diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..dd051801db53604981d2e0683e05da198d1b473f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,9 @@
+.python-version
+*venv*
+*__pycache__
+*__runtime_stats__*
+*.log
+data/
+
+# Mac storage system
+*.DS_Store
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..83cd09273783a57c3d3691d92db00045890e3404
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Production Metrology and Quality Management | RWTH Aachen University
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 3646343a8125515ec884737d051d768cdda73ef4..b9e1e06a626fbff462a8629bf1cbf95feb5f9136 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,216 @@
-# Method for Synthetic Dataset Generation
+# Method for Synthetic Dataset Generation of Fused Deposition Modeling (FDM) Parts
+This repository is an addon for Blender to simulate a 3D printer.
+Current stable version: 0.0.0
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
+## Installation
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+### Blender
-```
-cd existing_repo
-git remote add origin https://git-ce.rwth-aachen.de/wzl-mq-public/smopa3d/method-for-synthetic-dataset-generation.git
-git branch -M main
-git push -uf origin main
-```
+Download Blender installer [here](https://www.blender.org/download/).
-## Integrate with your tools
+### Addon instalation
-- [ ] [Set up project integrations](https://git-ce.rwth-aachen.de/wzl-mq-public/smopa3d/method-for-synthetic-dataset-generation/-/settings/integrations)
+This repository work as a Blender addon. To use it, you can either install it once - keep in mind that every time you change the code you will have to repeat the addon installation -, or use [VSCode 'Blender Development' extension by Jacques Luke](https://marketplace.visualstudio.com/items?itemName=JacquesLucke.blender-development).
-## Collaborate with your team
+#### VSCode 'Blender Development' extension
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
+Install the extension in VSCode Extensions Marketplace. Then, press `Ctrl/Cmd + P` and search for the `Blender: Start` function. Locate your installed Blender.
-## Test and Deploy
+After that, you Blender shall start with the extension already loaded. To run the functions in blender, press `F3` and search for the function you want to run. Currently, the main function is `object.node`.
-Use the built-in continuous integration in GitLab.
+To reload the addon after any change in the code, go back to VSCode and press `Ctrl/Cmd + P` and search for the `Blender: Reload Addons` function. You can now go back to Blender and find the function after pressing `F3`.
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+#### One time installation
-***
+Zip the `addon/` folder and follow the instructions in [here](https://docs.blender.org/manual/en/latest/editors/preferences/addons.html).
-# Editing this README
+## Usage
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
+The addon is composed of functions that can be called from Blender's menu. To open it, go to `Edit > Menu Search...`, and type `fdm_simulator`. The available functions are:
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+- `fdm_simulator.simulate`: simulates the 3D printing process of the selected object and plot the result in the Blender environment;
+- `fdm_simulator.draw_simulation`: draws a previously executed simulation result in the Blender environment;
+- `fdm_simulator.scan`: runs a measurement scan on the selected object simulating the behaviour of a laser line scanner and save it as `virtual_scan.npy` in a numpy array format with shape (n, 3), with each row representing (x, y, z) coordinates of the measured points.
-## Name
-Choose a self-explaining name for your project.
+It is also possible to execute the simulation outside the Blender environment. It is advantageous because Blender's addons run in a single thread, so the simulation can take a long time to finish. To do so, change the location of the gcode file in main.py and run it. The simulation will be saved in a pickle file to be later used in Blender via `fdm_simulator.draw_simulation` function.
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+**Important!** - the simulation saves temporary files in the path specified in the `saving_path` argument of the `addon.network.Network` class. Make sure to change it to a valid path in your system. It uses several GBs of disk space.
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+## Development
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+It not actually necessary to install python requirements since all the code is supposed to be run on Blender's python and the autoload module is configured to handle installations. But if you want to run any content outside Blender's environment, follow the instructions below.
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+1. Create and access virstual environment:
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+on Posix:
+``` bash
+python -m venv venv && source venv/bin/activate
+```
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+or on Windows:
+``` bash
+python -m venv venv && venv\\Scripts\\activate.bat
+```
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+2. Install dependencies
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+``` bash
+pip install -r requirements.txt
+```
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+## Changelog
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+**0.0.0** - 2023-11-29
+ - Initial release
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
## License
-For open source projects, say how it is licensed.
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+Below is a list of third-party libraries used in this project and their respective licenses:
+
+| Name | Version | License |
+|---------------------------|-----------|------------------------------------------------------------------|
+| asttokens | 2.2.1 | Apache 2.0 |
+| overrides | 7.3.1 | Apache License, Version 2.0 |
+| Cython | 0.29.35 | Apache Software License |
+| arrow | 1.2.3 | Apache Software License |
+| bleach | 6.0.0 | Apache Software License |
+| prometheus-client | 0.17.0 | Apache Software License |
+| q | 2.7 | Apache Software License |
+| requests | 2.31.0 | Apache Software License |
+| retrying | 1.3.4 | Apache Software License |
+| tenacity | 8.2.2 | Apache Software License |
+| tornado | 6.3.2 | Apache Software License |
+| tzdata | 2023.3 | Apache Software License |
+| websocket-client | 1.6.1 | Apache Software License |
+| packaging | 23.1 | Apache Software License; BSD License |
+| python-dateutil | 2.8.2 | Apache Software License; BSD License |
+| sniffio | 1.3.0 | Apache Software License; MIT License |
+| jupyterlab-pygments | 0.2.2 | BSD |
+| matplotlib-inline | 0.1.6 | BSD 3-Clause |
+| Flask | 2.2.5 | BSD License |
+| Jinja2 | 3.1.2 | BSD License |
+| MarkupSafe | 2.1.3 | BSD License |
+| Pygments | 2.15.1 | BSD License |
+| Send2Trash | 1.8.2 | BSD License |
+| Werkzeug | 2.2.3 | BSD License |
+| appnope | 0.1.3 | BSD License |
+| backcall | 0.2.0 | BSD License |
+| click | 8.1.6 | BSD License |
+| comm | 0.1.3 | BSD License |
+| contourpy | 1.1.0 | BSD License |
+| cycler | 0.11.0 | BSD License |
+| decorator | 5.1.1 | BSD License |
+| fastjsonschema | 2.17.1 | BSD License |
+| idna | 3.4 | BSD License |
+| imageio | 2.31.3 | BSD License |
+| ipykernel | 6.24.0 | BSD License |
+| ipython | 8.14.0 | BSD License |
+| ipython-genutils | 0.2.0 | BSD License |
+| ipywidgets | 8.1.1 | BSD License |
+| itsdangerous | 2.1.2 | BSD License |
+| joblib | 1.3.1 | BSD License |
+| jsonpointer | 2.4 | BSD License |
+| jupyter-events | 0.6.3 | BSD License |
+| jupyter_client | 8.3.0 | BSD License |
+| jupyter_core | 5.3.1 | BSD License |
+| jupyter_server | 2.7.0 | BSD License |
+| jupyter_server_terminals | 0.4.4 | BSD License |
+| jupyterlab-widgets | 3.0.9 | BSD License |
+| kiwisolver | 1.4.4 | BSD License |
+| lazy_loader | 0.3 | BSD License |
+| memory-profiler | 0.61.0 | BSD License |
+| mistune | 3.0.1 | BSD License |
+| nbclassic | 1.0.0 | BSD License |
+| nbclient | 0.8.0 | BSD License |
+| nbconvert | 7.6.0 | BSD License |
+| nbformat | 5.7.0 | BSD License |
+| nest-asyncio | 1.5.6 | BSD License |
+| networkx | 3.1 | BSD License |
+| notebook | 6.5.4 | BSD License |
+| notebook_shim | 0.2.3 | BSD License |
+| numpy | 1.25.0 | BSD License |
+| pandas | 2.0.2 | BSD License |
+| pandocfilters | 1.5.0 | BSD License |
+| prettytable | 3.9.0 | BSD License |
+| prompt-toolkit | 3.0.39 | BSD License |
+| psutil | 5.9.5 | BSD License |
+| pycparser | 2.21 | BSD License |
+| python-json-logger | 2.0.7 | BSD License |
+| scikit-image | 0.21.0 | BSD License |
+| scikit-learn | 1.3.0 | BSD License |
+| scipy | 1.11.0 | BSD License |
+| seaborn | 0.12.2 | BSD License |
+| terminado | 0.17.1 | BSD License |
+| threadpoolctl | 3.2.0 | BSD License |
+| tifffile | 2023.8.30 | BSD License |
+| tinycss2 | 1.2.1 | BSD License |
+| traitlets | 5.9.0 | BSD License |
+| webcolors | 1.13 | BSD License |
+| webencodings | 0.5.1 | BSD License |
+| widgetsnbextension | 4.0.9 | BSD License |
+| zstandard | 0.21.0 | BSD License |
+| pyzmq | 25.1.0 | BSD License; GNU Library or Lesser General Public License (LGPL) |
+| debugpy | 1.6.7 | Eclipse Public License 2.0 (EPL-2.0); MIT License |
+| ansi2html | 1.8.0 | GNU Lesser General Public License v3 or later (LGPLv3+) |
+| bpy | 3.5.0 | GPL-3.0 |
+| Pillow | 10.0.0 | Historical Permission Notice and Disclaimer (HPND) |
+| isoduration | 20.11.0 | ISC License (ISCL) |
+| pexpect | 4.8.0 | ISC License (ISCL) |
+| ptyprocess | 0.7.0 | ISC License (ISCL) |
+| dash-core-components | 2.0.0 | MIT |
+| dash-html-components | 2.0.0 | MIT |
+| dash-table | 5.0.0 | MIT |
+| ConfigArgParse | 1.5.5 | MIT License |
+| PeakUtils | 1.3.4 | MIT License |
+| PyWavelets | 1.4.1 | MIT License |
+| PyYAML | 6.0 | MIT License |
+| addict | 2.4.0 | MIT License |
+| anyio | 3.7.1 | MIT License |
+| argon2-cffi | 21.3.0 | MIT License |
+| argon2-cffi-bindings | 21.2.0 | MIT License |
+| attrs | 23.1.0 | MIT License |
+| beautifulsoup4 | 4.12.2 | MIT License |
+| cffi | 1.15.1 | MIT License |
+| charset-normalizer | 3.1.0 | MIT License |
+| dash | 2.11.1 | MIT License |
+| exceptiongroup | 1.1.2 | MIT License |
+| executing | 1.2.0 | MIT License |
+| fake-bpy-module-3.3 | 20230117 | MIT License |
+| fonttools | 4.40.0 | MIT License |
+| jedi | 0.18.2 | MIT License |
+| jsonschema | 4.18.0 | MIT License |
+| jsonschema-specifications | 2023.6.1 | MIT License |
+| open3d | 0.17.0 | MIT License |
+| parso | 0.8.3 | MIT License |
+| pickleshare | 0.7.5 | MIT License |
+| pip | 23.1.2 | MIT License |
+| pip-licenses | 4.3.3 | MIT License |
+| platformdirs | 3.8.1 | MIT License |
+| plotly | 5.15.0 | MIT License |
+| pure-eval | 0.2.2 | MIT License |
+| pyparsing | 3.0.9 | MIT License |
+| pyquaternion | 0.9.9 | MIT License |
+| pytz | 2023.3 | MIT License |
+| referencing | 0.29.1 | MIT License |
+| rfc3339-validator | 0.1.4 | MIT License |
+| rfc3986-validator | 0.1.1 | MIT License |
+| rpds-py | 0.8.10 | MIT License |
+| setuptools | 65.5.0 | MIT License |
+| six | 1.16.0 | MIT License |
+| soupsieve | 2.4.1 | MIT License |
+| stack-data | 0.6.2 | MIT License |
+| trimesh | 3.22.5 | MIT License |
+| uri-template | 1.3.0 | MIT License |
+| urllib3 | 2.0.3 | MIT License |
+| wcwidth | 0.2.6 | MIT License |
+| tqdm | 4.65.0 | MIT License; Mozilla Public License 2.0 (MPL 2.0) |
+| certifi | 2023.5.7 | Mozilla Public License 2.0 (MPL 2.0) |
+| fqdn | 1.5.1 | Mozilla Public License 2.0 (MPL 2.0) |
+| defusedxml | 0.7.1 | Python Software Foundation License |
+| matplotlib | 3.7.2 | Python Software Foundation License |
+| typing_extensions | 4.7.1 | Python Software Foundation License |
\ No newline at end of file
diff --git a/addon/__init__.py b/addon/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..44e13288c04b731c53a4534569e7af45737ed2dc
--- /dev/null
+++ b/addon/__init__.py
@@ -0,0 +1,40 @@
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTIBILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+import logging
+from .utils import init_logging, running_in_blender
+
+if 'SmoPa3D' not in logging.Logger.manager.loggerDict.keys():
+ init_logging('DEBUG')
+
+bl_info = {
+ "name" : "FDM Simulator",
+ "author" : "MateusHarano",
+ "description" : "This addon simulates the printing process of an FDM 3D printer.",
+ "blender" : (2, 80, 0),
+ "version" : (0, 0, 0),
+ "location" : "",
+ "warning" : "",
+ "category" : "Generic"
+}
+
+if running_in_blender:
+ from . import auto_load
+
+ auto_load.init()
+
+def register():
+ auto_load.register()
+
+def unregister():
+ auto_load.unregister()
diff --git a/addon/auto_load.py b/addon/auto_load.py
new file mode 100644
index 0000000000000000000000000000000000000000..360b2444ccce965f53f9938621d1a0786853199f
--- /dev/null
+++ b/addon/auto_load.py
@@ -0,0 +1,191 @@
+import os
+import bpy
+import sys
+import typing
+import inspect
+import pkgutil
+import importlib
+from pathlib import Path
+
+from .utils import running_in_blender
+
+__all__ = (
+ "init",
+ "register",
+ "unregister",
+)
+
+if running_in_blender:
+ blender_version = bpy.app.version
+
+modules = None
+ordered_classes = None
+
+def init():
+ ensure_site_packages([("scipy", "scipy"), ("open3d", "open3d"), ("tqdm", "tqdm"), ("memory_profiler", "memory_profiler"), ("seaborn", "seaborn"), ("skimage", "scikit-image"), ("sklearn", "scikit-learn"), ("pandas", "pandas"), ("matplotlib", "matplotlib"), ("peakutils", "PeakUtils")])
+
+ global modules
+ global ordered_classes
+
+ modules = get_all_submodules(Path(__file__).parent)
+ ordered_classes = get_ordered_classes_to_register(modules)
+
+def register():
+ for cls in ordered_classes:
+ bpy.utils.register_class(cls)
+
+ for module in modules:
+ if module.__name__ == __name__:
+ continue
+ if hasattr(module, "register"):
+ module.register()
+
+def unregister():
+ for cls in reversed(ordered_classes):
+ bpy.utils.unregister_class(cls)
+
+ for module in modules:
+ if module.__name__ == __name__:
+ continue
+ if hasattr(module, "unregister"):
+ module.unregister()
+
+
+# Import modules
+#################################################
+
+def get_all_submodules(directory):
+ return list(iter_submodules(directory, directory.name))
+
+def iter_submodules(path, package_name):
+ for name in sorted(iter_submodule_names(path)):
+ yield importlib.import_module("." + name, package_name)
+
+def iter_submodule_names(path, root=""):
+ for _, module_name, is_package in pkgutil.iter_modules([str(path)]):
+ if is_package:
+ sub_path = path / module_name
+ sub_root = root + module_name + "."
+ yield from iter_submodule_names(sub_path, sub_root)
+ else:
+ yield root + module_name
+
+
+# Find classes to register
+#################################################
+
+def get_ordered_classes_to_register(modules):
+ return toposort(get_register_deps_dict(modules))
+
+def get_register_deps_dict(modules):
+ my_classes = set(iter_my_classes(modules))
+ my_classes_by_idname = {cls.bl_idname : cls for cls in my_classes if hasattr(cls, "bl_idname")}
+
+ deps_dict = {}
+ for cls in my_classes:
+ deps_dict[cls] = set(iter_my_register_deps(cls, my_classes, my_classes_by_idname))
+ return deps_dict
+
+def iter_my_register_deps(cls, my_classes, my_classes_by_idname):
+ yield from iter_my_deps_from_annotations(cls, my_classes)
+ yield from iter_my_deps_from_parent_id(cls, my_classes_by_idname)
+
+def iter_my_deps_from_annotations(cls, my_classes):
+ for value in typing.get_type_hints(cls, {}, {}).values():
+ dependency = get_dependency_from_annotation(value)
+ if dependency is not None:
+ if dependency in my_classes:
+ yield dependency
+
+def get_dependency_from_annotation(value):
+ if blender_version >= (2, 93):
+ if isinstance(value, bpy.props._PropertyDeferred):
+ return value.keywords.get("type")
+ else:
+ if isinstance(value, tuple) and len(value) == 2:
+ if value[0] in (bpy.props.PointerProperty, bpy.props.CollectionProperty):
+ return value[1]["type"]
+ return None
+
+def iter_my_deps_from_parent_id(cls, my_classes_by_idname):
+ if bpy.types.Panel in cls.__bases__:
+ parent_idname = getattr(cls, "bl_parent_id", None)
+ if parent_idname is not None:
+ parent_cls = my_classes_by_idname.get(parent_idname)
+ if parent_cls is not None:
+ yield parent_cls
+
+def iter_my_classes(modules):
+ base_types = get_register_base_types()
+ for cls in get_classes_in_modules(modules):
+ if any(base in base_types for base in cls.__bases__):
+ if not getattr(cls, "is_registered", False):
+ yield cls
+
+def get_classes_in_modules(modules):
+ classes = set()
+ for module in modules:
+ for cls in iter_classes_in_module(module):
+ classes.add(cls)
+ return classes
+
+def iter_classes_in_module(module):
+ for value in module.__dict__.values():
+ if inspect.isclass(value):
+ yield value
+
+def get_register_base_types():
+ return set(getattr(bpy.types, name) for name in [
+ "Panel", "Operator", "PropertyGroup",
+ "AddonPreferences", "Header", "Menu",
+ "Node", "NodeSocket", "NodeTree",
+ "UIList", "RenderEngine",
+ "Gizmo", "GizmoGroup",
+ ])
+
+
+# Find order to register to solve dependencies
+#################################################
+
+def toposort(deps_dict):
+ sorted_list = []
+ sorted_values = set()
+ while len(deps_dict) > 0:
+ unsorted = []
+ for value, deps in deps_dict.items():
+ if len(deps) == 0:
+ sorted_list.append(value)
+ sorted_values.add(value)
+ else:
+ unsorted.append(value)
+ deps_dict = {value : deps_dict[value] - sorted_values for value in unsorted}
+ return sorted_list
+
+def ensure_site_packages(packages):
+ """ `packages`: list of tuples (<import name>, <pip name>) """
+
+ if not packages:
+ return
+
+ import site
+ import importlib
+ import importlib.util
+
+ user_site_packages = site.getusersitepackages()
+ if not user_site_packages in sys.path:
+ sys.path.append(user_site_packages)
+
+ modules_to_install = [module[1] for module in packages if not importlib.util.find_spec(module[0])]
+ if not modules_to_install:
+ return
+
+ if bpy.app.version < (2,91,0):
+ python_binary = bpy.app.binary_path_python
+ else:
+ python_binary = sys.executable
+
+ import subprocess
+ subprocess.run([python_binary, '-m', 'ensurepip'], check=True)
+ subprocess.run([python_binary, '-m', 'pip', 'install', *modules_to_install, "--user"], check=True)
+
+ importlib.invalidate_caches()
\ No newline at end of file
diff --git a/addon/command.py b/addon/command.py
new file mode 100644
index 0000000000000000000000000000000000000000..d842a8fc3a2b461b1ae1f248f2b75ee9bb91464f
--- /dev/null
+++ b/addon/command.py
@@ -0,0 +1,168 @@
+import os
+import numpy as np
+from skimage.measure import marching_cubes
+import logging
+log = logging.getLogger("SmoPa3D")
+
+from .gcode.parser import GcodeCommand
+from .node import Node, join_nodes, assign_values
+from .geometry import width_model
+
+class Command:
+ """Represents a command from the gcode file"""
+ def __init__(self, network, gcode:GcodeCommand, id:int) -> None:
+ self.network = network
+ self.gcode = gcode
+ self.id = id
+ self.nodes:list[Node] = []
+ self._vertices:np.ndarray = None
+ self._faces:np.ndarray = None
+ self.vertices_filepath:str = None
+ self.faces_filepath:str = None
+
+ # Update network to include this command
+ self.network.commands[id] = self
+
+ # Calculate distribution of nodes along the command
+ self.start = np.array((self.gcode.last_position['x'], self.gcode.last_position['y'], self.gcode.last_position['z']))
+ self.end = np.array((self.gcode.x if self.gcode.x is not None else self.gcode.last_position['x'], self.gcode.y if self.gcode.y is not None else self.gcode.last_position['y'], self.gcode.z if self.gcode.z is not None else self.gcode.last_position['z']))
+ self.trajectory_length = np.linalg.norm(self.end - self.start)
+
+ # Calculate properties
+ self.e = self.gcode.e
+ self.feedrate = (self.e - self.gcode.last_ocurrence('e')) / self.trajectory_length
+ self.speed = self.gcode.f if self.gcode.f is not None else self.gcode.last_ocurrence('f')
+
+ nominal_width = width_model(self.network.temperature, self.feedrate, self.speed)
+ self.node_distance = nominal_width * self.network.node_distance
+ self.qtd_nodes = round(self.trajectory_length / self.node_distance) + 1
+ self.nodes_coords = np.linspace(self.start, self.end, self.qtd_nodes)
+ if (self.start[0], self.start[1], self.start[2]) in self.network.coords: # Remove coincident starts and endings
+ self.nodes_coords = self.nodes_coords[1:]
+ self.qtd_nodes -= 1
+
+ if self.qtd_nodes <= 0: return
+ # volume_distribution = volume_profile(self.qtd_nodes, self.trajectory_length, self.speed) * self.total_extruded_volume
+ volume_distribution = np.ones(self.qtd_nodes)/self.qtd_nodes * self.total_extruded_volume
+
+ # Assign layer
+ self.layer = self.network.get_layer(self.start[2])
+
+ # Initialize nodes
+ for coord, volume in zip(self.nodes_coords, volume_distribution):
+ Node(self.network, self, self.layer, coord[0], coord[1], coord[2], volume)
+
+ @property
+ def total_extruded_volume(self,
+ constant:float = 0.0025726112346777796,
+ feedrate_multiplier:float = 1.812969202377806
+ ) -> float:
+ """Volume, in mm^3, of the filament deposited in the node
+ @param constant: constant value to calculate the area of the profile. Value retrieved experimentally
+ @param feedrate_multiplier: multiplier value to calculate the area of the profile. Value retrieved experimentally"""
+ area = constant + feedrate_multiplier * self.feedrate
+ volume = area * self.trajectory_length * self.network.extrusion_multiplier
+ # volume = self.feedrate * self.trajectory_length * np.pi * 1.75 ** 2 / 4 * self.network.extrusion_multiplier
+ return volume
+
+ @property
+ def vertices(self) -> np.ndarray:
+ """Vertices of the mesh. If the vertices are not saved, returns None"""
+ if self._vertices is None:
+ if self.vertices_filepath is not None:
+ return np.load(self.vertices_filepath)
+ else:
+ return None
+ else:
+ return self._vertices
+
+ @property
+ def faces(self) -> np.ndarray:
+ """Vertices of the mesh. If the vertices are not saved, returns None"""
+ if self._faces is None:
+ if self.faces_filepath is not None:
+ return np.load(self.faces_filepath)
+ else:
+ return None
+ else:
+ return self._faces
+
+def volume_profile(
+ qtd_nodes:int,
+ distance:float=100,
+ nozzle_speed:float=1000,
+ jerk:float=20,
+ acceleration:float=500
+ ):
+ times = []
+ umax = min(nozzle_speed, np.sqrt(jerk ** 2 + acceleration * distance))
+ s_ramp = (umax ** 2 - jerk ** 2) / (2 * acceleration)
+ for s in np.linspace(0, distance, qtd_nodes+1)[1:]:
+ if s <= s_ramp:
+ roots = np.roots((acceleration/2, jerk, -s))
+ roots = min(roots[roots > 0].real)
+ times.append(roots)
+ elif s <= (distance - s_ramp):
+ times.append((umax - jerk) / acceleration + (s - s_ramp) / umax)
+ else:
+ roots = np.roots((-acceleration/2, umax, (distance - s_ramp - s)))
+ roots = min(roots[roots > 0].real)
+ times.append((umax - jerk) / acceleration + (distance - 2*s_ramp)/umax + roots)
+ total_time = times[-1]
+ discretization = [times[i] - times[i-1] if i > 0 else times[i] for i in range(len(times))]
+ return (discretization / total_time).astype(float)
+
+def calculate_command_mesh(command:Command) -> tuple[str, str]:
+ """Calculate the mesh of the command. Returns the saved path of the vertices and faces"""
+ if command.qtd_nodes <= 0:
+ return None, None
+ elif command.qtd_nodes == 1:
+ if command.nodes[0].pointcloud is None: return None, None
+ joined_pcl = command.nodes[0].pointcloud
+ command.nodes[0].wipe()
+ else: # If there are more than one node, join the pointclouds into one collection of points (shape = (n, 3) [x, y, z])
+ joined_pcl = np.vstack([join_nodes(command.nodes[0], node) for node in command.nodes]).astype(np.int64)
+ for node in command.nodes:
+ node.wipe()
+
+ if joined_pcl is None or len(joined_pcl) == 0:
+ return None, None
+
+ # Shift the pointcloud to remove the negative values
+ shift = joined_pcl.min(0) - [1, 1, 1]
+ joined_pcl -= shift
+ volume_size = (joined_pcl.max(0) + [5,5,5])[[2,1,0]]
+
+ # Populate the voxel with the pointcloud
+ voxel = assign_values(np.zeros(volume_size, dtype=bool), joined_pcl)
+
+ # Calculate the mesh
+ vertices, faces, _, _ = marching_cubes(voxel)
+
+ # Positioning in the environment
+ vertices = (vertices.astype(np.float32)[:, ::-1] + shift - command.network.env.node_grid_size // 2 - 1) * command.network.env.resolution + command.nodes[0].coord
+
+ # Save the mesh to disk so it does not occupy memory
+ vert_path = os.path.join(command.network.saving_path, "commands", f"{command.id}", "vertices.npy")
+ face_path = os.path.join(command.network.saving_path, "commands", f"{command.id}", "faces.npy")
+ os.makedirs(os.path.dirname(vert_path), exist_ok=True)
+ os.makedirs(os.path.dirname(face_path), exist_ok=True)
+ np.save(vert_path, vertices)
+ np.save(face_path, faces)
+ del vertices, faces, joined_pcl
+ return vert_path, face_path
+
+class Layer:
+ """Represents a layer of the print"""
+ def __init__(self, network, z:float) -> None:
+ self.network = network
+ self.z = z
+ self.commands:list[Command] = []
+ self.nodes:list[Node] = []
+
+ def wipe_memory(self) -> None:
+ """Delete the nodes from the memory"""
+ for node in self.nodes:
+ node.wipe()
+ log.info(f'Layer {self.z} wiped')
+
diff --git a/addon/decorators.py b/addon/decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd4c05ebdfea11375b6c68e2b01c56b9c1c882ad
--- /dev/null
+++ b/addon/decorators.py
@@ -0,0 +1,144 @@
+# -*- coding: utf-8 -*-
+
+__all__ = ["factory", "runtime", "delay"]
+
+import os
+import time
+import random
+import functools
+from memory_profiler import memory_usage
+import matplotlib.pyplot as plt
+import logging
+
+log = logging.getLogger('SmoPa3D')
+
+def factory(**default_opts):
+ """
+ Factory function to create decorators for tasks's run methods. Default options for the decorator
+ function can be given in *default_opts*. The returned decorator can be used with or without
+ actual invocation. Example:
+ .. code-block:: python
+ @factory(digits=2)
+ def runtime(fn, opts, task, *args, **kwargs):
+ t0 = time.time()
+ try:
+ return fn(task, *args, **kwargs)
+ finally:
+ t1 = time.time()
+ diff = round(t1 - t0, opts["digits"])
+ print("runtime:")
+ print(diff)
+ ...
+ class MyTask():
+ @runtime
+ def run(self):
+ ...
+ # or
+ @runtime(digits=3):
+ def run(self):
+ ...
+ """
+ def wrapper(decorator):
+ @functools.wraps(decorator)
+ def wrapper(fn=None, **opts):
+ _opts = default_opts.copy()
+ _opts.update(opts)
+
+ def wrapper(fn):
+ @functools.wraps(fn)
+ def wrapper(*args, **kwargs):
+ return decorator(fn, _opts, *args, **kwargs)
+ return wrapper
+
+ return wrapper if fn is None else wrapper(fn)
+ return wrapper
+ return wrapper
+
+
+@factory(digits=2)
+def runtime(fn, opts, *args, **kwargs):
+ """
+ Decorator for inspecting a methods performance with a precision of *digits=2*.
+ """
+ t0 = time.time()
+ try:
+ return fn(*args, **kwargs)
+ finally:
+ t1 = time.time()
+ if (t1-t0) < 2:
+ diff = round(1000*(t1 - t0), opts["digits"])
+ log.debug(f"{fn.__name__} runtime: {diff} ms")
+ else:
+ diff = round((t1 - t0), opts["digits"])
+ log.debug(f"{fn.__name__} runtime: {diff} s")
+
+
+@factory(t=5, stddev=0, pdf="gauss")
+def delay(fn, opts, *args, **kwargs):
+ """ delay(t=5, stddev=0., pdf="gauss")
+ Wraps a bound method of a task and delays its execution by *t* seconds.
+ """
+ if opts["stddev"] <= 0:
+ t = opts["t"]
+ elif opts["pdf"] == "gauss":
+ t = random.gauss(opts["t"], opts["stddev"])
+ elif opts["pdf"] == "uniform":
+ t = random.uniform(opts["t"], opts["stddev"])
+ else:
+ raise ValueError(
+ "unknown delay decorator pdf '{}'".format(opts["pdf"]))
+
+ time.sleep(t)
+
+ return fn(*args, **kwargs)
+
+class Tracker:
+
+ def __init__(self) -> None:
+ self.runtime:list[float] = []
+ # self.memory:list[float] = []
+
+ def plot_results(self, function_name:str, path:str='./__runtime_stats__'):
+ os.makedirs(path, exist_ok=True)
+ fig, axs = plt.subplots(1, 1, layout='constrained')
+
+ # Plot runtime
+ axs.plot(self.runtime)
+ axs.set_ylabel('Runtime [ms]')
+ axs.set_xlabel('Iterations')
+ axs.set_title('Runtime')
+
+ # # Plot memory
+ # axs[1].plot(self.memory)
+ # axs[1].set_ylabel('Memory [MiB]')
+ # axs[1].set_xlabel('Iterations')
+ # axs[1].set_title('Memory')
+
+ print(f'Code statistics saved in {os.path.abspath(path)}')
+ fig.savefig(f"{path}/{function_name}.png")
+
+
+@factory()
+def track(fn, opts, *args, **kwargs):
+ """Keep track of the runtime of a function over time"""
+ if f"{fn.__name__}_tracker" not in globals().keys():
+ globals()[f"{fn.__name__}_tracker"] = Tracker()
+ tracker:Tracker = globals()[f"{fn.__name__}_tracker"]
+ t0 = time.time()
+ try:
+ # memory = max(memory_usage((fn, [*args], {**kwargs})))
+ return fn(*args, **kwargs)
+ finally:
+ t1 = time.time()
+ tracker.runtime.append(round((t1-t0)*1000, 1))
+ # if memory is not None: tracker.memory.append(memory)
+
+def save_tracking_stats():
+ """Save the statistics in charts"""
+ to_delete = []
+ for varname, value in globals().items():
+ if isinstance(value, Tracker):
+ value.plot_results(function_name=varname)
+ to_delete.append(varname)
+ for varname in to_delete:
+ del globals()[varname]
\ No newline at end of file
diff --git a/addon/gcode/__init__.py b/addon/gcode/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/addon/gcode/convert_gcode.py b/addon/gcode/convert_gcode.py
new file mode 100644
index 0000000000000000000000000000000000000000..67718530475dd32af4ea2a95f8b6046c09dac616
--- /dev/null
+++ b/addon/gcode/convert_gcode.py
@@ -0,0 +1,247 @@
+# -*- coding: utf-8 -*-
+"""
+@author: grh
+
+Project: SmoPa3D
+
+Description: This code is used for the manipulation of the gcode in order to generate slices
+"""
+import pandas as pd
+from random import sample, randint
+import re
+import numpy as np
+from scipy.interpolate import interp1d
+import os
+
+#===============================================================================
+## Generating of gcode slices in order to send them to the printer
+#===============================================================================
+def convertGcode2Slices(rel_path = "/data/printjobs/test_part.gcode"):
+
+ with open(os.path.join(os.environ['ROOT'], rel_path), "r") as gcode:
+ data = gcode.read() ### read in the G-Code
+
+ ##Get the layer height
+ layerheight_start = re.search(";Layer height: ", data).end()
+ layerheight_end = re.search(";MINX:", data).start()
+ layerheight = float(data[layerheight_start:layerheight_end])
+
+ ##Get the number of Layers
+ numberOfLayers_start = re.search(";LAYER_COUNT:", data).end()
+ numberOfLayers_end = re.search(";LAYER:0", data).start()
+ numberOfLayers = int(data[numberOfLayers_start:numberOfLayers_end])
+ numberOfLayers = numberOfLayers - 1 ## subtracting -1 in order to get the real number of layers (the counting starts at 0 -> for example if 169 layers are displayed, the last layer ist layer 168)
+
+ ##Get YMin und YMax for to select the measuring area
+ #Getting YMin
+ numberOfLayers_start = re.search(";MINY:", data).end()
+ numberOfLayers_end = re.search(";MINZ:", data).start()
+ YMin = float(data[numberOfLayers_start:numberOfLayers_end])
+
+ #Getting YMax
+ numberOfLayers_start = re.search(";MAXY:", data).end()
+ numberOfLayers_end = re.search(";MAXZ:", data).start()
+ YMax = float(data[numberOfLayers_start:numberOfLayers_end])
+
+ ##Seperate the first Layer from the G-Code
+ numberOfLayers_end = re.search(";LAYER:1", data).start()
+ slices = np.array(str(data[:numberOfLayers_end]))
+
+ i = 1 ## starting the counter at 1 because the 0 layer was already seperated
+ while i <= numberOfLayers:
+ ## Select start and end Layer (code is searching for ";LAYER:i" and "LAYER:i+1" in order to seperate each layer)
+ startLayer = re.search(";LAYER:" + str(i), data).start() ## getting the number of the character of the current layer in order to extract the layer
+
+ ## Seperate the other Layers from the G-Code
+ if i < numberOfLayers:
+ endLayer = re.search(";LAYER:" + str(i+1), data).start() ## getting the number of the character of the next layer in order to extract the layer
+ slices = np.append(slices, (str(data[startLayer:endLayer]))) ## seperating the layer and appending it to the numpy array
+
+ ## Seperate the last Layer from the G-Code
+ else:
+ slices = np.append(slices, (str(data[startLayer:]))) ## seperating the layer and appending it to the numpy array
+ i += 1
+
+ return numberOfLayers, layerheight, YMin, YMax, slices
+
+#===============================================================================
+## Generating of gcode with randomly induced defects (pores)
+#===============================================================================
+def convertGcode2SlicesWithDefects(rel_path = "/data/printjobs/test_part.gcode", output_path = "/data/printjobs/test_part_defective.gcode"):
+
+ with open(os.path.join(os.environ['ROOT'], rel_path), "r") as gcode:
+ data = gcode.read() ### read in the G-Code
+
+ ##Get the layer height
+ layerheight_start = re.search(";Layer height: ", data).end()
+ layerheight_end = re.search(";MINX:", data).start()
+ layerheight = float(data[layerheight_start:layerheight_end])
+
+ ##Get the number of Layers
+ numberOfLayers_start = re.search(";LAYER_COUNT:", data).end()
+ numberOfLayers_end = re.search(";LAYER:0", data).start()
+ numberOfLayers = int(data[numberOfLayers_start:numberOfLayers_end])
+ numberOfLayers = numberOfLayers - 1 ## subtracting -1 in order to get the real number of layers (the counting starts at 0 -> for example if 169 layers are displayed, the last layer ist layer 168)
+
+ ##Get YMin und YMax for to select the measuring area
+ #Getting YMin
+ numberOfLayers_start = re.search(";MINY:", data).end()
+ numberOfLayers_end = re.search(";MINZ:", data).start()
+ YMin = float(data[numberOfLayers_start:numberOfLayers_end])
+
+ #Getting YMax
+ numberOfLayers_start = re.search(";MAXY:", data).end()
+ numberOfLayers_end = re.search(";MAXZ:", data).start()
+ YMax = float(data[numberOfLayers_start:numberOfLayers_end])
+
+ with open(os.path.join(os.environ['ROOT'], rel_path), "r") as gcode:
+ ## Convert gcode into a Pandas Dataframe in order to process it
+ lines = gcode.readlines()
+ lines = [line.strip() for line in lines]
+ df = pd.Series(lines)
+
+ ## get the locations in the gcode, which contain those strings in order to seperate the code regarding the type which is printed
+ locations = df.loc[df.str.contains(";MESH:NONMESH|;TYPE:SKIN|;TYPE:FILL|;TYPE:WALL-INNER|;TYPE:WALL-OUTER", case= False)].index.values
+
+ i = 0
+ ## sets the resolution/size of the defects in mm
+ resolution = 1
+ ## go through each location. If its SKIN oder FILL, defects will be generated
+
+ while i < (len(locations)-1):
+ if df[locations[i]] == ";TYPE:SKIN" or df[locations[i]] == ";TYPE:FILL":
+
+ ## get each for each command block the commands in which material is extruded
+ position_commands = df[(locations[i]+2):locations[i+1]].loc[df.str.contains("G1", case= False)].index.values
+ position_commands = df[position_commands].loc[df.str.contains("X", case= False)].index.values
+ position_commands = df[position_commands].loc[df.str.contains("E", case= False)].index.values
+
+
+ ## creates random numbers in range of the len of the position commands in order get a random entry from position commands
+ len_commands = len(position_commands)
+ random_number_array = sample(range(0, len_commands), int(len_commands/20))
+ random_number_array.sort(reverse=True)
+
+ ## see if there are any random numbers created
+ if random_number_array != []:
+
+ ## go throug each printing section (FILL or SKIN) and create defects
+ j = 0
+ while j < int(len_commands/20):
+ ## Gets the x-position, y-position and e-position in order to get the starting point of the considered command
+ ## Get X
+ X_start = re.search("X", df[position_commands[random_number_array][j]]).end()
+ X_end = re.search("Y", df[position_commands[random_number_array][j]]).start()
+ x = float(df[position_commands[random_number_array][j]][X_start:X_end])
+ ## Get Y
+ Y_start = re.search("Y", df[position_commands[random_number_array][j]]).end()
+ Y_end = re.search("E", df[position_commands[random_number_array][j]]).start()
+ y = float(df[position_commands[random_number_array][j]][Y_start:Y_end])
+ ## Get E
+ E_start = re.search("E", df[position_commands[random_number_array][j]]).end()
+ e = float(df[position_commands[random_number_array][j]][E_start:])
+ ## Create a coordinate variable
+ coordinate = np.array((x,y))
+
+ ## Gets the previous x-position and y-position in order to get the starting point of the previous command
+ ## Get X
+ X_start = re.search("X", df[position_commands[random_number_array][j]-1]).end()
+ X_end = re.search("Y", df[position_commands[random_number_array][j]-1]).start()
+ prev_x = float(df[position_commands[random_number_array][j]-1][X_start:X_end])
+ ## Get Y
+ Y_start = re.search("Y", df[position_commands[random_number_array][j]-1]).end()
+ if re.search("E", df[position_commands[random_number_array][j]-1]) is not None:
+ Y_end = re.search("E", df[position_commands[random_number_array][j]-1]).start()
+ prev_y = float(df[position_commands[random_number_array][j]-1][Y_start:Y_end])
+ else:
+ prev_y = float(df[position_commands[random_number_array][j]-1][Y_start:])
+ ## Create a coordinate variable
+ prev_coordinates = np.array((prev_x,prev_y))
+
+ ## gets the total distance between the considered commands ("coordinates" and "prev_coordinates")
+ dist_total = np.linalg.norm(coordinate-prev_coordinates)
+
+ ## if the total distance is lower than 2mm the command will be executed but no filament is extruded -> the defect is then max 2 mm long
+ if dist_total < 2:
+ df[position_commands[random_number_array][j]] = "G1 X" + str(x) + " Y" + str(y) + " \nG92 E" + str(e)
+
+ ## if the total distance is higher than 2mm the "line" will be interupted on a random position and a defect will be inserted (nearly 1 mm long)
+ elif dist_total > 2:
+
+ ## Interpolation of the previous coordinates (from the previous command) and the current coordinates (from the considered command)
+ prev_e = e - (dist_total * 0.033) ## 0.033 is the distance the E-axis is moving per mm
+ increment = int(dist_total/resolution) ## total number of points which are created in the interpolated array
+ x_interplt=[prev_x,x]
+ y_interplt=[prev_y,y]
+ f=interp1d(x_interplt,y_interplt)
+ x_coordinates = np.linspace(prev_coordinates[0],coordinate[0],increment)
+ y_coordinates = f(x_coordinates)
+
+ ## creates a random number in order to select on position in the created "line" of the interpolation
+ random_number = randint(1, (len(x_coordinates)-1))
+
+ ## gets the coordinate before the "defect"
+ coordinate_before_defect = np.array((x_coordinates[random_number-1], y_coordinates[random_number-1]))
+
+ ## gets the coordinate after the "defect"
+ coordinate_after_defect = np.array((x_coordinates[random_number], y_coordinates[random_number]))
+
+ ## gets the distance between the previous coordinate and the coordinate before the "defect" in order to calculate the extrusion
+ dist_1 = np.linalg.norm(prev_coordinates-coordinate_before_defect)
+ e_value_1 = round(prev_e + (0.033 * dist_1),5)
+
+ ## gets the distance between the the coordinate before the "defect" and the coordinate after the "defect" in order to calculate the "extrusion"
+ dist_2 = np.linalg.norm(coordinate_before_defect-coordinate_after_defect)
+ e_value_2 = round(e_value_1 + (0.033 * dist_2),5)
+
+ ## if the random number is 1 and therefore the defect is just in the beginning of the line, the new code is generated
+ ## The defect in a 3 mm line looks like this |x--| (with - as printed normal and x as not printed and therefore a defect)
+ if random_number == 1:
+ df[position_commands[random_number_array][j]] = "G0 X" + str(coordinate_after_defect[0]) + " Y" + str(coordinate_after_defect[1]) + " \nG92 E" + str(e_value_2) + " \n" + df[position_commands[random_number_array][j]]
+
+ ## The defect in a 3 mm line looks like this |--x| (with - as printed normal and x as not printed and therefore a defect)
+ elif random_number == (len(x_coordinates)-1):
+ df[position_commands[random_number_array][j]] = "G1 X" + str(coordinate_before_defect[0]) + " Y" + str(coordinate_before_defect[1]) + " E" + str(e_value_1) + " \nG0 X" + str(x) + " Y" + str(y) + " \nG92 E" + str(e)
+
+ ## The defect in a 3 mm line looks like this |-x-| (with - as printed normal and x as not printed and therefore a defect)
+ else:
+ df[position_commands[random_number_array][j]] = "G1 X" + str(coordinate_before_defect[0]) + " Y" + str(coordinate_before_defect[1]) + " E" + str(e_value_1) + " \nG0 X" + str(coordinate_after_defect[0]) + " Y" + str(coordinate_after_defect[1]) + " \nG92 E" + str(e_value_2) + " \n" + df[position_commands[random_number_array][j]]
+ j +=1
+ i += 1
+
+ ## Convert Pandas Dataframe back into str in order to be processed as gcode
+ newlines = df.values.tolist()
+ newgcode = "\n".join(newlines)
+
+ ##Seperate the first Layer from the G-Code
+ numberOfLayers_end = re.search(";LAYER:1", newgcode).start()
+ slices = np.array(str(newgcode[:numberOfLayers_end]))
+
+ i = 1 ## starting the counter at 1 because the 0 layer was already seperated
+ while i <= numberOfLayers:
+ ## Select start and end Layer (code is searching for ";LAYER:i" and "LAYER:i+1" in order to seperate each layer)
+ startLayer = re.search(";LAYER:" + str(i), newgcode).start() ## getting the number of the character of the current layer in order to extract the layer
+
+ ## Seperate the other Layers from the G-Code
+ if i < numberOfLayers:
+ endLayer = re.search(";LAYER:" + str(i+1), newgcode).start() ## getting the number of the character of the next layer in order to extract the layer
+ slices = np.append(slices, (str(newgcode[startLayer:endLayer]))) ## seperating the layer and appending it to the numpy array
+
+ ## Seperate the last Layer from the G-Code
+ else:
+ slices = np.append(slices, (str(newgcode[startLayer:]))) ## seperating the layer and appending it to the numpy array
+ i += 1
+
+ #file = open(os.path.join(os.environ['ROOT'],utput_path), "w")
+ #file.write(newgcode)
+ #file.close
+
+ return numberOfLayers, layerheight, YMin, YMax, slices
+
+if __name__ == '__main__':
+ while True:
+ try:
+ convertGcode2SlicesWithDefects()
+ except AttributeError:
+ continue
+ break
\ No newline at end of file
diff --git a/addon/gcode/defects.py b/addon/gcode/defects.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddab8c085fa2b0528fa6ccf4fb254565d3b58e47
--- /dev/null
+++ b/addon/gcode/defects.py
@@ -0,0 +1,40 @@
+# TODO:
+# 1. Functions for each of the defects
+# a. ABC
+# b. Over extrusion
+# c. Under extrusion
+# d. Void
+# e. Geometric deviation
+from abc import ABC, abstractmethod
+
+from .parser import Command, Gcode
+
+class Defect(ABC):
+ def __init__(self, incidence_ratio:float, intensity:float) -> None:
+ """Parameters:
+ -----
+ * @param incidence_ratio ∈ [0, 1]: percentage of the gcode commands upon which the defect may incide\n
+ * @param intensity ∈ [0, 1]: percentage of the intensity of the defect"""
+ super().__init__()
+ assert 0 <= incidence_ratio <= 1, "incidence_ratio should be a float between 0 and 1"
+ assert 0 <= intensity <= 1, "incidence_ratio should be a float between 0 and 1"
+
+ self.incidence_ratio = incidence_ratio
+ self.intensity = intensity
+
+ @abstractmethod
+ def apply(self, command:Command) -> None:
+ pass
+
+class OverExtrusion(Defect):
+ def apply(self, command:Command) -> None:
+ if not (type(command) == float or type(command) == float): return
+ last_coords = command.get_last_position()
+ original_extrusion = command.e - last_coords['e']
+ command.e += original_extrusion * self.intensity
+
+if __name__ == '__main__':
+ gcode = Gcode('data/defects_scans/single_line_cross/no_defect/CE3PRO_single_line_cross.gcode')
+ defects = [OverExtrusion(1, 1)]
+ gcode.apply_defects(defects)
+ gcode.save('./source/gcode/gcode_with_defects.gcode')
\ No newline at end of file
diff --git a/addon/gcode/gcode2voxel.py b/addon/gcode/gcode2voxel.py
new file mode 100644
index 0000000000000000000000000000000000000000..f30b0aaa562c0fb0caeb67fbee779beb1170db11
--- /dev/null
+++ b/addon/gcode/gcode2voxel.py
@@ -0,0 +1,276 @@
+"""
+@author: grh, grh_mh
+
+Project: SmoPa3D
+
+Description: This code is used to generate a nominal out of the gcode given by a slicer.
+"""
+
+from scipy.spatial.transform import Rotation as R
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import re
+import logging
+from itertools import repeat
+import multiprocessing
+
+log = logging.getLogger('SmoPa3D')
+
+def gcode2dataframe(gcode_file_path:str):
+ """Reads a gcode file and transforms it into a pandas dataframe, each row representing a command.\n
+ Dataframe format:\n
+ ``` bash
+ +-------+---------+---------+---------+-----------+---------+-------+
+ | index | X-Value | Y-Value | Z-Value | Extrusion | Command | Layer |
+ +-------+---------+---------+---------+-----------+---------+-------+
+ | int | float | float | float | float | int | int |
+ """
+ with open(gcode_file_path, "r") as gcode:
+ data = gcode.read() ### read in the G-Code
+ numberOfLayers_start = re.search(";LAYER_COUNT:", data).end()
+ numberOfLayers_end = re.search(";LAYER:0", data).start()
+ numberOfLayers = int(data[numberOfLayers_start:numberOfLayers_end])
+
+ lines = [line.strip() for line in data.split('\n')]
+ data_size = len(lines)
+ df = pd.Series(lines)
+
+ gcode_values = np.zeros(((data_size),6))
+ amount_extracted_values = 0
+ amount_extracted_values_start_layer = 0
+
+
+ for i in range(numberOfLayers):
+ ### Selection of the i-th layer and separation of the code from the entire code block
+
+ startLayer = (";LAYER:" + str(i))
+ if i < (numberOfLayers-1):
+ startLayerplusone = (";LAYER:" + str(i+1))
+ layer_start = df[df==str(startLayer)].index.values[0]
+ layer_end = df[df==str(startLayerplusone)].index.values[0]
+ layer = df[(layer_start+1):layer_end]
+
+ elif i == (numberOfLayers-1):
+ layer = df[(layer_start+1):]
+
+ ### Separation of only G-code where extrude and process
+ layer = layer.drop(layer.loc[layer.str.contains(";", case= False)].index.values)
+ layer = layer.drop(layer.loc[layer.str.contains("M20", case= False)].index.values)
+ layer = layer.drop(layer.loc[layer.str.contains("Z", case= False)].index.values) ## Skipping commands in which Z changes
+ layer = layer.drop(layer.loc[~layer.str.contains("Y", case= False)].index.values)
+ layer = layer.drop(layer.loc[~layer.str.contains("X", case= False)].index.values)
+
+ for (j, position) in enumerate(layer):
+ ## Extraction of position in X
+ X_start = re.search("X", position).end()
+ X_end = re.search("Y", position).start()
+ X_position = float(position[X_start:X_end])
+ ## Extraction of position in Y
+ Y_start = re.search("Y", position).end()
+ if re.search("E", position) is not None:
+ Y_end = re.search("E", position).start()
+ Y_position = float(position[Y_start:Y_end])
+ else:
+ Y_position = float(position[Y_start:])
+
+ ## Extraction of position in Z
+ Z_position = 0.2 + (i*0.2)
+
+ ## Extraction of feedrate
+ if (re.search("E", position) is None) != True:
+ Extrusion_start = re.search("E", position).end()
+ Extrusion = float(position[Extrusion_start:])
+ else: Extrusion= float(0.00)
+
+ ## Assignment of layer count to the values
+ numberOfLayer = i
+
+ ## Assignment of the command number to the values
+ numberOfCommand = j
+
+ extracted_value = np.array((X_position,Y_position,Z_position, Extrusion, numberOfCommand, numberOfLayer))
+ gcode_values[amount_extracted_values: (amount_extracted_values + 1)] = extracted_value
+ amount_extracted_values += 1
+
+ gcode_values = gcode_values[:amount_extracted_values]
+
+ df2 = pd.DataFrame(gcode_values)
+ df2 = df2.set_axis(("X-Value", "Y-Value", "Z-Value", "Extrusion", "Command", "Layer"), axis='columns')
+ return df2
+
+def calculate_profile_dimensions(method:str, D:float=0.4, V_over_U:float=1.58, Rn:float=1, gap:float=0.2, alpha:float=1.505) -> tuple[float, float]:
+ """Calculates height and width of the strand. Available options:\n
+ * hebda
+ * xu
+ * comminal
+
+ References:\n
+ * [COMMINAL et al., 2018](https://doi.org/10.1016/j.addma.2017.12.013)\n
+ * [Hebda et al., 2019](http://hdl.handle.net/10454/16895)\n
+ * [Xu et al., 2022](https://hal-mines-paristech.archives-ouvertes.fr/hal-03766358)"""
+
+ if method == 'hebda':
+ W = D * alpha * np.sqrt(1/V_over_U)
+ H = D / alpha * np.sqrt(1 / V_over_U)
+ elif method == 'xu':
+ W = D * (1 - Rn/D + np.sqrt((Rn/D - 1)**2 + np.pi * D / (gap * V_over_U) * (Rn/D - 0.5)))
+ H = D**2 / (V_over_U * W)
+ elif method == 'comminal':
+ W = np.pi / 4 * 1 / V_over_U * D**2 / gap + gap * (1 - np.pi / 4)
+ H = gap
+ else:
+ log.warning('Calculation method for profile dimensions do not exist. Possible options are: hebda, xu and comminal')
+ W = 0
+ H = 0
+ return W, H
+
+def draw_profile(geometry:str='ellipse', width:float=0.374109, height:float=0.266511, resolution:float=0.07) -> pd.DataFrame:
+ """Creates a pinned ellipse profile in 2D to simulate the extrusion profile. It is made in Y = 0 plane.\n
+ Available format options:\n
+ * oblong
+ * ellipse
+ * pinned ellipse
+ * ellipse oblong
+
+ References:\n
+ * [COMMINAL et al., 2018](https://doi.org/10.1016/j.addma.2017.12.013)\n
+ * [Hebda et al., 2019](http://hdl.handle.net/10454/16895)\n
+ * [Xu et al., 2022](https://hal-mines-paristech.archives-ouvertes.fr/hal-03766358)"""
+
+ df = pd.DataFrame(columns=['x', 'y', 'z'])
+ z_vector = np.linspace(0, height, int(height / resolution) + 2)
+
+ # Draw contours right side
+ if geometry == 'oblong':
+ for z in z_vector:
+ angle = np.arcsin((z - height / 2) * 2/ height)
+ x = np.cos(angle) * height / 2 + (width - height) / 2
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+
+ elif geometry == 'ellipse':
+ for z in z_vector:
+ angle = np.arcsin((z - height / 2) * 2 / height)
+ x = np.cos(angle) * width / 2
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+
+ elif geometry == 'pinned ellipse':
+ x = 0
+ for z in z_vector[::-1]:
+ if x <= 0.95 * width / 2: # Upper side (ellipse)
+ angle = np.arcsin((z - height / 2) * 2 / height)
+ x = np.cos(angle) * width / 2
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+ else: # Lower side (rectangle)
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+
+ elif geometry == 'ellipse oblong':
+ for z in z_vector:
+ if z >= height / 2: # Upper side (ellipse)
+ angle = np.arcsin((z - height / 2) * 2 / height)
+ x = np.cos(angle) * width / 2
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+ else: # Lower side (oblong)
+ angle = np.arcsin((z - height / 2) * 2 / height)
+ x = np.cos(angle) * height/ 2 + (width - height) / 2
+ new_row = pd.DataFrame([[x, 0, z]], columns=['x', 'y', 'z'])
+ df = pd.concat([df, new_row])
+
+ # Mirror to the left side
+ df = pd.concat([df, df * [-1, 1, 1]])
+
+ # Fill up
+ for z in z_vector:
+ x = df[df.z == z].x.to_list()
+ x_vector = np.linspace(min(x), max(x), int((max(x) - min(x)) / resolution))[1:-1]
+ z_row = np.ones_like(x_vector) * z
+ new_row = new_row = pd.DataFrame.from_dict({'x':x_vector, 'y':np.zeros_like(x_vector), 'z':z_row})
+ df = pd.concat([df, new_row])
+ return df
+
+def draw_rounded_ending(profile:pd.DataFrame, steps:int=7):
+ """Draw a rounded ending by revoluting half of the profile in its Z axis"""
+ ending = pd.DataFrame(columns=profile.columns)
+ one_side_profile = profile[profile.x < 0]
+ for angle in np.linspace(0, np.pi, steps)[1:-1]:
+ r = R.from_euler('z', angle, False)
+ rotated = one_side_profile.to_numpy() @ r.as_matrix()
+ df_rotated = pd.DataFrame(rotated, columns=profile.columns)
+ ending = pd.concat([ending, df_rotated])
+ return ending
+
+def show_profile(profile:pd.DataFrame, axis:tuple=['x', 'z']):
+ """Plot the profile to check it graphically"""
+ fig, ax = plt.subplots()
+ ax.plot(profile[axis[0]], profile[axis[1]], 'ko')
+ ax.set_box_aspect(1)
+ ax.set_title('profile')
+ plt.gca().set_aspect('equal')
+ plt.show()
+
+def draw_tubular_point_cloud(pt0:tuple[float], pt1:tuple[float], profile:pd.DataFrame, resolution:float):
+ """Given two coordinates and a profile, returns a DataFrame representing a tubular point cloud between them.\n
+ Args:\n
+ - points = [x, y, z]\n
+ - profile = pd.DataFrame(columns=['x', 'y', 'z'])
+ """
+ x0, y0, z0 = pt0
+ x1, y1, z1 = pt1
+ angle = -np.arctan2(y1 - y0, x1 - x0)
+ r = R.from_euler('z', angle + np.pi/2, False).as_matrix()
+ rotated = profile.to_numpy() @ r
+
+ dist = np.linalg.norm(np.array(pt1) - np.array(pt0))
+ segments = int(dist/resolution) + 2
+ X = np.repeat(np.linspace(x0, x1, num=segments), len(rotated))
+ Y = np.repeat(np.linspace(y0, y1, num=segments), len(rotated))
+ Z = np.array([z0 for i in range(len(X))])
+
+ pcl = np.array([X, Y, Z]).transpose() + np.tile(rotated, (segments, 1))
+ df_pcl = pd.DataFrame(pcl, columns=['X', 'Y', 'Z'])
+
+ # Add rounded endings to both sides of tubular point cloud
+ ending_part = draw_rounded_ending(profile)
+ ending = pd.DataFrame(ending_part.to_numpy() @ r + np.array(pt1), columns=df_pcl.columns)
+ opposite_r = R.from_euler('z', angle - np.pi/2, False).as_matrix()
+ beginning = pd.DataFrame(ending_part.to_numpy() @ opposite_r + np.array(pt0), columns=df_pcl.columns)
+ df_pcl = pd.concat([df_pcl, beginning, ending])
+ return df_pcl
+
+def build_command(index:int, commands:pd.DataFrame, profile:pd.DataFrame, resolution:float):
+ """Builds one line of gcode command"""
+ start_row = commands.iloc[index - 1] if index > 0 else commands.iloc[0]
+ start_point = [start_row['X-Value'], start_row['Y-Value'], start_row['Z-Value']]
+ end_point = [commands.iloc[index]['X-Value'], commands.iloc[index]['Y-Value'], commands.iloc[index]['Z-Value']]
+ pcl = draw_tubular_point_cloud(start_point, end_point, profile, resolution)
+ return pcl
+
+def build_part(commands:pd.DataFrame, profile:pd.DataFrame, resolution:float, processes:int=2):
+ """Builds every line in gcode commands. Number of processes can be changed, but with caution."""
+ with multiprocessing.Pool(processes) as pool:
+ df_pcl = pd.concat(pool.starmap(build_command, zip(commands[commands.Extrusion > 0].index, repeat(commands), repeat(profile), repeat(resolution))))
+ return df_pcl
+
+# if __name__ == '__main__':
+# import generate_pointcloud as gp
+# import utils
+# import os
+# start = time.time()
+
+# gcode_path = utils.open_or_download('63847798504e7d63865e5769')
+# df = gcode2dataframe(gcode_path, create_pcl_for_each_layer=False)
+# resol = 0.07
+
+# W, H = calculate_profile_dimensions('hebda')
+# profile = draw_profile('oblong', W, H, resolution=resol)
+
+# pcl = build_part(df, profile, resol)
+
+# path_to_pcl = gp.generate_pcl(pcl, path="files/", name = "00_nominal_part", LLS ="")
+
+# log.info("I took {:.1f} s to generate realistic point cloud.".format(time.time() - start))
\ No newline at end of file
diff --git a/addon/gcode/parser.py b/addon/gcode/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..abf62cb4665ee081e71cfc10978aa953bd200c75
--- /dev/null
+++ b/addon/gcode/parser.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Any, Optional, TYPE_CHECKING
+import os, random
+
+import logging
+log = logging.getLogger("SmoPa3D")
+
+if TYPE_CHECKING:
+ from .defects import Defect
+
+@dataclass
+class GcodeCommand:
+ gcode: Gcode
+ id: int
+ command_line: Optional[str] = None
+ m: Optional[int] = None
+ s: Optional[int] = None
+ g: Optional[int] = None
+ f: Optional[float] = None
+ x: Optional[float] = None
+ y: Optional[float] = None
+ z: Optional[float] = None
+ e: Optional[float] = None
+
+ def __post_init__(self) -> None:
+ assert sum([self.command_line is not None, self.g is not None, self.m is not None]) == 1, "Either command_line or command must be input to Command class"
+ self.command_list = ['M', 'S', 'G', 'F', 'X', 'Y', 'Z', 'E']
+ if self.command_line[-1] == '\n': self.command_line = self.command_line[:-1]
+
+ if self.command_line is not None:
+ for arg in self.command_line.split(' '):
+ if arg[0] == ';': break
+ if arg[0] in self.command_list:
+ self.__dict__[arg[0].lower()] = float(arg[1:]) if len(arg[1:]) > 0 else ''
+
+ def __str__(self) -> str:
+ if not self.is_transformable: return self.command_line
+ parameters = []
+ for arg in self.command_list:
+ value = self.__dict__[arg.lower()]
+ if value is None:
+ continue
+ elif type(value) == str:
+ parameters.append(value)
+ else:
+ parameters.append(arg + str(round(value, 5)))
+ return ' '.join(parameters)
+
+ def format(self) -> str:
+ return self.__str__()
+
+ @property
+ def last_position(self, parameters_to_search:list[str]=['x', 'y', 'z', 'e']) -> dict:
+ """Locate the last ocurrence of coordinates x, y, z, e in parent gcode. If no previous ocurrence is found, returns `None`\n
+ ```
+ return {x: float, y: float, z: float, e: float}
+ ```"""
+ coords = {param: None for param in parameters_to_search}
+ for i in range(self.id-1, 0, -1):
+ if all([pos is not None for pos in coords.values()]):
+ break
+ for coord in coords.keys():
+ if self.gcode.commands[i].__dict__[coord] is not None and self.gcode.commands[i].__dict__[coord] != '' and coords[coord] is None:
+ coords[coord] = self.gcode.commands[i].__dict__[coord]
+ return coords
+
+ def last_ocurrence(self, parameter:str) -> Optional[float]:
+ """Locate the last ocurrence of the parameter in parent gcode. If no previous ocurrence is found, returns `None`"""
+ if parameter.upper() not in self.command_list:
+ log.warning(f'No previous ocurrence of parameter {parameter} not found in command list')
+ return None
+
+ for i in range(self.id-1, 0, -1):
+ if self.gcode.commands[i].__dict__[parameter] is not None and self.gcode.commands[i].__dict__[parameter] != '':
+ return self.gcode.commands[i].__dict__[parameter]
+
+ @property
+ def is_transformable(self) -> bool:
+ """Check if command is transformable, i.e., if it:\n
+ * is not part of printer setup
+ * is not part of printer unset
+ * is not a comment
+ * is not M command
+ * has moved
+ * has extruded
+ """
+ is_setup = self.id <= self.gcode.setup_end
+ is_unset = self.id >= self.gcode.unset_start
+ is_comment = self.command_line[0] == ';'
+ is_m_command = self.m is not None
+ has_moved = sum([self.x is not None, self.y is not None, self.z is not None]) > 0
+ has_extruded = self.e is not None
+ return not any([is_setup, is_unset, is_comment, is_m_command, not has_moved, not has_extruded])
+
+ def __setattr__(self, __name: str, __value: Any) -> None:
+ self.__dict__[__name] = __value
+
+
+class Gcode:
+ def __init__(self, path:str) -> None:
+ assert os.path.exists(path), f"File path {os.path.abspath(path)} does not exist"
+ self.path = path
+
+ # Read gcode
+ with open(path, 'r') as gcodefile:
+ gcode = gcodefile.readlines()
+ self.gcode = gcode
+ self.setup_end = next(i for (i, row) in enumerate(self.gcode) if ';LAYER_COUNT' in row)
+ self.unset_start = next(i for i in range(len(self.gcode)-1, 0, -1) if ';TIME_ELAPSED' in self.gcode[i])
+ self.commands = [GcodeCommand(self, command_line=command, id=i) for (i, command) in enumerate(gcode) if len(command.replace('\n', '')) > 0]
+
+
+ def apply_defects(self, defectset:list[Defect], overlap:bool=False) -> None:
+ """Transform gcode by applying randomly the set of defects. Together with the transformed gcode, it outputs a label in json format indicating the coordinates of each of the synthetized defects.\n
+ Parameters:
+ -----
+ * @param overlap: if false, each command can contain a maximum of 1 defect. If True, more than one defect can happen in each command\n"""
+
+ # if not overlap: assert if sum is equal or less than 1
+ transformable = [command for command in self.commands if command.is_transformable]
+ total_commands = len(transformable)
+ for defect in defectset:
+ sample = random.sample(transformable, int(defect.incidence_ratio * total_commands))
+ for command in sample:
+ if not overlap:
+ transformable.remove(command)
+ command = defect.apply(command)
+
+ def save(self, path:str=None) -> None:
+ if path is None:
+ path = self.path.replace('.gcode', '_with_defects.gcode')
+
+ with open(path, 'w') as fle:
+ for command in self.commands:
+ fle.write(command.format() + '\n')
\ No newline at end of file
diff --git a/addon/geometry.py b/addon/geometry.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bde9c9d503e0b1df86c004c7a7dc2a0f3c475d5
--- /dev/null
+++ b/addon/geometry.py
@@ -0,0 +1,173 @@
+import numpy as np
+import open3d as o3d
+from scipy.spatial import KDTree
+from scipy.spatial.transform import Rotation as R
+import logging
+
+log = logging.getLogger("SmoPa3D")
+
+def apply_revolution(f_of_z, height:int, width:int) -> np.ndarray:
+ """Create a solid by revolutioning a function of z over the z-axis.
+ @param f_of_z: function that returns a float by given z value (in grid units)
+ @param height: length in z of the volume in grid units
+ @param width: length in x and y of the volume in grid units"""
+ if width % 2 == 0: width += 1 # Volume must be uneven
+ max_x = width // 2 + 1
+ solid = np.zeros((height, max_x, width), dtype=int) # z, y, x
+ for z in range(height):
+ for y in range(max_x):
+ if f_of_z(z) < y:
+ break
+ x_surface = round(np.sqrt(f_of_z(z) ** 2 - y ** 2))
+ x_array = [1 if x < x_surface else 0 for x in range(max_x)]
+ solid[z, y] = np.array([*x_array[::-1], *x_array[1:]])
+ solid = np.hstack((solid[:, ::-1], solid[:, 1:]))
+ return solid
+
+def place_ellipsoid(space:np.ndarray, w:int, l:int, h:int, direction:np.ndarray, center:np.ndarray) -> np.ndarray:
+ """Function to place an ellipsoid in a 3D space
+ @param space: 3D numpy array (environment)
+ @param w: width of the ellipsoid
+ @param l: length of the ellipsoid
+ @param h: height of the ellipsoid
+ @param direction: direction of the ellipsoid
+ @param center: center of the ellipsoid (z, y, x)"""
+
+ # # Create a rotation matrix from the direction vector
+ # enclosed_space = np.zeros((h, max(w, l), max(w, l)))
+ h += 1
+ direction = direction / np.linalg.norm(direction)
+ r = R.from_euler('z', np.arctan2(direction[1], direction[0]), degrees=False)
+ r = R.from_matrix(r.as_matrix()[::-1,::-1].T)
+
+ # Create a grid of points
+ z,y,x = np.meshgrid(np.arange(space.shape[0]), np.arange(space.shape[1]), np.arange(space.shape[2]), indexing='ij')
+
+ # Get the points relative to the center of the space
+ points = np.vstack((z.ravel(), y.ravel(), x.ravel())) - center.reshape(-1, 1)
+
+ # Rotate the points by the rotation matrix
+ points = r.apply(points.T).T
+
+ # Ellipsoid equation
+ inside = (points[0, :] / h) ** 2 + (points[1, :] / w) ** 2 + (points[2, :] / l) ** 2 < 0.98
+
+ # Cilinder equation
+ # inside = ((points[0, :] / h) ** 2 + (points[1, :] / w) ** 2 <= 1) * (abs(points[2, :] / l) <= 1)
+
+ # Set the value of the points inside the ellipsoid to 1
+ space.ravel()[inside] = 1
+ return space
+
+def calculate_shell(voxel:np.ndarray) -> np.ndarray:
+ """Remove the inner points of the geometry"""
+ dz, dy, dx = np.gradient(voxel)
+ grads = np.absolute(dz) + np.absolute(dy) + np.absolute(dx)
+ shell = grads * voxel
+ shell = np.where(shell != 0)
+ hull = np.array(shell[::-1]).transpose()
+ return hull
+
+def calculate_mesh(pointcloud:np.ndarray, simplify_factor:int=32, resolution:float=0.02) -> tuple[np.ndarray]:
+ """Calculate and simplify mesh by a given voxel. Returns vertices coordinates and a list of faces, each one given by a list of indexes of the vertices that make part of the face."""
+ basis = KDTree(pointcloud)
+ radius = resolution * (np.sqrt(3)+0.1)
+
+ neighbours_list = basis.query_ball_point(pointcloud, r=radius, p=2, return_length=False)
+ del basis
+
+ gradients = np.zeros((len(pointcloud), 3))
+ for i, neighbours in enumerate(neighbours_list):
+ if len(neighbours) >=27: continue
+ mean = np.mean(pointcloud[neighbours], axis=0)
+ gradients[i] = mean - pointcloud[i]
+ norm = np.linalg.norm(gradients[i])
+ gradients[i] = gradients[i] / norm if norm > 0 else np.zeros(3)
+
+ xyz = np.reshape(pointcloud[gradients.any(1)], (-1, 3))
+ normals = np.reshape(gradients[gradients.any(1)] * -1, (-1, 3))
+ del gradients, pointcloud
+ if len(normals) == 0: return np.zeros((0, 3)), np.zeros((0, 3))
+
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ pcd.normals = o3d.utility.Vector3dVector(normals)
+
+ mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=9, scale=2, n_threads=1)
+ # vertices_to_remove = densities < np.quantile(densities, 0.01)
+ # mesh.remove_vertices_by_mask(vertices_to_remove)
+
+ # Simplify mesh
+ if simplify_factor > 0:
+ voxel_size = max(mesh.get_max_bound() - mesh.get_min_bound()) / simplify_factor
+ mesh = mesh.simplify_vertex_clustering(
+ voxel_size=voxel_size,
+ contraction=o3d.geometry.SimplificationContraction.Average)
+ vertices = np.asarray(mesh.vertices)
+ faces = np.asarray(mesh.triangles)
+
+ pcd.clear()
+ mesh.clear()
+ densities.clear()
+ return vertices, faces
+
+def reconstruct_pointcloud_mesh(pointcloud:np.ndarray, simplify_factor:int=32) -> tuple[np.ndarray]:
+ xyz = np.reshape(pointcloud, (-1, 3))
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ pcd.estimate_normals()
+ last_normals = np.asarray(pcd.normals)
+ pcd.normals = o3d.utility.Vector3dVector(np.absolute(last_normals))
+ radii = [0.16, 0.32]
+ mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_ball_pivoting(
+ pcd, o3d.utility.DoubleVector(radii))
+
+ # Simplify mesh
+ if simplify_factor > 0:
+ voxel_size = max(mesh.get_max_bound() - mesh.get_min_bound()) / simplify_factor
+ mesh = mesh.simplify_vertex_clustering(
+ voxel_size=voxel_size,
+ contraction=o3d.geometry.SimplificationContraction.Average)
+ vertices = np.asarray(mesh.vertices)
+ faces = np.asarray(mesh.triangles)
+ pcd.clear()
+ mesh.clear()
+ return vertices, faces
+
+def shift_array(array:np.ndarray, shift_vector:np.ndarray):
+ if any([shift > array.shape[i] for (i, shift) in enumerate(shift_vector)]): return np.zeros_like(array)
+ H, W, D = array.shape
+ dx, dy, dz = shift_vector
+ dx, dy, dz = int(dx), int(dy), int(dz)
+ augmented_volume = np.zeros((H+abs(dz), W+abs(dy), D+abs(dx)))
+ augmented_volume[max(0, dz):H+max(0, dz), max(0, dy):W+max(0, dy), max(0, dx):D+max(0, dx)] = array
+ shifted_array = augmented_volume[max(0, -dz):H+max(0, -dz), max(0, -dy):W+max(0, -dy), max(0, -dx):D+max(0, -dx)]
+ return shifted_array
+
+def visualize(voxel:np.array) -> None:
+ import matplotlib.pyplot as plt
+ fig = plt.figure()
+ ax = fig.add_subplot(projection='3d')
+
+ x, y, z = calculate_shell(voxel).transpose()
+ ax.scatter3D(x, y, z, c=z, cmap='Greens')
+
+ ax.set_xlabel('X')
+ ax.set_ylabel('Y')
+ ax.set_zlabel('Z')
+ plt.show()
+
+def width_model(temperature:float, feedrate:float, speed:float) -> float:
+ """Returns the width of the filament in mm for a given temperature, speed and feedrate. Formulation is based on experimental values."""
+ root = np.cbrt(feedrate)
+ return (
+ -1.2411217231463934
+ + 0.004062025031923957 * temperature
+ - 0.0001331731552701792 * speed
+ + 2.984109335460154 * root
+ )
+
+def height_model(area:float, width:float) -> float:
+ """Returns the height of the filament in mm for a given area and width.
+ Calculation based on the area of the ellipse."""
+ return 2 * area / (np.pi * width / 2)
\ No newline at end of file
diff --git a/addon/lls.py b/addon/lls.py
new file mode 100644
index 0000000000000000000000000000000000000000..18fd1ccdcb2fe6930ee602d7f3ef182471361af5
--- /dev/null
+++ b/addon/lls.py
@@ -0,0 +1,156 @@
+from __future__ import annotations
+import bpy
+from mathutils import Vector
+import numpy as np
+import logging
+import itertools
+from tqdm import tqdm
+from tqdm.contrib.itertools import product
+import concurrent.futures
+
+from . import main
+
+log = logging.getLogger('SmoPa3D')
+
+class LaserLineScannerOperator(bpy.types.Operator):
+ bl_idname = "fdm_simulator.scan"
+ bl_label = "Simulate Laser Line Scanner"
+
+ def execute(self, context):
+ pcl = scan(draw=False, x_resolution=0.05, y_resolution=0.05, y_range=[40, 60])
+ np.save("virtual_scan.npy", pcl)
+ return {'FINISHED'}
+
+def scan(
+ x_range: tuple[float, float] = (95.306, 97.306),
+ x_resolution: float = 0.02,
+ y_range: tuple[float, float] = (-10, 10),
+ y_resolution: float = 1,
+ angle_range: tuple[float, float] = (-0.243, 0.243),
+ z: float = 130,
+ draw: bool = False
+ ) -> np.ndarray:
+ """Simulate the laser line scanner. Returns the point cloud of the scan in the format `np.array([[x0, y0, z0], ..., [xn, yn, zn]])`.\n
+ Arguments:
+ @param x_range: The limits of the laser line scanner source in the x axis
+ @param x_resolution: The resolution of the x axis. Resolution of the lls is 0.003 ~ 0.05 mm according to the datasheet
+ @param y_range: The range of the y axis
+ @param y_resolution: The resolution of the y axis. Resolution of the encoder is
+ @param angle_range: The range of the angle of the lls. Default value assessed experimentally
+ @param z: The height of the lls. Default value assessed experimentally
+ @param draw: Whether to draw the beams and the point cloud in the scene
+ """
+ bpy.context.view_layer.update()
+
+ width = 2 * z * np.tan(max(angle_range)) + abs(x_range[1] - x_range[0])
+ x_divisions = round(width / x_resolution)
+ x_range = np.linspace(*x_range, x_divisions)
+ y_range = np.arange(*y_range, y_resolution)
+ angle_range = np.linspace(*angle_range, x_divisions)
+ log.info("Casting beams")
+ beams = [Beam((x, y, z), (angle, 0, -1)) for (x, angle), y in product(zip(x_range, angle_range), y_range)]
+ with concurrent.futures.ThreadPoolExecutor() as executor:
+ executor.map(lambda beam: beam.cast(), beams)
+
+ if draw:
+ log.info("Drawing beams")
+ for beam in tqdm(Beam._instances):
+ beam.draw()
+
+ pointcloud = np.zeros((len(Beam._instances), 3))
+ i = 0
+ for beam in Beam._instances:
+ if beam.is_hit and beam.is_read:
+ pointcloud[i] = beam.hit_position
+ i += 1
+ pointcloud = pointcloud[:i]
+
+ del Beam._instances[:]
+ return pointcloud
+
+class Beam:
+ """Class for handling the beam of the laser line scanner."""
+ id_iter = itertools.count() # Iterator for assigning unique ids to beams
+ _instances:list[Beam] = [] # List of all instances of this class
+
+ def __init__(self, origin:tuple[float, float, float], direction:tuple[float, float, float]) -> None:
+ self.origin = origin
+ self.direction = direction
+ self.id = next(self.id_iter)
+ self.detector_offset = (0, -65, 0) # The offset of the detector from the origin of the beam
+ self.detector_position = Vector(self.origin) + Vector(self.detector_offset)
+ Beam._instances.append(self)
+
+ def __getitem__(self, id:int) -> Beam:
+ return self._instances[id]
+
+ def cast(self) -> tuple[bool, tuple[float, float, float]]:
+ """Cast the beam and return the result."""
+ reading = bpy.context.scene.ray_cast(bpy.context.view_layer.depsgraph, Vector(self.origin), Vector(self.direction))
+ self.is_hit, self.hit_position, self.hit_normal, self.hit_index, self.hit_object, self.hit_matrix = reading
+
+ # Check if the beam can be seen by the detector
+ if self.is_hit:
+ detector_direction = self.detector_position - Vector(self.hit_position)
+ detector_direction.normalize()
+ reading = bpy.context.scene.ray_cast(bpy.context.view_layer.depsgraph, self.hit_position + Vector((0, 0, 0.001)), detector_direction)
+ self.is_read = not reading[0] # If the beam is blocked by an object
+ if not self.is_read:
+ self.detector_position = reading[1]
+ return reading
+
+ def draw(self) -> None:
+ """Draw the path of the beam and the point where it hits."""
+ if not self.is_hit: return
+ color = green() if self.is_read else red()
+ if self.is_hit:
+ # Draw an icosphere where the beam hits
+ hit_pointer = reference_icosphere()
+ hit_pointer.location = self.hit_position
+ hit_pointer.active_material = color
+ main.group(hit_pointer, f"Laser beams/{self.id}")
+
+ # Draw the beam path
+ emission_beam = main.add_bezier(self.origin, self.hit_position)
+ emission_curve = emission_beam.data
+ emission_curve.dimensions = '3D'
+ emission_curve.bevel_depth = 0.02
+ emission_curve.bevel_resolution = 3
+ emission_curve.materials.append(color)
+ emission_curve.name = "Beam path"
+ main.group(emission_beam, f"Laser beams/{self.id}")
+ reading_beam = main.add_bezier(self.hit_position, self.detector_position)
+ reading_curve = reading_beam.data
+ reading_curve.dimensions = '3D'
+ reading_curve.bevel_depth = 0.02
+ reading_curve.bevel_resolution = 3
+ reading_curve.materials.append(color)
+ reading_curve.name = "Beam path"
+ main.group(reading_beam, f"Laser beams/{self.id}")
+
+def reference_icosphere() -> bpy.types.Object:
+ """Create a reference icosphere and return it."""
+ if "Reference Icosphere" in bpy.data.objects:
+ icosphere = bpy.data.objects["Reference Icosphere"]
+ icosphere = icosphere.copy()
+ return icosphere
+ bpy.ops.mesh.primitive_ico_sphere_add(radius=0.02, enter_editmode=False, location=(0, 0, 0))
+ icosphere = bpy.context.object
+ icosphere.data.name = "Reference Icosphere"
+ try:
+ bpy.context.scene.collection.children["Collection"].objects.unlink(icosphere)
+ except:
+ pass
+
+ icosphere = icosphere.copy()
+ return icosphere
+
+def red():
+ material = bpy.data.materials.new("Red")
+ material.diffuse_color = (1, 0, 0, 1)
+ return material
+
+def green():
+ material = bpy.data.materials.new("Green")
+ material.diffuse_color = (0, 1, 0, 1)
+ return material
\ No newline at end of file
diff --git a/addon/metrics.py b/addon/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..8305d6ae79a0c88405ba51afd188377764fa5414
--- /dev/null
+++ b/addon/metrics.py
@@ -0,0 +1,226 @@
+import os
+import open3d as o3d
+import numpy as np
+from scipy.spatial import KDTree
+from scipy.spatial.transform import Rotation as R
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm.contrib.itertools import product
+sns.set()
+
+from .pointcloud_processing.process_pointclouds import layerize_pointclouds
+from .pointcloud_processing.calibration import prepare_dataset, execute_global_registration, draw_registration_result
+
+def get_samples(translation:np.ndarray=np.array((0, 0, 0)), rotation:float=30, plot: bool = True):
+ square = np.zeros((10, 10))
+ square[3:7, 3:7] = 1
+ straight = np.array(np.where(square == 1))
+ straight = np.vstack((straight, np.zeros((1, len(straight[0])))))
+
+ rotation_matrix = R.as_matrix(R.from_euler('z', rotation, degrees=True))
+ transformed = (rotation_matrix @ straight)
+ transformed = transformed + translation.reshape(3, 1)
+
+ if plot:
+ plot_squares(straight, transformed)
+
+ return straight.T, transformed.T
+
+def get_one_missplaced(deviation:np.ndarray=np.array([-1, 0, 0]), deviation_index:tuple[float, float, float]=[0, 0, 0], plot:bool=True):
+ straight, transformed = get_samples(rotation=0, plot=False)
+ transformed[deviation_index] += deviation
+ if plot:
+ plot_squares(straight, transformed)
+ return straight, transformed
+
+def plot_squares(pcd1:np.ndarray, pcd2:np.ndarray):
+ plt.plot(pcd1[:, 0], pcd1[:, 1], 'o')
+ plt.plot(pcd2[:, 0], pcd2[:, 1], 'o')
+ plt.legend(['straight', 'transformed'])
+
+
+def plot3d(pcls:list[np.ndarray], alpha:float=0.4, size_multiplier:float=1, figsize:tuple[int]=(10, 10)) -> plt.Figure:
+ """Plots a list of point clouds in 3D"""
+ fig = plt.figure(figsize=figsize)
+ fig.tight_layout()
+ ax = fig.add_subplot(projection='3d')
+ if not isinstance(pcls, list):
+ pcls = [pcls]
+ try:
+ s = size_multiplier * 0.06/(pcls[0][:, 0].max()-pcls[0][:, 0].min())
+ except:
+ s = size_multiplier * 0.06
+ for pcl in pcls:
+ if len(pcl) == 0:
+ continue
+ x, y, z = pcl[:, 0], pcl[:, 1], pcl[:, 2]
+ ax.set_box_aspect((np.ptp(x), np.ptp(y), np.ptp(z)))
+ ax.scatter(x, y, z, s=s, marker='o', alpha=alpha)
+ ax.set_xlabel('X')
+ ax.set_ylabel('Y')
+ ax.set_zlabel('Z')
+ return fig
+
+def RMSE(reference:np.ndarray, target:np.ndarray, workers:int=-1) -> float:
+ octree = KDTree(reference)
+ distances, indices = octree.query(target, k=1, p=2, workers=workers)
+ rmse = np.sqrt(np.mean(distances**2))
+ return rmse
+
+def calculate_accuracy(reference:np.ndarray, target:np.ndarray, threshold:float, workers:int=-1) -> tuple[float, np.ndarray]:
+ """Calculates the accuracy of the target point cloud compared to the reference point cloud.
+ If reference and target are swept, metric is called 'completeness'.
+ Returns the accuracy and the indices of the inaccurate points, i.e. the points of target that do not match any of the reference."""
+ octree = KDTree(reference)
+ distances, indices = octree.query(target, k=1, distance_upper_bound=threshold, p=2, workers=workers)
+ accuracy = np.array(distances <= threshold, dtype=int).sum() / len(distances)
+ return accuracy, np.array(range(len(target)))[distances > threshold]
+
+def f1_score(reference:np.ndarray, target:np.ndarray, threshold:float, workers:int=-1, plot_results:bool=False) -> tuple[float, np.ndarray, np.ndarray]:
+ """Calculates the F1 score between two pointclouds. Reference and target are interchangeable.
+ Returns the score, the indices of the inaccurate points of the reference and the indices of the uncomplete points of the target."""
+ accuracy, inaccurate_ids = calculate_accuracy(reference, target, threshold, workers=workers)
+ completeness, uncomplete_ids = calculate_accuracy(target, reference, threshold, workers=workers)
+ score = 2 * (accuracy * completeness) / (accuracy + completeness)
+ if plot_results:
+ inaccurate = target[inaccurate_ids]
+ accurate = target[np.setdiff1d(np.arange(len(target)), inaccurate_ids)]
+ uncomplete = reference[uncomplete_ids]
+ complete = reference[np.setdiff1d(np.arange(len(reference)), uncomplete_ids)]
+ matched = np.concatenate([accurate, complete], axis=0)
+ plot3d([matched, inaccurate, uncomplete])
+ return score, inaccurate_ids, uncomplete_ids
+
+def EMD(reference:np.ndarray, target:np.ndarray, workers:int=-1) -> float:
+ octree = KDTree(target)
+ distances, indices = octree.query(reference, k=1, p=2, workers=workers)
+ return distances.sum()
+
+def average_squared_distance(reference:np.ndarray, target:np.ndarray, neighbours:int=1, workers:int=-1) -> np.ndarray:
+ """Calculates the average squared distance to neighbours of all target's points to the reference"""
+ octree = KDTree(reference)
+ distances, indices = octree.query(target, k=neighbours, p=2, workers=workers)
+ return distances**2 / neighbours
+
+def k_chamf(reference:np.ndarray, target:np.ndarray, neighbours:int=1, workers:float=-1) -> float:
+ """Calculates the k-Nearest Chamfer distance between two point clouds. Reference and target are interchangeable."""
+ first_term = average_squared_distance(reference, target, neighbours, workers=workers)
+ first_term = np.mean(first_term)
+ second_term = average_squared_distance(target, reference, neighbours, workers=workers)
+ second_term = np.mean(second_term)
+ return first_term + second_term
+
+def load_pointcloud(path:str, bed_path:str, roi:list[list[float]]=[[None, None], [None, None], [None, None]], intensity_threshold:int=60, plot:bool=True) -> np.ndarray:
+ """Load the pointcloud from npy file, and layerize it by comparing with its bed.
+ @param path: the path to the npy file of the measurement point cloud
+ @param bed: the path to the npy file of the bed point cloud
+ @param roi: region that will be used. The rest of the part is cut off. Leave it as `None` not to define a limit.
+ Format: [[x0, x1], [y0, y1], [z0, z1]]
+ @param intensity_threshold: the threshold of the intensity of the point cloud. Points with intensity below this threshold will be removed.
+ @param plot: plot the point cloud using matplotlib if requested"""
+ pcl = np.load(path)
+ bed = np.load(bed_path)[:, :3]
+
+ pcl = pcl[pcl[:, 3] > intensity_threshold]
+ pcl = pcl[:, :3]
+ _, pcl = layerize_pointclouds([bed, pcl]) # Layerize pointclouds
+ pcl[:, 2] = -pcl[:, 2] + max(pcl[:, 2]) # Invert z axis
+ pcl = pcl[pcl[:, 2] < 100] # Removing outliers
+ for i in range(3):
+ if roi[i][0]is not None: pcl = pcl[:, i] > roi[i][0]
+ if roi[i][1]is not None: pcl = pcl[:, i] < roi[i][1]
+
+ if plot:
+ plot3d(pcl)
+
+ return pcl
+
+def icp(source:np.ndarray, target:np.ndarray, voxel_size:float=0.5) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ """Performs Iteractive Closest Point registration on two point clouds.
+ The source point cloud is transformed to match the target point cloud.
+ Returns the transformed source point cloud, the target point cloud and the transformation matrix.
+ """
+ threshold = voxel_size * 0.4
+
+ pcd1 = o3d.geometry.PointCloud()
+ pcd1.points = o3d.utility.Vector3dVector(np.reshape(source, (-1, 3)))
+ pcd2 = o3d.geometry.PointCloud()
+ pcd2.points = o3d.utility.Vector3dVector(np.reshape(target, (-1, 3)))
+
+ source_pcl, target_pcl, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(voxel_size, source=pcd1, target=pcd2)
+ result_ransac = execute_global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size)
+
+ result_icp = o3d.pipelines.registration.registration_icp(
+ source_pcl, target_pcl, threshold, result_ransac.transformation,
+ o3d.pipelines.registration.TransformationEstimationPointToPoint(),
+ o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=5000))
+
+ transformation = result_icp.transformation
+ return apply_transformation(source, transformation), target, transformation
+
+def apply_transformation(source:np.ndarray, transformation:np.ndarray) -> np.ndarray:
+ """Applies a transformation matrix to a point cloud"""
+ source = np.hstack((source, np.ones((len(source), 1))))
+ source = transformation @ source.T
+ source = source.T
+ return source[:, :3]
+
+def remove_noise(pointcloud:np.ndarray, threshold:float=0.5, workers:int=-1) -> np.ndarray:
+ """Removes noise from a point cloud. Returns the pointcloud without noise and log the deletion ratio, i.e. the percentage of points that were removed."""
+ basis = KDTree(pointcloud)
+ distances, indices = basis.query(pointcloud, k=[2], p=2, distance_upper_bound=threshold, workers=workers)
+ not_noise = distances[:, 0] < threshold
+ remove_ratio = 1-not_noise.sum()/len(pointcloud)
+ print(f"Removed {remove_ratio*100:.2f}% of the pointcloud")
+ return pointcloud[not_noise]
+
+def load_dataset(path="../data/metrics/measurements") -> list[tuple[str, str]]:
+ files = []
+ for filename in os.listdir(path):
+ if not filename.endswith(".npy") or filename.startswith("bed") or int(filename.split(".")[0][-1]) > 1:
+ continue
+ run = filename.split("_")[1]
+ bed = os.path.join(path, f"bed_before_printing_{run}_0.npy")
+ files.append((bed, os.path.join(path, filename)))
+ return files
+
+def evaluate_metrics(dataset:list[tuple[str, str]], results_path:str="../data/metrics/results") -> None:
+ """Evaluates the metrics on a dataset of point clouds."""
+ os.makedirs(results_path, exist_ok=True)
+ with open(os.path.join(results_path, "results.csv"), "w") as f:
+ f.write("target,source,rmse,f1,emd,chamfer\n")
+ for s, t in product(dataset, dataset):
+ s_file = os.path.split(s[1])[-1]
+ t_file = os.path.split(t[1])[-1]
+ if s == t:
+ continue
+ elif 'printing_0' in s_file or 'printing_0' in t_file: # Ignore the first measurement
+ continue
+ elif 'measurement' not in s_file and 'measurement' not in t_file: # Only compare scenarios to as-is
+ continue
+
+ source = load_pointcloud(s[1], s[0], plot=False)
+ target = load_pointcloud(t[1], t[0], plot=False)
+
+ source = remove_noise(source, 0.2)
+ target = remove_noise(target, 0.2)
+
+ source, _, _ = icp(source, target)
+
+ rmse = RMSE(source, target)
+ f1, _, _ = f1_score(source, target, 0.15, plot_results=False)
+ emd = EMD(source, target)
+ chamfer = k_chamf(source, target)
+
+ with open(os.path.join(results_path, "results.csv"), "a") as f:
+ f.write(",".join((
+ os.path.split(t[1])[-1],
+ os.path.split(s[1])[-1],
+ str(round(rmse, 4)),
+ str(round(f1, 4)),
+ str(round(emd, 4)),
+ str(round(chamfer, 4))
+ )))
+ f.write("\n")
+
+ del source, target
\ No newline at end of file
diff --git a/addon/network.py b/addon/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..627aca4132807db07da6f52fc91a8de7d6c900d9
--- /dev/null
+++ b/addon/network.py
@@ -0,0 +1,136 @@
+import os
+import pickle
+import numpy as np
+from scipy.spatial import KDTree
+from tqdm import tqdm
+from concurrent.futures import ThreadPoolExecutor as Pool
+import logging
+log = logging.getLogger("SmoPa3D")
+
+from .gcode.parser import Gcode
+from .node import Node, Environment
+from .command import Command, Layer, calculate_command_mesh
+from .decorators import runtime, track
+
+class Network:
+ """Class to create and manage Nodes from the given gcode
+ @param gcode_path: path to the gcode file
+ @param resolution: the concentration of the pointcloud per mm
+ @param node_size: the lenght, in mm, of the pointcloud around the node
+ @param filament_thickness: the diameter, in mm, of the filament used in the printing simulation
+ """
+ def __init__(self, gcode_path:str, resolution:float, node_size:float, node_distance:float=0.2, extrusion_multiplier:float=1, saving_path:str='data/simulation/pickle') -> None:
+ log.info('Creating network...')
+ self.saving_path = saving_path
+ os.makedirs(self.saving_path, exist_ok=True)
+
+ self.gcode = Gcode(path=gcode_path)
+ self.env = Environment(resolution=resolution, node_size=node_size)
+ self.node_distance = node_distance
+ self.extrusion_multiplier = extrusion_multiplier
+ self.nodes:list[Node] = []
+ self.commands:dict[int, Command] = {}
+ self.layers:dict[float, Layer] = {}
+ self.coords:list[tuple[float, float, float]] = []
+
+ # Get layer height from gcode
+ self.layer_height = None
+ for command in self.gcode.gcode[:self.gcode.setup_end]:
+ if ';Layer height: ' in command:
+ self.layer_height = float(command.split(';Layer height: ')[1])
+ break
+ if self.layer_height is None:
+ log.warning('Layer height not found in gcode. Using 0.2 mm as default')
+ self.layer_height = 0.2
+
+ self.create_network()
+
+ @runtime
+ def create_network(self) -> None:
+ """Create the nodes from the gcode commands"""
+ empty_commands = 0
+ for (command_id, gcode_command) in enumerate(self.gcode.commands):
+ if gcode_command.m == 109: # Set extruder temperature
+ self.temperature = gcode_command.s
+ if not gcode_command.is_transformable: continue # Only considers commands that extrude filament
+
+ # Create command and nodes
+ command = Command(self, gcode_command, id=command_id)
+ if command.qtd_nodes == 0:
+ if empty_commands < 15: log.warning(f'Command {command.id} has no nodes')
+ empty_commands += 1
+ continue
+
+ self.tree = KDTree(self.coords)
+ log.warning(f'{empty_commands} ({empty_commands/len(self.commands)*100:.2f}%) commands have no nodes, and will not be simulated')
+
+ def get_layer(self, z:float) -> Layer:
+ """Get the layer of the given z coordinate"""
+ if z not in self.layers:
+ self.layers[z] = Layer(self, z)
+ return self.layers[z]
+
+ @runtime
+ def simulate_printer(self, node_limit:int=-1, workers:int=-1, optmize_memory:bool=True) -> None:
+ """Run the calculations of each node
+ @param node_limit: limit the number of nodes to be calculated. If -1, all nodes will be calculated
+ @param workers: number of processes to be used in the calculation. If -1, all available cores will be used
+ @param optmize_memory: if True, the nodes will be saved after each iteration, and the previous layers will be deleted from memory"""
+ log.info('Simulating printer...')
+
+ current_layer = self.nodes[0].layer
+ for (i, node) in enumerate(tqdm(self.nodes[:node_limit])):
+ obstacles = self.calculate_obstacles(node, workers=workers)
+ node.iterate_volume(obstacles)
+ node.save()
+
+ if node.layer != current_layer:
+ if optmize_memory:
+ threshold_in_use = node.layer.z - self.layer_height * 3 # Delete layers that are not in the search radius of the calculate_obstacles function
+ layers_to_wipe = [layer for layer in self.layers.values() if layer.z < threshold_in_use]
+ for layer in layers_to_wipe:
+ layer.wipe_memory()
+ current_layer = node.layer
+
+ @track
+ def calculate_obstacles(self, node:Node, workers:int) -> np.ndarray:
+ search_radius = self.env.node_size / np.sqrt(3)
+ query = self.tree.query_ball_point(node.coord, search_radius, p=2, workers=workers)
+ # Restrict teh search in the z axis to range between the node and 2 layers below
+ neighbours = [self.nodes[x] for x in query if self.nodes[x].z >= node.z - 2 * self.layer_height and self.nodes[x].z <= node.z]
+ return node.obstacles(neighbour_nodes=neighbours) # TODO: processes = workers
+
+ def calculate_meshes(self, processes:int=None) -> None:
+ """Calculate the meshes of each command. Pass processes as 0 not to use multiprocessing"""
+ log.info('Calculating meshes...')
+ if processes == 0:
+ for command in tqdm(self.commands.values()):
+ command.vertices_filepath, command.faces_filepath = calculate_command_mesh(command)
+ else:
+ with Pool(processes) as pool:
+ meshes = list(tqdm(pool.map(calculate_command_mesh, (list(self.commands.values()))), total=len(self.commands)))
+
+ log.info('Assigning meshes to commands...')
+ for command, (vertices_path, faces_path) in zip(self.commands.values(), meshes):
+ command.vertices_filepath = vertices_path
+ command.faces_filepath = faces_path
+
+
+ def save(self, filename:str="network") -> None:
+ """Save the network in a pickle file"""
+ if filename[-4:] != ".pkl": filename += ".pkl"
+ try:
+ with open(os.path.join(self.saving_path, filename), "wb") as fle:
+ pickle.dump(self, fle)
+ log.info(f'Network saved in {os.path.join(self.saving_path, filename)}')
+ except Exception as e:
+ log.warning(f'Could not save network due to {e}')
+
+def load_network(path:str="data/simulation/pickle/network.pkl") -> Network:
+ """Load the network from a pickle file"""
+ with open(path, "rb") as fle:
+ return pickle.load(fle)
+
+if __name__ == '__main__':
+ net = Network('test/sample.gcode', 0.01, 2)
+ net.simulate_printer()
\ No newline at end of file
diff --git a/addon/node.py b/addon/node.py
new file mode 100644
index 0000000000000000000000000000000000000000..4734c7c8f64be53552620a5f850eb81a04386dc6
--- /dev/null
+++ b/addon/node.py
@@ -0,0 +1,266 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+ from .network import Network
+ from .command import Command, Layer
+
+from dataclasses import dataclass
+import os
+import numpy as np
+import logging
+from concurrent.futures import ThreadPoolExecutor as Pool
+from functools import partial
+
+from . import geometry as geo
+
+log = logging.getLogger('SmoPa3D')
+
+@dataclass
+class Environment:
+ resolution: float # Size, in mm, of the edge of the cubic grid unit
+ node_size: float # Size, in mm, of the edge of the cubic volume around the node
+ # Node grid size must be uneven
+
+ def __post_init__(self) -> None:
+ # Create the volume
+ self.node_grid_size = round(self.node_size / self.resolution)
+ if self.node_grid_size % 2 == 0: self.node_grid_size += 1 # Node grid size must be uneven
+ self.volume = np.zeros((self.node_grid_size, self.node_grid_size, self.node_grid_size))
+ self.calculate_nozzle()
+
+ def calculate_nozzle(self, D:float=0.4, angle:float=0.716, height:float=2.5) -> np.array:
+ """Create the 3D model of the nozzle in the environment grid.
+ ----
+ Arguments
+ ----
+ @param D: Diameter of nozzle, in mm
+ @param angle: angle of the trunk of cone of the nozzle, in rad"""
+ z0 = self.node_grid_size // 2 # Tip of the nozzle is always the center of the volume
+ height = height / self.resolution
+ radius = D / (2 * self.resolution)
+ B = 1 / np.tan(angle)
+ A = radius - z0 * B
+ final_diameter = A + (height + z0) * B
+
+ def nozzle_2d(z:int) -> float:
+ """Build the nozzle in 2D.
+ @param z: coordinate in grid units"""
+ if z < z0:
+ return 0
+ elif z < z0 + height:
+ return A + z * B
+ else:
+ return final_diameter
+
+ revolutionized = geo.apply_revolution(nozzle_2d, self.node_grid_size, self.node_grid_size).astype(int)
+ self.nozzle = np.where(revolutionized > 0)
+ self.nozzle = np.array(self.nozzle[::-1]).transpose()
+ return self.nozzle
+
+ def bed(self, node_z:float) -> np.array:
+ """Create the 3D model of the printing bed in the environment grid placing the bed according to the
+ position in z of the node.
+ ----
+ Arguments
+ ----
+ @param node_z: position, in mm, between the bottom of the nozzle in the node (that corresponds to the
+ top of the printed filament)"""
+ z0 = self.node_grid_size//2 - 1 - round(node_z/self.resolution)
+ bed = np.zeros_like(self.volume)
+ if z0 > 0:
+ bed[:z0] = 1
+ return bed
+
+class Node:
+ def __init__(self, network:Network, command:Command, layer:Layer, x:float, y:float, z:float, filament_volume:float) -> None:
+ """@param index: index of the node in the network
+ @param x: Coordinate position of the center of the node
+ @param y: Coordinate position of the center of the node
+ @param z: Coordinate position of the center of the node
+ @param filament_volume: Volume, in mm^3, of the filament deposited in the node"""
+ self.network = network
+ self.command = command
+ self.x = x
+ self.y = y
+ self.z = z
+ self.filament_volume = filament_volume
+ self._placed_filament = None # Voxel reprensenting the volume occupied by the node. It is None until iterate_volume() is computed
+ self._pointcloud = None # Pointcloud of the filament, in (x, y, z) coordinates. It is None until iterate_volume() is computed
+ self.active:bool = True # Set as False when the node is saved and wiped from memory
+ self.simulated:bool = False # Set as True when the node is simulated
+
+ # Update Network to include this node
+ self.network.nodes.append(self)
+ self.index = len(self.network.nodes) - 1
+ self.network.coords.append((self.x, self.y, self.z))
+
+ # Update Command to include this node
+ self.command.nodes.append(self)
+
+ # Update Layer to include this node
+ self.layer = layer
+ self.layer.nodes.append(self)
+
+ @property
+ def coord(self) -> np.ndarray:
+ """(x, y, z)"""
+ return np.array((self.x, self.y, self.z))
+
+ def __repr__(self) -> str:
+ return f"({self.x}, {self.y}, {self.z})"
+
+ def save(self) -> None:
+ """Save the node data in npy files"""
+ if self.active:
+ self.saving_path = os.path.join(self.network.saving_path, 'nodes', f'{self.index}')
+ os.makedirs(self.saving_path, exist_ok=True)
+ np.save(os.path.join(self.saving_path, 'placed_filament.npy'), self.placed_filament)
+ np.save(os.path.join(self.saving_path, 'pointcloud.npy'), self.pointcloud)
+ else:
+ log.warn('Node is already inactive. Cannot save it.')
+
+ def load(self) -> None:
+ """Load the node from the npy files"""
+ if self.saving_path is None:
+ log.warning('Node was not saved before or saving path could not be found. Cannot load it.')
+ return
+
+ self._placed_filament = np.load(os.path.join(self.saving_path, 'placed_filament.npy'), allow_pickle=True)
+ self._pointcloud = np.load(os.path.join(self.saving_path, 'pointcloud.npy'), allow_pickle=True)
+
+ def wipe(self) -> None:
+ """Delete the node from the memory, but keeps it in the network to be loaded back again if necessary"""
+ if not self.active: return
+ del self._placed_filament
+ del self._pointcloud
+ self.active = False
+
+ @property
+ def placed_filament(self) -> np.ndarray:
+ """Voxel reprensenting the volume occupied by the node. If node is inactive, load it from the npy files"""
+ if not self.active:
+ self.load()
+ return self._placed_filament
+
+ @property
+ def pointcloud(self) -> np.ndarray:
+ """Pointcloud of the filament, in (x, y, z) coordinates. If node is inactive, load it from the npy files"""
+ if not self.active:
+ self.load()
+ return self._pointcloud
+
+ def backpropagate_feedrate(self, volume:float=None, constant:float=0.0025726112346777796, feedrate_multiplier:float=1.812969202377806) -> float:
+ """Calculate the feedrate that would be necessary to deposit the given volume of filament
+ @param volume: filament volume, in mm^3
+ @param constant: constant value to calculate the area of the profile. Value retrieved experimentally
+ @param feedrate_multiplier: multiplier value to calculate the area of the profile. Value retrieved experimentally"""
+ area = volume / self.command.trajectory_length * self.command.qtd_nodes / self.network.extrusion_multiplier
+ return (area - constant) / feedrate_multiplier
+ # return volume / (self.command.trajectory_length / self.command.qtd_nodes * np.pi * 1.75 ** 2 / 4 * self.network.extrusion_multiplier)
+
+ def draw_revolutionized_profile(self, ground:int, volume:float=None) -> np.ndarray:
+ """Draw a drop of the profile with the given volume.
+ @param ground: z coordinate of the ground level
+ @param volume: filament volume, in mm^3"""
+ if volume is None:
+ volume = self.filament_volume
+ volume_multiplier = volume / self.filament_volume
+
+ self.applied_feedrate = self.backpropagate_feedrate(volume)
+ self.width = geo.width_model(self.network.temperature, self.applied_feedrate, self.command.speed)
+ self.length = 1.5 * np.cbrt(volume_multiplier) * self.command.trajectory_length / self.command.qtd_nodes
+ self.height = 6 * volume / (np.pi * self.width * self.length)
+
+ h = round(self.height / self.network.env.resolution / 2)
+ w = round(self.width / self.network.env.resolution / 2)
+ l = round(self.length / self.network.env.resolution / 2)
+ grid = self.network.env.volume.copy()
+ if any(np.array(grid.shape) < np.array((w, l, h))):
+ log.warning(f'Node size is too small for node {self.index}. It was cut to fit in the simulation.')
+ center = np.array(grid.shape)//2 - 1
+ # if self.height > self.network.layer_height:
+ # center[0] = ground + (center[0] - ground)//2
+ # else:
+ # center[0] = ground + h
+ center[0] = center[0] - round(self.network.layer_height / self.network.env.resolution / 2)
+ geo.place_ellipsoid(grid, w, l, h, self.command.end - self.command.start, center=center)
+ return grid
+
+ def iterate_volume(self, obstacles:np.ndarray, increment_offset:float=0.1, precision:float=0.1, max_iterations:int=30) -> np.ndarray:
+ """Calculates the volume that results from the intersection between the deposited filament of the node
+ and the environment given by obstacles. Then expands the volume by the interference plus an increment_offset
+ and calculates the volume interference again, until the interference volume reaches a value as low as the
+ precision, or it gets to the max iterations.
+ @param obstacles: an array representing the interacting environment, given by the obstacles method
+ @param increment_offset: the extra volume, in percentage, that is increased during each iteration
+ to get faster to the result
+ @param precision: the acceptable error, in percentage, between the nominal_volume and the actual volume got from the iterations
+ @param max_iterations: the maximum size of the loop that is conducted to get to the final volume"""
+
+ precision = self.filament_volume * precision # Convert the units to mm^3
+
+ virtual_volume = self.filament_volume # In the end of the iterations: virtual volume = self.filament_volume + intersection_volume
+ # ground_level = np.where(obstacles[:obstacles.shape[0]//2, obstacles.shape[1]//2, obstacles.shape[2]//2])[0].max() # Get the ground level of the obstacles
+ for it in range(max_iterations):
+ if virtual_volume < 0:
+ log.warning(f'Negative filament volume in node {self.index}. It is ignored in the simulation.')
+ virtual_volume = 0
+ return None
+ drawn_profile = self.draw_revolutionized_profile(ground=0, volume=virtual_volume)
+ molded_profile = 1*((drawn_profile - obstacles) > 0)
+ molded_volume = molded_profile.sum() * self.network.env.resolution**3
+ diff = self.filament_volume - molded_volume
+
+ if abs(diff) <= precision:
+ break
+ else:
+ virtual_volume += diff * (1 + increment_offset)
+ if it == max_iterations - 1:
+ log.warning(f'Node {self.index} did not converge. Difference of {diff*10**6:.0f} μm^3 ({(diff/self.filament_volume)*100:.2f}% difference) between nominal and actual volume.')
+ self._placed_filament = molded_profile.astype(int)
+ self._pointcloud = np.where(self._placed_filament > 0)
+ self._pointcloud = np.array(self._pointcloud[::-1]).transpose()
+ self.simulated = True
+ return self._placed_filament
+
+ def obstacles(self, neighbour_nodes:list[Node], processes:int=None) -> np.ndarray:
+ """Places all obstacles in the grid, including the nozzle, the bed (if applicable) and the adjacent nodes that already have
+ their volumes calculated.
+ @param neighbour_nodes: list of Node objects that must be considered in the surroundings of the node"""
+ if processes is not None and processes == 0:
+ neighbours_pts = np.empty((0, 3))
+ for nbr in neighbour_nodes:
+ neighbours_pts = np.concatenate([neighbours_pts, place_obstacle(self, nbr)])
+ else:
+ with Pool(max_workers=processes) as pool:
+ self_place_obstacle = partial(place_obstacle, self)
+ neighbours_pts = np.concatenate(list(pool.map(self_place_obstacle, neighbour_nodes)))
+ filter_in_boundaries = np.all((neighbours_pts >= 0) & (neighbours_pts < self.network.env.node_grid_size), axis=1)
+ neighbours = assign_values(self.network.env.volume.copy(), neighbours_pts[filter_in_boundaries])
+ return (self.network.env.bed(self.z) + neighbours) > 0
+
+def place_obstacle(main_node:Node, obstacle_node:Node) -> np.ndarray:
+ neighbours_pts = np.empty((0, 3))
+ shift_vector = np.around((obstacle_node.coord - main_node.coord)/main_node.network.env.resolution)
+ if any(np.abs(shift_vector) > main_node.network.env.node_grid_size): return np.empty((0, 3)) # Ignore nodes that are too far away (more than the node grid size)
+ if obstacle_node.z == main_node.z and (obstacle_node in main_node.command.nodes or obstacle_node.placed_filament is None): # Add the nozzle of the next and last nodes even if they have not been placed yet
+ new_neighbour = obstacle_node.network.env.nozzle + shift_vector
+ neighbours_pts = np.concatenate([neighbours_pts, new_neighbour])
+ if not obstacle_node.placed_filament is None: # if the filament has been placed, add it to the neighbours_pts
+ new_neighbour = obstacle_node.pointcloud + shift_vector
+ neighbours_pts = np.concatenate([neighbours_pts, new_neighbour])
+ return neighbours_pts
+
+def assign_values(volume, neighbours_pts) -> np.ndarray:
+ """Assigns 1 to the neighbours_pts in the volume array.\n
+ Works exactly as `volume[neighbours_pts] = 1` but more efficient."""
+ ravel = np.ravel_multi_index(neighbours_pts[:, [2, 1, 0]].astype(int).T, volume.shape)
+ np.put(volume, ravel, 1)
+ return volume
+
+def join_nodes(main_node:Node, obstacle_node:Node):
+ """Join the pointclouds of two nodes, placing the obstacle_node in the position relative to the main_node"""
+ if obstacle_node.placed_filament is None: return np.empty((0, 3))
+ shift_vector = np.around((obstacle_node.coord - main_node.coord)/main_node.network.env.resolution)
+ new_neighbour = obstacle_node.pointcloud + shift_vector
+ return new_neighbour
\ No newline at end of file
diff --git a/addon/pointcloud.py b/addon/pointcloud.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b99fb7629e11589921b24e2f8f307413fe3011b
--- /dev/null
+++ b/addon/pointcloud.py
@@ -0,0 +1,64 @@
+import random
+import numpy as np
+from .utils import running_in_blender
+if running_in_blender:
+ import bpy
+ import bmesh
+
+
+class PointCloud:
+ """Class for handling pointclouds."""
+ def __init__(self, path:str) -> None:
+ self.pcl:np.ndarray = np.load(path)
+ self.correct_z_axis()
+
+ def correct_z_axis(self) -> float:
+ """Invert z axis, then get the height of the bed from the pointcloud and move pointcloud so that the bed is at z=0."""
+ self.pcl[:, 2] = -self.pcl[:, 2]
+ z_values = self.pcl[:, 2].copy()
+ z_values.sort()
+ mode = z_values[int(len(z_values)*0.4):int(len(z_values)*0.6)]
+ bed_z = mode.mean() - 5* mode.std()
+
+ self.pcl[:, 2] = self.pcl[:, 2] - bed_z
+ return bed_z
+
+ def crop_ROI(self, x:tuple[float, float]=(None, None), y:tuple[float, float]=(None, None), z:tuple[float, float]=(None, None)) -> np.ndarray:
+ """Crop the pointcloud to the region of interest."""
+ if x[0] is not None: self.pcl = self.pcl[self.pcl[:, 0] > x[0]]
+ if x[1] is not None: self.pcl = self.pcl[self.pcl[:, 0] < x[1]]
+ if y[0] is not None: self.pcl = self.pcl[self.pcl[:, 1] > y[0]]
+ if y[1] is not None: self.pcl = self.pcl[self.pcl[:, 1] < y[1]]
+ if z[0] is not None: self.pcl = self.pcl[self.pcl[:, 2] > z[0]]
+ if z[1] is not None: self.pcl = self.pcl[self.pcl[:, 2] < z[1]]
+ return self.pcl
+
+ def downsample(self, select_rate:float=0.05) -> np.ndarray:
+ # Decrease amount of points
+ sample = random.sample(range(len(self.pcl)), int(len(self.pcl) * select_rate))
+ self.pcl = self.pcl[sample]
+ return self.pcl
+
+ def place_pointcloud(self, name:str, parent:bpy.types.Object, move:tuple[float, float, float]=None) -> bpy.types.Object:
+ """Plot pointcloud given by x, y and z data in blender as points"""
+ mesh_data = bpy.data.meshes.new(name)
+ bm = bmesh.new()
+
+ for v in self.pcl[:, :3]:
+ bm.verts.new(v)
+
+ bm.to_mesh(mesh_data)
+ mesh_obj = bpy.data.objects.new(mesh_data.name, mesh_data)
+
+ parent.parent = mesh_obj
+ parent.parent_type = 'OBJECT'
+
+ # Move pointcloud
+ if move is not None:
+ mesh_obj.location = move
+
+ # Visuals
+ mesh_obj.display.show_shadows = False
+ mesh_obj.show_all_edges = False
+ mesh_obj.show_instancer_for_viewport = False
+ return mesh_obj
\ No newline at end of file
diff --git a/addon/pointcloud_processing/PCL.py b/addon/pointcloud_processing/PCL.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ae7147ba3f5c6ed885e25d98f0bba8acc66368f
--- /dev/null
+++ b/addon/pointcloud_processing/PCL.py
@@ -0,0 +1,756 @@
+import logging
+from pprint import pformat
+import math
+import numpy as np
+import numpy.matlib
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from scipy.linalg import expm, norm
+import scipy.stats as stats
+import os
+import plotly.graph_objs as go
+from .utils import bundle_adjust
+import peakutils as peakutils
+from skimage.transform import (hough_line, hough_line_peaks)
+from skimage.feature import canny
+from skimage.morphology import opening, closing
+from sklearn.cluster import KMeans
+import open3d as o3d
+
+# TODO: recycle this code into a helpers module or class
+
+log = logging.getLogger('SmoPa3D')
+
+def histogram1d(data, bins='auto'):
+ n, bins, patches = plt.hist(data, bins, facecolor='blue', alpha=0.5, density=True)
+ return n, bins, patches
+
+
+def segmentation(heatmap, threshold):
+ return np.where(heatmap > threshold, 1.0, 0.0)
+
+
+def to_grayscale(heatmap):
+ maxGray = max(heatmap.flatten())
+ return np.uint8(heatmap / maxGray * 255)
+
+
+def bin_centers(binedges):
+ return [(binedges[idx]+binedges[idx+1])/2 for idx in range(len(binedges[:-1]))]
+
+
+def cluster(points, n_clusters=10):
+ kmeans = KMeans(n_clusters=n_clusters)
+ kmeans.fit(points)
+ cluster_centers = kmeans.cluster_centers_
+ labels = kmeans.labels_
+ return cluster_centers, labels
+
+
+def to_parameterform(angle, distance):
+ x0 = distance * np.cos(angle)
+ y0 = distance * np.sin(angle)
+ x1 = x0 - distance * np.sin(angle)
+ y1 = y0 + distance * np.cos(angle)
+ return x0, x1, y0, y1
+
+
+def intersection(x1, y1, x2, y2, x3, y3, x4, y4):
+ px = ((x1*y2-y1*x2)*(x3-x4)-(x1-x2)*(x3*y4-y3*x4)) / ((x1-x2)*(y3-y4)-(y1-y2)*(x3-x4))
+ py = ((x1*y2-y1*x2)*(y3-y4)-(y1-y2)*(x3*y4-y3*x4)) / ((x1-x2)*(y3-y4)-(y1-y2)*(x3-x4))
+ return [px, py]
+
+
+def remove_outliers(intersections, centers, labels, min_Npoints=3):
+ # remove outlier (clusters with less than min_Npoints in the cluster intersections)
+ sorted_labels = labels.argsort()
+ labels = labels[sorted_labels]
+ unique, counts = np.unique(labels, return_counts=True)
+ occurences = dict(zip(unique, counts))
+
+ bad_clusters = [key for key, val in occurences.items() if val < min_Npoints]
+
+ for bad_cluster in bad_clusters:
+ bad_cluster_idx = np.nonzero(labels == bad_cluster)
+ labels = np.delete(labels, bad_cluster_idx)
+ intersections = np.delete(intersections, bad_cluster_idx, axis=0)
+ centers = np.delete(centers, bad_clusters, axis=0)
+ return intersections, centers, labels
+
+
+def apply_hough_transform(heatmap, useEdges=True):
+ grayscale = opening(heatmap.T)
+ if useEdges:
+ grayscale = canny(grayscale/255.)
+ grayscale = closing(grayscale, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]))
+ grayscale = np.where(grayscale != 0.0, 255, 0)
+ h, theta, d = hough_line(grayscale)
+ return grayscale, h, theta, d
+
+
+def find_intersections(angles, distances, xBins, yBins, num_xbins, num_ybins):
+ intX = []
+ intY = []
+ for angle, dist in zip(angles, distances):
+ x1, x2, y1, y2 = to_parameterform(angle, dist)
+ for angle2, dist2 in zip(angles, distances):
+ x0 = 0
+ y0 = 0
+ if angle != angle2 and dist != dist2:
+ x3, x4, y3, y4 = to_parameterform(angle2, dist2)
+ X, Y = intersection(x1, y1, x2, y2, x3, y3, x4, y4)
+ xIntersect = X
+ yIntersect = Y
+ intX.append(xIntersect)
+ intY.append(yIntersect)
+ else:
+ continue
+ return np.array([intX, intY]).T
+
+
+class LaserscannerSystem:
+ """
+ Class representing the system of two laserscanners.
+
+ Functionality:
+ - trigger the data acquisition
+ - extract the pointClouds from the file given by the laser sensors
+ """
+
+ def __init__(self, pcl1, pcl2):
+ self.cloud1 = self.format_cloud(pcl1)
+ self.cloud2 = self.format_cloud(pcl2)
+ self.cloud1.name = "PCL1"
+ self.cloud2.name = "PCL2"
+ self.merged_cloud = self.join_clouds()
+ self.calibration_parameters = None
+
+ def add_calibration_parameters(self, filename):
+ import json
+ with open(filename, 'r') as json_file:
+ json_data = json_file.read()
+ self.calibration_parameters = json.loads(json_data)
+
+ def calibrate_pcls(self):
+ if self.calibration_parameters is not None:
+ log.info("Starting calibration")
+
+ self.merged_cloud.projection2D('x', 'y', suffix='before_calibration')
+ self.merged_cloud.projection2D('x', 'z', suffix='before_calibration')
+ self.merged_cloud.projection2D('y', 'z', suffix='before_calibration')
+
+ self.cloud1.shift('z', float(self.calibration_parameters['delta1_z']))
+ self.cloud2.shift('z', float(self.calibration_parameters['delta2_z']))
+
+ self.cloud1.rotate('x', 'z', float(self.calibration_parameters['theta1_xz']))
+ self.cloud1.rotate('y', 'z', float(self.calibration_parameters['theta1_yz']))
+ self.cloud2.rotate('x', 'z', float(self.calibration_parameters['theta2_xz']))
+ self.cloud2.rotate('y', 'z', float(self.calibration_parameters['theta2_yz']))
+
+ self.cloud1.shift('x', float(self.calibration_parameters['delta1_x']))
+ self.cloud1.shift('y', float(self.calibration_parameters['delta1_y']))
+ self.cloud1.rotate('x', 'y', float(self.calibration_parameters['theta1_xy']))
+
+ self.cloud2.shift('x', float(self.calibration_parameters['delta2_x']))
+ self.cloud2.shift('y', float(self.calibration_parameters['delta2_y']))
+ self.cloud2.rotate('x', 'y', float(self.calibration_parameters['theta2_xy'])-np.pi)
+
+ self.cloud1.update()
+ self.cloud2.update()
+
+ self.merged_cloud = self.join_clouds()
+ self.merged_cloud.show3DCloud(points=157, suffix="beforeCalib")
+
+ self.cloud2.move_rotate(np.array(self.calibration_parameters['R'])*0, np.array(self.calibration_parameters['T']))
+
+ self.merged_cloud = self.join_clouds()
+ self.merged_cloud.show3DCloud(points=157, suffix="afterCalib")
+ self.merged_cloud.projection2D('x', 'y', suffix='after_calibration')
+ self.merged_cloud.projection2D('x', 'z', suffix='after_calibration')
+ self.merged_cloud.projection2D('y', 'z', suffix='after_calibration')
+
+ def format_cloud(self, cloud):
+ return PointCloud(cloud)
+
+ def join_clouds(self):
+ return PointCloud(np.concatenate([self.cloud1._data, self.cloud2._data]), name="MergedCloud")
+
+ def calibrate(self):
+ calibration = Calibration(self.cloud1, self.cloud2)
+
+ # normalize to the minimum in z direction
+ print("Normalize to minimum")
+ calibration.normalize_to_minimum('z')
+
+ # correct the tilt in z direction
+ print("Correct the tilt in z direction")
+ calibration.z_leveling()
+
+ # shift both point clouds to bed level. This should be where the highest point density is found
+ print("Bed Leveling")
+ calibration.bed_leveling()
+ self.cloud1.clean('z', 0.5, 1)
+ self.cloud1.clean('y', 20, 100)
+ self.cloud1.clean('x', 10, 40)
+ self.cloud2.clean('z', 0.5, 1)
+
+ self.merged_cloud = self.join_clouds()
+
+ # for the calibration the assumption is, that only very narrow parts are printed
+ # any noisy artifacts should be removed
+
+ self.cloud1.clean('z', -2, 1)
+ self.cloud2.clean('z', -2, 1)
+
+
+ # extract a region around the floor with a minimum height of 0.5mm for the calibration in the x-y plane
+ subcloud1 = self.cloud1.extract_subcloud('z', 0.5, 1)
+ subcloud1.name = "Sub1"
+ subcloud2 = self.cloud2.extract_subcloud('z', 0.5, 1)
+ subcloud2.name = "Sub2"
+
+ # find the origin of the calibration structure by clustering of heap points of hough lines
+ angle_xy1, center1 = Calibration.calibrateXY(subcloud1, num_xbins=300, num_ybins=300, useEdges=False, min_distance=3)
+ angle_xy2, center2 = Calibration.calibrateXY(subcloud2, num_xbins=300, num_ybins=300, useEdges=False, min_distance=2, min_Npoints=3)
+
+ self.cloud1.shift('x', center1[0])
+ self.cloud1.shift('y', center1[1])
+
+ self.cloud2.shift('x', center2[0])
+ self.cloud2.shift('y', center2[1])
+
+ self.cloud1.rotate('x', 'y', angle_xy1)
+ self.cloud2.rotate('x', 'y', angle_xy2+np.pi)
+
+ R, T = calibration.RBFMerge()
+
+ calibration.calibration_parameters['theta1_xy'] = angle_xy1
+ calibration.calibration_parameters['theta2_xy'] = angle_xy2
+ calibration.calibration_parameters['delta1_x'] = center1[0]
+ calibration.calibration_parameters['delta1_y'] = center1[1]
+
+ calibration.calibration_parameters['delta2_x'] = center2[0]
+ calibration.calibration_parameters['delta2_y'] = center2[1]
+
+ calibration.calibration_parameters['R'] = R.tolist()
+ calibration.calibration_parameters['T'] = T.tolist()
+
+ self.cloud2.move_rotate(R, T)
+
+ calibration.save_parameters_to_file("data/Calibration_Parameters.txt")
+
+
+class Calibration:
+ """
+ Class to takes two point clouds and performs processing steps to determine
+ the 3 rotational angles and the three shifts between two point clouds
+
+ Parameters:
+ - 2 Point cloud objects (probably numpy arrays)
+ - Calibration parameters
+
+ Functionality:
+ - Perform normalizations
+ - Save and visualize the Point clouds
+ - calculate the angles and shifts
+ - save/ return the calibration parameters
+ """
+ def __init__(self, cloud1, cloud2):
+ self.cloud1 = cloud1
+ self.cloud2 = cloud2
+ self.suffix = ""
+ self.calibration_parameters = {'delta1_x': None, 'delta2_x': None, 'delta1_y': None, 'delta2_y': None, 'delta1_z': None, 'delta2_z': None, 'theta1_xy': None, 'theta2_xy': None, 'theta1_xz': None, 'theta2_xz': None, 'theta1_yz': None, 'theta2_yz': None, 'R': None, 'T': None}
+
+ def save_parameters_to_file(self, filename):
+ import json
+ if not os.path.exists(os.path.dirname(filename)):
+ os.mkdir(os.path.dirname(filename))
+ with open(filename, 'w') as json_file:
+ dict_json = json.dump(self.calibration_parameters, json_file)
+
+ def normalize_to_minimum(self, dim):
+ min1 = min(self.cloud1.dim[dim])
+ min2 = min(self.cloud2.dim[dim])
+ self.cloud1.dim[dim] -= min1
+ self.cloud2.dim[dim] -= min2
+ self.suffix = ''.join([self.suffix, 'NZ'])
+
+ if self.calibration_parameters['delta1_z'] is None:
+ self.calibration_parameters['delta1_z'] = min1
+ else:
+ self.calibration_parameters['delta1_z'] += min1
+
+ if self.calibration_parameters['delta2_z'] is None:
+ self.calibration_parameters['delta2_z'] = min2
+ else:
+ self.calibration_parameters['delta2_z'] += min2
+
+ self.cloud1.update()
+ self.cloud2.update()
+
+ def z_leveling(self):
+ self.suffix = ''.join([self.suffix, 'ZL'])
+
+ num_xbins = 201
+ num_ybins = 801
+ self.cloud1.projection2D('x', 'z', suffix="before_zleveled")
+
+ ##log.info("Requiring threshold for segmentation")
+ ##threshold = float(input("Enter threshold value according to control plot: "))
+ theta_xz, intercept = self.get_rotational_angle(self.cloud1, 'x', 'z', num_xbins, num_ybins, suffix=self.suffix, name=self.cloud1.name)
+ self.cloud1.rotate('x', 'z', theta_xz, 0, intercept)
+ self.cloud1.correct_projection('x', theta_xz, min(self.cloud1.dim['x']))
+ self.cloud1.dim['z'] -= intercept
+
+ self.calibration_parameters['theta1_xz'] = theta_xz
+ if self.calibration_parameters['delta1_z'] is None:
+ self.calibration_parameters['delta1_z'] = intercept
+ else:
+ self.calibration_parameters['delta1_z'] += intercept
+ self.cloud1.update()
+
+ self.cloud1.projection2D('x', 'z', suffix="after_zleveled")
+ self.cloud1.projection2D('y', 'z', suffix="before_zleveled")
+
+ ##log.info("Requiring threshold for segmentation")
+ ##threshold = float(input("Enter threshold value according to control plot: "))
+ theta_yz, intercept = self.get_rotational_angle(self.cloud1, 'y', 'z', num_xbins, num_ybins, suffix=self.suffix, name=self.cloud1.name)
+ self.cloud1.rotate('y', 'z', theta_yz, 0, intercept)
+ self.cloud1.correct_projection('y', theta_yz, min(self.cloud1.dim['y']))
+
+ self.cloud1.dim['z'] -= intercept
+ self.calibration_parameters['theta1_yz'] = theta_yz
+ if self.calibration_parameters['delta1_z'] is None:
+ self.calibration_parameters['delta1_z'] = intercept
+ else:
+ self.calibration_parameters['delta1_z'] += intercept
+ self.cloud1.update()
+
+ self.cloud1.projection2D('y', 'z', suffix="after_zleveled")
+ self.cloud2.projection2D('x', 'z', suffix="before_zleveled")
+
+
+ ##log.info("Requiring threshold for segmentation")
+ ##threshold = float(input("Enter threshold value according to control plot: "))
+ theta_xz, intercept = self.get_rotational_angle(self.cloud2, 'x', 'z', num_xbins, num_ybins, suffix=self.suffix, name=self.cloud2.name)
+ self.cloud2.rotate('x', 'z', theta_xz, 0, intercept)
+ self.cloud2.correct_projection('x', theta_xz, min(self.cloud2.dim['x']))
+
+ self.cloud2.dim['z'] -= intercept
+ self.calibration_parameters['theta2_xz'] = theta_xz
+ if self.calibration_parameters['delta2_z'] is None:
+ self.calibration_parameters['delta2_z'] = intercept
+ else:
+ self.calibration_parameters['delta2_z'] += intercept
+ self.cloud2.update()
+
+ self.cloud2.projection2D('x', 'z', suffix="after_zleveled")
+ self.cloud2.projection2D('y', 'z', suffix="before_zleveled")
+
+ ##log.info("Requiring threshold for segmentation")
+ ##threshold = float(input("Enter threshold value according to control plot: "))
+ theta_yz, intercept = self.get_rotational_angle(self.cloud2, 'y', 'z', num_xbins, num_ybins, suffix=self.suffix, name=self.cloud2.name)
+ self.cloud2.rotate('y', 'z', theta_yz, 0, intercept)
+ self.cloud2.correct_projection('y', theta_yz, min(self.cloud2.dim['y']))
+ self.cloud2.dim['z'] -= intercept
+ self.calibration_parameters['theta2_yz'] = theta_yz
+ if self.calibration_parameters['delta2_z'] is None:
+ self.calibration_parameters['delta2_z'] = intercept
+ else:
+ self.calibration_parameters['delta2_z'] += intercept
+ self.cloud2.update()
+
+ ##self.cloud2.projection2D('y', 'z', suffix="after_zleveled")
+
+
+ def bed_leveling(self):
+ self.cloud1.normalize_hist_to_maximum('z', 800)
+ self.cloud2.normalize_hist_to_maximum('z', 800)
+ self.cloud1.update()
+ self.cloud2.update()
+
+ def get_rotational_angle(self, cloud, dim1, dim2, num_xbins, num_ybins, xlims=None, ylims=None, suffix=None, name=None):
+ # segmentation
+
+ ybins = None
+ xbins = None
+ """
+ ##grayscale, xbins, ybins = cloud.projection2D(dim1, dim2, num_xbins=num_xbins, num_ybins=num_ybins, suffix=suffix, xlims=None, ylims=None)
+ cleaned_grayscale = np.where(grayscale > threshold, 1.0, 0.0)
+ ##extent = [xbins[0], xbins[-1], ybins[0], ybins[-1]]
+
+ # get the relevant points for the fit
+ x_Reg = np.repeat(xbins[:-1], cleaned_grayscale.shape[1])
+ y_Reg = np.tile(ybins[:-1], cleaned_grayscale.shape[0])*cleaned_grayscale.flatten()
+
+ relevant_points = np.where(cleaned_grayscale.flatten() != 0.0, True, False)
+
+ x_Reg = x_Reg[relevant_points]
+ y_Reg = y_Reg[relevant_points]
+ """
+ x_Reg = cloud.dim[dim1]
+ y_Reg = cloud.dim[dim2]
+
+ if ybins is None:
+ ybins = np.linspace(min(y_Reg), max(y_Reg), num_ybins)
+ if xbins is None:
+ xbins = np.linspace(min(x_Reg), max(x_Reg), num_xbins)
+
+ # Do the fit
+ if np.all(x_Reg == x_Reg[0]):
+ slope= 0
+ intercept = 0
+ else:
+ slope, intercept, r_value, p_value, std_err = stats.linregress(x_Reg, y_Reg)
+
+
+ def line(x, slope, intercept):
+ return intercept + slope*x
+ y_Fit = line(xbins, slope, intercept)
+
+ # Now correct the full data and return a control plot
+ angle = np.arctan(slope)
+ ##log.info("angle: {ANGLE}".format(ANGLE=angle))
+ """
+ plt.plot(x_Reg, y_Reg, 'o', label='original data')
+ plt.plot(x_Reg, intercept + slope*x_Reg, 'r', label='fitted line')
+ plt.legend()
+ plt.show()
+ """
+
+ return angle, intercept
+
+ @staticmethod
+ def calibrateXY(cloud, num_xbins=300, num_ybins=300, suffix="", min_distance=2, useEdges=True, min_Npoints=5, chosenCenter=None):
+ find_centers = True
+ get_angle = True
+ print("Find centers for shifting")
+ while find_centers or get_angle:
+
+ xbins = np.linspace(min(cloud.dim['x'])-10, max(cloud.dim['x'])+10, num_xbins)
+ ybins = np.linspace(min(cloud.dim['y'])-10, max(cloud.dim['y'])+10, num_ybins)
+ grayscale, xbins, ybins = cloud.projection2D('x', 'y', xbins=xbins, ybins=ybins, num_xbins=num_xbins, num_ybins=num_ybins, suffix=suffix, xlims=None, ylims=None)
+
+ # calculate scale to turn values back into mm
+ min_y = min(ybins)
+ min_x = min(xbins)
+ m_y = (max(ybins) - min(ybins))/num_ybins
+ m_x = (max(xbins) - min(xbins))/num_xbins
+
+ grayscale = to_grayscale(grayscale)
+
+ xCenters = bin_centers(xbins)
+ yCenters = bin_centers(ybins)
+
+ extent = [xCenters[0], xCenters[-1], yCenters[0], yCenters[-1]]
+
+ useEdges = input("Apply hough transform to edges? Y/N: ")
+ if useEdges == "Y":
+ useEdges = True
+ else:
+ useEdges = False
+
+ min_distance = int(input("Choose a minimum distance (Default: {MIN_DISTANCE}): ".format(MIN_DISTANCE=min_distance)))
+
+ grayscale, h, theta, d = apply_hough_transform(grayscale, useEdges=useEdges)
+
+ fig, axes = plt.subplots(1, 2, figsize=(15, 6))
+ ax = axes.ravel()
+ ax[0].imshow(grayscale, cmap=cm.viridis, origin='lower')
+ ax[0].set_title('Input')
+ ax[1].imshow(grayscale, cmap=cm.viridis, origin='lower')
+
+ angles = []
+ angles_mm = []
+ distances = []
+ intersections = []
+ hough_lines = {'x0': [], 'x1': []}
+ for _, angle, dist in zip(*hough_line_peaks(h, theta, d, min_distance=min_distance)):
+ if angle < 0:
+ angle += 2*np.pi
+
+ angles.append(angle)
+ distances.append(dist)
+ y0 = (dist - 0 * np.cos(angle)) / np.sin(angle)
+ y1 = (dist - grayscale.shape[1] * np.cos(angle)) / np.sin(angle)
+ x0 = 0
+ x1 = grayscale.shape[1]
+ hough_lines['x0'].append((m_x*x0+min_x, m_y*y0+min_y))
+ hough_lines['x1'].append((m_x*x1+min_x, m_y*y1+min_y))
+ ax[1].plot((x0, x1), (y0, y1), '-r', zorder=1)
+
+ ax[1].set_xlim(-10, num_xbins+10)
+ ax[1].set_ylim(-10, num_ybins+10)
+ ax[1].set_title('Hough_lines')
+
+ intersections = find_intersections(angles, distances, xCenters, yCenters, num_xbins, num_ybins)
+
+ centers, labels = cluster(intersections)
+ min_Npoints = int(input("Choose a minimum intersection points to find suited clusters (Default: {MIN_POINTS}): ".format(MIN_POINTS=min_Npoints)))
+
+ intersections, centers, labels = remove_outliers(intersections, centers, labels, min_Npoints=min_Npoints)
+
+ for idx, center in enumerate(centers):
+ ax[1].scatter(center[0], center[1], marker="o", color="g", zorder=11)
+ ax[1].text(center[0]+5, center[1]+5, str(idx), color="g")
+ if not os.path.exists("figures"):
+ os.mkdir("figures")
+
+ plt.savefig("figures/{NAME}{SUFFIX}_houghlines_x_y.png".format(NAME=cloud.name, SUFFIX="_{SUFFIX}".format(SUFFIX=suffix if suffix is not None else "")), format="png")
+
+ fig, ax2 = plt.subplots(1, 1, figsize=(15, 10))
+ ax2.imshow(grayscale, cmap=cm.viridis, origin='lower')
+ ax2.scatter(intersections[:, 0], intersections[:, 1], marker="+", color="b", zorder=10, s=50)
+ ax2.set_xlim(-10, num_xbins)
+ ax2.set_ylim(-10, num_ybins)
+ for idx, center in enumerate(centers):
+ ax2.scatter(center[0], center[1], marker="o", color="g", zorder=11)
+ ax2.text(center[0]+5, center[1]+5, str(idx), color="g")
+ plt.tight_layout()
+ plt.savefig("figures/{NAME}{SUFFIX}_intersections_x_y.png".format(NAME=cloud.name, SUFFIX="_{SUFFIX}".format(SUFFIX=suffix if suffix is not None else "")), format="png")
+
+ find_centers_input = input("Centers and hough lines, ok? Y/N: ")
+ if (find_centers_input == 'Y' and find_centers):
+ find_centers = False
+ # transform from bins to mm
+ centers[:, 0] *= m_x
+ centers[:, 0] += min_x
+ centers[:, 1] *= m_y
+ centers[:, 1] += min_y
+
+ # Let user decide which center to take and shift data accordingly
+ log.info(pformat(centers))
+ id_center = int(input("Enter index to use for coordinate origin for cloud: "))
+ origin = centers[id_center]
+ log.info("{CENTER} was chosen".format(CENTER=origin))
+
+ log.info("--- shift original cloud to new origin")
+ cloud.shift('x', origin[0])
+ cloud.shift('y', origin[1])
+ suffix += "C"
+ log.info("--- done shifting to new origin")
+
+ elif (find_centers_input == 'Y' and not find_centers):
+ # Take the endpoint of one hough line to get the rotational angle for align the two clouds
+ get_angle = False
+ print(pformat(hough_lines['x0']))
+ id_hline = int(input("Enter index to use for line end for rotating subcloud1: "))
+ hline = hough_lines['x0'][id_hline]
+ print("{LINE} was chosen".format(LINE=hline))
+
+ x, y = hline
+ angle_xy = math.atan2(y, x)
+ # cloud.rotate('x', 'y', angle_xy)
+ cloud.projection2D('x', 'y', suffix="sdf")
+
+ else:
+ log.info("Repeat calculation of hough lines and clustering")
+ find_centers = True
+
+ return angle_xy, origin
+
+ def RBFMerge(self):
+ top_cloud1 = self.cloud1.extract_subcloud('z', 0.1, 5)
+ top_cloud2 = self.cloud2.extract_subcloud('z', 0.1, 5)
+ data_top_cloud1 = top_cloud1._data
+ data_top_cloud2 = top_cloud2._data
+
+ # Take random samples because cloud sizes have to be equal (n of entries)
+ size = min([data_top_cloud1.shape[0], data_top_cloud2.shape[0]])
+ print(size)
+
+ rand_data_cloud1 = data_top_cloud1 #np.empty(shape=(size,3))
+ rand_data_cloud2 = data_top_cloud2 # np.empty(shape=(size,3))
+ # for i in range(0, size):
+ # rand_data_cloud1[i] = random.choice(data_top_cloud1)
+ # rand_data_cloud2[i] = random.choice(data_top_cloud2)
+
+ # Points have to be in order for algorithm, norm allows sorting
+ sort_rand_cloud1 = np.append(rand_data_cloud1, np.empty(shape=(rand_data_cloud1.shape[0],1)), axis = 1)
+ sort_rand_cloud2 = np.append(rand_data_cloud2, np.empty(shape=(rand_data_cloud2.shape[0],1)), axis = 1)
+ for i in range(0, sort_rand_cloud1.shape[0]):
+ sort_rand_cloud1[i,3] = np.linalg.norm(sort_rand_cloud1[i,0:2])
+ for i in range(0, sort_rand_cloud2.shape[0]):
+ sort_rand_cloud2[i,3] = np.linalg.norm(sort_rand_cloud2[i,0:2])
+
+ # Conversion to tuples only to allow sorting
+ dtype_cloud_tuples = [('x', float), ('y', float), ('z', float), ('norm', float)]
+ tuples_sort_cloud1 = np.empty(sort_rand_cloud1.shape[0], dtype=dtype_cloud_tuples)
+ tuples_sort_cloud2 = np.empty(sort_rand_cloud2.shape[0], dtype=dtype_cloud_tuples)
+ for i in range(0, tuples_sort_cloud1.shape[0]):
+ tuples_sort_cloud1[i] = (sort_rand_cloud1[i,0],sort_rand_cloud1[i,1],\
+ sort_rand_cloud1[i,2],sort_rand_cloud1[i,3])
+ for i in range(0, tuples_sort_cloud2.shape[0]):
+ tuples_sort_cloud2[i] = (sort_rand_cloud2[i,0],sort_rand_cloud2[i,1],\
+ sort_rand_cloud2[i,2],sort_rand_cloud2[i,3])
+
+ sorted_cloud1 = np.array(tuples_sort_cloud1,dtype=dtype_cloud_tuples)
+ sorted_cloud2 = np.array(tuples_sort_cloud2,dtype=dtype_cloud_tuples)
+ sorted_cloud1.sort(order='norm')
+ sorted_cloud2.sort(order='norm')
+
+ # Restore original arrays to allow algorithm to work
+ cloud1_final = np.empty((sorted_cloud1.shape[0],3))
+ cloud1_final[:,0] = sorted_cloud1['x']
+ cloud1_final[:,1] = sorted_cloud1['y']
+ cloud1_final[:,2] = sorted_cloud1['z']
+
+
+ cloud2_final = np.empty((sorted_cloud2.shape[0],3))
+ cloud2_final[:,0] = sorted_cloud2['x']
+ cloud2_final[:,1] = sorted_cloud2['y']
+ cloud2_final[:,2] = sorted_cloud2['z']
+
+ R, T, adjusted = bundle_adjust(cloud1_final[:size], cloud2_final[:size])
+
+ return R, T
+
+
+class PointCloud:
+ """
+ Class for PointCloud data
+
+ Functionality:
+ - Load point cloud
+ - Perform a rotation
+ - merge with another point cloud
+ - show point cloud
+ - save it
+ """
+
+ def __init__(self, point_cloud, name=""):
+ # point_cloud should be a numpy array with 3 columns and N entries.
+ self._data = point_cloud
+ self._x = self._data[:, 0]
+ self._y = self._data[:, 1]
+ self._z = self._data[:, 2]
+ self.dim = {'x': self._x, 'y': self._y, 'z': self._z}
+ self.name = name
+
+ def update(self):
+ self._x = self.dim['x']
+ self._y = self.dim['y']
+ self._z = self.dim['z']
+ self._data = np.array([self._x, self._y, self._z]).transpose()
+
+ def prepareScatter(self, points, color):
+ return go.Scatter3d(x=(self._x[::points]), y=(self._y[::points]), z=(self._z[::points]), mode='markers', marker=dict(size=3, color=self._z[::points], colorscale='Viridis', opacity=0.8))
+
+
+ def projection2D(self, dim1, dim2, xbins=None, ybins=None, num_xbins=200, num_ybins=200, suffix=None, xlims=None, ylims=None):
+ # Build 2D projection
+ x = self.dim[dim1]
+ y = self.dim[dim2]
+ if ybins is None:
+ ybins = np.linspace(min(y), max(y), num_ybins)
+ if xbins is None:
+ xbins = np.linspace(min(x), max(x), num_xbins)
+
+ ##fig, ax = plt.subplots()
+ heatmap, xedges, yedges = np.histogram2d(x, y, bins=[xbins, ybins], density=True)
+ extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
+ """
+ im = ax.imshow(heatmap.T, extent=extent, origin='lower')
+ ax.set_xlabel(dim1)
+ ax.set_ylabel(dim2)
+ if xlims is not None:
+ ax.set_xlim(xlims)
+ if ylims is not None:
+ ax.set_ylim(ylims)
+ fig.colorbar(im)
+ ax.axis('equal')
+ if not os.path.exists("figures"):
+ os.mkdir("figures")
+ plt.savefig("figures/{NAME}{SUFFIX}_2Dprojection_{X}_{Y}.png".format(NAME=self.name, SUFFIX="_{SUFFIX}".format(SUFFIX=suffix if suffix is not None else ""), X=dim1, Y=dim2), format="png")
+ plt.show()
+ """
+ return heatmap, xedges, yedges
+
+ def rotation_matrix(self, axis, angle):
+ return expm(np.cross(np.eye(3), axis/norm(axis)*angle))
+
+ def rotate(self, dim1, dim2, angle, pointx=0, pointy=0):
+ xData = self.dim[dim1]
+ yData = self.dim[dim2]
+ xDataPrime = pointx + np.cos(angle)*(xData - pointx) + np.sin(angle)*(yData - pointy)
+ yDataPrime = pointy - np.sin(angle)*(xData - pointx) + np.cos(angle)*(yData - pointy)
+ self.dim[dim1] = xDataPrime
+ self.dim[dim2] = yDataPrime
+ self.update()
+
+ def move_rotate(self, R, T):
+ temp = numpy.matmul(R, self._data.T).T + T
+ self.dim['x'] = temp[:,0]
+ self.dim['y'] = temp[:,1]
+ self.dim['z'] = temp[:,2]
+ self.update()
+
+
+ def skew(self, dim1, dim2, k):
+ xData = self.dim[dim1]
+ yData = self.dim[dim2]
+
+ xDataPrime = xData + k*yData
+ yDataPrime = yData
+ self.dim[dim1] = xDataPrime
+ self.dim[dim2] = yDataPrime
+ self.update()
+
+
+ def correct_projection(self, dim, angle, reference_point = 0):
+ """
+ Scales the axis to take into account the effect of a tilted sesnor projecting the height profile on a plane sensor
+ """
+ data = self.dim[dim]
+ data -= reference_point
+ data /= np.cos(angle)
+ data += reference_point
+ self.dim[dim] = data
+ self.update()
+
+
+ def normalize_hist_to_maximum(self, dim, num_bins, threshold=0.5, min_dist=100):
+ data = self.dim[dim]
+ count, bins, patches = histogram1d(data, bins=int(num_bins))
+ indexes = peakutils.indexes(count, thres=threshold, min_dist=min_dist)
+ maxzidx = np.argmax(count[indexes])
+ maximum = bins[indexes][maxzidx]
+ self.shift(dim, maximum)
+
+ def extract_subcloud(self, dim, lvalue=-999, hvalue=999):
+ # extracts a part of the the cloud given the
+ subcloud = PointCloud(self._data[np.where(self.dim[dim] > lvalue, True, False)])
+ subcloud = subcloud._data[np.where(subcloud.dim[dim] < hvalue, True, False)]
+ return PointCloud(subcloud)
+
+ def clean(self, dim, lvalue=-999, hvalue=999):
+ hdata = PointCloud(self._data[np.where(self.dim[dim] > lvalue, True, False)])
+ ldata = PointCloud(hdata._data[np.where(hdata.dim[dim] < hvalue, True, False)])
+ self._data = ldata._data
+ self._x = self._data[:, 0]
+ self._y = self._data[:, 1]
+ self._z = self._data[:, 2]
+ self.dim = {'x': self._x, 'y': self._y, 'z': self._z}
+ return PointCloud(ldata._data)
+
+ def shift(self, dim, value):
+ self.dim[dim] -= value
+ self.update()
+
+ def save_to_npz(self, outname, *args, **kwargs):
+ np.savez(outname, self._data, *args, **kwargs)
+
+ def transform_to_np(self):
+ x = self._data[:, 0]
+ y = self._data[:, 1]
+ z = self._data[:, 2]
+ nparray = np.concatenate((x.reshape(-1,1),y.reshape(-1,1),z.reshape(-1,1)), axis = 1)
+
+ return nparray
+
+ def show3DCloud(self):
+ npy = self.transform_to_np()
+ pcl = o3d.geometry.PointCloud()
+ pcl.points = o3d.utility.Vector3dVector(npy)
+ o3d.visualization.draw_geometries([pcl])
+
diff --git a/addon/pointcloud_processing/__init__.py b/addon/pointcloud_processing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/addon/pointcloud_processing/calibration.py b/addon/pointcloud_processing/calibration.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b92e13c42bb56695bdcfeb90b1c7191062fcd61
--- /dev/null
+++ b/addon/pointcloud_processing/calibration.py
@@ -0,0 +1,533 @@
+# -*- coding: utf-8 -*-
+"""
+@author: grh
+
+Project: SmoPa3D
+
+Description: This code is used to calibrate the laser light section sensors.
+For this purpose, a standardized component must be placed on the build platform, which is scanned.
+Based on the captured data, calibration data is generated, which must be applied to all captured data.
+"""
+import os
+import json
+import logging
+import copy
+import numpy as np
+import open3d as o3d
+import matplotlib.pyplot as plt
+from json import JSONEncoder
+from .PCL import PointCloud, Calibration
+
+from . import generate_pointcloud as gp
+
+logger = logging.getLogger('SmoPa3D') # Logger should be defined at demonstrator-orchestration
+
+#===============================================================================
+## Helper functions
+#===============================================================================
+## Look up "global registration open3d" http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html
+def draw_registration_result(source, target, transformation):
+ source_temp = copy.deepcopy(source)
+ target_temp = copy.deepcopy(target)
+ source_temp.paint_uniform_color([1, 0.706, 0])
+ target_temp.paint_uniform_color([0, 0.651, 0.929])
+ source_temp.transform(transformation)
+ o3d.visualization.draw_geometries([source_temp, target_temp],
+ zoom=0.4459,
+ front=[0.9288, -0.2951, -0.2242],
+ lookat=[1.6784, 2.0612, 1.4451],
+ up=[-0.3402, -0.9189, -0.1996])
+
+## Look up "global registration open3d" http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html
+def preprocess_registration(pcd, voxel_size):
+ print(":: Downsample with a voxel size %.3f." % voxel_size)
+ pcd_down = pcd.voxel_down_sample(voxel_size)
+
+ radius_normal = voxel_size * 2
+ print(":: Estimate normal with search radius %.3f." % radius_normal)
+ pcd_down.estimate_normals(
+ o3d.geometry.KDTreeSearchParamHybrid(radius=radius_normal, max_nn=30))
+
+ radius_feature = voxel_size * 5
+ print(":: Compute FPFH feature with search radius %.3f." % radius_feature)
+ pcd_fpfh = o3d.pipelines.registration.compute_fpfh_feature(
+ pcd_down,
+ o3d.geometry.KDTreeSearchParamHybrid(radius=radius_feature, max_nn=100))
+ return pcd_down, pcd_fpfh
+
+## Look up "global registration open3d" http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html
+def prepare_dataset(voxel_size, source, target):
+ print(":: Load two point clouds and disturb initial pose.")
+ trans_init = np.asarray([[0.0, 0.0, 1.0, 0.0], [1.0, 0.0, 0.0, 0.0],
+ [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]])
+ ##source.transform(trans_init)
+ ##draw_registration_result(source, target, np.identity(4))
+
+ source_down, source_fpfh = preprocess_registration(source, voxel_size)
+ target_down, target_fpfh = preprocess_registration(target, voxel_size)
+ return source, target, source_down, target_down, source_fpfh, target_fpfh
+
+## Look up "global registration open3d" http://www.open3d.org/docs/release/tutorial/pipelines/global_registration.html
+def execute_global_registration(source_down, target_down, source_fpfh,
+ target_fpfh, voxel_size):
+ distance_threshold = voxel_size * 1.5
+ print(":: RANSAC registration on downsampled point clouds.")
+ print(" Since the downsampling voxel size is %.3f," % voxel_size)
+ print(" we use a liberal distance threshold %.3f." % distance_threshold)
+ result = o3d.pipelines.registration.registration_ransac_based_on_feature_matching(
+ source_down, target_down, source_fpfh, target_fpfh, False, distance_threshold,
+ o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
+ 4, [
+ o3d.pipelines.registration.CorrespondenceCheckerBasedOnEdgeLength(
+ 0.9),
+ o3d.pipelines.registration.CorrespondenceCheckerBasedOnDistance(
+ distance_threshold)
+ ], o3d.pipelines.registration.RANSACConvergenceCriteria(4000000, 500))
+ return result
+
+## Function for displaying in- and outliers
+def display_inlier_outlier(cloud, ind):
+ inlier_cloud = cloud.select_by_index(ind)
+ outlier_cloud = cloud.select_by_index(ind, invert=True)
+
+ print("Showing outliers (red) and inliers (gray): ")
+ outlier_cloud.paint_uniform_color([1, 0, 0])
+ inlier_cloud.paint_uniform_color([0.8, 0.8, 0.8])
+ o3d.visualization.draw_geometries([inlier_cloud, outlier_cloud],
+ zoom=0.3412,
+ front=[0.4257, -0.2125, -0.8795],
+ lookat=[2.6172, 2.0475, 1.532],
+ up=[-0.0694, -0.9768, 0.2024])
+## Function for removal of outliers
+def remove_outlier(cloud):
+ ## Downsample the point cloud with a voxel of 0.02
+ voxel_down_pcl = cloud.voxel_down_sample(voxel_size=0.02)
+ ## Statistical oulier removal
+ cloud, ind = voxel_down_pcl.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+ ##display_inlier_outlier(voxel_down_pcl, ind)
+ return cloud
+
+def preprocess_pointcloud(pcl):
+ ## Converting of the pcl into an numpy array in order to process them
+ npy = np.asarray(pcl.points)
+
+ ## Inverting the Z-values making it easier to process them
+ npy[:,2] = npy[:,2]*-1
+
+ ## Creating PointCloud classes (self made class from PCL.py)
+ pcl = PointCloud(npy)
+
+
+ ## Creating an X,Y and Z-area where only the relevant points are kept
+ pcl.clean('z', -1000, 300)
+
+ return pcl
+
+## Definition of class in order to encode np arrays to json data
+class NumpyArrayEncoder(JSONEncoder):
+ def default(self, obj):
+ if isinstance(obj, np.ndarray):
+ return obj.tolist()
+ return JSONEncoder.default(self, obj)
+
+#===============================================================================
+## Creation of calibration parameters using three different Pointclouds
+## 1. Surface of building platform
+## 2. Surface of printed part on building platform
+## 3. Features in order to calculate a transformation
+#===============================================================================
+def get_calibration_parameters(
+ calib_plate_1 = os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements", "plate_LLS1.ply"),
+ calib_plate_2=os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements","plate_LLS2.ply"),
+ surface_layer_1=os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements","part_surface_LLS1.ply"),
+ surface_layer_2=os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements","part_surface_LLS2.ply"),
+ last_layer_1=os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements","last_layer_LLS1.ply"),
+ last_layer_2=os.path.join(os.getcwd(), "data", "calibration", "calibration_measurements","last_layer_LLS2.ply"),
+ parameters_path:str=(os.getcwd() + "/data/calibration/calibration_files/transformation_parameters.json"),
+ view_steps:bool=logger.level <= 20
+ ):
+
+ #===============================================================================
+ ## Calibration of building plate
+ #===============================================================================
+ ## Import the calibration pcls
+ pcl_1 = o3d.io.read_point_cloud(calib_plate_1)
+ pcl_2 = o3d.io.read_point_cloud(calib_plate_2)
+
+ ## Removing outliers
+ pcl_1 = remove_outlier(pcl_1)
+ pcl_2 = remove_outlier(pcl_2)
+
+ ## Preprocessing of Pointclouds
+ pcl1 = preprocess_pointcloud(pcl_1)
+ pcl2 = preprocess_pointcloud(pcl_2)
+
+ ## Creating a Calibration class (self made class from PCL.py)
+ calibration = Calibration(pcl1, pcl2)
+
+ ## Normalize to the minimum in z direction
+ calibration.normalize_to_minimum('z')
+
+ ## Correct the tilt in z direction
+ calibration.z_leveling()
+
+ ## Shift both point clouds to bed level. This should be where the highest point density is found
+ calibration.bed_leveling()
+
+ if view_steps: plt.show()
+
+ ## Setting the zero plane by taking the top 99% quantile and shifting the zero point of the z-axis to that value
+ data_1 = np.reshape(pcl1._data, (-1, 3))
+ quant_1 = np.quantile(data_1[:,2], 0.99)
+ data_1[:,2] = data_1[:,2] - quant_1
+
+ data_2 = np.reshape(pcl2._data, (-1, 3))
+ quant_2 = np.quantile(data_2[:,2], 0.99)
+ data_2[:,2] = data_2[:,2] - quant_2
+
+ """
+ ## Visualization
+ plt.plot(data_1[:,1], data_1[:,2], 'o', label='updated data 1')
+ plt.legend()
+ plt.show()
+ plt.plot(data_2[:,1], data_2[:,2], 'o', label='updated data 2')
+ plt.legend()
+ plt.show()
+ """
+
+ ## Getting the calibration parameters
+ plate_delta1_z = float(calibration.calibration_parameters['delta1_z']) + quant_1
+ plate_delta2_z = float(calibration.calibration_parameters['delta2_z']) + quant_2
+ plate_theta1_xz = float(calibration.calibration_parameters['theta1_xz'])
+ plate_theta1_yz = float(calibration.calibration_parameters['theta1_yz'])
+ plate_theta2_xz = float(calibration.calibration_parameters['theta2_xz'])
+ plate_theta2_yz = float(calibration.calibration_parameters['theta2_yz'])
+
+
+ logger.info("Both LLS are calibrated regarding the building plate")
+ #===============================================================================
+ ## Calibration of even surface of printed calibration parts
+ #===============================================================================
+ ## Import the calibration pcls
+ pcl_1 = o3d.io.read_point_cloud(surface_layer_1)
+ pcl_2 = o3d.io.read_point_cloud(surface_layer_2)
+
+ ## Removing outliers
+ pcl_1 = remove_outlier(pcl_1)
+ pcl_2 = remove_outlier(pcl_2)
+
+ ## Preprocessing of Pointclouds
+ pcl1 = preprocess_pointcloud(pcl_1)
+ pcl1.clean('y', 50, 90)
+ pcl1.clean('x', -30, -5)
+ pcl2 = preprocess_pointcloud(pcl_2)
+ pcl2.clean('y', 10, 50)
+ pcl2.clean('x', 5, 30)
+
+ ## Calibrating with the previous calibration parameters
+ ## Normalize to the minimum in z direction
+ pcl1.shift('z', plate_delta1_z)
+ pcl2.shift('z', plate_delta2_z)
+ ## Correct the tilt in z direction
+ pcl1.rotate('x', 'z', plate_theta1_xz)
+ pcl1.rotate('y', 'z', plate_theta1_yz)
+ pcl2.rotate('x', 'z', plate_theta2_xz)
+ pcl2.rotate('y', 'z', plate_theta2_yz)
+ ## Cutting out the relevant area
+ pcl1.clean('z', 0, 10)
+ pcl2.clean('z', 0, 10)
+
+ ## Creating a Calibration class (self made class from PCL.py)
+ surface_calibration = Calibration(pcl1, pcl2)
+
+ ## Correct the tilt in z direction
+ surface_calibration.z_leveling()
+
+
+ ## Visualization
+ data_1 = np.reshape(pcl1._data, (-1, 3))
+ data_2 = np.reshape(pcl2._data, (-1, 3))
+
+ if view_steps:
+ plt.show()
+ plt.plot(data_1[:,0], data_1[:,2], 'o', label='xz updated data 1')
+ plt.legend()
+ plt.show()
+ plt.plot(data_1[:,1], data_1[:,2], 'o', label='yz updated data 1')
+ plt.legend()
+ plt.show()
+ plt.plot(data_2[:,0], data_2[:,2], 'o', label='xz updated data 2')
+ plt.legend()
+ plt.show()
+ plt.plot(data_2[:,1], data_2[:,2], 'o', label='yz updated data 2')
+ plt.legend()
+ plt.show()
+
+
+ ## Adapting the previous calibration parameters
+ ##delta1_z = plate_delta1_z + float(surface_calibration.calibration_parameters['delta1_z'])
+ delta1_z = plate_delta1_z
+ ##delta2_z = plate_delta2_z + float(surface_calibration.calibration_parameters['delta2_z'])
+ delta2_z = plate_delta2_z
+ theta1_xz = plate_theta1_xz + float(surface_calibration.calibration_parameters['theta1_xz'])
+ theta1_yz = plate_theta1_yz + float(surface_calibration.calibration_parameters['theta1_yz'])
+ theta2_xz = plate_theta2_xz + float(surface_calibration.calibration_parameters['theta2_xz'])
+ theta2_yz = plate_theta2_yz + float(surface_calibration.calibration_parameters['theta2_yz'])
+
+
+ #===============================================================================
+ ## Test if the calibration worked
+ #===============================================================================
+ pcl_1 = o3d.io.read_point_cloud(surface_layer_1)
+ pcl_2 = o3d.io.read_point_cloud(surface_layer_2)
+
+ ## Removing outliers
+ pcl_1 = remove_outlier(pcl_1)
+ pcl_2 = remove_outlier(pcl_2)
+
+ ## Preprocessing of Pointclouds
+ pcl1 = preprocess_pointcloud(pcl_1)
+ pcl2 = preprocess_pointcloud(pcl_2)
+
+ ## Calibrating with the previous calibration parameters
+ ## Normalize to the minimum in z direction
+ pcl1.shift('z', delta1_z)
+ pcl2.shift('z', delta2_z)
+ ## Correct the tilt in z direction
+ pcl1.rotate('x', 'z', theta1_xz)
+ pcl1.rotate('y', 'z', theta1_yz)
+ pcl2.rotate('x', 'z', theta2_xz)
+ pcl2.rotate('y', 'z', theta2_yz)
+
+ pcl1.clean('z', -10, 10)
+ pcl2.clean('z', -10, 10)
+
+ data_1 = np.reshape(pcl1._data, (-1, 3))
+ data_2 = np.reshape(pcl2._data, (-1, 3))
+
+ ## Visualization
+ if view_steps:
+ plt.plot(data_1[:,0], data_1[:,2], 'o', label='updated data 1')
+ plt.legend()
+ plt.show()
+ plt.plot(data_2[:,0], data_2[:,2], 'o', label='updated data 2')
+ plt.legend()
+ plt.show()
+
+ logger.info("Finished calibration of the even surface")
+ #===============================================================================
+ ## Calibration of features of printed calibration parts and merging of both pointclouds
+ #===============================================================================
+
+ ## Import the calibration pcls
+ pcl_1 = o3d.io.read_point_cloud(last_layer_1)
+ pcl_2 = o3d.io.read_point_cloud(last_layer_2)
+
+ ## Preprocessing of Pointclouds
+ pcl1 = preprocess_pointcloud(pcl_1)
+ pcl2 = preprocess_pointcloud(pcl_2)
+
+ ## Calibrating with the previous calibration parameters
+ ## Normalize to the minimum in z direction
+ pcl1.shift('z', delta1_z)
+ pcl2.shift('z', delta2_z)
+ ## Correct the tilt in z direction
+ pcl1.rotate('x', 'z', theta1_xz)
+ pcl1.rotate('y', 'z', theta1_yz)
+ pcl2.rotate('x', 'z', theta2_xz)
+ pcl2.rotate('y', 'z', theta2_yz)
+ ## Cutting out the relevant area
+ pcl1.clean('z', 1.1, 10)
+ pcl1.clean('y', 50, 90)
+ pcl1.clean('x', -30, -5)
+
+ pcl2.clean('z', 1.1, 10)
+ pcl2.clean('y', 10, 50)
+ pcl2.clean('x', 5, 30)
+
+ ## Creating again numpy arrays in order to create PointCloud classes (this time we use the class from open3d)
+ npy1 = pcl1.transform_to_np()
+ pcd1 = o3d.geometry.PointCloud()
+ pcd1.points = o3d.utility.Vector3dVector(npy1)
+
+ npy2 = pcl2.transform_to_np()
+ pcd2 = o3d.geometry.PointCloud()
+ pcd2.points = o3d.utility.Vector3dVector(npy2)
+
+ ## Transfer of point clouds for processing in the registration algorithms
+ target = pcd2
+ source = pcd1
+
+ ## Setting up of variables for the registration
+ voxel_size = 0.5
+ threshold = voxel_size * 0.4
+
+ ## extracting geometric features from the original pointclouds
+ source, target, source_down, target_down, source_fpfh, target_fpfh = prepare_dataset(voxel_size, source= source, target=target)
+
+ ## Using the RANSAC for global registration
+ result_ransac = execute_global_registration(source_down, target_down, source_fpfh, target_fpfh, voxel_size)
+
+ ## Local registration using the icp-algorithm -> for more: http://www.open3d.org/docs/latest/tutorial/Basic/icp_registration.html
+ result_icp = o3d.pipelines.registration.registration_icp(
+ source, target, threshold, result_ransac.transformation,
+ o3d.pipelines.registration.TransformationEstimationPointToPoint(),
+ o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=5000))
+
+ ## vizualisation of the results
+ if view_steps: draw_registration_result(source, target, result_icp.transformation)
+
+ logger.info(result_icp.fitness)
+ logger.info(result_icp.inlier_rmse)
+
+ ## Initalisation of dict for saving of JSON file
+ data = {
+ "delta1_z" : float(delta1_z),
+ "delta2_z" : float(delta2_z),
+ "theta1_xz" : float(theta1_xz),
+ "theta1_yz" : float(theta1_yz),
+ "theta2_xz" : float(theta2_xz),
+ "theta2_yz" : float(theta2_yz),
+ "fitness" : result_icp.fitness,
+ "inlier_rmse" : result_icp.inlier_rmse,
+ "transformation_matrix" : result_icp.transformation
+ }
+
+ # Serializing json
+ json_object = json.dumps(data, cls=NumpyArrayEncoder, indent=10)
+
+ # Writing to sample.json
+ path = os.path.dirname(parameters_path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ with open(parameters_path, "w") as outfile:
+ outfile.write(json_object)
+
+ return calibration, result_icp
+
+def calibrate_pcls(Layerheight, pcl1 =None, pcl2=None, calib_path = "/data/calibration/calibration_files/transformation_parameters.json", filetype = "NumpyArray"):
+ ## import the calibration parameters
+ path = (os.getcwd() + calib_path)
+ with open(path, "r") as read_file:
+ calib_params = json.load(read_file)
+
+ transformation_matrix_icp = np.asarray(calib_params["transformation_matrix"])
+ delta1_z = calib_params['delta1_z']
+ delta2_z = calib_params['delta2_z']
+ theta1_xz = calib_params['theta1_xz']
+ theta1_yz = calib_params['theta1_yz']
+ theta2_xz = calib_params['theta2_xz']
+ theta2_yz = calib_params['theta2_yz']
+
+ ## Import the to be calibrated parts and converting them into an numpy array in order to process them
+ if type(pcl1) == str and filetype == "Pointcloud":
+ path_pcl1 = os.getcwd() + "/data/pointclouds/raw/" + pcl1
+ pcl_1 = o3d.io.read_point_cloud(path_pcl1)
+ npy_1 = np.asarray(pcl_1.points)
+ elif pcl1 == None and filetype == "Pointcloud":
+ path_pcl1 = os.getcwd() + "/data/pointclouds/raw/" + str(Layerheight) + "_LLS1"
+ pcl_1 = o3d.io.read_point_cloud(path_pcl1)
+ npy_1 = np.asarray(pcl_1.points)
+ elif type(pcl1) == str and filetype == "NumpyArray":
+ path_pcl1 = os.getcwd() + "/data/Numpy_Arrays/raw/" + pcl1
+ npy_1 = np.load(path_pcl1)
+ elif pcl1 == None and filetype == "NumpyArray":
+ path_pcl1 = os.getcwd() + "/data/Numpy_Arrays/raw/" + str(Layerheight) + "_LLS1"
+ npy_1 = np.load(path_pcl1)
+ else:
+ npy_1 = pcl1
+
+ if type(pcl2) == str and filetype == "Pointcloud":
+ path_pcl2 = os.getcwd() + "/data/pointclouds/raw/" + pcl2
+ pcl_2 = o3d.io.read_point_cloud(path_pcl2)
+ npy_2 = np.asarray(pcl_2.points)
+ elif pcl2 == None and filetype == "Pointcloud":
+ path_pcl2 = os.getcwd() + "/data/pointclouds/raw/" + str(Layerheight) + "_LLS2"
+ pcl_2 = o3d.io.read_point_cloud(path_pcl2)
+ npy_2 = np.asarray(pcl_2.points)
+ elif type(pcl2) == str and filetype == "NumpyArray":
+ path_pcl2 = os.getcwd() + "/data/Numpy_Arrays/raw/" + pcl2
+ npy_2 = np.load(path_pcl2)
+ elif pcl2 == None and filetype == "NumpyArray":
+ path_pcl2 = os.getcwd() + "/data/Numpy_Arrays/raw/" + str(Layerheight) + "_LLS2"
+ npy_2 = np.load(path_pcl2)
+ else:
+ npy_2 = pcl2
+
+ ## Inverting the Z-values making it easier to process them
+ npy_1[:,2] = npy_1[:,2]*-1
+ npy_2[:,2] = npy_2[:,2]*-1
+
+ ## Creating PointCloud classes (self made class from PCL.py)
+ pcl1 = PointCloud(npy_1)
+ pcl2 = PointCloud(npy_2)
+
+ ## Creating an Z-area where points are kept (removing outliers)
+ pcl1.clean('z', -1000, -100)
+ pcl2.clean('z', -1000, -100)
+
+ ## Calibrating with the calibration parameters
+ ## Normalize to the minimum in z direction
+ pcl1.shift('z', delta1_z)
+ pcl2.shift('z', delta2_z)
+ ## Correct the tilt in z direction
+ pcl1.rotate('x', 'z', theta1_xz)
+ pcl1.rotate('y', 'z', theta1_yz)
+ pcl2.rotate('x', 'z', theta2_xz)
+ pcl2.rotate('y', 'z', theta2_yz)
+
+ pcl1.clean('z', 0.3, 300)
+ pcl2.clean('z', 0.3, 300)
+ ## Creating again numpy arrays in order to create PointCloud classes (this time we use the class from open3d)
+ npy_1 = pcl1.transform_to_np()
+ pcd1 = o3d.geometry.PointCloud()
+ pcd1.points = o3d.utility.Vector3dVector(npy_1)
+
+ npy_2 = pcl2.transform_to_np()
+ pcd2 = o3d.geometry.PointCloud()
+ pcd2.points = o3d.utility.Vector3dVector(npy_2)
+
+ ## visualization of the results
+ draw_registration_result(pcd1, pcd2, transformation_matrix_icp)
+
+ ## transforming npy3 into the right coordinate system using the generated transformation
+ npy1_transformed = np.asarray(pcd1.transform(transformation_matrix_icp).points)
+
+ ## merging of both pointclouds
+ merged_pcl = np.append(npy1_transformed, npy_2, axis=0)
+
+ ## saving the merged pcl as .npy
+ height = Layerheight
+ gp.save_as_npArray(Data=merged_pcl, name=str(height), path = "/data/Numpy_Arrays/merged/")
+
+ ## saving the merged pcl as .ply
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(merged_pcl)
+ pcl_path = os.getcwd() + "/data/pointclouds/merged/" + str(height) + "_merged.ply"
+ o3d.io.write_point_cloud(pcl_path, pcd)
+
+ return merged_pcl
+
+
+if __name__ == "__main__":
+ pass
+ # ##pcl_1, pcl_2 = calibration_acquisition()
+ # ##gp.save_as_npArray(pcl_1, Layerheight="pcl_1_calib", path = "/data/Calibration_PCLs/")
+ # ##gp.save_as_npArray(pcl_2, Layerheight="pcl_2_calib", path = "/data/Calibration_PCLs/")
+ # file_path = os.path.join(os.environ['ROOT'], 'files/calibration_parameters.json')
+ # get_calibration_parameters(
+ # '63848113504e7d63865e576b',
+ # '6384811d504e7d63865e576c',
+ # '63848128504e7d63865e576e',
+ # '63848130504e7d63865e576f',
+ # '63848137504e7d63865e5771',
+ # '6384813d504e7d63865e5772',
+ # file_path,
+ # False
+ # )
+ # product = db.initiate_from_db('63847797504e7d63865e5767')
+ # user = db.initiate_from_db(os.environ['USER_ID'])
+ # nominal = db.State('Calibrated', 'Printjob is done and the parameters are obtained.', product, 3)
+ # nominal.post()
+ # transf_json = db.Files('Calibration Parameters', 'Transformation parameters to join the data from both sensors.', nominal, user, 'Parameters', localfilepath=file_path)
+ # transf_json.post()
diff --git a/addon/pointcloud_processing/generate_pointcloud.py b/addon/pointcloud_processing/generate_pointcloud.py
new file mode 100644
index 0000000000000000000000000000000000000000..285e4eb1e4f8b58c319081cfc64bf4403e30f7a9
--- /dev/null
+++ b/addon/pointcloud_processing/generate_pointcloud.py
@@ -0,0 +1,63 @@
+import numpy as np
+import open3d as o3d
+import pandas as pd
+import os
+
+## Generation of a pandas DataFrame and preprocessing of the data
+def generate_df(xyz_profile):
+ data = np.reshape(xyz_profile, (-1, 4))
+ df = pd.DataFrame(data)
+ df = df.set_axis(("X", "Y", "Z", "Intensität"), axis='columns')
+ return df
+
+### Generates a Pointcloud out of a pandas DataFrame and saves it as .ply
+def generate_pcl(Data, path="/data/pointclouds/raw/", name = "unknownName", LLS = "unknownLLS"):
+ if type(Data) != np.ndarray:
+ df = Data
+ xyz= df.iloc[:,[0, 1, 2]].to_numpy()
+ else:
+ xyz= Data[:,[0, 1, 2]]
+ xyz = np.reshape(xyz, (-1, 3))
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ filepath = os.path.join(os.environ['ROOT'], path, str(name) + LLS + ".ply")
+ o3d.io.write_point_cloud(filepath, pcd)
+ print("Profil wurde als pcl gespeichert!")
+ return filepath
+
+### Saves a pandas DataFrame as hdf5
+def save_as_hdf(DataFrame, path = "/data/DataFrame/Ergebnisse.h5"):
+ df = DataFrame
+ filepath = os.path.join(os.environ['ROOT'], path)
+ df.to_hdf(filepath, key="DataFrame")
+ print("Profil wurde als .HDF5 gespeichert!")
+
+def save_as_npArray(Data, name, LLS, path = "/data/Numpy_Arrays/") -> str:
+ """Return the path to the saved file"""
+ Array = Data
+ filepath = os.path.join(os.environ['ROOT'], path, str(name) + LLS + ".npy")
+ np.save(filepath,Array)
+ return filepath
+
+if __name__ == "__main__":
+ path = "D:/DATA/"
+
+ data = np.array([])
+ df2 = pd.DataFrame(columns=["X", "Y", "Z", "Sensor", "Intensität"])
+
+ samples = os.listdir(path)
+ i = 0
+ for sample in samples:
+ print("Layer " + str(i))
+ sample_path = str(path + str(i) + ".npy")
+ array = np.load(sample_path)
+
+ df = generate_df(array)
+ df = df[df.Intensität != 0]
+ df = df[df.Z > 20]
+ df_max = df.quantile(q=0.2)
+ df = df[df.Z < (df_max["Z"])]
+ df2 = pd.concat([df2, df])
+ i += 1
+
+ generate_pcl(df2)
\ No newline at end of file
diff --git a/addon/pointcloud_processing/process_pointclouds.py b/addon/pointcloud_processing/process_pointclouds.py
new file mode 100644
index 0000000000000000000000000000000000000000..e16a705b9966f17f1296798d3e0a7737faac3670
--- /dev/null
+++ b/addon/pointcloud_processing/process_pointclouds.py
@@ -0,0 +1,140 @@
+import numpy as np
+import open3d as o3d
+import os
+import json
+import copy
+from tqdm import tqdm
+from scipy import spatial
+
+from . import generate_pointcloud as gp
+from .PCL import PointCloud
+
+def draw_registration_result(source, target, transformation):
+ source_temp = copy.deepcopy(source)
+ target_temp = copy.deepcopy(target)
+ source_temp.paint_uniform_color([1, 0.706, 0])
+ target_temp.paint_uniform_color([0, 0.651, 0.929])
+ source_temp.transform(transformation)
+ o3d.visualization.draw_geometries([source_temp, target_temp],
+ zoom=0.4459,
+ front=[0.9288, -0.2951, -0.2242],
+ lookat=[1.6784, 2.0612, 1.4451],
+ up=[-0.3402, -0.9189, -0.1996])
+
+def merge_pointclouds(pcl1, pcl2, saving_path:str, calibration_filepath:str="data/calibration/calibration_files/transformation_parameters.json") -> np.ndarray:
+ """Merge two pointclouds, usually obtained from the laser scanners, by applying transformations described by a calibration json.
+ Pointclouds can be inputed as open3d pointclouds, numpy arrays or paths to those formats (.ply or .npy)."""
+
+ ## import the calibration parameters
+ with open(calibration_filepath, "r") as read_file:
+ calib_params = json.load(read_file)
+ transformation_matrix_icp = np.asarray(calib_params["transformation_matrix"])
+ delta1_z = calib_params['delta1_z']
+ delta2_z = calib_params['delta2_z']
+ theta1_xz = calib_params['theta1_xz']
+ theta1_yz = calib_params['theta1_yz']
+ theta2_xz = calib_params['theta2_xz']
+ theta2_yz = calib_params['theta2_yz']
+
+ ## Import the to be calibrated parts and converting them into an numpy array in order to process them
+ if type(pcl1) != type(pcl2):
+ raise NameError('Given point clouds are in different types. Try again with a same type.')
+ if type(pcl1) == str:
+ # if not os.path.isdir(pcl1) or not os.path.isdir(pcl2):
+ # raise NameError('Given point clouds do not exist.')
+ extension = pcl1.split('.')[-1].lower()
+ if extension == 'ply':
+ pcl1 = o3d.io.read_point_cloud(pcl1)
+ pcl2 = o3d.io.read_point_cloud(pcl2)
+ elif extension == 'npy':
+ pcl1 = np.load(pcl1)
+ pcl2 = np.load(pcl2)
+ if type(pcl1) == o3d.geometry.PointCloud:
+ pcl1 = np.asarray(pcl1.points)
+ pcl2 = np.asarray(pcl2.points)
+ if type(pcl1) != np.ndarray or type(pcl2) != np.ndarray:
+ raise NameError('Files could not be properly opened.')
+
+ # ## Inverting the Z-values making it easier to process them
+ pcl1[:,2] = pcl1[:,2]*-1
+ pcl2[:,2] = pcl2[:,2]*-1
+
+ ## Creating PointCloud classes (self made class from PCL.py)
+ pcl1 = PointCloud(pcl1)
+ pcl2 = PointCloud(pcl2)
+
+ ## Creating an Z-area where points are kept (removing outliers)
+ ##pcl1.clean('z', -1000, -500)
+ ##pcl2.clean('z', -1000, -500)
+
+ ## Calibrating with the calibration parameters
+ ## Normalize to the minimum in z direction
+ pcl1.shift('z', delta1_z)
+ pcl2.shift('z', delta2_z)
+ ## Correct the tilt in z direction
+ pcl1.rotate('x', 'z', theta1_xz)
+ pcl1.rotate('y', 'z', theta1_yz)
+ pcl2.rotate('x', 'z', theta2_xz)
+ pcl2.rotate('y', 'z', theta2_yz)
+
+ # pcl1.clean('z', 0.3, 300)
+ # pcl2.clean('z', 0.3, 300)
+
+ ## Creating again numpy arrays in order to create PointCloud classes (this time we use the class from open3d)
+ pcl1 = pcl1.transform_to_np()
+ pcd1 = o3d.geometry.PointCloud()
+ pcd1.points = o3d.utility.Vector3dVector(pcl1)
+
+ pcl2 = pcl2.transform_to_np()
+ pcd2 = o3d.geometry.PointCloud()
+ pcd2.points = o3d.utility.Vector3dVector(pcl2)
+
+ ## visualization of the results
+ ##draw_registration_result(pcd1, pcd2, transformation_matrix_icp)
+
+ ## transforming npy3 into the right coordinate system using the generated transformation
+ npy1_transformed = np.asarray(pcd1.transform(transformation_matrix_icp).points)
+
+ ## merging of both pointclouds
+ merged_pcl = np.append(npy1_transformed, pcl2, axis=0)
+
+ ## saving the merged pcl as .npy
+ ##gp.save_as_npArray(Data=merged_pcl, name=str(layer), LLS="_merged", path = "/data/Numpy_Arrays/merged/")
+
+ ## saving the merged pcl as .ply
+ if len(saving_path) > 0:
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(merged_pcl)
+ if saving_path[-4:] != '.ply':
+ saving_path += '.ply'
+ o3d.io.write_point_cloud(saving_path, pcd)
+
+ return merged_pcl
+
+def layerize_pointclouds(layers, distance_threshold:float=0.2, workers:int=-1):
+ """Given a list of point clouds, sorted from bottom to top, iterates so that each layer represents only the added points."""
+ diff_layers = [layers[0]]
+ for i in tqdm(range(1, len(layers))):
+ basis = spatial.KDTree(layers[i-1])
+ raw_addition = layers[i]
+ dists, idx = basis.query(raw_addition, 1, p=2, distance_upper_bound=distance_threshold, workers=workers)
+ addition = raw_addition[dists == np.inf]
+ diff_layers.append(addition)
+ return diff_layers
+
+if __name__ == '__main__':
+ folder = 'data/pointclouds/raw'
+ calibration_path = 'data/calibration/calibration_files/transformation_parameters.json'
+ merged_layers = []
+ for i in range(20):
+ saving_path = 'data/pointclouds/layerize/%d_merged.ply' % i
+ merged_pcl = merge_pointclouds(
+ os.path.join(folder, '%d_LLS1.ply' % i),
+ os.path.join(folder, '%d_LLS2.ply' % i),
+ saving_path,
+ calibration_path
+ )
+ merged_layers.append(merged_pcl)
+ layers = layerize_pointclouds(merged_layers)
+ for (i, layer) in enumerate(layers):
+ gp.generate_pcl(layer, path="data/pointclouds/layerize/piece/layerized/", name = "", LLS =str(i))
\ No newline at end of file
diff --git a/addon/pointcloud_processing/utils.py b/addon/pointcloud_processing/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..22ffdcec906dc1d888b06f5e12431a7dab9f6a1a
--- /dev/null
+++ b/addon/pointcloud_processing/utils.py
@@ -0,0 +1,123 @@
+import numpy
+import logging
+import os
+
+def bundle_adjust(set1, set2, printer = True):
+ if set1.shape != set2.shape:
+ raise Exception("Can only bundle adjust equally shaped sets of points")
+ dim = set1.shape[1]
+ m = numpy.hstack((set1, set2))
+ mean = numpy.mean(m, axis=0)
+ m -= mean
+ H = numpy.matlib.zeros([dim,dim])
+ for d in m:
+ for i in range(dim):
+ for j in range(dim):
+ H[i,j] += d[j+dim]*d[i]
+
+ U, s, V = numpy.linalg.svd(H)
+
+ det = numpy.linalg.det(V.T*U.T)
+ R = numpy.asarray(V.T*numpy.diag([1,1,det])*U.T).T
+ T = -numpy.matmul(R, mean[dim:(2*dim)].T).T + mean[0:dim]
+ adjusted = numpy.matmul(R, set2.T).T + T
+ vol = numpy.linalg.norm(set1 - adjusted, axis=1)
+ if printer:
+ print("--- Bundle Adjust ---")
+ print("Rotation")
+ print(R)
+ print("Translation")
+ print(T)
+ print("Before: {}".format(numpy.mean(numpy.linalg.norm(set1 - set2, axis=1))))
+ print("After: {}".format(numpy.mean(vol)))
+ print("Maximum deviations: {}\t{}\t{}".format(*numpy.sort(vol)[-3:].tolist()))
+
+ return R, T, adjusted
+
+def quaternion(R, isprecise=False): #Gohlke
+ M = numpy.array(R)
+ if isprecise:
+ q = numpy.empty((4, ))
+ t = numpy.trace(M)
+ if t > M[3, 3]:
+ q[0] = t
+ q[3] = M[1, 0] - M[0, 1]
+ q[2] = M[0, 2] - M[2, 0]
+ q[1] = M[2, 1] - M[1, 2]
+ else:
+ i, j, k = 0, 1, 2
+ if M[1, 1] > M[0, 0]:
+ i, j, k = 1, 2, 0
+ if M[2, 2] > M[i, i]:
+ i, j, k = 2, 0, 1
+ t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3]
+ q[i] = t
+ q[j] = M[i, j] + M[j, i]
+ q[k] = M[k, i] + M[i, k]
+ q[3] = M[k, j] - M[j, k]
+ q = q[[3, 0, 1, 2]]
+ q *= 0.5 / numpy.sqrt(t * M[3, 3])
+ else:
+ m00 = M[0, 0]
+ m01 = M[0, 1]
+ m02 = M[0, 2]
+ m10 = M[1, 0]
+ m11 = M[1, 1]
+ m12 = M[1, 2]
+ m20 = M[2, 0]
+ m21 = M[2, 1]
+ m22 = M[2, 2]
+ # symmetric matrix K
+ K = numpy.array([[m00-m11-m22, 0.0, 0.0, 0.0],
+ [m01+m10, m11-m00-m22, 0.0, 0.0],
+ [m02+m20, m12+m21, m22-m00-m11, 0.0],
+ [m21-m12, m02-m20, m10-m01, m00+m11+m22]])
+ K /= 3.0
+ # quaternion is eigenvector of K that corresponds to largest eigenvalue
+ w, V = numpy.linalg.eigh(K)
+ q = V[[3, 0, 1, 2], numpy.argmax(w)]
+ if q[0] < 0.0:
+ numpy.negative(q, q)
+ return q
+
+def rotation_matrix(q):
+ a, b, c, d = q
+ return numpy.array(
+ [
+ [a**2 + b**2 - c**2 - d**2, 2*(b*c - a*d), 2*(b*d + a*c)],
+ [2*(b*c + a*d), a**2 -b**2 + c**2 - d**2, 2*(c*d - a*b)],
+ [2*(b*d - a*c), 2*(c*d + a*b), a**2 - b**2 - c**2 + d**2]
+
+ ]
+ )
+
+def euler(q):
+ a, b, c, d = q
+
+ alpha = numpy.arctan2(2*(a*b + c*d), a** - b**2 -c**2 + d**2)
+ beta = numpy.arcsin(2*(a*c - b*d))
+ gamma = numpy.arctan2(2*(a*d + b*c), a**2 + b**2 - c**2 - d**2)
+
+ return [alpha, beta, gamma]
+
+def init_logging(log_level=None):
+ if log_level is None and 'LOG_LEVEL' in os.environ:
+ log_level = os.environ['LOG_LEVEL']
+ else:
+ log_level = logging.WARNING
+ logFormatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] [%(module)-10s] %(message)s", datefmt='%d/%m/%Y %H:%M:%S')
+ rootLogger = logging.getLogger("SmoPa3D")
+
+ fileHandler = logging.FileHandler(os.path.join(os.environ['ROOT'], "smopa3d.log"))
+ fileHandler.setFormatter(logFormatter)
+ fileHandler.setLevel(logging.WARNING)
+ rootLogger.addHandler(fileHandler)
+
+ consoleHandler = logging.StreamHandler()
+ consoleHandler.setFormatter(logFormatter)
+ consoleHandler.setLevel(log_level)
+ rootLogger.addHandler(consoleHandler)
+
+ rootLogger.setLevel(log_level)
+ return rootLogger
+
diff --git a/addon/simulation.py b/addon/simulation.py
new file mode 100644
index 0000000000000000000000000000000000000000..78ae8464385c85f09809fe92e1387c6f930ef77d
--- /dev/null
+++ b/addon/simulation.py
@@ -0,0 +1,163 @@
+import bpy
+import bmesh
+import os
+import numpy as np
+from tqdm import tqdm
+import logging
+
+from . import geometry as geo
+from .node import Node
+from .network import Network, load_network
+from .pointcloud import PointCloud
+from . import utils
+
+log = logging.getLogger('SmoPa3D')
+
+class SimulateOperator(bpy.types.Operator):
+ bl_idname = "fdm_simulator.simulate"
+ bl_label = "Simulate an FDM 3D printer"
+
+ def execute(self, context):
+ net = Network('test/benchy-02infill.gcode', 0.02, 1, node_distance=2/3)
+ net.simulate_printer()
+ net.calculate_meshes(processes=None)
+ net.save("benchy.pkl")
+
+ log.info('Plotting...')
+ for command in tqdm(net.commands.values()):
+ if command.vertices is None or len(command.faces) == 0: continue
+ node_mesh = place_mesh(command.vertices, command.faces, f'Command {command.id}')
+ group(node_mesh, f'Simulation/layer {command.layer.z}') # Group by layer
+ return {'FINISHED'}
+
+class DrawSimulationOperator(bpy.types.Operator):
+ bl_idname = "fdm_simulator.draw_simulation"
+ bl_label = "Draw an already simulated FDM 3D printed part"
+
+ def execute(self, context):
+ net = load_network('data/simulation/over/benchy.pkl')
+
+ log.info('Plotting...')
+ for command in tqdm(net.commands.values()):
+ if command.vertices is None or len(command.faces) == 0: continue
+ node_mesh = place_mesh(command.vertices, command.faces, f'Command {command.id}')
+ group(node_mesh, f'Simulation/layer {command.layer.z}') # Group by layer
+ return {'FINISHED'}
+
+class LoadScanOperator(bpy.types.Operator):
+ bl_idname = "fdm_simulator.load_scan_point_cloud"
+ bl_label = "Load Scan in point cloud format"
+
+ def execute(self, context):
+ path = "data/awk_wzl-printed_in_windows-2_lls"
+ scans = {int(os.path.split(fle)[-1].split('_')[0]): os.path.join(path, fle) for fle in os.listdir(path) if fle[-5] == '2'}
+ layers = sorted(scans.keys())
+ colors = utils.color_range(len(scans))
+ for layer, color in tqdm(zip(layers[::-1], colors)):
+ fle = scans[layer]
+ pointcloud = PointCloud(fle)
+ pointcloud.crop_ROI((0, 25), (23, 33), (0, 10))
+ pointcloud.downsample(0.5)
+
+ # Create an icosphere to use as an instance
+ bpy.ops.mesh.primitive_cube_add(size=0.05)
+ icosphere = bpy.context.object
+ icosphere.active_material = color
+
+ # Place instances at each vertex of the point cloud
+ placed_pointcloud = pointcloud.place_pointcloud(f'Layer {layer}', icosphere, (97.615, -10.637, 0))
+ group(placed_pointcloud, 'Scan')
+
+ return {'FINISHED'}
+
+class LoadScanMeshOperator(bpy.types.Operator):
+ bl_idname = "fdm_simulator.load_scan_mesh"
+ bl_label = "Load Scan as a mesh"
+
+ def execute(self, context):
+ path = "data/awk_wzl-printed_in_windows-2_lls"
+ scans = {int(os.path.split(fle)[-1].split('_')[0]): os.path.join(path, fle) for fle in os.listdir(path) if fle[-5] == '2'}
+ layers = sorted(scans.keys())
+ colors = utils.color_range(len(scans))
+ for layer, color in tqdm(zip(layers[::-1], colors)):
+ fle = scans[layer]
+ pointcloud = PointCloud(fle)
+ pointcloud.crop_ROI((0, 25), (23, 33), (0, 10))
+ pcl = pointcloud.pcl[:, :3]
+ vertices, faces = geo.reconstruct_pointcloud_mesh(pcl, 0)
+ placed_pointcloud = place_mesh(vertices, faces, f'Layer {layer}')
+
+ group(placed_pointcloud, 'Scan')
+
+ return {'FINISHED'}
+
+def group(obj:bpy.types.Object, groupName:str):
+ """Group object by layer. If groupName is passed as a route, all parent layers will be created."""
+ folders = os.path.normpath(groupName).split(os.sep)
+ parent_folder = bpy.context.scene.collection
+ for folder in folders:
+ if not folder in parent_folder.children:
+ bpy.ops.collection.create(name=folder)
+ parent_folder.children.link(bpy.data.collections[folder])
+ parent_folder = parent_folder.children[folder]
+ parent_folder.objects.link(obj)
+
+def place_mesh(pointcloud:np.array, faces:np.array, name:str) -> bpy.types.Object:
+ """Plot pointcloud given by x, y and z data in blender as mesh"""
+ mesh_data = bpy.data.meshes.new(name)
+ mesh_data.from_pydata(pointcloud, [], faces)
+ bm = bmesh.new()
+ bm.from_mesh(mesh_data)
+ bm.to_mesh(mesh_data)
+ mesh_obj = bpy.data.objects.new(mesh_data.name, mesh_data)
+ return mesh_obj
+
+def place_node(node:Node) -> None:
+ if node.placed_filament is None: return
+ vertices, faces = geo.calculate_mesh(node.placed_filament, 16)
+ if len(vertices) == 0:
+ log.warning(f"Node {node} could not be plotted due to lack of normals.")
+ return
+ vertices *= node.network.env.resolution
+ positioned_node = vertices - np.repeat((1+ node.network.env.node_grid_size)//2, 3) * node.network.env.resolution + node.coord
+ node_mesh = place_mesh(positioned_node, faces, 'Node')
+ group(node_mesh, 'Layer ' + str(round(node.z, 2))) # Group by layer
+
+def place_node_by_close_points(node:Node) -> None:
+ """Calculate mesh by finding points close to each other"""
+ if node.placed_filament is None: return
+ vertices = geo.calculate_shell(node.placed_filament)
+ vertices = geo.downsample(vertices, 0.5)
+ faces = geo.find_triangles(vertices, 2)
+ positioned_node = vertices - np.repeat((1+ node.network.env.node_grid_size)//2, 3) + node.coord / node.network.env.resolution
+ node_mesh = place_mesh(positioned_node, faces, 'Node')
+ group(node_mesh, 'Layer ' + str(round(node.z, 2))) # Group by layer
+
+def place_obstacles(node:Node, net:Network) -> None:
+ search_radius = net.env.node_size * np.sqrt(2)
+ query = net.tree.query_ball_point(node.coord, search_radius)
+ neighbours:list[Node] = [net.nodes[x] for x in query]
+
+ for nbr in neighbours:
+ if nbr.placed_filament is None and nbr != node: continue
+ log.debug(f'Plotting node: {nbr} ...')
+ shift_vector = np.around(nbr.coord/node.network.env.resolution) - np.around(node.coord/node.network.env.resolution)
+ shifted_node = geo.shift_array(nbr.placed_filament, shift_vector)
+ vertices, faces = geo.calculate_mesh(shifted_node, 16)
+ if len(vertices) == 0: continue
+ positioned_node = vertices - np.repeat((1+ node.network.env.node_grid_size)//2, 3) + node.coord / node.network.env.resolution
+ node_mesh = place_mesh(positioned_node, faces, 'Neighbour')
+
+ log.debug('Plotting nozzle...')
+ vertices, faces = geo.calculate_mesh(node.network.env.nozzle, 16)
+ positioned_node = vertices - np.repeat((1+ node.network.env.node_grid_size)//2, 3) + node.coord / node.network.env.resolution
+ node_mesh = place_mesh(positioned_node, faces, 'Nozzle')
+ group(node_mesh, 'Nozzle')
+
+ log.debug('Plotting bed...')
+ bed = node.network.env.bed(node.z)
+ if len(bed != 0) > 0:
+ vertices, faces = geo.calculate_mesh(bed, 16)
+ positioned_node = vertices - np.repeat((1+ node.network.env.node_grid_size)//2, 3) + node.coord / node.network.env.resolution
+ node_mesh = place_mesh(positioned_node, faces, 'Bed')
+ group(node_mesh, 'Bed')
\ No newline at end of file
diff --git a/addon/utils.py b/addon/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1224f0134603635fae59685e4cce16b815b6b025
--- /dev/null
+++ b/addon/utils.py
@@ -0,0 +1,62 @@
+import sys
+import logging
+import numpy as np
+running_in_blender = 'Blender' in sys.executable
+if running_in_blender:
+ import bpy
+
+def init_logging(log_level='WARNING'):
+ logFormatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] [%(module)-10s] %(message)s", datefmt='%d/%m/%Y %H:%M:%S')
+ rootLogger = logging.getLogger("SmoPa3D")
+
+ fileHandler = logging.FileHandler("smopa3d.log")
+ fileHandler.setFormatter(logFormatter)
+ fileHandler.setLevel(logging.WARNING)
+ rootLogger.addHandler(fileHandler)
+
+ consoleHandler = logging.StreamHandler()
+ consoleHandler.setFormatter(logFormatter)
+ consoleHandler.setLevel(log_level)
+ rootLogger.addHandler(consoleHandler)
+
+ rootLogger.setLevel(log_level)
+ return rootLogger
+
+def color_range(slices:int) -> list:
+ """Returns a list of colors for the given number of slices"""
+ def red(i:int) -> float:
+ """graph = \_"""
+ # return max(0, 1-2*i/slices)
+ return 0
+
+ def green(i:int) -> float:
+ """graph = _/"""
+ # return max(0, 2*i/slices-1)
+ return max(i/slices - 0.5, 0) * 2
+
+ def blue(i:int) -> float:
+ """graph = /\\"""
+ # return 1-(red(i)+green(i))
+ return min(1, 0.2 + 0.8 * 2 * i/slices) - max(i/slices - 0.5, 0) * 2
+
+ colors = []
+ for slice in range(slices):
+ color_values = (red(slice), green(slice), blue(slice), 1)
+ color = bpy.data.materials.new(f"Color_{slice}")
+ color.use_nodes = True
+ tree = color.node_tree
+ nodes = tree.nodes
+ bsdf = nodes["Principled BSDF"]
+ bsdf.inputs["Base Color"].default_value = color_values
+ color.diffuse_color = color_values
+ colors.append(color)
+ return colors
+
+def npy_to_ply(path:str):
+ import open3d as o3d
+ xyz = np.load(path)
+ xyz = np.reshape(xyz, (-1, 3))
+ pcd = o3d.geometry.PointCloud()
+ pcd.points = o3d.utility.Vector3dVector(xyz)
+ filepath = path.replace(".npy", ".ply")
+ o3d.io.write_point_cloud(filepath, pcd)
\ No newline at end of file
diff --git a/experiments/__init__.py b/experiments/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiments/assessment.py b/experiments/assessment.py
new file mode 100644
index 0000000000000000000000000000000000000000..c142c29d754ea468ebe9abec59d3bd905abdb388
--- /dev/null
+++ b/experiments/assessment.py
@@ -0,0 +1,257 @@
+import os
+import random
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from tqdm.contrib.itertools import product
+from sklearn.preprocessing import SplineTransformer
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import r2_score
+import seaborn as sns
+sns.set()
+import utils
+
+log = utils.init_logging("DEBUG")
+
+def load(results_path:str = "data/experiments/results/v2",
+ temperature:int = 200,
+ speed:int = 1000,
+ layer:int = 2,
+ ):
+ # Load the point cloud
+ pcl_path = os.path.join(results_path, f"temperature_{temperature}_speed_{speed}", f"{layer}_LLS2.npy")
+ npy = np.load(pcl_path)
+
+ # Invert z axis
+ npy[:, 2] = -npy[:, 2] + max(npy[:, 2])
+
+ # Crop to ROI
+ roi = [[None, None], [0, None], [None, 10]]
+ npy = utils.crop_to_roi(npy, roi=roi)
+ return npy
+
+def get_strand_rois() -> dict[list[list[float]]]:
+ """Definition of the ROI of each strand.
+ strand_rois[feeding_rate] = [[x_min, x_max], [y_min, y_max], [z_min, z_max]] """
+ strands_rois = {0.03: [[-28, -25], [40, 80], [None, None]]}
+ for i in range(1, 20):
+ strands_rois[0.03 + i * 0.01] = [[strands_rois[0.03][0][0] + i * 3, strands_rois[0.03][0][1] + i *3], *strands_rois[0.03][1:]]
+ return strands_rois
+
+def calculate_profile(strand:np.ndarray, layer:int, plot:bool=True):
+ # Noise removal
+ # Firstly, we split the lower third of the strand into two: left and right parts
+ # Then we calculate the gap between the left and right parts, add a secure margin of 10% to both ends and remove any points outside that range
+ upper_threshold = min(strand[:, 2]) + (max(strand[:, 2]) - min(strand[:, 2])) * 2/3
+ lower_threshold = min(strand[:, 2]) + (max(strand[:, 2]) - min(strand[:, 2])) * 1/3
+ upper_piece = strand[strand[:, 2] > upper_threshold]
+ lower_piece = strand[strand[:, 2] < lower_threshold]
+ left_part = lower_piece[lower_piece[:, 0] < upper_piece[:, 0].mean()] # The left part of the bed before the strand
+ right_part = lower_piece[lower_piece[:, 0] > upper_piece[:, 0].mean()] # The right part of the bed after the strand
+ bed_z = lower_piece[:, 2].mean() # The z coordinate of the bed
+
+ # Calculate height
+ top_10 = sorted(strand[:, 2])[-10:] # Get the top 10 highest points
+ height = np.mean(top_10) - bed_z
+ x_height = strand[strand[:, 2].argmax(), 0]
+ last_layer_height = height / (layer + 1) + bed_z
+ if layer > 0: strand = strand[strand[:, 2] > last_layer_height] # Exclude previous layers
+
+ if len(left_part) > 0 and len(right_part) > 0:
+ lower_gap = min(right_part[:, 0]) - max(left_part[:, 0])
+ strand = strand[strand[:, 0] > (max(left_part[:, 0]) - lower_gap * 0.1)] # Remove noise from the left side
+ strand = strand[strand[:, 0] < (min(right_part[:, 0]) + lower_gap * 0.1)] # Remove noise from the right side
+ strand = strand[strand[:, 2] > bed_z] # Remove noise from the bottom
+ if len(strand) == 0:
+ print("No strand left after noise removal")
+ return (None, None, None) if plot else (None, None)
+ else:
+ print("No gap between the left and right parts of the bed")
+ return (None, None, None) if plot else (None, None)
+
+ # Calcultate bed
+ # Assuming the data can be sliced in 3 parts by z axis, the upper part should represent the strand and the lower should represent the bed
+ # Bed is then calculated as the mean of the lower part
+ upper_threshold = min(strand[:, 2]) + (max(strand[:, 2]) - min(strand[:, 2])) * 2/3
+ lower_threshold = min(strand[:, 2]) + (max(strand[:, 2]) - min(strand[:, 2])) * 1/3
+ upper_piece = strand[strand[:, 2] > upper_threshold]
+ middle_piece = strand[(strand[:, 2] > lower_threshold)]
+ middle_piece = middle_piece[(middle_piece[:, 2] < upper_threshold)]
+ lower_piece = strand[strand[:, 2] < lower_threshold]
+ left_part = lower_piece[lower_piece[:, 0] < upper_piece[:, 0].mean()] # The left part of the bed before the strand
+ right_part = lower_piece[lower_piece[:, 0] > upper_piece[:, 0].mean()] # The right part of the bed after the strand
+
+ if len(lower_piece) == 0 or len(upper_piece) == 0:
+ return (None, None, None) if plot else (None, None)
+
+ # Calculate width
+ # We calculate the width by three ways:
+ # * difference between opposite points in the middle part
+ # * difference between opposite points in the upper part
+ # * gap size in the center
+ # We then exclude any values outside the standart deviation and the width will be the mean of the remaining values
+ middle_left = middle_piece[middle_piece[:, 0] < upper_piece[:, 0].mean()]
+ middle_right = middle_piece[middle_piece[:, 0] > upper_piece[:, 0].mean()]
+ middle_width = np.mean(middle_right[:, 0]) - np.mean(middle_left[:, 0]) if len(middle_left) > 0 and len(middle_right) > 0 else None
+ upper_width = max(upper_piece[:, 0]) - min(upper_piece[:, 0])
+ lower_gap = min(right_part[:, 0]) - max(left_part[:, 0]) if len(left_part) > 0 and len(right_part) > 0 else None
+ widths = np.array([width for width in [middle_width, upper_width, lower_gap] if width is not None])
+ width = upper_width
+ if len(widths) == 0:
+ return None, None
+ elif len(widths) == 1:
+ width = widths[0]
+ elif len(widths) >= 2:
+ width = widths.mean()
+
+ if plot:
+ width_start = upper_piece[:, 0].mean() - width/2
+ width_end = upper_piece[:, 0].mean() + width/2
+ fig, ax = plt.subplots()
+ ax.plot(strand[:, 0], strand[:, 2], 'ko')
+ # ax.plot(xaxis, interp(xaxis), '-')
+ ax.plot([min(strand[:, 0]), max(strand[:, 0])], [bed_z, bed_z], '-k') # plot bed
+ ax.plot([min(strand[:, 0]), max(strand[:, 0])], [upper_threshold, upper_threshold], '--k') # plot upper threshold
+ ax.plot([min(strand[:, 0]), max(strand[:, 0])], [lower_threshold, lower_threshold], '--k') # plot lower threshold
+ ax.plot([x_height, x_height], [bed_z, bed_z + height], '-g') # plot height
+ ax.plot([width_start, width_end], [np.mean([lower_threshold, upper_threshold]), np.mean([lower_threshold, upper_threshold])], '-r') # plot width
+ ax.legend(['measurements', 'bed', 'upper third', 'lower third', 'height', 'width'])
+ ax.set_box_aspect(1)
+ ax.set_title('Front view')
+ plt.close()
+ return fig, height, width
+
+ return height, width
+
+def assess_width_and_height(strand:np.ndarray, layer:int) -> dict:
+ step = 0.2
+ heights = []
+ widths = []
+ for y_slice in np.arange(min(strand[:, 1]), max(strand[:, 1]), step):
+ # Cut a straight line
+ slice = strand[strand[:, 1] >= y_slice]
+ slice = slice[slice[:, 1] < (y_slice + step)]
+ if len(slice) < 10: continue
+ height, width = calculate_profile(slice, layer, False)
+ if height is None or width is None: continue
+ heights.append(height)
+ widths.append(width)
+
+ stats = pd.DataFrame({'height':heights, 'width':widths})
+
+ return {'width':{'mean': stats.width.mean(), 'std': stats.width.std()}, 'height':{'mean': stats.height.mean(), 'std': stats.height.std()}}
+
+def assess_results() -> pd.DataFrame:
+ results = pd.DataFrame(columns=['temperature', 'speed', 'run_number', 'layer', 'feeding_rate', 'extrusion_type', 'width_mean', 'width_std', 'height_mean', 'height_std'])
+
+ strands_rois = get_strand_rois()
+ for temperature, speed, run_number, layer, feeding_rate, extrusion_type in product([180, 200], [500, 700, 1000, 1200], range(1, 4), range(3), strands_rois.keys(), ['one_line', 'overextrusion']):
+ measurement = load(temperature=temperature, speed=speed, exp_number=run_number, layer=layer)
+ roi = strands_rois[feeding_rate][extrusion_type]
+ strand = utils.crop_to_roi(measurement, roi=roi)
+ stats = assess_width_and_height(strand, layer)
+ results.loc[len(results)] = {'temperature':temperature, 'speed':speed, 'run_number':run_number, 'layer':layer, 'feeding_rate':feeding_rate, 'extrusion_type': extrusion_type, 'width_mean':stats['width']['mean'], 'width_std':stats['width']['std'], 'height_mean':stats['height']['mean'], 'height_std':stats['height']['std']}
+
+ # Clean up
+ del measurement, strand, stats
+ return results
+
+def assess_width_and_height_by_regression(strand:np.ndarray, last_layer_height:float, plot:bool=True):
+ # Model fitting
+ X = strand[:, 0].reshape(-1, 1)
+ Z = strand[:, 2].reshape(-1, 1)
+
+ model = Pipeline([
+ ('spline', SplineTransformer(n_knots=50, degree=10)),
+ ('linear', LinearRegression())])
+ model = model.fit(X, Z)
+ pred = model.predict(X)
+ r2 = r2_score(Z, pred)
+
+ domain = np.linspace(min(strand[:, 0]), max(strand[:, 0]), 3000)
+ pred_curve = model.predict(domain.reshape(-1, 1))
+ prediction = np.column_stack([domain, pred_curve])
+
+ # Noise removal
+ prediction = prediction[prediction[:, 0] > np.percentile(prediction[:, 0], 5)] # Remove noise from the left side
+ prediction = prediction[prediction[:, 0] < np.percentile(prediction[:, 0], 95)] # Remove noise from the left side
+ center = prediction[prediction[:, 0] > np.median(X) - 0.5]
+ center = center[center[:, 0] < np.median(X) + 0.5]
+ top = center[center[:, 1] > np.percentile(center[:, 1], 85)] # Top 15% highest points
+ x_center = top[top[:, 1].argmax(), 0]
+ lower_third = prediction[prediction[:, 1] < np.percentile(prediction[:, 1], 50)]
+ left_side = lower_third[lower_third[:, 0] < x_center]
+ right_side = lower_third[lower_third[:, 0] > x_center]
+ lower_gap = right_side[:, 0].min() - left_side[:, 0].max()
+ prediction = prediction[prediction[:, 0] > (left_side[:, 0].max() - lower_gap * 0.1)] # Remove noise from the left side
+ prediction = prediction[prediction[:, 0] < (right_side[:, 0].min() + lower_gap * 0.1)] # Remove noise from the right side
+
+ # Calculation of characteristics
+ bed = np.median(lower_third[:, 1])
+ basis = bed + last_layer_height
+
+ # Height is the highest point minus the basis
+ highest_point = prediction[prediction[:, 1].argmax()]
+ raw_height = highest_point[1]
+ height = raw_height - basis
+ half_height = basis + height/2
+
+ # Width is calculated at half height
+ left = prediction[prediction[:, 0] < highest_point[0]]
+ right = prediction[prediction[:, 0] > highest_point[0]]
+ if len(left) == 0 or len(right) == 0: # If the highest point is at the edge of the bed, we can't calculate the width
+ return (None, None, None) if plot else (None, None)
+ width_left = left[np.abs(left[:, 1] - half_height).argmin()]
+ width_right = right[np.abs(right[:, 1] - half_height).argmin()]
+ width = width_right[0] - width_left[0]
+
+ # Area is calculated as the area under the curve minus the area under the basis
+ basis_curve = prediction[:, 1].copy()
+ basis_curve[basis_curve > basis] = basis
+ basis_area = np.trapz(basis_curve, prediction[:, 0])
+ area = np.trapz(prediction[:, 1], prediction[:, 0]) - basis_area
+
+ if plot:
+ fig, ax = plt.subplots()
+ ax.plot(strand[:, 0], strand[:, 2], 'bo', markersize=0.02, label='measurement points') # real data
+ ax.plot([min(strand[:, 0]), max(strand[:, 0])], [bed, bed], '-k', label='bed') # plot bed
+ ax.plot([min(strand[:, 0]), max(strand[:, 0])], [bed + last_layer_height, bed + last_layer_height], '--k', label='last layer hight') # plot last layer height
+ ax.plot([highest_point[0], highest_point[0]], [basis, highest_point[1]], '-g', label='height') # plot height
+ ax.plot([width_left[0], width_right[0]], [half_height, half_height], '-m', label='width') # plot width
+ ax.fill_between(prediction[:, 0], np.ones_like(prediction[:, 0]) * bed, basis_curve, color='b', alpha=0.2, label='area') # area
+ ax.plot(prediction[:, 0], prediction[:, 1], 'r-', label='regression') # regression
+ ax.legend(loc='upper left', bbox_to_anchor=(1, 0.5))
+ ax.set_xlabel('x [mm]')
+ ax.set_ylabel('z [mm]')
+ ax.set_box_aspect(1)
+ ax.set_title('Front view')
+ plt.close()
+ return fig, height, width, area, r2
+ else:
+ return height, width, area, r2
+
+def assess_by_regression():
+ results = pd.DataFrame(columns=['temperature', 'speed', 'layer', 'feeding_rate', 'width', 'height', 'area', 'determination'])
+
+ strands_rois = get_strand_rois()
+ for temperature, speed, feeding_rate in product([180, 200], [500, 1200], strands_rois.keys()):
+ last_layer_height = 0
+ for layer in range(3):
+ measurement = load(temperature=temperature, speed=speed, layer=layer)
+ roi = strands_rois[feeding_rate]
+ strand = utils.crop_to_roi(measurement, roi=roi)
+ try:
+ fig, height, width, area, r2 = assess_width_and_height_by_regression(strand, last_layer_height=last_layer_height, plot=True)
+ except Exception as e:
+ print(f"Error while assessing {temperature}, {speed}, {layer}, {feeding_rate}")
+ NameError(e)
+
+ results.loc[len(results)] = {'temperature':temperature, 'speed':speed, 'layer':layer, 'feeding_rate':feeding_rate, 'width':width, 'height':height, 'area': area, 'determination':r2}
+ fig.savefig(f"data/experiments/assessment/v2/img/{temperature}_{speed}_{layer}_{feeding_rate}.png")
+ last_layer_height += height
+ return results
+
+if __name__ == '__main__':
+ results = assess_by_regression()
+ results.to_csv('data/experiments/assessment/v2/assessments_v1.csv', index=False)
\ No newline at end of file
diff --git a/experiments/doe.py b/experiments/doe.py
new file mode 100644
index 0000000000000000000000000000000000000000..abfd1ea381478fb9f6f2334fb38481b87cfba9f6
--- /dev/null
+++ b/experiments/doe.py
@@ -0,0 +1,61 @@
+"""Module to manage the design of experiments (DOE) to learn about the profile's cross-section"""
+
+import os
+
+def generate_experiment(folderpath:str="data/experiments/gcode", temperature:int=200, speed:int=1000, layer_count:int=3) -> None:
+ """Generates a gcode file with the given temperature and speed settings"""
+ # Setup
+ os.makedirs(folderpath, exist_ok=True)
+ gcode = setup(temperature=temperature, layer_count=layer_count)
+
+ # Generate gcode
+ global e
+ e = 0
+ y_start = 20
+ length = 100
+ for (layer_number, z) in enumerate([0.2, 0.4, 0.6]):
+ gcode += f";LAYER:{layer_number}\n"
+ direction = 1
+ for i in range(20):
+ gcode += print_strand(speed=speed, x=70 + i * 3, y=y_start + max(0, -direction * length), z=z, length=length * direction, feeding_rate=0.03 + i * 0.01)
+ direction *= -1
+
+ gcode += end()
+
+ with open(f"{folderpath}/temperature_{temperature}_speed_{speed}.gcode", "w") as fle:
+ fle.write(gcode)
+
+
+def print_strand(speed:int, x:float, y:float, z:float, length:float, feeding_rate:float) -> str:
+ """Returns a string to print a strand at the given coordinates"""
+ global e
+ e += abs(feeding_rate * length)
+ y_final = y + length
+ move_up = f"G0 F6000 Z{round(z + 0.4, 1)}\n"
+ place_in_start = f"G0 F6000 X{round(x, 3)} Y{round(y, 3)}\n"
+ move_down = f"G0 F6000 Z{round(z, 1)}\n"
+ extrude = f"G1 F{round(speed, 3)} Y{round(y_final, 3)} E{round(e, 3)}\n"
+ return move_up + place_in_start + move_down + extrude
+
+def setup(temperature:int, layer_count:int) -> str:
+ """Returns a string to setup the printer with the given temperature"""
+ with open("doe/template_startup.gcode", "r") as fle:
+ template = fle.read()
+
+ template = template.replace("{$TEMPERATURE}", str(temperature))
+ template = template.replace("{$LAYER_COUNT}", str(layer_count))
+ return template
+
+def end() -> str:
+ """Returns a string to end the printing process"""
+ with open("doe/template_end.gcode", "r") as fle:
+ template = fle.read()
+ return template
+
+if __name__ == "__main__":
+ def main():
+ for temp in [180, 200]:
+ for speed in [500, 1200]:
+ generate_experiment(folderpath='data/experiments/gcode/v2', temperature=temp, speed=speed)
+
+ main()
\ No newline at end of file
diff --git a/experiments/results.ipynb b/experiments/results.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b12fe8aa372bcd13587addd0b7feda28d4f13a84
--- /dev/null
+++ b/experiments/results.ipynb
@@ -0,0 +1,1199 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>temperature</th>\n",
+ " <th>speed</th>\n",
+ " <th>layer</th>\n",
+ " <th>feeding_rate</th>\n",
+ " <th>width</th>\n",
+ " <th>height</th>\n",
+ " <th>area</th>\n",
+ " <th>determination</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>0</td>\n",
+ " <td>0.03</td>\n",
+ " <td>0.232047</td>\n",
+ " <td>0.113483</td>\n",
+ " <td>0.027571</td>\n",
+ " <td>0.566229</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>1</td>\n",
+ " <td>0.03</td>\n",
+ " <td>0.343069</td>\n",
+ " <td>0.186798</td>\n",
+ " <td>0.060071</td>\n",
+ " <td>0.844442</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>2</td>\n",
+ " <td>0.03</td>\n",
+ " <td>0.456030</td>\n",
+ " <td>0.214355</td>\n",
+ " <td>0.093566</td>\n",
+ " <td>0.895364</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>0</td>\n",
+ " <td>0.04</td>\n",
+ " <td>0.422093</td>\n",
+ " <td>0.141609</td>\n",
+ " <td>0.058275</td>\n",
+ " <td>0.793031</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>1</td>\n",
+ " <td>0.04</td>\n",
+ " <td>0.427048</td>\n",
+ " <td>0.181689</td>\n",
+ " <td>0.074651</td>\n",
+ " <td>0.943355</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>235</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>1</td>\n",
+ " <td>0.21</td>\n",
+ " <td>1.212323</td>\n",
+ " <td>0.319786</td>\n",
+ " <td>0.358358</td>\n",
+ " <td>0.979150</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>236</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>2</td>\n",
+ " <td>0.21</td>\n",
+ " <td>1.129647</td>\n",
+ " <td>0.294548</td>\n",
+ " <td>0.302866</td>\n",
+ " <td>0.987627</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>237</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>0</td>\n",
+ " <td>0.22</td>\n",
+ " <td>1.231542</td>\n",
+ " <td>0.340750</td>\n",
+ " <td>0.393009</td>\n",
+ " <td>0.968387</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>238</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>1</td>\n",
+ " <td>0.22</td>\n",
+ " <td>0.851429</td>\n",
+ " <td>0.320425</td>\n",
+ " <td>0.272002</td>\n",
+ " <td>0.976035</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>239</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>2</td>\n",
+ " <td>0.22</td>\n",
+ " <td>1.188328</td>\n",
+ " <td>0.230621</td>\n",
+ " <td>0.234240</td>\n",
+ " <td>0.945466</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>240 rows × 8 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " temperature speed layer feeding_rate width height area \\\n",
+ "0 180 500 0 0.03 0.232047 0.113483 0.027571 \n",
+ "1 180 500 1 0.03 0.343069 0.186798 0.060071 \n",
+ "2 180 500 2 0.03 0.456030 0.214355 0.093566 \n",
+ "3 180 500 0 0.04 0.422093 0.141609 0.058275 \n",
+ "4 180 500 1 0.04 0.427048 0.181689 0.074651 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "235 200 1200 1 0.21 1.212323 0.319786 0.358358 \n",
+ "236 200 1200 2 0.21 1.129647 0.294548 0.302866 \n",
+ "237 200 1200 0 0.22 1.231542 0.340750 0.393009 \n",
+ "238 200 1200 1 0.22 0.851429 0.320425 0.272002 \n",
+ "239 200 1200 2 0.22 1.188328 0.230621 0.234240 \n",
+ "\n",
+ " determination \n",
+ "0 0.566229 \n",
+ "1 0.844442 \n",
+ "2 0.895364 \n",
+ "3 0.793031 \n",
+ "4 0.943355 \n",
+ ".. ... \n",
+ "235 0.979150 \n",
+ "236 0.987627 \n",
+ "237 0.968387 \n",
+ "238 0.976035 \n",
+ "239 0.945466 \n",
+ "\n",
+ "[240 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "sns.set()\n",
+ "\n",
+ "df = pd.read_csv('../data/experiments/assessment/v2/assessments_v1.csv')\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup plots\n",
+ "\n",
+ "SMALL_SIZE = 16\n",
+ "MEDIUM_SIZE = SMALL_SIZE * 1.2\n",
+ "BIGGER_SIZE = SMALL_SIZE * 1.5\n",
+ "\n",
+ "plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n",
+ "plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title\n",
+ "plt.rc('axes', labelsize=SMALL_SIZE) # fontsize of the x and y labels\n",
+ "plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n",
+ "plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n",
+ "plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n",
+ "plt.rc('figure', titlesize=MEDIUM_SIZE) # fontsize of the figure title"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "11 (out of 240) data points disconsidered because of low determination\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"{df[df.determination <= 0.7].shape[0]} (out of {df.shape[0]}) data points disconsidered because of low determination\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pre processing\n",
+ "\n",
+ "* Calculate gap distance\n",
+ "* Remove data of layer = 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/c0/l87ylxnd52v7n4kq_c122hg00000gn/T/ipykernel_7555/2155986236.py:9: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " df['gap'] = (1 + df.layer) * 0.2 - df.last_layer_height\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>temperature</th>\n",
+ " <th>speed</th>\n",
+ " <th>layer</th>\n",
+ " <th>feeding_rate</th>\n",
+ " <th>width</th>\n",
+ " <th>height</th>\n",
+ " <th>area</th>\n",
+ " <th>determination</th>\n",
+ " <th>last_layer_height</th>\n",
+ " <th>gap</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>1</td>\n",
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+ " <td>0.844442</td>\n",
+ " <td>0.113483</td>\n",
+ " <td>0.286517</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>0</td>\n",
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+ " <td>0.141609</td>\n",
+ " <td>0.058275</td>\n",
+ " <td>0.793031</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>1</td>\n",
+ " <td>0.04</td>\n",
+ " <td>0.427048</td>\n",
+ " <td>0.181689</td>\n",
+ " <td>0.074651</td>\n",
+ " <td>0.943355</td>\n",
+ " <td>0.141609</td>\n",
+ " <td>0.258391</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>0</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.462910</td>\n",
+ " <td>0.172832</td>\n",
+ " <td>0.077505</td>\n",
+ " <td>0.874186</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>180</td>\n",
+ " <td>500</td>\n",
+ " <td>1</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.519121</td>\n",
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+ " <td>0.091320</td>\n",
+ " <td>0.957285</td>\n",
+ " <td>0.172832</td>\n",
+ " <td>0.227168</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
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+ " <th>232</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>1</td>\n",
+ " <td>0.20</td>\n",
+ " <td>1.273103</td>\n",
+ " <td>0.311648</td>\n",
+ " <td>0.361049</td>\n",
+ " <td>0.987496</td>\n",
+ " <td>0.340510</td>\n",
+ " <td>0.059490</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>234</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>0</td>\n",
+ " <td>0.21</td>\n",
+ " <td>1.142737</td>\n",
+ " <td>0.359291</td>\n",
+ " <td>0.382367</td>\n",
+ " <td>0.967821</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>235</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>1</td>\n",
+ " <td>0.21</td>\n",
+ " <td>1.212323</td>\n",
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+ " <td>0.358358</td>\n",
+ " <td>0.979150</td>\n",
+ " <td>0.359291</td>\n",
+ " <td>0.040709</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>237</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>0</td>\n",
+ " <td>0.22</td>\n",
+ " <td>1.231542</td>\n",
+ " <td>0.340750</td>\n",
+ " <td>0.393009</td>\n",
+ " <td>0.968387</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.200000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>238</th>\n",
+ " <td>200</td>\n",
+ " <td>1200</td>\n",
+ " <td>1</td>\n",
+ " <td>0.22</td>\n",
+ " <td>0.851429</td>\n",
+ " <td>0.320425</td>\n",
+ " <td>0.272002</td>\n",
+ " <td>0.976035</td>\n",
+ " <td>0.340750</td>\n",
+ " <td>0.059250</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>152 rows × 10 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " temperature speed layer feeding_rate width height area \\\n",
+ "1 180 500 1 0.03 0.343069 0.186798 0.060071 \n",
+ "3 180 500 0 0.04 0.422093 0.141609 0.058275 \n",
+ "4 180 500 1 0.04 0.427048 0.181689 0.074651 \n",
+ "6 180 500 0 0.05 0.462910 0.172832 0.077505 \n",
+ "7 180 500 1 0.05 0.519121 0.185009 0.091320 \n",
+ ".. ... ... ... ... ... ... ... \n",
+ "232 200 1200 1 0.20 1.273103 0.311648 0.361049 \n",
+ "234 200 1200 0 0.21 1.142737 0.359291 0.382367 \n",
+ "235 200 1200 1 0.21 1.212323 0.319786 0.358358 \n",
+ "237 200 1200 0 0.22 1.231542 0.340750 0.393009 \n",
+ "238 200 1200 1 0.22 0.851429 0.320425 0.272002 \n",
+ "\n",
+ " determination last_layer_height gap \n",
+ "1 0.844442 0.113483 0.286517 \n",
+ "3 0.793031 0.000000 0.200000 \n",
+ "4 0.943355 0.141609 0.258391 \n",
+ "6 0.874186 0.000000 0.200000 \n",
+ "7 0.957285 0.172832 0.227168 \n",
+ ".. ... ... ... \n",
+ "232 0.987496 0.340510 0.059490 \n",
+ "234 0.967821 0.000000 0.200000 \n",
+ "235 0.979150 0.359291 0.040709 \n",
+ "237 0.968387 0.000000 0.200000 \n",
+ "238 0.976035 0.340750 0.059250 \n",
+ "\n",
+ "[152 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "last_layer_height = []\n",
+ "for row in df.iloc:\n",
+ " if row.layer == 0:\n",
+ " last_layer_height.append(0)\n",
+ " else:\n",
+ " last_layer_height.append(df[(df.temperature == row.temperature) & (df.speed == row.speed) & (df.feeding_rate == row.feeding_rate) & (df.layer < row.layer)].height.sum())\n",
+ "df[\"last_layer_height\"] = last_layer_height\n",
+ "df = df[df.determination > 0.7]\n",
+ "df['gap'] = (1 + df.layer) * 0.2 - df.last_layer_height\n",
+ "df = df[df.layer <= 1]\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = df[[\"temperature\", \"speed\", \"feeding_rate\", \"layer\", \"gap\", \"width\", \"area\"]].values\n",
+ "height = df[[\"height\"]].values\n",
+ "width = df[[\"width\"]].values\n",
+ "area = df[[\"area\"]].values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Width model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1500x1000 with 6 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "eval = \"width\"\n",
+ "fig, axes = plt.subplots(3, 2, figsize=(15, 10))\n",
+ "fig.suptitle(\"Influence of parameters on the width of the printed strand\", weight=\"bold\")\n",
+ "fig.tight_layout(pad=3.0)\n",
+ "for i, (param, unit) in enumerate(zip([\"temperature\", \"speed\", \"layer\", \"feeding_rate\", \"gap\", \"area\"], [' [°C]', ' [mm/s]', '', '', '[mm]', ' [mm²]'])):\n",
+ " axes[i//2, i%2].plot(df[param], df[eval], 'o')\n",
+ " if param == 'feeding_rate': param = 'feed rate'\n",
+ " axes[i//2, i%2].title.set_text(f'{param[0].upper()}{param[1:]}')\n",
+ " axes[i//2, i%2].set_xlabel(f\"{param}{unit}\")\n",
+ " axes[i//2, i%2].set_ylabel(f\"{eval} [mm]\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Test of the models in the literature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Results of the model proposed by Xu et al. (2022)\n",
+ "--------------------------------------------------\n",
+ "Linear Regression r^2 on test data : -2.795\n",
+ "Prediction: 0.06649036758114679\n",
+ "Real value: 0.3430693379119418\n",
+ "\n",
+ "Results of the model proposed by Hebda et al. (2019)\n",
+ "--------------------------------------------------\n",
+ "Linear Regression r^2 on test data : -5.525\n",
+ "Prediction: 0.12124355652982143\n",
+ "Real value: 0.3430693379119418\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import r2_score\n",
+ "\n",
+ "def xu(X, D=0.4, rn=1):\n",
+ " t, s, f, l, g, w, a = X.T\n",
+ " w = D*(1 - rn/D + np.sqrt((rn/D-1)**2 + np.pi*D*f/g * (rn/D - 0.5)**2))\n",
+ " return w\n",
+ "\n",
+ "predxu = xu(X)\n",
+ "\n",
+ "r2 = r2_score(width, predxu)\n",
+ "print('Results of the model proposed by Xu et al. (2022)')\n",
+ "print('-'*50)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {predxu[0]}\\nReal value: {width[0][0]}\")\n",
+ "\n",
+ "def hebda(X, D=0.4, alpha=1.75):\n",
+ " t, s, f, l, g, w, a = X.T\n",
+ " w = alpha * D * np.sqrt(f)\n",
+ " return w\n",
+ "\n",
+ "predhebda = hebda(X)\n",
+ "\n",
+ "r2 = r2_score(width, predhebda)\n",
+ "print('\\nResults of the model proposed by Hebda et al. (2019)')\n",
+ "print('-'*50)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {predhebda[0]}\\nReal value: {width[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Feature Engineering\n",
+ "\n",
+ "**Hypothesis**:\n",
+ "* As suggested by Comminal et al., the width is proportional to the square root of the feeding rate\n",
+ "* The layer should not matter, although the graph suggests differently\n",
+ "* Parameters with low coefficients can be removed from the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import FunctionTransformer\n",
+ "\n",
+ "def feature_extraction(X:np.ndarray) -> np.ndarray:\n",
+ " temperature, speed, feeding_rate, layer = X[:, 0], X[:, 1], X[:, 2], X[:, 3]\n",
+ " sqrt_fr = np.sqrt(feeding_rate)\n",
+ " cbrt_fr = np.cbrt(feeding_rate)\n",
+ " fr2 = feeding_rate ** 2\n",
+ " t_f = temperature * feeding_rate\n",
+ " return np.column_stack([temperature, speed, cbrt_fr])\n",
+ "\n",
+ "FeatureExtraction = FunctionTransformer(feature_extraction)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Linear Regression r^2 on test data : 0.881\n",
+ "Prediction: 0.3506883578110451\n",
+ "Real value: 0.3430693379119418\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.metrics import r2_score\n",
+ "\n",
+ "width_model = Pipeline([('transf', FeatureExtraction),\n",
+ " # ('poly', PolynomialFeatures(degree=3)),\n",
+ " ('linear', LinearRegression())])\n",
+ "\n",
+ "width_model = width_model.fit(X, width)\n",
+ "pred = width_model.predict(X)\n",
+ "r2 = r2_score(width, pred)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {pred[0][0]}\\nReal value: {width[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients:\n",
+ "temperature: 0.004062025031923957\n",
+ "speed: -0.0001331731552701792\n",
+ "cbrt_fr: 2.984109335460154\n",
+ "intercept: -1.2411217231463934\n"
+ ]
+ }
+ ],
+ "source": [
+ "def print_coefs_and_params_names(model, coef_names = ['temperature', 'speed', 'cbrt_fr']):\n",
+ " linear_model = model.named_steps['linear']\n",
+ " transformer = model.named_steps['transf']\n",
+ " if hasattr(transformer, 'get_feature_names_out'):\n",
+ " coef_names = transformer.get_feature_names_out(coef_names)\n",
+ " coefficients = linear_model.coef_[0]\n",
+ " \n",
+ " print('Coefficients:')\n",
+ " for coef_name, coef_value in zip(coef_names, coefficients):\n",
+ " print(f'{coef_name}: {coef_value}')\n",
+ " print(f'intercept: {linear_model.intercept_[0]}')\n",
+ " \n",
+ "print_coefs_and_params_names(width_model)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1500x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from itertools import product\n",
+ "\n",
+ "domain = np.linspace(df.feeding_rate.min(), df.feeding_rate.max(), 100)\n",
+ "def my_function(domain, temp, speed):\n",
+ " # return np.cbrt(domain) * 3.5 * temp/200 -0.8\n",
+ " return width_model.predict(np.column_stack([np.ones_like(domain) * temp, np.ones_like(domain) * speed, domain, np.ones_like(domain)]))\n",
+ "\n",
+ "fig, axs = plt.subplots(1, 2, figsize=(15, 8), sharey=True)\n",
+ "colors = {(200, 500): \"#aa0000\", (200, 1200): \"#ff0000\", (180, 500): \"#008800\", (180, 1200): \"#00bb00\"}\n",
+ "for temp, speed in product([180, 200], [500, 1200]):\n",
+ " legend = f\"Temperature: {temp}ºC, Speed: {speed }mm/s\"\n",
+ " pltdf = df[(df.temperature == temp) & (df.speed == speed)]\n",
+ " for layer in range(2):\n",
+ " ldf = pltdf[pltdf.layer == layer]\n",
+ " axs[layer].plot(ldf.feeding_rate, ldf.width, 'o', label=legend, markersize=4, color=colors[(temp, speed)])\n",
+ " axs[layer].plot(domain, my_function(domain, temp, speed), color=colors[(temp, speed)], label=\"model\")\n",
+ "for ax in axs:\n",
+ " ax.set_xlabel(\"Feeding rate [mm/s]\")\n",
+ " ax.set_ylabel(\"Width [mm]\")\n",
+ " ax.set_title(f\"Layer {axs.tolist().index(ax)}\", weight=\"bold\")\n",
+ "fig.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Height model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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fWff/zTffaMqUKabanp1NmzbZirnZfe5nzJjhMOp71qxZGj9+vF2RJWu7du/erYEDBzpcM52Jjo7WkCFDHM7ZjHbFxMTozTffNL2/GTNm2D6TWV9benq67feg9P+focyfD4vF4vI1sTCvywEBAU6fn/W6nOG9996zFXOzHuPChQv65JNP7Lbl9drlqg8++MBWzM3a9pSUFA0fPlw7duzI8fnLli2zFXOzXrtc8cknn9iKudm9BwsWLLC9376+vgoKCnJ4vzMWA8z8R9tPPvnEoZib9XMUGRmpF1980eGYo0aNcijmSv/fT7NmzdKJEyfMvDwHeb2uBwUFOSx0mHG9zPo7K4Oz3y8HDx7UO++843CdyXounDt3Th9//HGOryc9PV0DBgywFXOzyzFGjx7t8LwXXnjBoZibcWzDMJweEwC8HQVdACii4uLi5O/vrzfffFObN2/W1q1b9Z///McuJvNIlM2bN9uNcJKuJtwZ2z25sEiGyMhIuy+GjRs31k8//aQdO3bol19+sRvlNHPmTB08eDDHfcXGxqp169ZavHixduzYoZ9//lnXX3+97fG0tDT9+eeftp9TUlLsinH+/v56+eWXFRUVpW3btmns2LF2X9I+++wz2xcQd7Y7qzFjxtgV4tq0aaMlS5Zox44dWr9+vfr06WN77MqVKxo+fLiteOGJ9zQxMVF33323IiIitG3bNk2fPl3VqlWzPX7s2DHNmjXL9vPKlSsVHR1t+3nQoEHasmWLdu7cqYiICLvboWNjY3X06FGnxx86dKi2bt2qtWvX6r333pOPj4/Onj1rtwhThw4dtG7dOu3YsUNRUVF66KGH7Paxbdu2HPefkpKiu+66S2vWrNGOHTscCvCJiYmqUKGCZsyYoR07dmjWrFl2o6o2bdqkK1eu2H7+9ttv7V7T/fffr7Vr12rbtm2aOnWqqlSpYnvsvffeU1pamqSri0otWbLE7thPP/20w3v67rvv2o7n4+OjV155RRs3btSmTZs0evRoW8EhISFBn376aY6vW5Jq1qypZcuWadu2bZo8ebJtVFzmP1Tceeedtr7Ztm2bPvjgA7uiRuYR1LmJj4+3Gy3t4+Oj559/Xn/99Zd27NihefPmqX79+rbHt27dqq+++irH/cXGxqpMmTIaO3astm7dqr///lu9evWyi8k6+i4no0aN0ubNm1W1alXbthtvvNHW/zmNdk5NTdUDDzygtWvXaufOnZo1a5bdNCQ7d+7UuXPnbD//9NNPdu9nu3bttHz5cu3YsUM//PCD3Wj3zz//PM+LMKWmpqpmzZqaNWuWtm/frvXr19tNY2EYhtauXWv7+eLFi3YFvnLlymnSpEnasmWLIiMj1bt3b7vX9OOPP5puy+TJk+0K+A0bNtTChQu1fft2LV++XG3bttXly5ddem3t27fX8uXLtXPnTi1atMg2cl+6OkJv165dkqRnnnnG4f2rWrWqy9fEwrwuf/PNN06fn/mczSw1NVUDBgzQhg0btH37dk2YMMHud9ratWvtimp5vXa5KjExUfXq1dP8+fO1fft2/fLLL2rbtq3t8ZSUFI0dO9bpPpo3b67ff/9dW7Zs0dixYx1GYZoRGxur8PBwzZ07V9u3b9fq1avVokULu5h169ZJ+v9rwdNPP233+JIlS7R582aNGjVKknT48GFNnTrV9njGZ3Dbtm1avXq13ZQJq1at0u+//277+fDhw3bTvvj7++ull15SVFSUbbRwfu42yOt1ffPmzfr666/t9vX2229r8+bNThdDzOn3y8yZM22fjYCAAE2YMEE7duzQjh079P3339vdlZH1DyiZGYahuLg49e7dW3/88Yd27NihyZMn2xWZ//nnH506dcr28/r16+3ywKCgIL333nv6+++/FRUVpeHDh2f7h2EAuFZQ0AWAImzo0KHq1auXAgMDVaJECQ0cONBuPrrMt78VBZm/NPj5+WncuHGqX7++fHx8VLt2bX3++ecqWbJktvFZlS9fXpMnT5bVapXFYtENN9yg559/3i4m8+uPjIy0S/T79Omj/v37KyQkRP7+/urataseffRRXX/99brrrrvUo0cP26ggd7Y7swsXLtgV9SpXrqzJkyerbt26kq4WWV577TV17tzZFnPkyBFFRESY2n9e1KtXT59++qmqV68uf39/tWnTRl9//bXdaJqlS5fa/t2lSxf98ssv+uyzz/TSSy9p0KBBtr6oXr267rvvPrv9nz9/Psdj16lTR88884z8/f1VqVIl3XbbbZKujtRdvXq1vvrqKw0bNkzvvPOOypcvL0kKCQnRY489ZvoYVapU0UcffaTKlSvLYrHo/vvvtytYS1e/vLZu3Vo+Pj668cYb7eaDTEtLs7vdM/MX8htuuEHvvfeeKlWqJD8/P7Vt29ZuJOKpU6eyHY2Vk6NHj2rjxo22n3v27GmbJzYwMFA9e/a0KywtX75c8fHxOe7vP//5j2rVqiU/Pz+1b9/eVojMPJrMz8/P9v6VKFFC999/vyZNmqSZM2cqMjJSn3/+uen2//e//7UrUPbt21fPPvus7Xbppk2bavLkyXZFi+nTpztdXX306NHq2rWrSpQooVKlSmnkyJF2t197+poXHh5ue4+lq4WfBx980C7m5MmTtn9nPj9CQ0P12WefqWbNmvLx8VHjxo3tiqpJSUlOCya5GTt2rG688Ub5+PioXLlyDtMaZL7+ZR71KEkjR45Uhw4dVKJECYWFhen1119X48aNbY+bLegahqHly5fbfi5durS+++47NWjQQD4+PqpZs6a+/PLLHOdzzU758uVt/SZdfQ+eeOIJu5jMfZ5fRfG6bEaHDh00bNgwlS1bVj4+PurUqZNdGy9fvlxg167MSpYsqcmTJ6tJkyby8/NT7dq1NXnyZLt5gNetW+f0jxkjR45UxYoVVaJECXXt2jVPd5+UKFFCX3/9tZo1ayYfHx9VqVLFbv5dyfXrx08//WR318kHH3ygG2+8UX5+fna/azJk/hytWLHCrsDep08fPfnkkwoJCVFwcLAeeOABp9MH5cZT1/Wc5PT75ZVXXtGCBQv03nvv6d1331WnTp3k4+MjX19fNW/eXDfffLNtHxcuXHB6jA4dOuiNN95QhQoVZLFY1L59e3Xv3t0uJvN7mPlaJEkvvvii/v3vfys4OFghISF64okn9OSTT+bzlQNA0cUcugBQhGW38Fft2rVtt9k5u325MGQedVSpUiVdvnzZYTRrlSpVbLc0Zx2llNnNN9/sMHqldu3adj9nfv1ZR35kLtJlePnll/Xyyy97tN2Z/fnnn3ajPTOK81k98cQTWrFihe3n9evXO52LNj8eeOABh1sh69Spo0aNGtlGvu7bt09XrlxRiRIlbEXtjL5PSUnR3r17tWPHDm3cuNFuVKAkp8W6zF/ssqpUqZIqVapkW8Dl8OHD2rFjhzZv3mw36im3Y7Rt29ZhjuMKFSrYFuDx8/NTu3bt7B7PWnzKuP385MmTdkWyunXrOty+nHlhQunquWF2cbFNmzbZ/VyrVi2H8y5zMTolJUU7duywG/2WWU7926lTJ9sfIZYuXarly5ercePGatmypVq0aKGWLVvmuBieM1nf+6xFOOnq+9qtWzfNnTtX0tWRozt27Mh2kTBfX1+HxdMyFofLuKXW09e8Dh06ONyynHkko3S1MCtdPQ8zjxa//vrrdfr0abupYDJu7c6YnmDz5s0Oo47NqFixoho2bGi3LetIzox2ZRwns8qVKzucW7Vr19b27dslXZ16ITk52eFW7KyOHTtmV5i77bbbHM6doKAg3X333aanmLjpppscros59bk7FMXrshkdO3Z02Ja1nzI+H56+dmV26623OlxDAwICdNddd+mbb76RdPUPAbt27cr22lWqVCk1adLE5eNm1bhxY4e5oJ3lDGZk/hz5+/urTJkyDp+jWrVq2fo6c3zWqYH+/e9/O+y/a9eueuONN5xOVZQTT13Xc5LT75cSJUqoYcOGtutTQkKCdu3apW3btikqKkqRkZG22Nymdskub3P2Hmbt46zFX0n617/+pYkTJzo9LgB4Kwq6AFCEVaxY0WFb5i+eeV1MIz+c3ZaZebGW48ePO10IRZLdIipZZffasxZ4M7/+mJgYu8cqVKjg9NiZubPdmWUUETNk/WKS0/bMX8TdLafV02vUqGErTqWnpysuLs7Wh6mpqfrpp59st6M6K644Oyeze08zW79+vebPn6/IyEinI3mcHSO79z1zgTc0NNSh4Jv1lsyM/Wc+LyTpl19+0S+//JLzC5D5cyO7/X/wwQe5ztF8+PDhbIsiAQEBOX55f+WVV3Ts2DHbiuWpqal2t437+vqqVatW6tu3b7ZfqHOS+fwuVapUju9v1vP75MmT2RZ0y5Ytm+2CgwV5zcvuNWQtcmZcA+Pi4uwKFJs2bXLbtSOr7BYty6ldkuO5lXmKheykpKTo2LFjqlOnjtO4rKPjc7qeZF2cyxlXX1t+FcXrshlm+injj12evnZl5ux3SmY53VmR2+8Fs1zNGczI3I8pKSm5fr5PnTpl+8NI5tHSUvb9FBwcrLCwMIf8xQxPXdez4+z3i3R1uos5c+ZoxYoV2rNnT54/r7nlvJLsit+Z+7hcuXLZTmFRvXr1PLUFALwBBV0AKMKyGy2V04IpnpBdUp55ZJOZeGcyL7SVVXav3ZUV4p2N4szKne3OLLfRbhmyjs4pyPc4Q9Yvuhm3cyYmJqp///52I49q1KihZs2aqUmTJoqJibEtZJWbrCuAZzZ69Gjbok+SFBYWZjtG9erVTa9Undt8edkVDHOSly+lZs8NyfF9NyOnKRec9W2ZMmU0ffp0rV27VkuWLNEff/xhV1zJmI/6zz//1KOPPqrXX3/dVFvcfX7ntL+C/DxkN1Izp+uOp8+PzFxpl+Tecysv+3VlJKSrry2/vOm6nFnmaX8yFIVzMydZf6fk1H/Orl2u8ES+lNd+rFChgsOxc2qLKwsSZuap63p2nL1HBw8eVN++fW1FaR8fHzVs2FBNmjRRs2bN9OuvvzosOJsTV9/DzI+5u38BwBtQ0AWAIsyTX2qzkzUhzu5LubN58MqVK2cbxVS3bl2HRaFc4eprz3r76PHjxx1Gm508eVLbt29XnTp1VKNGDVvxz53tzizriKp//vkn27iMqRwy5LQojjv8888/2Y7WyTxqLePWUunqAkgZxdyAgAB9/fXXatOmjS32+++/N33snIqp69evtyvmvvTSS+rfv7/tfMyp3zwt6znVu3dvvfHGG27bf1hYmN3Pn3/+eZ5v6c6tUG2xWHT77bfr9ttvl2EY2rdvn7Zs2aK//vpLy5cvt33WZ86cqYcfftg2n6gzFStWtE2FkJCQoDNnzmQ7wsrs+V3Q17v8Klu2rHx9fW1Fn/bt22vy5MmF3Kqrsp5bkZGRDtvcsd/MCyZmltviiIWpKF6X3c3T167Mcuq/rCOhM+ZFz8qVP7I544nrR7ly5WxTVQQFBZmebinjuZlFR0c75CRxcXF5XihR8sx1PTvO3qPXXnvNVsytVauWpkyZYvdZyTo1T26vxxXlypWzTYFx7tw5JSYmOozSLcrXIgDILxZFAwDYBAcH2/2c3QIiW7ZsyfH5mefBO3z4sEMiff78eT399NP64IMP9OOPP2r//v35a3AmWW/hzjz3YYa5c+dq8ODB6tq1q5o1a2ZbkMpT7W7ZsqXd4i6zZ8/OdvX3b7/91u7nW2+91dT+8+K///2vw+jl6Oho21yaklS/fn1buzPPX1u7dm27Yq4kW0HPjJxG0GT9wvfQQw/ZxbpyDHeqXr263W2m69evdxjts379eg0ZMkTjxo3T0qVL7eZPzfp6s474yzpv5Jo1axzaMGXKFI0YMUJff/21Vq1aZZuLNauc+vbgwYP69ttvNXLkSD388MNau3atLBaLwsPD9dBDD+njjz/W0KFD7Z6zb9++bPeV1U033WT3c9bzWJJOnz5t9weSsLAwh7lgPSlzgSAvo1adKVGihOrVq2f7ecuWLQ7Fmf379+vZZ5/Vp59+qv/+9785FkDdLeu5lV1R5ZVXXtFbb72lWbNm2W7bzk2NGjXsVq3//fffHV5zXFyc2/4olpP8vK9F5brsyXMzv9cuV0RGRurYsWN221JTU+0WrPL19VX9+vWzfX5hjnzOWkDMOqo48+coMTFRf/31l93jKSkpGjRokN59913NnTvXbk7tzIsOStLixYsdju/KH0Qzy+91PbffTVnl9B4lJCTYzQV/++232xVz09PTtWvXLtOvy1WZ+zg9PT3bRSfz2scA4A0o6AIAbLIubPLTTz/ZVhdPS0vTlClTsi06Zbjvvvts/05JSdFzzz2njRs3KjU1VTExMXr11Ve1evVqfffddxo5cqTpIoIZbdu2tWv/Dz/8oC+++ELnz59XamqqIiIi7BbpCQ0NtX0Z8FS7y5UrZzca9tSpU3rmmWdsi9qdO3dOo0ePtls93Wq1qn379nnqAzP279+v559/XidOnFBaWpq2bNmiAQMG2N1a2q1bN9u/M4/S3rt3r3788Uelpqbq/PnzGj9+vH744Qe7/efl9sasI8E/+eQTXbp0SUlJSVq8eLFGjx6d72PkhcVi0b333mv7+dChQxo6dKiOHj0qwzC0d+9evfXWW1q2bJkmTJigF154wW5Ov6y3j+7bt08pKSm2kX/h4eF2BcGFCxdq/PjxOn/+vNLT0xUREaHPPvtMCxcu1Mcff6y33nrL5dXfY2Nj9eGHH+rHH3/U5s2b9cYbbygyMlKpqalKT0/X3r17tWzZMrvnmJ1/ulu3bna3gU+fPl0TJ0603bq/detWPf3003ZF6MwjrwtC5vfg2LFjunjxos6fP++228sznx+xsbF69tlntWfPHqWnpys6OlqvvfaaVq5cqcmTJ2v48OEOCyp5yt133203qu69997TkiVLdPnyZSUlJWn8+PFasGCB5syZo1GjRmnmzJmm9muxWHT33Xfbfo6Pj9cTTzyhvXv3KjU1Vbt27dKTTz6Zr1GHZmR+Xy9cuKATJ04oPj7e1FykReW6nPk1HDx4UElJSTp58qRbFoDL77XLFSkpKXr66adtC5NGR0dr8ODBdouwdezY0W1TK7hT1mv0zp07bfNJS/afb+nq3SNr1qzRlStXFB8fr3feeUcrVqzQ9OnT9cYbb9jNU9ylSxe7z+BXX32l6dOnKykpSZcuXdL333+vCRMm5Knd+b2uZ33dGfPeZl04LzdZp+BavHixtmzZIsMwdPjwYf3nP/9xGOnubNouV2XOVaSr17nFixcrJSVFcXFx+vLLLzV//ny3HQ8AihqmXAAA2GSMnssYEXn69Gl16dJFN9xwg06fPq2YmBi7Fduz6tSpk9q2bWtb1Xjfvn3q1auX3W3JGWrVqqWePXu6re3+/v4aOXKkhgwZIunqaI3x48dr/Pjx2R7/P//5j+3Llifb/dprr2nTpk22xVX+/PNP3XPPPfL393coTAYFBemjjz7yeMErIiJCERER8vPzcxitW7t2bbvXd+ONN9oKHenp6Ro5cqRef/31HOcWzEuhrFWrVpo9e7bt5zlz5uj777+XxWLJduSQp4tFmQ0cOFBLly61FYpWrFihFStWZHtudO/eXeHh4bafy5Ytq8DAQNvov4iICDVp0kTp6enasGGDypYtq5EjR+rxxx9XWlqaDMPQF198oS+++CLXc9asli1bqlOnTrbi1MmTJ9WvXz/5+PjIYrE4HMNqtaply5am9l2uXDm9+eabeuWVVyRdPT8+++wzffbZZ9me323atNHjjz/uUvvzq0qVKrYi6qlTp9SmTRvbteHOO+/M9/579eql+fPn2z4jf//9t+67775s37/WrVt79I81mZUvX14DBw7U559/LunqZ2bYsGGyWCwOn6vAwEA9//zzpvc9cOBALVmyRAkJCZKk7du3695775WPj49tv9m9/+5UpUoV27+TkpJ0xx13yDAMvfzyy6bOsaJwXa5SpYqt2LVjxw41b95c6enpWrBggVtGsefn2uWqAwcO6MEHH8z2d0rJkiVdOr8KUtZpNJ5//nn5+vrqtttu06RJk9SgQQP16NHD9ofLkydPasCAAdn2YVhYmJ566inbz+XLl9cTTzyhiRMnSrr6R/F3331X7733nt1n0FlOlZP8Xtczf34kacaMGZo1a5YCAwPtRtzmJiwsTLVr17ZNu3H+/Hk99NBD2fZPhosXL+Y4/Yar6tevr3vvvVeLFi2SdHUU9Ysvvqjhw4fLMAzbiOu89DEAeANG6AIA7Lz22mt2i9QkJydr586diomJUbly5fTRRx85ff4XX3yhG2+80W5b1sS+Zs2amjZtmtvmzsvQpUsXvfnmmw6jGDMf32Kx6JlnnlGPHj0KpN0VK1bUtGnTHL6gZy0aVK5cWdOmTbMbsekJgwYNst2Km/WLd7Vq1TRx4kS79/+5555zGLmduV/q169vV+gwe7t+ZnfddZduueUWu22GYdi+8IaFhdl9AXXnVB25CQsL03fffecw72bWc6Njx456++237bb5+vo6zFec8Zoy5mxu06aNPvjgA4cFj7Luf9iwYerevXueXsMHH3ygdu3aObQjuz9WfPnll7YF8cx44IEHNHr0aId5C7Oe33feeacmTpzo8gjj/MpatM3o/4w7D/KrRIkS+u6773TDDTfYbc/at02bNtWXX37plmOa9eyzz6pPnz522zJ/rqSrxdyJEyc6zO3pTOXKlTVu3LgcV5+3Wq0aMGCA3WOunFNmZPe+GoZh+n0tCtdlT5+b+bl2uaJ79+62fsz6OyUgIECffPJJnudu9bSbb77ZYaqptLQ02/VZkt566y116dLFISazsLAwTZ061WGO6cGDB9vdASTZfwYffPBBuxHvrsjPdb1ChQpq3ry5w3MvXbpkanHEzN544w2HxUgztyHrZ8zdv7/ffvtth/wh43pgsVj0/PPPq2nTpm49JgAUFYzQBQDYadGihebNm6dJkyYpKipKcXFxqlq1qjp16qQnnngi19vlSpUqpZkzZ2rx4sX6+eeftXPnTsXGxsrf31+1a9dW586d9eijj3rs9stevXrppptu0vTp0xUZGalTp04pPT1dFSpUUMuWLdWrVy+HLzKebnedOnU0d+5crVixQkuWLNGePXt09uxZBQYG6oYbblDnzp318MMPZ7vau7s1atRIixYt0meffaa1a9fa3t8uXbpowIABKl26tF18xYoVbdNXrFmzRmfPnlVAQICuv/563X333erTp4/69++vv//+W5L0yy+/6Pnnn3dpcRMfHx9NmjRJU6ZM0eLFi3XkyBFZLBZVq1ZNt956q5588kl9//33toLYli1bdOLEiQJbpCg8PFy//vqrZs2apZUrV+rw4cOKj49XqVKl1KBBA9133322EYpZvf322woNDdXy5ct1/vx5hYSEqGHDhnYF0G7duqlFixaaMWOG1q1bpxMnTig5OVlhYWFq2bKlevfu7fDHBleUKlVK33zzjSIiIrRo0SJt375dMTExMgxDZcqUUd26ddWpUyf17NnTobBsRs+ePXXbbbdp4cKF+u233xQdHa34+HiVK1dOzZs3V48ePTw6L7QzDz30kJKSkjR37lwdPXpUQUFBql27tmrVquW2Y1SqVEk//fST5s+fr+XLl2vfvn2Ki4tTYGCg6tatq3vuuUcPPvig2/+AZcZrr72mu+++W/PmzdPff/+tmJgYpaenq2rVqrr11lvVr18/Va9e3eX93nLLLVqwYIEmTZqk9evXKzY2VlWrVtU999yjp59+2mHeyrycV87ceuut+vjjj/Xdd9/pwIEDKlGihGrUqKEGDRqY3kdhX5cHDx4sX19fLVq0SKdPn1apUqUUHh6e7cKCeZWfa5dZoaGhmjNnjiZNmqQlS5bo5MmTKlu2rG655RY988wzuv766932etwtNDRUU6dO1eeff65NmzYpPT1dlStXVtu2bW0x/v7+GjdunFa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",
+ "text/plain": [
+ "<Figure size 1500x1200 with 6 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "eval = \"height\"\n",
+ "fig, axes = plt.subplots(3, 2, figsize=(15, 12))\n",
+ "fig.suptitle(\"Influence of parameters on the height of the printed strand\", weight=\"bold\")\n",
+ "fig.tight_layout(pad=3.0)\n",
+ "for i, (param, unit) in enumerate(zip([\"temperature\", \"speed\", \"layer\", \"feeding_rate\", \"gap\", \"area\"], [' [°C]', ' [mm/s]', '', '', '[mm]', ' [mm²]'])):\n",
+ " axes[i//2, i%2].plot(df[param], df[eval], 'o')\n",
+ " if param == 'feeding_rate': param = 'feed rate'\n",
+ " axes[i//2, i%2].title.set_text(f'{param[0].upper()}{param[1:]}')\n",
+ " axes[i//2, i%2].set_xlabel(f\"{param}{unit}\")\n",
+ " axes[i//2, i%2].set_ylabel(f\"{eval} [mm]\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Linear Regression r^2 on test data : -21013005883.420\n",
+ "Prediction: 0.18622350017540157\n",
+ "Real value: 0.1867976188659668\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import PolynomialFeatures\n",
+ "\n",
+ "polyreg = Pipeline([('poly', PolynomialFeatures(degree=3)),\n",
+ " ('linear', LinearRegression())])\n",
+ "\n",
+ "polyreg = polyreg.fit(X[df.layer != 0], height[df.layer != 0])\n",
+ "pred = polyreg.predict(X)\n",
+ "r2 = r2_score(height, pred)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {pred[0][0]}\\nReal value: {height[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Feature Engineering\n",
+ "\n",
+ "**Hypothesis**:\n",
+ "* Parameters with low coefficients can be removed from the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import FunctionTransformer\n",
+ "\n",
+ "def height_features(X:np.ndarray) -> np.ndarray:\n",
+ " temperature, speed, feeding_rate, layer, gap, width, area = X[:, 0], X[:, 1], X[:, 2], X[:, 3], X[:, 4], X[:, 5], X[:, 6]\n",
+ " fw = feeding_rate / width\n",
+ " sqrtf = np.sqrt(feeding_rate)\n",
+ " w2 = width ** 2\n",
+ " sqrtfw = np.sqrt(fw)\n",
+ " return np.column_stack([feeding_rate, temperature, speed, np.cbrt(feeding_rate)])\n",
+ "\n",
+ "HeightFeatures = FunctionTransformer(height_features)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Linear Regression r^2 on test data : 0.801\n",
+ "Prediction: 0.16186860238094286\n",
+ "Real value: 0.1867976188659668\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.metrics import r2_score\n",
+ "\n",
+ "height_model = Pipeline([('transf', HeightFeatures),\n",
+ " ('linear', LinearRegression())])\n",
+ "# fit to an order-3 polynomial data\n",
+ "height_model = height_model.fit(X[df.layer != 0], height[df.layer != 0])\n",
+ "pred = height_model.predict(X[df.layer != 0])\n",
+ "r2 = r2_score(height[df.layer != 0], pred)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {pred[0][0]}\\nReal value: {height[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x157836a10>"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1500x800 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "eval = \"height\"\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(15, 8), sharey=True)\n",
+ "fig.suptitle(eval)\n",
+ "fig.tight_layout(pad=3.0)\n",
+ "\n",
+ "for layer in [0, 1, 2]:\n",
+ " for temp_no, temperature in enumerate([180, 200]):\n",
+ " for speed_no, speed in enumerate([500, 700, 1000, 1200]):\n",
+ " color_base = np.zeros((3,))\n",
+ " color_base[temp_no] = 1\n",
+ " current = df[(df.temperature == temperature) & (df.speed == speed) & (df.layer == layer)]\n",
+ " if len(current) == 0: continue\n",
+ " axes[layer].plot(current[\"feeding_rate\"], current[eval], 'o', markersize=5, color=color_base * ((3 + speed_no) / 7), label=f\"temp {temperature} speed {speed}\")#+ np.array([0, 0, 1]) * speed_no / 4)\n",
+ " predictions_mean = []\n",
+ " for fr in np.unique(current[\"feeding_rate\"]):\n",
+ " current_fr = current[current.feeding_rate == fr]\n",
+ " params = current_fr[[\"temperature\", \"speed\", \"feeding_rate\", \"layer\", \"gap\", \"width\", \"area\"]].values.mean(axis=0)\n",
+ " predictions_mean.append(height_model.predict(params.reshape(1, -1))[0])\n",
+ " axes[layer].plot(np.unique(current[\"feeding_rate\"]), predictions_mean, color=color_base * ((3 + speed_no) / 7), label=f\"temp {temperature} speed {speed}\")#+ np.array([0, 0, 1]) * speed_no / 4\n",
+ " axes[layer].title.set_text(f\"Layer {layer}\")\n",
+ " axes[layer].set_xlabel(\"Feeding rate\")\n",
+ " axes[layer].set_ylabel(\"Width mean\")\n",
+ "handles, labels = axes[-1].get_legend_handles_labels()\n",
+ "fig.legend(handles, labels, loc='upper left')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Model based on ellipse formula"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Linear Regression r^2 on test data : 0.975\n",
+ "Prediction: 0.18659585343559454\n",
+ "Real value: 0.1867976188659668\n"
+ ]
+ }
+ ],
+ "source": [
+ "def geometry_model(X:np.ndarray) -> np.ndarray:\n",
+ " temperature, speed, feeding_rate, layer, gap, w, area = X[:, 0], X[:, 1], X[:, 2], X[:, 3], X[:, 4], X[:, 5], X[:, 6]\n",
+ " # w = width_model.predict(X)\n",
+ " # area = area_model.predict(X)\n",
+ " delta = w ** 2 * (np.pi**2 + 8*np.pi + 16) + 32 * 0.9 * area * (np.pi - 4)\n",
+ " b = w * (np.pi + 4)\n",
+ " denominator = 8 - 2 * np.pi\n",
+ " h = (b - np.sqrt(delta)) / denominator\n",
+ " return np.column_stack([h])\n",
+ "\n",
+ "GeometryFeatures = FunctionTransformer(geometry_model)\n",
+ "\n",
+ "height_model = Pipeline([('transf', GeometryFeatures),\n",
+ " ('linear', LinearRegression())])\n",
+ "# fit to an order-3 polynomial data\n",
+ "height_model = height_model.fit(X, height)\n",
+ "pred = height_model.predict(X)\n",
+ "r2 = r2_score(height, pred)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {pred[0][0]}\\nReal value: {height[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients:\n",
+ "h: 1.1091118401975177\n",
+ "intercept: -0.023084143477631602\n"
+ ]
+ }
+ ],
+ "source": [
+ "print_coefs_and_params_names(height_model, ['h'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x157813df0>]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "markersize = 2\n",
+ "plt.plot(df.width, pred, 'o', markersize=markersize)\n",
+ "plt.plot(df.width, height, 'o', markersize=markersize)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusions\n",
+ "\n",
+ "Height can be calculated with the analytical model of the ellipse, varying with the width and the area. However, if the parameters passed to it are the predictions, i.e. the models of area and width, the height prediction presents a high error, probably due to a combination of errors in the area and width models.\n",
+ "\n",
+ "The area model, though, has a multiplier, probably because the format may vary."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Area model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1500x1000 with 6 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "eval = \"area\"\n",
+ "fig, axes = plt.subplots(3, 2, figsize=(15, 10))\n",
+ "fig.suptitle(\"Influence of parameters on the area of the printed strand\", weight=\"bold\")\n",
+ "fig.tight_layout(pad=3.0)\n",
+ "for i, (param, unit) in enumerate(zip([\"temperature\", \"speed\", \"layer\", \"feeding_rate\", \"gap\", \"area\"], [' [°C]', ' [mm/s]', '', '', '[mm]', ' [mm²]'])):\n",
+ " axes[i//2, i%2].plot(df[param], df[eval], 'o')\n",
+ " if param == 'feeding_rate': param = 'feed rate'\n",
+ " axes[i//2, i%2].title.set_text(f'{param[0].upper()}{param[1:]}')\n",
+ " axes[i//2, i%2].set_xlabel(f\"{param}{unit}\")\n",
+ " axes[i//2, i%2].set_ylabel(f\"{eval} [mm]\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Linear Regression r^2 on test data : 0.843\n",
+ "Prediction: 0.056961687306011956\n",
+ "Real value: 0.0600707403988143\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import FunctionTransformer\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.metrics import r2_score\n",
+ "\n",
+ "def area_features(X:np.ndarray) -> np.ndarray:\n",
+ " temperature, speed, feeding_rate, layer, gap, w, area = X[:, 0], X[:, 1], X[:, 2], X[:, 3], X[:, 4], X[:, 5], X[:, 6]\n",
+ " fr2 = feeding_rate ** 2\n",
+ " t_f = temperature * feeding_rate\n",
+ " s_f = speed * feeding_rate\n",
+ " sqrt = np.sqrt(feeding_rate)\n",
+ " w2 = w ** 2\n",
+ " return np.column_stack([feeding_rate])\n",
+ "\n",
+ "AreaFeatures = FunctionTransformer(area_features)\n",
+ "\n",
+ "area_model = Pipeline([('transf', AreaFeatures),\n",
+ " ('linear', LinearRegression())])\n",
+ "# fit to an order-3 polynomial data\n",
+ "area_model = area_model.fit(X, area)\n",
+ "pred = area_model.predict(X)\n",
+ "r2 = r2_score(area, pred)\n",
+ "print(f\"Linear Regression r^2 on test data : {r2:.3f}\")\n",
+ "print(f\"Prediction: {pred[0][0]}\\nReal value: {area[0][0]}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Coefficients:\n",
+ "feeding_rate: 1.812969202377806\n",
+ "intercept: 0.0025726112346777796\n"
+ ]
+ }
+ ],
+ "source": [
+ "print_coefs_and_params_names(area_model, coef_names = ['feeding_rate', 'fr2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The volume measured does not match the theoretical volume calculated by the E parameter of the gcode."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.3608385656749022"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "area_ratio = (df.feeding_rate * np.pi * 1.75**2 / 4) / df.area - 1\n",
+ "area_ratio = area_ratio[~np.isinf(area_ratio)]\n",
+ "area_ratio.mean()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/template_end.gcode b/experiments/template_end.gcode
new file mode 100644
index 0000000000000000000000000000000000000000..7a223822a21c3c10ab10c218778665e02ccc14cd
--- /dev/null
+++ b/experiments/template_end.gcode
@@ -0,0 +1,28 @@
+;TIME_ELAPSED:0
+M107
+G91 ;Relative positioning
+G1 E-2 F2700 ;Retract a bit
+G1 E-2 Z0.2 F2400 ;Retract and raise Z
+G1 X5 Y5 F3000 ;Wipe out
+G1 Z10 ;Raise Z more
+G90 ;Absolute positioning
+
+G1 X0 Y220 ;Present print
+M106 S0 ;Turn-off fan
+M106 P1 S0 ;Turn-off fan
+M104 S0 ;Turn-off hotend
+M140 S0 ;Turn-off bed
+
+M84 X Y E ;Disable all steppers but Z
+
+M82 ;absolute extrusion mode
+M104 S0
+;End of Gcode
+;SETTING_3 {"global_quality": "[general]\\nversion = 4\\nname = Standard Quality
+;SETTING_3 #2\\ndefinition = creality_ender3pro\\n\\n[metadata]\\ntype = qualit
+;SETTING_3 y_changes\\nquality_type = standard\\nsetting_version = 20\\n\\n[valu
+;SETTING_3 es]\\nadhesion_type = none\\n\\n", "extruder_quality": ["[general]\\n
+;SETTING_3 version = 4\\nname = Standard Quality #2\\ndefinition = creality_ende
+;SETTING_3 r3pro\\n\\n[metadata]\\ntype = quality_changes\\nquality_type = stand
+;SETTING_3 ard\\nsetting_version = 20\\nposition = 0\\n\\n[values]\\ninfill_spar
+;SETTING_3 se_density = 100\\n\\n"]}
\ No newline at end of file
diff --git a/experiments/template_startup.gcode b/experiments/template_startup.gcode
new file mode 100644
index 0000000000000000000000000000000000000000..0eb743c6e009de185e6ba80199aad5ad2e07950e
--- /dev/null
+++ b/experiments/template_startup.gcode
@@ -0,0 +1,36 @@
+;FLAVOR:Marlin
+;TIME:265
+;Filament used: 0.113878m
+;Layer height: 0.2
+;MINX:60
+;MINY:20
+;MINZ:0.2
+;MAXX:120
+;MAXY:30
+;MAXZ:0.6
+;Generated with Cura_SteamEngine main
+M82 ;absolute extrusion mode
+; Ender 3 Custom Start G-code
+G92 E0 ; Reset Extruder
+G28 ; Home all axes
+M104 S175 ; Start heating up the nozzle most of the way
+M190 S60 ; Start heating the bed, wait until target temperature reached
+M109 S{$TEMPERATURE} ; Finish heating the nozzle
+G1 Z2.0 F3000 ; Move Z Axis up little to prevent scratching of Heat Bed
+G1 X0.1 Y20 Z0.3 F5000.0 ; Move to start position
+G1 X0.1 Y200.0 Z0.3 F1500.0 E15 ; Draw the first line
+G1 X0.4 Y200.0 Z0.3 F5000.0 ; Move to side a little
+G1 X0.4 Y20 Z0.3 F1500.0 E30 ; Draw the second line
+G92 E0 ; Reset Extruder
+G1 Z2.0 F3000 ; Move Z Axis up little to prevent scratching of Heat Bed
+G1 X5 Y20 Z0.3 F5000.0 ; Move over to prevent blob squish
+G92 E0
+G92 E0
+G1 F1500 E-6.5
+;LAYER_COUNT:{$LAYER_COUNT}
+;LAYER:0
+M107
+M106 S85
+M106 P1 S85
+;TYPE:WALL-INNER
+G1 F1500 E0
diff --git a/experiments/utils.py b/experiments/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e847a6b6bd5e791d62b3f0a5f9abdf97de10fcc9
--- /dev/null
+++ b/experiments/utils.py
@@ -0,0 +1,62 @@
+import logging
+import matplotlib.pyplot as plt
+import numpy as np
+
+def plot_pointclouds(pointcloud:np.array, markersize:float=0.01) -> tuple[plt.Figure, plt.Axes]:
+ fig, ax = plt.subplots(3, 1, figsize=(10,10))
+ fig.suptitle("Raw data view")
+ fig.tight_layout(pad=3.0)
+
+ # XY
+ ax[0].plot(pointcloud[:,1], pointcloud[:,0], 'o', markersize=markersize)
+ # ax[0].set_aspect('equal')
+ ax[0].title.set_text('YX')
+ ax[0].set_xlabel('Y')
+ ax[0].set_ylabel('X')
+
+ # XZ
+ ax[1].plot(pointcloud[:,0], pointcloud[:,2], 'o', markersize=markersize)
+ ax[1].set_aspect('equal')
+ ax[1].title.set_text('XZ')
+ ax[1].set_xlabel('X')
+ ax[1].set_ylabel('Z')
+
+ # YZ
+ ax[2].plot(pointcloud[:,1], pointcloud[:,2], 'o', markersize=markersize)
+ ax[2].set_aspect('equal')
+ ax[2].title.set_text('XZ')
+ ax[2].set_xlabel('Y')
+ ax[2].set_ylabel('Z')
+
+ return fig, ax
+
+def crop_to_roi(pointcloud:np.array, roi:list[list[float]]):
+ """
+ Crop pointcloud to region of interest (roi)
+ roi: [[min x, max x], [min y, max y], [min z, max z]]
+ """
+ pcl = pointcloud.copy()
+ if roi[0][0] is not None: pcl = pcl[pcl[:, 0] > roi[0][0]] # min x
+ if roi[0][1] is not None: pcl = pcl[pcl[:, 0] < roi[0][1]] # max x
+ if roi[1][0] is not None: pcl = pcl[pcl[:, 1] > roi[1][0]] # min y
+ if roi[1][1] is not None: pcl = pcl[pcl[:, 1] < roi[1][1]] # max y
+ if roi[2][0] is not None: pcl = pcl[pcl[:, 2] > roi[2][0]] # min z
+ if roi[2][1] is not None: pcl = pcl[pcl[:, 2] < roi[2][1]] # max z
+ return pcl
+
+def init_logging(log_level='WARNING'):
+ logFormatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] [%(module)-10s] %(message)s", datefmt='%d/%m/%Y %H:%M:%S')
+ rootLogger = logging.getLogger("SmoPa3D")
+
+ fileHandler = logging.FileHandler("smopa3d.log")
+ fileHandler.setFormatter(logFormatter)
+ fileHandler.setLevel(logging.WARNING)
+ rootLogger.addHandler(fileHandler)
+
+ consoleHandler = logging.StreamHandler()
+ consoleHandler.setFormatter(logFormatter)
+ consoleHandler.setLevel(log_level)
+ rootLogger.addHandler(consoleHandler)
+
+ rootLogger.setLevel(log_level)
+ return rootLogger
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..11f9d408b0807d20005a8a69fd8a69010bdab934
--- /dev/null
+++ b/main.py
@@ -0,0 +1,14 @@
+from addon.network import Network, load_network
+from addon.decorators import save_tracking_stats
+
+def simulation():
+ nodes_to_simulate = -1
+ net = Network('test/benchy.gcode', 0.02, 1, node_distance=2/3, extrusion_multiplier=1, saving_path='data/simulation/over')
+ net.simulate_printer(node_limit=nodes_to_simulate)
+ net.calculate_meshes(processes=None)
+ net.save("benchy.pkl")
+ save_tracking_stats()
+
+if __name__ =='__main__':
+ simulation()
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70afb7a4d92a7b7e096b51dfa9df3518ccf0efa2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+bpy
+mathutils
+memory_profiler
+numpy
+open3d
+scipy
+tqdm
\ No newline at end of file
diff --git a/test/benchy.gcode b/test/benchy.gcode
new file mode 100644
index 0000000000000000000000000000000000000000..b0f2766b8e538c2257e2ef16cb7fe295a4294a0d
--- /dev/null
+++ b/test/benchy.gcode
@@ -0,0 +1,9858 @@
+;FLAVOR:Marlin
+;TIME:640
+;Filament used: 0.172783m
+;Layer height: 0.2
+;MINX:100.804
+;MINY:40.523
+;MINZ:0.2
+;MAXX:119.166
+;MAXY:49.827
+;MAXZ:15
+;Generated with Cura_SteamEngine main
+M82 ;absolute extrusion mode
+; Ender 3 Custom Start G-code
+G92 E0 ; Reset Extruder
+G28 ; Home all axes
+M104 S175 ; Start heating up the nozzle most of the way
+M190 S60 ; Start heating the bed, wait until target temperature reached
+M109 S200 ; Finish heating the nozzle
+G1 Z2.0 F3000 ; Move Z Axis up little to prevent scratching of Heat Bed
+G1 X0.1 Y20 Z0.3 F5000.0 ; Move to start position
+G1 X0.1 Y200.0 Z0.3 F1500.0 E15 ; Draw the first line
+G1 X0.4 Y200.0 Z0.3 F5000.0 ; Move to side a little
+G1 X0.4 Y20 Z0.3 F1500.0 E30 ; Draw the second line
+G92 E0 ; Reset Extruder
+G1 Z2.0 F3000 ; Move Z Axis up little to prevent scratching of Heat Bed
+G1 X5 Y20 Z0.3 F5000.0 ; Move over to prevent blob squish
+G92 E0
+G92 E0
+G1 F1500 E-6.5
+;LAYER_COUNT:75
+;LAYER:0
+M107
+;MESH:3DBenchy.stl
+G0 F6000 X109.853 Y47.153 Z0.2
+;TYPE:WALL-INNER
+G1 F1500 E0
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+G0 F6000 X109.841 Y47.553
+;TYPE:WALL-OUTER
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+;TYPE:SKIN
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+;MESH:NONMESH
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+;TIME_ELAPSED:15.128097
+;LAYER:1
+M106 S85
+M106 P1 S85
+;TYPE:WALL-INNER
+;MESH:3DBenchy.stl
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+M107
+G91 ;Relative positioning
+G1 E-2 F2700 ;Retract a bit
+G1 E-2 Z0.2 F2400 ;Retract and raise Z
+G1 X5 Y5 F3000 ;Wipe out
+G1 Z10 ;Raise Z more
+G90 ;Absolute positioning
+
+G1 X0 Y220 ;Present print
+M106 S0 ;Turn-off fan
+M106 P1 S0 ;Turn-off fan
+M104 S0 ;Turn-off hotend
+M140 S0 ;Turn-off bed
+
+M84 X Y E ;Disable all steppers but Z
+
+M82 ;absolute extrusion mode
+M104 S0
+;End of Gcode
+;SETTING_3 {"global_quality": "[general]\\nversion = 4\\nname = Standard Quality
+;SETTING_3 #2\\ndefinition = creality_ender3pro\\n\\n[metadata]\\ntype = qualit
+;SETTING_3 y_changes\\nquality_type = standard\\nsetting_version = 20\\n\\n[valu
+;SETTING_3 es]\\nadhesion_type = none\\nsupport_type = everywhere\\n\\n", "extru
+;SETTING_3 der_quality": ["[general]\\nversion = 4\\nname = Standard Quality #2\
+;SETTING_3 \ndefinition = creality_ender3pro\\n\\n[metadata]\\ntype = quality_ch
+;SETTING_3 anges\\nquality_type = standard\\nsetting_version = 20\\nposition = 0
+;SETTING_3 \\n\\n[values]\\ninfill_pattern = grid\\nsupport_offset = 0.0\\n\\n"]
+;SETTING_3 }