate_output_path(self, output_filename):
|
||||
"""生成输出文件路径"""
|
||||
# 确保文件名有正确的扩展名
|
||||
if not output_filename.lower().endswith('.glb'):
|
||||
output_filename += '.glb'
|
||||
|
||||
# 生成输出路径
|
||||
output_dir = folder_paths.get_output_directory() if FOLDER_PATHS_AVAILABLE else "output"
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# 添加时间戳避免文件名冲突
|
||||
import time
|
||||
timestamp = str(int(time.time()))
|
||||
name_parts = output_filename.rsplit('.', 1)
|
||||
if len(name_parts) == 2:
|
||||
timestamped_filename = f"{name_parts[0]}_{timestamp}.{name_parts[1]}"
|
||||
else:
|
||||
timestamped_filename = f"{output_filename}_{timestamp}"
|
||||
|
||||
return os.path.join(output_dir, timestamped_filename)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# 节点注册 - 独立注册，ComfyUI会自动发现此文件
|
||||
# 节点注册 - 更新注册信息
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"GLBPointCloudProcessor": GLBPointCloudProcessor,
|
||||
"GLBPointCloudBounds": GLBPointCloudBounds,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"GLBPointCloudProcessor": "VVL GLB Point Cloud Processor",
|
||||
"GLBPointCloudBounds": "VVL GLB Point Cloud Bounds Visualizer",
|
||||
}
|
||||
|
||||
# # 添加节点信息，帮助ComfyUI更好地识别
|
||||
|
||||
Reference in New Issue
Block a user