217 lines
9.2 KiB
Markdown
217 lines
9.2 KiB
Markdown
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# Documentation
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- Class name: CameraPoseVisualizer
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- Category: KJNodes/misc
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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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。
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# Input types
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## Required
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- pose_file_path
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- 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。
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- Comfy dtype: STRING
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- Python dtype: str
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- base_xval
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- zval
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- scale
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- 调整整个可视化的比例，允许放大或缩小场景。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- use_exact_fx
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- 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。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- relative_c2w
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- 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。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- use_viewer
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- 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。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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## Optional
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- cameractrl_poses
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- 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。
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- Comfy dtype: CAMERACTRL_POSES
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- Python dtype: list
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# Output types
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- image
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- 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。
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- Comfy dtype: IMAGE
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- Python dtype: matplotlib.figure.Figure
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- ui
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- 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。
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class CameraPoseVisualizer:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"pose_file_path": ("STRING", {"default": '', "multiline": False}),
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"base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}),
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"zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}),
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"scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}),
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"use_exact_fx": ("BOOLEAN", {"default": False}),
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"relative_c2w": ("BOOLEAN", {"default": True}),
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"use_viewer": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "plot"
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CATEGORY = "KJNodes/misc"
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DESCRIPTION = """
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Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose
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or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot.
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"""
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def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None):
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import io
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from torchvision.transforms import ToTensor
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x_min = -2.0 * scale
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x_max = 2.0 * scale
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y_min = -2.0 * scale
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y_max = 2.0 * scale
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z_min = -2.0 * scale
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z_max = 2.0 * scale
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plt.rcParams['text.color'] = '#999999'
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self.fig = plt.figure(figsize=(18, 7))
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self.fig.patch.set_facecolor('#353535')
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self.ax = self.fig.add_subplot(projection='3d')
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self.ax.set_facecolor('#353535') # Set the background color here
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self.ax.grid(color='#999999', linestyle='-', linewidth=0.5)
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self.plotly_data = None # plotly data traces
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self.ax.set_aspect("auto")
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self.ax.set_xlim(x_min, x_max)
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self.ax.set_ylim(y_min, y_max)
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self.ax.set_zlim(z_min, z_max)
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self.ax.set_xlabel('x', color='#999999')
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self.ax.set_ylabel('y', color='#999999')
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self.ax.set_zlabel('z', color='#999999')
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for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels():
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text.set_color('#999999')
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print('initialize camera pose visualizer')
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if pose_file_path != "":
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with open(pose_file_path, 'r') as f:
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poses = f.readlines()
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w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]]
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fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]]
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print(poses)
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elif cameractrl_poses is not None:
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poses = cameractrl_poses
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w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses]
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fxs = [pose[1] for pose in cameractrl_poses]
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else:
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raise ValueError("Please provide either pose_file_path or cameractrl_poses")
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total_frames = len(w2cs)
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transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4)
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last_row = np.zeros((1, 4))
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last_row[0, -1] = 1.0
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w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
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c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w)
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for frame_idx, c2w in enumerate(c2ws):
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self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval,
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zval=(fxs[frame_idx] if use_exact_fx else zval))
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# Create the colorbar
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cmap = mpl.cm.rainbow
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norm = mpl.colors.Normalize(vmin=0, vmax=total_frames)
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colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical')
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# Change the colorbar label
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colorbar.set_label('Frame', color='#999999') # Change the label and its color
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# Change the tick colors
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colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color
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# Change the tick frequency
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# Assuming you want to set the ticks at every 10th frame
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ticks = np.arange(0, total_frames, 10)
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colorbar.ax.yaxis.set_ticks(ticks)
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plt.title('')
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plt.draw()
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buf = io.BytesIO()
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plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
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buf.seek(0)
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img = Image.open(buf)
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tensor_img = ToTensor()(img)
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buf.close()
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tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0)
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if use_viewer:
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time.sleep(1)
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plt.show()
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return (tensor_img,)
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def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3):
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from mpl_toolkits.mplot3d.art3d import Poly3DCollection
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vertex_std = np.array([[0, 0, 0, 1],
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[base_xval, -base_xval * hw_ratio, zval, 1],
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[base_xval, base_xval * hw_ratio, zval, 1],
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[-base_xval, base_xval * hw_ratio, zval, 1],
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[-base_xval, -base_xval * hw_ratio, zval, 1]])
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vertex_transformed = vertex_std @ extrinsic.T
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meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]],
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[vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]],
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[vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]],
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[vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]],
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[vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]]
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color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map)
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self.ax.add_collection3d(
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Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25))
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def customize_legend(self, list_label):
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from matplotlib.patches import Patch
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list_handle = []
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for idx, label in enumerate(list_label):
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color = plt.cm.rainbow(idx / len(list_label))
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patch = Patch(color=color, label=label)
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list_handle.append(patch)
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plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle)
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def get_c2w(self, w2cs, transform_matrix, relative_c2w):
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if relative_c2w:
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target_cam_c2w = np.array([
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[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 1, 0],
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[0, 0, 0, 1]
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])
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abs2rel = target_cam_c2w @ w2cs[0]
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ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
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else:
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ret_poses = [np.linalg.inv(w2c) for w2c in w2cs]
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ret_poses = [transform_matrix @ x for x in ret_poses]
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return np.array(ret_poses, dtype=np.float32)
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```
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