diff --git a/nodes.py b/nodes.py index 0170901..aa779cf 100644 --- a/nodes.py +++ b/nodes.py @@ -4352,19 +4352,12 @@ class CameraPoseVisualizer: @classmethod def INPUT_TYPES(s): return {"required": { - "pose_file_path": ("STRING", {"default": 'pose file path here', "multiline": False}), - "sample_stride": ("INT", {"default": 1,"min": 0, "max": 100, "step": 1}), - "frames": ("INT", {"default": 16,"min": 0, "max": 100, "step": 1}), - "base_xval": ("FLOAT", {"default": 0.5,"min": 0, "max": 100, "step": 0.01}), - "zval": ("FLOAT", {"default": 2.0,"min": 0, "max": 100, "step": 0.01}), - "use_exact_fx": ("BOOLEAN", {"default": True}), + "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}), - "x_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}), - "x_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}), - "y_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}), - "y_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}), - "z_min": ("FLOAT", {"default": -5.0,"min": -100, "max": 100, "step": 0.01}), - "z_max": ("FLOAT", {"default": 5.0,"min": -100, "max": 100, "step": 0.01}), "use_viewer": ("BOOLEAN", {"default": False}), }, "optional": { @@ -4376,26 +4369,40 @@ class CameraPoseVisualizer: FUNCTION = "plot" CATEGORY = "KJNodes/misc" DESCRIPTION = """ -Visualizes the camera poses from a .txt file with -RealEstate camera intrinsics and coordinates in a 3D plot. +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, sample_stride, frames, base_xval, zval, use_exact_fx, relative_c2w, x_min, x_max, y_min, y_max, z_min, z_max, use_viewer, cameractrl_poses=None): + 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') - self.ax.set_ylabel('y') - self.ax.set_zlabel('z') + 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() @@ -4409,26 +4416,35 @@ RealEstate camera intrinsics and coordinates in a 3D plot. else: raise ValueError("Please provide either pose_file_path or cameractrl_poses") - cropped_length = frames * sample_stride total_frames = len(w2cs) - start_frame_ind = random.randint(0, max(0, total_frames - cropped_length - 1)) - end_frame_ind = min(start_frame_ind + cropped_length, total_frames) - frame_ind = np.linspace(start_frame_ind, end_frame_ind - 1, frames, dtype=int) - w2cs = [w2cs[x] for x in frame_ind] 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 / frames, hw_ratio=1/1, base_xval=base_xval, - zval=(fxs[frame_idx] if use_exact_fx else zval)) + 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=frames) - self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical', label='Frame Number') - plt.title('Extrinsic Parameters') + 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) @@ -4459,7 +4475,7 @@ RealEstate camera intrinsics and coordinates in a 3D plot. 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=0.35)) + Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) def customize_legend(self, list_label): from matplotlib.patches import Patch