orthographic-projector 0.1.9

Generate orthographic projections from a Point Cloud.

Orthographic Projector

A Rust-based Python library for generating cubic orthographic projections from point clouds.

Prerequisites

• Python 3.10 or later
• rustc 1.77.2 or later
• pip

Setup

From PyPI

pip install orthographic-projector
# or
python -m pip install orthographic-projector

From source

# Clone this repository
https://github.com/akaTsunemori/orthographic_projector.git

# cd into the project folder
cd orthographic_projector

# Setup and activate the conda environment
conda env create -f environment.yml
conda activate orthographic-projector

# Compile the project into a python module using maturin
maturin develop -r

Usage

import cv2
import numpy as np
import open3d as o3d
import orthographic_projector
import time


def save_projections(projections):
    for i in range(len(projections)):
        image = projections[i].astype(np.uint8)
        image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        cv2.imwrite(f"projection_{i}.png", image_bgr)


# Load the point cloud through open3d
PC_PATH = "./examples/redandblack_vox10_1550.ply"
pc = o3d.io.read_point_cloud(PC_PATH)
points, colors = np.asarray(pc.points), np.asarray(pc.colors)

# orthographic_projector parameters
precision = 10
filtering = 2
crop = True
save = True

t0 = time.time()
# The generate_projections function can be used for generating the projections
images, ocp_maps = orthographic_projector.generate_projections(
    points, colors, precision, filtering
)
# The crop parameter could optionally be passed to the generate_projections function,
# but it can also be called after the generation process
if crop:
    images, ocp_maps = orthographic_projector.apply_cropping(images, ocp_maps)
t1 = time.time()
print(f"Done. Time taken: {(t1-t0):.2f} s")

# The save_projections function is just an example intended for visualization of the results
if save:
    save_projections(images)

This example is also available in the examples/example_generate_projections.py folder.

Results

These are the generated projections to be expected from the provided example.

Spoiler

References

• Orthographic projector

  [1] D. Graziosi, O. Nakagami, S. Kuma, A. Zaghetto, T. Suzuki, and A. Tabatabai "An overview of ongoing point cloud compression standardization activities: video-based (V-PCC) and geometry-based (G-PCC)".

  [2] Osman Topçu "3D Reconstruction Of Point Clouds Using Multi-View Orthographic Projections".

• Source for the example point cloud

  [3] E. Alexiou, I. Viola, T. M. Borges, T. A. Fonseca, R. L. De Queiroz, and T. Ebrahimi, “A comprehensive study of the rate-distortion performance in mpeg point cloud compression,” APSIPA Transactions on Signal and Information Processing, vol. 8, 2019

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated, you can simply open an issue.

License

MIT License

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GitHub @akaTsunemori