diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem new file mode 100644 index 0000000..b85c803 --- /dev/null +++ b/.gradio/certificate.pem @@ -0,0 +1,31 @@ +-----BEGIN CERTIFICATE----- +MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw +TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh +cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 +WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu +ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY +MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc +h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ +0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U +A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW +T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH +B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC +B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv 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+mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d +emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc= +-----END CERTIFICATE----- diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..eaa9c9e --- /dev/null +++ b/__init__.py @@ -0,0 +1,3 @@ +from .comfyui_vggt_nodes import NODE_CLASS_MAPPINGS + +__all__ = ['NODE_CLASS_MAPPINGS'] \ No newline at end of file diff --git a/comfyui_vggt_nodes.py b/comfyui_vggt_nodes.py new file mode 100644 index 0000000..46950c4 --- /dev/null +++ b/comfyui_vggt_nodes.py @@ -0,0 +1,521 @@ +import os +import json +import tempfile +from typing import List, Any + +import cv2 +import numpy as np +import torch +from PIL import Image + +# 尝试导入 ComfyUI 的类型标记 +try: + from comfy.comfy_types import IO +except ImportError: + class IO: + VIDEO = "VIDEO" + IMAGE = "IMAGE" + +# 导入 VGGT 相关函数 +try: + from vggt.models.vggt import VGGT + from vggt.utils.load_fn import load_and_preprocess_images + from vggt.utils.pose_enc import pose_encoding_to_extri_intri +except Exception as e: + VGGT = None + _VGGT_IMPORT_ERROR = e +else: + _VGGT_IMPORT_ERROR = None + +# ----------------------------------------------------------------------------- +# 工具函数 +# ----------------------------------------------------------------------------- + +def _extract_video_frames(video_path: str, interval: int, max_frames: int) -> List[np.ndarray]: + """按照给定间隔提取视频帧 (BGR).""" + cap = cv2.VideoCapture(video_path) + frames = [] + idx = 0 + while len(frames) < max_frames: + ret, frame = cap.read() + if not ret: + break + if idx % interval == 0: + frames.append(frame.copy()) + idx += 1 + cap.release() + return frames + +def _matrices_to_json(intrinsic, extrinsic) -> (str, str): + """将相机矩阵转换为 JSON 字符串。""" + num_views = extrinsic.shape[0] + intrinsics_list = [] + poses_list = [] + for i in range(num_views): + K = intrinsic[i].tolist() + Rt = extrinsic[i].tolist() + # 相机位置 world 坐标 ( -R^T * t ) + R = np.array(Rt)[:3, :3] + t = np.array(Rt)[:3, 3] + position = (-R.T @ t).tolist() + intrinsics_list.append({ + "view_id": i, + "intrinsic_matrix": K + }) + poses_list.append({ + "view_id": i, + "extrinsic_matrix": Rt, + "position": position + }) + return ( + json.dumps({"cameras": intrinsics_list}, ensure_ascii=False, indent=2), + json.dumps({"poses": poses_list}, ensure_ascii=False, indent=2), + ) + +def _create_traj_preview(extrinsic: torch.Tensor) -> torch.Tensor: + """根据相机外参创建3D轨迹可视化 (返回 1xHxWxC)。""" + ext = extrinsic.cpu().numpy() # (N,3,4) + positions = [] + orientations = [] + + for mat in ext: + R = mat[:3, :3] + t = mat[:3, 3] + pos = -R.T @ t + positions.append(pos) + # 提取相机朝向（Z轴方向） + forward = R[:, 2] # 相机朝向 + orientations.append(forward) + + positions = np.array(positions) + orientations = np.array(orientations) + + if positions.shape[0] < 2: + # 少于两帧给提示图 + return _create_insufficient_data_image() + + # 创建组合视图：顶视图 + 侧视图 + 3D投影 + canvas_width, canvas_height = 800, 600 + canvas = np.ones((canvas_height, canvas_width, 3), dtype=np.float32) * 0.95 + + # 绘制标题 + cv2.putText(canvas, "VGGT Camera Trajectory Analysis", (20, 30), + cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0.1, 0.1, 0.1), 2) + + # 1. 顶视图 (Top View) - 左上角 + top_view = _create_top_view(positions, orientations, size=(350, 250)) + canvas[60:310, 20:370] = top_view + cv2.putText(canvas, "Top View (X-Z)", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0.2, 0.2, 0.2), 1) + + # 2. 侧视图 (Side View) - 右上角 + side_view = _create_side_view(positions, orientations, size=(350, 250)) + canvas[60:310, 420:770] = side_view + cv2.putText(canvas, "Side View (X-Y)", (420, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0.2, 0.2, 0.2), 1) + + # 3. 伪3D视图 - 下方 + pseudo_3d_view = _create_pseudo_3d_view(positions, orientations, size=(720, 250)) + canvas[330:580, 40:760] = pseudo_3d_view + cv2.putText(canvas, "3D Perspective View", (40, 350), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0.2, 0.2, 0.2), 1) + + # 添加统计信息 + _add_trajectory_stats(canvas, positions, orientations) + + return torch.from_numpy(canvas).unsqueeze(0) + +def _create_insufficient_data_image(): + """创建数据不足的提示图像""" + canvas = np.ones((600, 800, 3), dtype=np.float32) * 0.9 + cv2.putText(canvas, "Insufficient Camera Data", (200, 280), + cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0.3, 0.3, 0.3), 2) + cv2.putText(canvas, "Need at least 2 frames", (250, 320), + cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0.5, 0.5, 0.5), 1) + return torch.from_numpy(canvas).unsqueeze(0) + +def _create_top_view(positions, orientations, size=(350, 250)): + """创建顶视图 (X-Z平面)""" + canvas = np.ones((size[1], size[0], 3), dtype=np.float32) * 0.98 + + # 计算投影范围 + x, z = positions[:, 0], positions[:, 2] + x_range, z_range = _get_padded_range(x), _get_padded_range(z) + + # 绘制网格 + _draw_grid(canvas, size, color=(0.9, 0.9, 0.9)) + + # 转换坐标 + pts_2d = _normalize_coords(np.column_stack([x, z]), x_range, z_range, size, margin=20) + + # 绘制轨迹线 + _draw_trajectory_line(canvas, pts_2d, color=(0.2, 0.4, 0.8), thickness=2) + + # 绘制相机位置和朝向 + for i, (pt, orient) in enumerate(zip(pts_2d, orientations)): + # 相机位置 + color = _get_camera_color(i, len(pts_2d)) + cv2.circle(canvas, tuple(pt.astype(int)), 4, color, -1) + + # 朝向指示器 (X-Z投影) + orient_2d = np.array([orient[0], orient[2]]) # X-Z投影 + orient_2d = orient_2d / (np.linalg.norm(orient_2d) + 1e-8) * 15 + end_pt = pt + orient_2d + cv2.arrowedLine(canvas, tuple(pt.astype(int)), tuple(end_pt.astype(int)), + color, 1, tipLength=0.3) + + # 帧编号 + cv2.putText(canvas, str(i), (int(pt[0]+6), int(pt[1]+6)), + cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0,0,0), 1) + + return canvas + +def _create_side_view(positions, orientations, size=(350, 250)): + """创建侧视图 (X-Y平面)""" + canvas = np.ones((size[1], size[0], 3), dtype=np.float32) * 0.98 + + # 计算投影范围 + x, y = positions[:, 0], positions[:, 1] + x_range, y_range = _get_padded_range(x), _get_padded_range(y) + + # 绘制网格 + _draw_grid(canvas, size, color=(0.9, 0.9, 0.9)) + + # 转换坐标 (注意Y轴翻转) + pts_2d = _normalize_coords(np.column_stack([x, -y]), x_range, (-y_range[1], -y_range[0]), size, margin=20) + + # 绘制轨迹线 + _draw_trajectory_line(canvas, pts_2d, color=(0.8, 0.4, 0.2), thickness=2) + + # 绘制相机位置 + for i, pt in enumerate(pts_2d): + color = _get_camera_color(i, len(pts_2d)) + cv2.circle(canvas, tuple(pt.astype(int)), 4, color, -1) + cv2.putText(canvas, str(i), (int(pt[0]+6), int(pt[1]+6)), + cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0,0,0), 1) + + return canvas + +def _create_pseudo_3d_view(positions, orientations, size=(720, 250)): + """创建伪3D视图""" + canvas = np.ones((size[1], size[0], 3), dtype=np.float32) * 0.98 + + # 应用3D->2D投影变换 (等距投影) + # 旋转角度 + angle_x, angle_y = np.pi/6, np.pi/4 # 30度和45度 + + cos_x, sin_x = np.cos(angle_x), np.sin(angle_x) + cos_y, sin_y = np.cos(angle_y), np.sin(angle_y) + + # 3D到2D投影矩阵 + proj_matrix = np.array([ + [cos_y, sin_x*sin_y, 0], + [0, cos_x, sin_x] + ]) + + # 投影3D点 + pts_3d = positions.T # (3, N) + pts_2d = proj_matrix @ pts_3d # (2, N) + pts_2d = pts_2d.T # (N, 2) + + # 归一化到画布 + x_range = _get_padded_range(pts_2d[:, 0]) + y_range = _get_padded_range(pts_2d[:, 1]) + pts_canvas = _normalize_coords(pts_2d, x_range, y_range, size, margin=30) + + # 绘制3D网格效果 + _draw_3d_grid(canvas, size) + + # 根据深度排序绘制 + depths = positions[:, 2] # Z坐标作为深度 + depth_order = np.argsort(depths) + + # 绘制轨迹线（带深度渐变） + for i in range(len(depth_order)-1): + idx1, idx2 = depth_order[i], depth_order[i+1] + if abs(idx1 - idx2) == 1: # 连续帧 + pt1, pt2 = pts_canvas[idx1], pts_canvas[idx2] + # 深度渐变色 + depth_ratio = (depths[idx1] - depths.min()) / (depths.max() - depths.min() + 1e-8) + color = (0.2 + 0.6*depth_ratio, 0.4, 0.8 - 0.4*depth_ratio) + cv2.line(canvas, tuple(pt1.astype(int)), tuple(pt2.astype(int)), color, 2) + + # 绘制相机（带深度大小变化） + for i in depth_order: + pt = pts_canvas[i] + depth_ratio = (depths[i] - depths.min()) / (depths.max() - depths.min() + 1e-8) + radius = int(3 + 4 * depth_ratio) # 近大远小 + color = _get_camera_color(i, len(positions)) + + # 相机主体 + cv2.circle(canvas, tuple(pt.astype(int)), radius, color, -1) + cv2.circle(canvas, tuple(pt.astype(int)), radius+1, (0,0,0), 1) + + # 朝向指示（3D投影） + orient_3d = orientations[i] + orient_2d = proj_matrix @ orient_3d + orient_2d = orient_2d / (np.linalg.norm(orient_2d) + 1e-8) * (10 + 5*depth_ratio) + end_pt = pt + orient_2d + cv2.arrowedLine(canvas, tuple(pt.astype(int)), tuple(end_pt.astype(int)), + color, max(1, int(depth_ratio*2+1)), tipLength=0.3) + + # 帧编号 + cv2.putText(canvas, str(i), (int(pt[0]+8), int(pt[1]+8)), + cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) + + return canvas + +def _get_padded_range(coords, padding=0.1): + """获取带边距的坐标范围""" + min_val, max_val = coords.min(), coords.max() + span = max_val - min_val + if span < 1e-8: + span = 1.0 + margin = span * padding + return (min_val - margin, max_val + margin) + +def _normalize_coords(coords, x_range, y_range, size, margin=20): + """归一化坐标到画布""" + x_norm = (coords[:, 0] - x_range[0]) / (x_range[1] - x_range[0]) + y_norm = (coords[:, 1] - y_range[0]) / (y_range[1] - y_range[0]) + + canvas_coords = np.column_stack([ + x_norm * (size[0] - 2*margin) + margin, + (1 - y_norm) * (size[1] - 2*margin) + margin # 翻转Y轴 + ]) + return canvas_coords + +def _draw_grid(canvas, size, color=(0.9, 0.9, 0.9)): + """绘制网格""" + h, w = size[1], size[0] + # 垂直线 + for x in range(0, w, w//8): + cv2.line(canvas, (x, 0), (x, h), color, 1) + # 水平线 + for y in range(0, h, h//6): + cv2.line(canvas, (0, y), (w, y), color, 1) + +def _draw_3d_grid(canvas, size): + """绘制3D网格效果""" + h, w = size[1], size[0] + color = (0.85, 0.85, 0.85) + + # 斜向网格线 + for i in range(0, w, w//12): + cv2.line(canvas, (i, 0), (i + h//3, h), color, 1) + for i in range(0, h, h//8): + cv2.line(canvas, (0, i), (w, i), color, 1) + +def _draw_trajectory_line(canvas, points, color, thickness=2): + """绘制轨迹线""" + for i in range(len(points)-1): + cv2.line(canvas, tuple(points[i].astype(int)), + tuple(points[i+1].astype(int)), color, thickness) + +def _get_camera_color(index, total): + """获取相机颜色（彩虹渐变）""" + if total <= 1: + return (0.5, 0.5, 0.5) + + ratio = index / (total - 1) + if ratio < 0.2: + return (0.0, 0.8, 0.0) # 绿色起点 + elif ratio > 0.8: + return (0.8, 0.0, 0.0) # 红色终点 + else: + # 蓝色中间段 + blue_intensity = 0.3 + 0.5 * np.sin(ratio * np.pi) + return (0.0, 0.2, blue_intensity) + +def _add_trajectory_stats(canvas, positions, orientations): + """添加轨迹统计信息""" + # 计算统计数据 + total_distance = 0 + for i in range(1, len(positions)): + total_distance += np.linalg.norm(positions[i] - positions[i-1]) + + max_height = positions[:, 1].max() + min_height = positions[:, 1].min() + height_range = max_height - min_height + + # 显示统计信息 + stats_x, stats_y = 40, 590 + font_scale, thickness = 0.45, 1 + color = (0.1, 0.1, 0.1) + + stats_text = [ + f"Frames: {len(positions)}", + f"Distance: {total_distance:.2f}m", + f"Height Range: {height_range:.2f}m", + f"Avg Height: {positions[:, 1].mean():.2f}m" + ] + + for i, text in enumerate(stats_text): + cv2.putText(canvas, text, (stats_x + i*150, stats_y), + cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness) + +# ----------------------------------------------------------------------------- +# 节点实现 +# ----------------------------------------------------------------------------- + +_VGGT_MODEL = None # 全局缓存模型 + +class VGGTVideoCameraNode: + """VGGT 视频相机参数估计节点""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "video": (IO.VIDEO, { + "tooltip": "来自 LoadVideo 的视频对象，或直接输入视频文件路径" + }), + }, + "optional": { + "video_path": ("STRING", { + "default": "", + "tooltip": "备用视频路径" + }), + "frame_interval": ("INT", { + "default": 5, "min": 1, "max": 50, "step": 1 + }), + "max_frames": ("INT", { + "default": 60, "min": 5, "max": 200, "step": 5 + }), + "device": (["auto", "cuda", "cpu"], { + "default": "auto" + }), + } + } + + RETURN_TYPES = ("STRING", "IMAGE", "STRING") + RETURN_NAMES = ("intrinsics_json", "trajectory_preview", "poses_json") + FUNCTION = "estimate" + CATEGORY = "VGGT" + + def __init__(self): + global _VGGT_MODEL + if VGGT is None: + print(f"[VGGT node] 导入 VGGT 失败: {_VGGT_IMPORT_ERROR}") + _VGGT_MODEL = None + return + if _VGGT_MODEL is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + try: + _VGGT_MODEL = VGGT.from_pretrained("facebook/VGGT-1B").to(device) + _VGGT_MODEL.eval() + print("[VGGT node] VGGT 模型加载完成") + except Exception as e: + print(f"[VGGT node] 加载模型失败: {e}") + _VGGT_MODEL = None + + # --------------------------------------------------------- + def _resolve_video_path(self, video: Any, fallback: str) -> str: + if video is None: + return fallback + # 如果 video 是字符串 + if isinstance(video, str): + return video + # 常见属性 + attrs = ["_VideoFromFile__file", "path", "video_path", "_path", "file_path"] + for attr in attrs: + if hasattr(video, attr): + val = getattr(video, attr) + if isinstance(val, str): + return val + return fallback + + # --------------------------------------------------------- + def estimate(self, video=None, video_path: str = "", frame_interval: int = 5, max_frames: int = 60, device: str = "auto"): + try: + if _VGGT_MODEL is None: + raise RuntimeError("VGGT 模型未加载，无法推理") + if device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + + # 更兼容的dtype选择，避免BFloat16不支持问题 + if device == "cuda": + try: + # 尝试使用BFloat16，如果不支持则fallback到Float16 + dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16 + except: + dtype = torch.float16 + else: + dtype = torch.float32 + + # 解析视频路径 + vid_path = self._resolve_video_path(video, video_path) + if not vid_path or not os.path.exists(vid_path): + raise FileNotFoundError(f"找不到视频文件: {vid_path}") + + frames = _extract_video_frames(vid_path, frame_interval, max_frames) + if not frames: + raise RuntimeError("无法从视频中提取帧") + + # 将帧保存为 PNG 以复用官方预处理 + with tempfile.TemporaryDirectory() as tmpdir: + img_paths = [] + for i, frm in enumerate(frames): + rgb = cv2.cvtColor(frm, cv2.COLOR_BGR2RGB) + p = os.path.join(tmpdir, f"frame_{i:04d}.png") + # 使用PIL保存RGB图片，避免cv2的BGR问题 + Image.fromarray(rgb).save(p) + img_paths.append(p) + + imgs = load_and_preprocess_images(img_paths).to(device) + print(f"[VGGT node] 加载图片数量: {len(img_paths)}, 处理后形状: {imgs.shape}") + + with torch.no_grad(): + # 使用与demo_gradio.py相同的调用方式：直接调用model() + try: + with torch.amp.autocast(device_type="cuda", dtype=dtype): + predictions = _VGGT_MODEL(imgs) # 直接调用整个模型 + except: + # Fallback to old API + try: + with torch.cuda.amp.autocast(dtype=dtype): + predictions = _VGGT_MODEL(imgs) # 直接调用整个模型 + except: + # 如果autocast有问题，直接运行 + predictions = _VGGT_MODEL(imgs) # 直接调用整个模型 + + # 从predictions中提取pose_enc + pose_enc = predictions["pose_enc"] + print(f"[VGGT node] pose_enc形状: {pose_enc.shape}") + + # extrinsic (1,N,3,4), intrinsic (1,N,3,3) + extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, imgs.shape[-2:]) + + # 如果有批次维度则去除 + if len(extrinsic.shape) == 4: # (1,N,3,4) + extrinsic = extrinsic[0] # (N,3,4) + if len(intrinsic.shape) == 4: # (1,N,3,3) + intrinsic = intrinsic[0] # (N,3,3) + + extrinsic = extrinsic.cpu() + intrinsic = intrinsic.cpu() + print(f"[VGGT node] 最终矩阵形状 - extrinsic: {extrinsic.shape}, intrinsic: {intrinsic.shape}") + + # JSON 输出 + intrinsics_json, poses_json = _matrices_to_json(intrinsic.numpy(), extrinsic.numpy()) + + # 轨迹图像 + traj_tensor = _create_traj_preview(extrinsic) + + return (intrinsics_json, traj_tensor, poses_json) + + except Exception as e: + err = f"VGGT 估计错误: {e}" + print(err) + empty_img = torch.ones((1, 400, 400, 3), dtype=torch.float32) * 0.1 + err_json = json.dumps({"success": False, "error": err}, ensure_ascii=False, indent=2) + return (err_json, empty_img, err_json) + +# ----------------------------------------------------------------------------- +# 节点注册 +# ----------------------------------------------------------------------------- + +NODE_CLASS_MAPPINGS = { + "VGGTVideoCameraNode": VGGTVideoCameraNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "VGGTVideoCameraNode": "VGGT Video Camera Estimator", +} \ No newline at end of file diff --git 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