# Documentation - Class name: CameraPoseVisualizer - Category: KJNodes/misc - Output node: False - Repo Ref: https://github.com/comfyanonymous/ComfyUI CameraPoseVisualizer节点用于在3D图表中可视化相机姿态。它可以从指定的包含相机内参和坐标的文件中读取数据，或直接使用提供的相机控制姿态。该节点支持调整可视化比例、基准值，并可选择是否使用精确焦距或相对的相机到世界坐标变换，从而增强对给定空间中相机位置和方向的理解。 # Input types ## Required - pose_file_path - 指定包含相机姿态的文件路径，如果直接提供相机控制姿态，则为空字符串。此参数对确定要可视化的相机姿态来源至关重要。 - Comfy dtype: STRING - Python dtype: str - base_xval - 确定相机金字塔可视化的基准x值，影响图表中相机表示的定位。 - Comfy dtype: FLOAT - Python dtype: float - zval - 设置相机金字塔可视化的z值，影响图表中相机表示的深度定位。 - Comfy dtype: FLOAT - Python dtype: float - scale - 调整整个可视化的比例，允许放大或缩小场景。 - Comfy dtype: FLOAT - Python dtype: float - use_exact_fx - 布尔标志，决定是否使用相机姿态中的精确焦距或默认值，影响可视化的准确性。 - Comfy dtype: BOOLEAN - Python dtype: bool - relative_c2w - 确定相机到世界坐标变换是否为相对变换，影响相机表示的方向和定位。 - Comfy dtype: BOOLEAN - Python dtype: bool - use_viewer - 布尔标志，指示是否使用外部查看器进行可视化，提供替代的显示方法。 - Comfy dtype: BOOLEAN - Python dtype: bool ## Optional - cameractrl_poses - 直接提供用于可视化的相机控制姿态，作为指定姿态文件路径的替代方案。 - Comfy dtype: CAMERACTRL_POSES - Python dtype: list # Output types - image - 输出是相机姿态的3D图表视觉表示，展示相机的空间排列和方向。 - Comfy dtype: IMAGE - Python dtype: matplotlib.figure.Figure - ui - 输出是相机姿态的3D图表视觉表示，展示相机的空间排列和方向。 ## Usage tips - Infra type: `CPU` - Common nodes: unknown ## Source code ```python class CameraPoseVisualizer: @classmethod def INPUT_TYPES(s): return {"required": { "pose_file_path": ("STRING", {"default": '', "multiline": False}), "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), "use_exact_fx": ("BOOLEAN", {"default": False}), "relative_c2w": ("BOOLEAN", {"default": True}), "use_viewer": ("BOOLEAN", {"default": False}), }, "optional": { "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}), } } RETURN_TYPES = ("IMAGE",) FUNCTION = "plot" CATEGORY = "KJNodes/misc" DESCRIPTION = """ Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. """ def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None): import matplotlib as mpl import matplotlib.pyplot as plt import io from torchvision.transforms import ToTensor x_min = -2.0 * scale x_max = 2.0 * scale y_min = -2.0 * scale y_max = 2.0 * scale z_min = -2.0 * scale z_max = 2.0 * scale plt.rcParams['text.color'] = '#999999' self.fig = plt.figure(figsize=(18, 7)) self.fig.patch.set_facecolor('#353535') self.ax = self.fig.add_subplot(projection='3d') self.ax.set_facecolor('#353535') # Set the background color here self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) self.plotly_data = None # plotly data traces self.ax.set_aspect("auto") self.ax.set_xlim(x_min, x_max) self.ax.set_ylim(y_min, y_max) self.ax.set_zlim(z_min, z_max) self.ax.set_xlabel('x', color='#999999') self.ax.set_ylabel('y', color='#999999') self.ax.set_zlabel('z', color='#999999') for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): text.set_color('#999999') print('initialize camera pose visualizer') if pose_file_path != "": with open(pose_file_path, 'r') as f: poses = f.readlines() w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]] fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]] print(poses) elif cameractrl_poses is not None: poses = cameractrl_poses w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses] fxs = [pose[1] for pose in cameractrl_poses] else: raise ValueError("Please provide either pose_file_path or cameractrl_poses") total_frames = len(w2cs) transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) last_row = np.zeros((1, 4)) last_row[0, -1] = 1.0 w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs] c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w) for frame_idx, c2w in enumerate(c2ws): self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval, zval=(fxs[frame_idx] if use_exact_fx else zval)) # Create the colorbar cmap = mpl.cm.rainbow norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') # Change the colorbar label colorbar.set_label('Frame', color='#999999') # Change the label and its color # Change the tick colors colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color # Change the tick frequency # Assuming you want to set the ticks at every 10th frame ticks = np.arange(0, total_frames, 10) colorbar.ax.yaxis.set_ticks(ticks) plt.title('') plt.draw() buf = io.BytesIO() plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) buf.seek(0) img = Image.open(buf) tensor_img = ToTensor()(img) buf.close() tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) if use_viewer: time.sleep(1) plt.show() return (tensor_img,) def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3): from mpl_toolkits.mplot3d.art3d import Poly3DCollection vertex_std = np.array([[0, 0, 0, 1], [base_xval, -base_xval * hw_ratio, zval, 1], [base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, base_xval * hw_ratio, zval, 1], [-base_xval, -base_xval * hw_ratio, zval, 1]]) vertex_transformed = vertex_std @ extrinsic.T meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) self.ax.add_collection3d( Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) def customize_legend(self, list_label): from matplotlib.patches import Patch list_handle = [] for idx, label in enumerate(list_label): color = plt.cm.rainbow(idx / len(list_label)) patch = Patch(color=color, label=label) list_handle.append(patch) plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) def get_c2w(self, w2cs, transform_matrix, relative_c2w): if relative_c2w: target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] else: ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] ret_poses = [transform_matrix @ x for x in ret_poses] return np.array(ret_poses, dtype=np.float32) ```