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b24c748 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import os
from sklearn.decomposition import PCA
from matplotlib.animation import FuncAnimation
def features_to_colors(features):
"""
Convert high-dimensional feature matrix to RGB colors, considering cosine similarity.
Args:
features (numpy.ndarray): Feature matrix of shape (N, D).
Returns:
colors (numpy.ndarray): RGB colors of shape (N, 3).
"""
# Normalize features to unit length for cosine similarity
norms = np.linalg.norm(features, axis=1, keepdims=True) + 1e-6 # Avoid division by zero
features = features / norms # Normalize to unit length
# Reduce to 3 dimensions if necessary
if features.shape[1] > 3:
pca = PCA(n_components=3)
features = pca.fit_transform(features)
# Normalize reduced features to [0, 1]
features = (features - features.min(axis=0)) / (features.ptp(axis=0) + 1e-6)
# Map directly to RGB
colors = features
return colors
def visualize_points_3d(tem_pts, points_name, num_frames=360, **kwargs):
output_video_path=f'{points_name}_visualization.mp4'
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Scatter plot of points
ax.scatter(tem_pts[:, 0], tem_pts[:, 1], tem_pts[:, 2], **kwargs)
# Hide grid and axes
ax.grid(False)
ax.axis('off')
# Configure axes limits for better visibility
max_extent = np.max(np.abs(tem_pts))
ax.set_xlim([-max_extent, max_extent])
ax.set_ylim([-max_extent, max_extent])
ax.set_zlim([-max_extent, max_extent])
def update(frame):
ax.view_init(elev=30, azim=frame)
return fig,
anim = FuncAnimation(fig, update, frames=num_frames, interval=100)
# Save as a video
anim.save(output_video_path, fps=30, writer='ffmpeg')
print(f"Visualization saved to {output_video_path}")
def visualize_two_sets_3d(
points1,
points2,
vis_name,
points1_name='Set 1',
points2_name='Set 2',
color1='red',
color2='green',
num_frames=360,
output_video_path='two_sets_visualization.mp4',
**kwargs,
):
"""
Visualize two sets of 3D points in a single animated plot.
Parameters:
- points1 (np.ndarray): First set of points, shape (N, 3).
- points2 (np.ndarray): Second set of points, shape (M, 3).
- points1_name (str): Label for the first point set.
- points2_name (str): Label for the second point set.
- color1 (str): Color for the first point set.
- color2 (str): Color for the second point set.
- num_frames (int): Number of frames in the animation.
- output_video_path (str): Path to save the output video.
"""
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Scatter plots for both point sets
scatter1 = ax.scatter(points1[:, 0], points1[:, 1], points1[:, 2],
c=color1, **kwargs)
scatter2 = ax.scatter(points2[:, 0], points2[:, 1], points2[:, 2],
c=color2, **kwargs)
# Hide grid and axes for a cleaner look
ax.grid(False)
ax.axis('off')
# Determine the combined maximum extent for all points
all_points = np.vstack((points1, points2))
max_extent = np.max(np.abs(all_points)) * 1.1 # Slightly larger for padding
ax.set_xlim([-max_extent, max_extent])
ax.set_ylim([-max_extent, max_extent])
ax.set_zlim([-max_extent, max_extent])
# Function to update the view for each frame
def update(frame):
ax.view_init(elev=30, azim=frame)
return fig,
# Create the animation
anim = FuncAnimation(fig, update, frames=num_frames, interval=50, blit=True)
# Save the animation as a video file
# from matplotlib.animation import FuncAnimation, FFMpegWriter
print('------------------>', output_video_path)
anim.save(f"{vis_name}_{output_video_path}", fps=30, writer='ffmpeg')
plt.close(fig) # Close the figure to free memory
print(f"Visualization saved to {vis_name}_{output_video_path}") |