Question about Anomaly Map Visualization for 3D AD&S #3
Comments
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Same question here. |
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Same, I would like to visualize the xyz image. Did you manage to do it? |
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was any able to visulize anomaly maps?? |
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The paper of Patchcore has some problem. We suggest that you pay attention to this problem, so as not to step on the pit again. |
I don't do research anymore, but I might be able to help. |
Can you please give some suggestions about visulization?? Thank you |
What kind of problems??? |
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I will help you find the code this weekend, too long haven't done this work, a little forgot. Of course, it is better that the author can open source. |
Thank you... I will wait for your response. Appreciate it |
I'm sorry. I didn't manage my code well. The following I have written from memory, I hope it will be useful. import numpy as np
import open3d as o3d
import matplotlib.pyplot as plt
# Function to load point cloud data
def load_point_cloud_data(point_cloud_file):
"""
Load point cloud data from a .npy file.
The file should contain a 3D tensor with shape (3, 224, 224).
Args:
point_cloud_file (str): Path to the .npy file containing the point cloud.
Returns:
np.ndarray: A (N, 3) array of points where N is the total number of points.
"""
# Load the point cloud tensor
point_cloud = np.load(point_cloud_file)
# Flatten the x, y, z components
x, y, z = point_cloud[0].flatten(), point_cloud[1].flatten(), point_cloud[2].flatten()
# Combine them into a single array of 3D points
points = np.vstack((x, y, z)).T
return points
# Function to load RGB image data
def load_rgb_image(rgb_image_file):
"""
Load RGB image data and map it to corresponding point cloud points.
Args:
rgb_image_file (str): Path to the .png file containing the RGB image.
Returns:
np.ndarray: A (N, 3) array of RGB values normalized to the range [0, 1].
"""
# Read the RGB image
rgb_image = plt.imread(rgb_image_file)
# Flatten the image and normalize RGB values to [0, 1]
return rgb_image.reshape(-1, 3) / 255.0
# Function to load precomputed outlier mask
def load_outlier_mask(outlier_mask_file):
"""
Load the outlier mask from a .npy file.
The file should contain a tensor with shape (1, 224, 224).
Args:
outlier_mask_file (str): Path to the .npy file containing the outlier mask.
Returns:
np.ndarray: A boolean array of shape (N,) indicating outlier points.
"""
# Load the outlier mask tensor
outlier_mask = np.load(outlier_mask_file)
# Flatten and convert to a boolean array
return outlier_mask.flatten().astype(bool)
# Function to visualize the point cloud
def visualize_point_cloud(points, colors, outliers):
"""
Visualize the point cloud with normal points and outliers.
Args:
points (np.ndarray): A (N, 3) array of point cloud data.
colors (np.ndarray): A (N, 3) array of RGB values for the points.
outliers (np.ndarray): A boolean array indicating outlier points.
"""
# Create an Open3D PointCloud object
pcd = o3d.geometry.PointCloud()
# Assign points and their colors
pcd.points = o3d.utility.Vector3dVector(points)
pcd.colors = o3d.utility.Vector3dVector(colors)
# Separate normal points and outliers
normal_pcd = pcd.select_by_index(np.where(~outliers)[0])
outlier_pcd = pcd.select_by_index(np.where(outliers)[0])
# Color outliers red
outlier_pcd.paint_uniform_color([1, 0, 0])
# Visualize the normal points and outliers together
o3d.visualization.draw_geometries([normal_pcd, outlier_pcd])
# Main function
def main():
"""
Main pipeline to:
1. Load point cloud data.
2. Load corresponding RGB image.
3. Use the model-provided outlier mask.
4. Visualize the results.
"""
# File paths
point_cloud_file = "0.tff" # Path to the point cloud file (assumed .npy format)
rgb_image_file = "0.png" # Path to the RGB image file
outlier_mask_file = "outlier_mask.npy" # Path to the precomputed outlier mask
# Load the point cloud data
points = load_point_cloud_data(point_cloud_file)
# Load the corresponding RGB image
colors = load_rgb_image(rgb_image_file)
# Load the precomputed outlier mask
outliers = load_outlier_mask(outlier_mask_file)
# Output statistics
print(f"Total points: {len(points)}, Outliers: {np.sum(outliers)}")
# Visualize the point cloud with outliers
visualize_point_cloud(points, colors, outliers)
if __name__ == "__main__":
main() |
Thanks for your insightful paper about 3D AD&S and the released neat code! I am trying to make some visualizations of the 3D data and blend an anomaly score map on it. However, I have a problem with the camera intrinsic parameter settings. Do you know how to visualize the point cloud with the given camera parameters using Open3D?
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