package consisting of the scripts for operating on the point cloud / photogrammetric data
Project description
@geospatial-pipelines/data_preparation:
This package provides the tools and utils for the first stage of the operation after fetching data: allowing users to do operations of the current raw dataset of pointcloud (.las, .laz, .pcd) for operations like: - Cropping (2D/3D cropping) - conversion of the coordinates - combination of point clouds - conversion across various formats.
installation and setup:
- Install from the pip directory:
pip install pointcloud_extraction_toolkit
. - Or via the source by cloning the whole geospatial-pipeline and then installing the file via the
pip install -r requirements.txt
.
Various packages:
package_file | remarks |
---|---|
cameras.utils | consist of methods to use the API across colmap / neuralangelo for photogrammetry |
pdal.pipeline_generation | scripts to generate the pipeline for PDAL to do necessary transformations |
cropping | Script to crop the given portion that you want to fetch. |
threed_pointcloud | script that integrates the open3D for 3D data cropping at microlevel |
API's :
There are colab tutorials in test/
folder that explain the various usecase, but now try to fetch the
- Import the cameras package for the photogrammetry pipeline processing to fetch the pointcloud
Important: You need to setup colmap before this package in order to work.
from pointcloud_extraction_toolkit.cameras.utils import ColmapDataParsing
import os
colmap_progressing = ColmapDataParsing(filepath="demo.mp4", output_dir="./demo_output")
colmap_progressing.convert_photo_to_video(downsampling_rate=5)
## also fetch the image metadata for the algorithm / reviewer in order to showcase the details.
## now fetching the image metadata from the given details in order to later on do the required transformation on the specific frame <> pose basis if needed.
files = './output/imgs'
data_info = {}
for filename in os.listdir(files):
full_path = os.path.join(files, filename)
imagemetadata = colmap_progressing.get_image_metadata(full_path)
data_info[filename].append(imagemetadata)
## and finally the colmap transformation
await colmap_processing.colmap_transformation()
## in the results you seem to see some of the outputs are not compatible with the alignment then run the following method to fix and rerun colmap_transformation().
## analyze_colmap_images(self,camera_bin_path, transform_file, camera_depth, coordinates_adjust = ["0", "0", "0", "1"] )
colmap_processing.analyze_colmap_images(camera_bin_path= files + "stereo/camera.bin" , transform_file= files + "transforms.json", camera_depth = "", coordinates_adjust = [] )
- Tutorial for 3D data processing directly:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for pointcloud_extraction-0.0.4.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | f7326a99f5d5921f3acddca741ea14da8efd9e1223c5743e7e64f68df4cf2cac |
|
MD5 | 476fe50c39e9ee58e67e05e9d9d89441 |
|
BLAKE2b-256 | 3f7560d0b5cdb45de71a145f94e6d5c12f9865d0a5765c3d9772c121f4ebd5d2 |
Close
Hashes for pointcloud_extraction-0.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5ad0791d3e2d023abd0341f80af72f28154436b53ee85f1b85cf780b33939088 |
|
MD5 | 81a261be476fcc9edff9b86b03c28485 |
|
BLAKE2b-256 | ff934df0cf1553aa6fb2d751cfd5a29478e78d56ab95a269e8cb608a3a2408a1 |