| Name | pyopf JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | |
| Summary | Python library for I/O and manipulation of projects under the Open Photogrammetry Format (OPF) |
| upload_time | 2023-06-26 11:06:55 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
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| keywords |
opf
photogrammetry
|
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## Python Open Photogrammetry Format (OPF)
This repository provides a Python package for reading, writing and manipulating projects in the OPF format.
For more information about what OPF is and its full specification, please refer to https://www.github.com/Pix4D/opf-spec
### Installation
The tool can be installed using `pip` with the following command:
```shell
pip install pyopf
```
This command installs the `pyopf` package and tools.
### Structure of the PyOPF repository
The `pyopf` library can be found under `src/pyopf`. The library implements easy parsing and writing of OPF projects in Python.
Below is a small example, printing the calibrated position and orientation of a camera, knowing its ID.
```python
from pyopf.io import load
from pyopf.resolve import resolve
from pyopf.uid64 import Uid64
# Path to the example project file.
project_path = "spec/examples/project.json"
# We are going to search for the calibrated position of the camera with this ID
camera_id = Uid64(hex = "0x57282923")
# Load the json data and resolve the project, i.e. load the project items as named attributes.
project = load(project_path)
project = resolve(project)
# Many objects are optional in OPF. If they are missing, they are set to None.
if project.calibration is None:
print("No calibration data.")
exit(1)
# Filter the list of calibrated cameras to find the one with the ID we are looking for.
calibrated_camera = [camera for camera in project.calibration.calibrated_cameras.cameras if camera.id == camera_id]
# Print the pose of the camera.
print("The camera {} is calibrated at:".format(camera_id), calibrated_camera[0].position)
print("with orientation", calibrated_camera[0].orientation_deg)
```
The custom attributes are stored per node in the `custom_attributes` dictionary. This dictionary might be `None` if
the `Node` has no associated custom attributes. Below is an example of setting a custom attribute.
```python
import numpy as np
from pathlib import Path
from pyopf.pointcloud import GlTFPointCloud
pcl = GlTFPointCloud.open(Path('dense_pcl/dense_pcl.gltf'))
# Generate a new point attribute as a random vector of 0s and 1s
# The attribute must have one scalar per point
new_attribute = np.random.randint(0, 2, size=len(pcl.nodes[0]))
# The attribute must have the shape (number_of_points, 1)
new_attribute = new_attribute.reshape((-1, 1))
# Supported types for custom attributes are np.float32, np.uint32, np.uint16, np.uint8
new_attribute = new_attribute.astype(np.uint32)
# Set the new attribute as a custom attribute for the node
# By default, nodes might be missing custom attributes, so the dictionary might have to be created
if pcl.nodes[0].custom_attributes is not None:
pcl.nodes[0].custom_attributes['point_class'] = new_attribute
else:
pcl.nodes[0].custom_attributes = {'point_class': new_attribute}
pcl.write(Path('out/out.gltf'))
```
### OPF Tools
We provide a few tools as command line scripts to help manipulate OPF projects in different ways.
#### Merging
The main use case for merging projects is to be able to process smaller sections of a project independently.
For the merging to succeed the sub projects must be in the same coordinate reference system. Note that the tool doesn't support merging the content of most OPF extensions, which will then be dropped in the merged project.
Two objects are considered identical if they have the same ID even if they are in different projects. If this assumption is violated, the merging fails. For example, the same camera ID cannot be associated with two different image URIs.
The only exception are the sensors, whose IDs are always regenerated and for which no attempt is made at finding common and equally calibrated sensors.
The point clouds are merged based on their label.
Only core project items support merging:
* camera list
* input cameras
* projected input cameras
* input control points
* projected control points
* calibration (calibrated cameras, calibrated control points, tracks)
* point clouds
* constraints
All extensions are dropped.
The merging tool can be called using
`opf_merge project_1.opf project_2.opf project_3.opf output_directory`
#### Undistorting
A tool to undistort images is provided. The undistorted images will be stored in their original location, but in an `undistort` directory. Only images taken with a perspective camera, for which the sensor has been calibrated will be undistorted.
This tool can be used as
`opf_undistort project.opf`
#### Cropping
We call "cropping" the operation of preserving only the region of interest of the project (as defined by the Region of
Interest OPF extension).
The project to be cropped *MUST* contain an item of type `ext_pix4d_region_of_interest`.
During the cropping process, only the control points and the part of the point clouds which are contained in the ROI are kept.
Cameras which do not see any remaining points from the point clouds are discarded.
Also, cropping uncalibrated projects is not supported.
The following project items are updated during cropping:
* Point Clouds (including tracks)
* Cameras (input, projected, calibrated, camera list)
* GCPs
The rest of the project items are simply copied.
The cropping tool can be called using
`opf_crop project_to_crop.opf output_directory`
#### Convert to NeRF
This tool converts OPF projects to NeRF. NeRF consists of transforms file(s), which contain information about distortion, intrinsic and extrinsinc parameters of cameras. Usually it is split in `transforms_train.json` and `transforms_test.json` files, but can sometimes also have only the train one. The split can be controlled with the parameter `--train-frac`, for example `--train-frac 0.7` will randomly assign 70% of images for training, and the remaining 30% for testing. If this parameter is unspecified or set to 1.0, only the `transforms_train.json` will be generated. Sometimes an additional `transforms_val.json` is required. It is to evaluate from new points of view, but the generation of new point of views is not managed by this tool, so it can just be a copy of `transforms_test.json` renamed.
The tool can also convert input images to other image formats using `--out-img-format`. An optional output directory can be given with `--out-img-dir`, otherwise the images are written to the same directory as the input ones. If `--out-img-dir` is used without `--out-img-format`, images will be copied. When copying or converting an image, the input directory layout is preserved.
When `--out-img-dir` is used, the tree structure of where input images are stored will be copied to the output image directory. In other words, if all images are stored in the same directory, the folder specified by `--out-img-dir` will only contain the images. If images are stored in different folders/subfolders, the `--out-img-dir` folder will contain the same folders/subfolders starting from the first common folder.
Only calibrated projects with only perspective cameras are supported. Remote files are not supported.
##### Examples
Different NeRFs require different parameter settings, by default all values are set to work with Instant-NeRF, so it can be used as:
`opf2nerf project.opf --output-extension`
DirectVoxGo only works with PNG image files, and contrary to Instant-NeRF it doesn't flip cameras orientation with respect to OPF. Thus it can be used as:
`opf2nerf project.opf --out-img-format png --out-img-dir ./images --no-camera-flip`
## License and citation
If you use this work in your research or projects, we kindly request that you cite it as follows:
The Open Photogrammetry Format Specification, Grégoire Krähenbühl, Klaus Schneider-Zapp, Bastien Dalla Piazza, Juan Hernando, Juan Palacios, Massimiliano Bellomo, Mohamed-Ghaïth Kaabi, Christoph Strecha, Pix4D, 2023, retrived from https://pix4d.github.io/opf-spec/
Copyright (c) 2023 Pix4D SA
All scripts and/or code contained in this repository are licensed under Apache License 2.0.
Third party documents or tools that are used or referred to in this specification are licensed under their own terms by their respective copyright owners.
Raw data
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"description": "## Python Open Photogrammetry Format (OPF)\n\nThis repository provides a Python package for reading, writing and manipulating projects in the OPF format.\nFor more information about what OPF is and its full specification, please refer to https://www.github.com/Pix4D/opf-spec\n\n### Installation\n\nThe tool can be installed using `pip` with the following command:\n\n```shell\npip install pyopf\n```\n\nThis command installs the `pyopf` package and tools.\n\n\n### Structure of the PyOPF repository\n\nThe `pyopf` library can be found under `src/pyopf`. The library implements easy parsing and writing of OPF projects in Python.\n\nBelow is a small example, printing the calibrated position and orientation of a camera, knowing its ID.\n\n```python\nfrom pyopf.io import load\n\nfrom pyopf.resolve import resolve\nfrom pyopf.uid64 import Uid64\n\n# Path to the example project file.\nproject_path = \"spec/examples/project.json\"\n\n# We are going to search for the calibrated position of the camera with this ID\ncamera_id = Uid64(hex = \"0x57282923\")\n\n# Load the json data and resolve the project, i.e. load the project items as named attributes.\nproject = load(project_path)\nproject = resolve(project)\n\n# Many objects are optional in OPF. If they are missing, they are set to None.\nif project.calibration is None:\n print(\"No calibration data.\")\n exit(1)\n\n# Filter the list of calibrated cameras to find the one with the ID we are looking for.\ncalibrated_camera = [camera for camera in project.calibration.calibrated_cameras.cameras if camera.id == camera_id]\n\n# Print the pose of the camera.\nprint(\"The camera {} is calibrated at:\".format(camera_id), calibrated_camera[0].position)\nprint(\"with orientation\", calibrated_camera[0].orientation_deg)\n```\n\nThe custom attributes are stored per node in the `custom_attributes` dictionary. This dictionary might be `None` if\nthe `Node` has no associated custom attributes. Below is an example of setting a custom attribute.\n\n```python\nimport numpy as np\nfrom pathlib import Path\nfrom pyopf.pointcloud import GlTFPointCloud\n\npcl = GlTFPointCloud.open(Path('dense_pcl/dense_pcl.gltf'))\n\n# Generate a new point attribute as a random vector of 0s and 1s\n# The attribute must have one scalar per point\nnew_attribute = np.random.randint(0, 2, size=len(pcl.nodes[0]))\n\n# The attribute must have the shape (number_of_points, 1)\nnew_attribute = new_attribute.reshape((-1, 1))\n# Supported types for custom attributes are np.float32, np.uint32, np.uint16, np.uint8\nnew_attribute = new_attribute.astype(np.uint32)\n\n# Set the new attribute as a custom attribute for the node\n# By default, nodes might be missing custom attributes, so the dictionary might have to be created\nif pcl.nodes[0].custom_attributes is not None:\n pcl.nodes[0].custom_attributes['point_class'] = new_attribute\nelse:\n pcl.nodes[0].custom_attributes = {'point_class': new_attribute}\n\npcl.write(Path('out/out.gltf'))\n```\n\n### OPF Tools\n\nWe provide a few tools as command line scripts to help manipulate OPF projects in different ways.\n\n#### Merging\n\nThe main use case for merging projects is to be able to process smaller sections of a project independently.\nFor the merging to succeed the sub projects must be in the same coordinate reference system. Note that the tool doesn't support merging the content of most OPF extensions, which will then be dropped in the merged project.\nTwo objects are considered identical if they have the same ID even if they are in different projects. If this assumption is violated, the merging fails. For example, the same camera ID cannot be associated with two different image URIs.\nThe only exception are the sensors, whose IDs are always regenerated and for which no attempt is made at finding common and equally calibrated sensors.\n\nThe point clouds are merged based on their label.\n\nOnly core project items support merging:\n* camera list\n* input cameras\n* projected input cameras\n* input control points\n* projected control points\n* calibration (calibrated cameras, calibrated control points, tracks)\n* point clouds\n* constraints\n\nAll extensions are dropped.\n\nThe merging tool can be called using\n\n`opf_merge project_1.opf project_2.opf project_3.opf output_directory`\n\n\n#### Undistorting\n\nA tool to undistort images is provided. The undistorted images will be stored in their original location, but in an `undistort` directory. Only images taken with a perspective camera, for which the sensor has been calibrated will be undistorted.\n\nThis tool can be used as\n\n`opf_undistort project.opf`\n\n#### Cropping\n\nWe call \"cropping\" the operation of preserving only the region of interest of the project (as defined by the Region of\nInterest OPF extension).\nThe project to be cropped *MUST* contain an item of type `ext_pix4d_region_of_interest`.\n\nDuring the cropping process, only the control points and the part of the point clouds which are contained in the ROI are kept.\nCameras which do not see any remaining points from the point clouds are discarded.\nAlso, cropping uncalibrated projects is not supported.\n\nThe following project items are updated during cropping:\n* Point Clouds (including tracks)\n* Cameras (input, projected, calibrated, camera list)\n* GCPs\n\nThe rest of the project items are simply copied.\n\nThe cropping tool can be called using\n\n`opf_crop project_to_crop.opf output_directory`\n\n#### Convert to NeRF\n\nThis tool converts OPF projects to NeRF. NeRF consists of transforms file(s), which contain information about distortion, intrinsic and extrinsinc parameters of cameras. Usually it is split in `transforms_train.json` and `transforms_test.json` files, but can sometimes also have only the train one. The split can be controlled with the parameter `--train-frac`, for example `--train-frac 0.7` will randomly assign 70% of images for training, and the remaining 30% for testing. If this parameter is unspecified or set to 1.0, only the `transforms_train.json` will be generated. Sometimes an additional `transforms_val.json` is required. It is to evaluate from new points of view, but the generation of new point of views is not managed by this tool, so it can just be a copy of `transforms_test.json` renamed.\n\nThe tool can also convert input images to other image formats using `--out-img-format`. An optional output directory can be given with `--out-img-dir`, otherwise the images are written to the same directory as the input ones. If `--out-img-dir` is used without `--out-img-format`, images will be copied. When copying or converting an image, the input directory layout is preserved.\n\nWhen `--out-img-dir` is used, the tree structure of where input images are stored will be copied to the output image directory. In other words, if all images are stored in the same directory, the folder specified by `--out-img-dir` will only contain the images. If images are stored in different folders/subfolders, the `--out-img-dir` folder will contain the same folders/subfolders starting from the first common folder.\n\nOnly calibrated projects with only perspective cameras are supported. Remote files are not supported.\n\n##### Examples\n\nDifferent NeRFs require different parameter settings, by default all values are set to work with Instant-NeRF, so it can be used as:\n\n`opf2nerf project.opf --output-extension`\n\nDirectVoxGo only works with PNG image files, and contrary to Instant-NeRF it doesn't flip cameras orientation with respect to OPF. Thus it can be used as:\n\n`opf2nerf project.opf --out-img-format png --out-img-dir ./images --no-camera-flip`\n\n## License and citation\n\nIf you use this work in your research or projects, we kindly request that you cite it as follows:\n\nThe Open Photogrammetry Format Specification, Gr\u00e9goire Kr\u00e4henb\u00fchl, Klaus Schneider-Zapp, Bastien Dalla Piazza, Juan Hernando, Juan Palacios, Massimiliano Bellomo, Mohamed-Gha\u00efth Kaabi, Christoph Strecha, Pix4D, 2023, retrived from https://pix4d.github.io/opf-spec/\n\nCopyright (c) 2023 Pix4D SA\n\nAll scripts and/or code contained in this repository are licensed under Apache License 2.0.\n\nThird party documents or tools that are used or referred to in this specification are licensed under their own terms by their respective copyright owners.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. 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