Add SAP
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---
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layout: about
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title: about
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title: bio
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permalink: /
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subtitle: Researcher in computer vision, machine learning and remote sensing, Ph.D.
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profile:
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@ -28,10 +28,10 @@ learning** on a very large scale! Before that, I had the chance to work
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as a postdoc on the [SIXP][sixp] project. I have worked on plant species
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and very high resolution multispectral imagery with **semantic
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segmentation** on a very a fine scale! Earlier, I did my [Ph.D.
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thesis][thesis] in [LETG Rennes][letg] and [IRISA's OBELIX
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team][obelix]. The topic was to propose new and efficient ways of
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processing **3D point clouds** from **LiDAR data**, using
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**morphological hierarchies** and deep learning.
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thesis][thesis] in [LETG Rennes][letg], [IRISA's OBELIX team][obelix]
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and [Tellus Environment][tellus]. The topic was to propose new and
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efficient ways of processing **3D point clouds** from **LiDAR data**,
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using **morphological hierarchies** and deep learning.
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I am currently looking for new adventures!
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@ -40,7 +40,7 @@ I am currently looking for new adventures!
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[thesis]: assets/pdf/Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf
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[obelix]: http://www-obelix.irisa.fr/
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[tellus]: https://tellus-environment.com/
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[letg]: https://letg.cnrs.fr/
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[aj]: https://lavionjaune.com/
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[sixp]: https://sixp.inria.fr/
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layout: page
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title: projects
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permalink: /projects/
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description: A growing collection of your cool projects.
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description: Just a small list of the projects lying around in my folders. It may be updated at any time, with new or not so new content!
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nav: true
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nav_order: 2
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display_categories: [thesis, other]
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@ -3,8 +3,8 @@ layout: page
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title: project 1
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description: a project with a background image
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img: assets/img/12.jpg
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importance: 1
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category: thesis
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importance: 9
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category: demo
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---
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Every project has a beautiful feature showcase page.
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145
_projects/sap.md
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---
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layout: page
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title: SAP
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description: Python package to compute morphological hierarchies of images and more.
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img: /assets/img/sap.svg
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importance: 1
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category: thesis
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---
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SAP (for Simple Attribute Profiles) is a Python package to easily
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compute attribute profiles of images. I have developed this package as
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part of my PhD thesis.
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The source code is available on [github][git]. I used this project to
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experiments CI/CD with gitlab pipelines (the project was initially
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hosted on the INRIA's gitlab) and lately with Github [actions].
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[Testing][test], [code coverage][cover], release publishing on
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[PyPI][pypi] and [online documentation][doc] are all automatically
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updated.
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[git]: https://github.com/fguiotte/sap
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[doc]: https://python-sap.rtfd.io
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[actions]: https://github.com/fguiotte/sap/actions
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[pypi]: https://pypi.org/project/sap/
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[test]: https://github.com/fguiotte/sap/tree/master/test
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[cover]: https://app.codecov.io/gh/fguiotte/sap/tree/master/sap
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## Installation
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To start tinkering images with the package, you just have to:
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```bash
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pip install sap
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```
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## Quick start
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A small Python snippet to get you started quickly:
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```python
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import sap
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import numpy as np
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import matplotlib.pyplot as plt
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image = np.random.random((512, 512))
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t = sap.MaxTree(image)
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area = t.get_attribute('area')
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filtered_image = t.reconstruct(area < 100)
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plt.imshow(filtered_image)
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plt.show()
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```
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## Slower launch
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This package is a combination of three submodules.
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### Trees
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The first submodule `sap.trees` is to build trees from images, to compute
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attributes, and to filter them.
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For example, we can build the max-tree of an image, compute the area
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attributes of the nodes and reconstruct a filtered image removing nodes
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with area less than 100 pixels:
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```python
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t = sap.MaxTree(image)
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area = t.get_attribute('area')
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filtered_image = t.reconstruct(area < 100)
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```
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### Profiles
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The second submodule `sap.profiles` is provided to compute *Attribute
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Profiles* (and other profiles) of images. The submodule contains the
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utils to easily concatenate the profiles (*Extended Attribute Profiles*)
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and to display them.
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```python
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import imageio.v3 as iio # Reads and writes images
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import sap
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image = iio.imread('image.png')
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ap = sap.attribute_profiles(image, {'area': [100, 1000]})
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sap.show_profiles(ap)
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```
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{.img-fluid .rounded .z-depth-1}
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### Spectra
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The third submodule is `sap.spectra`. We use it to compute Pattern
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Spectra of trees and to display them. Pattern Spectra can be useful to
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set thresholds of attribute filters and Attribute Profiles.
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```python
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import rasterio as rio # Reads and writes geospatial raster data
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from matplotlib import pyplot as plt # Display plots and images
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import sap
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dsm = rio.open('dsm.tif').read()[0]
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max_tree = sap.MaxTree(dsm)
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plt.imshow(max_tree.reconstruct())
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plt.show()
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```
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{.img-fluid .z-depth-1 .rounded}
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```python
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ps = sap.spectrum2d(max_tree, 'area', 'compactness', x_log=True)
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sap.show_spectrum(*ps)
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plt.xlabel('area')
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plt.ylabel('compactness')
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plt.colorbar()
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plt.title('SAP 2D spectrum')
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plt.show()
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```
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{.img-fluid .z-depth-1 .rounded}
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To go further, please have a look to the [online documentation][doc].
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This package has been used, amongst other projects, to perform an
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experimental comparison of the attribute profiles and their variations
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in a published paper [^1].
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[^1]: Deise Santana Maia, Minh-Tan Pham, Erchan Aptoula, Florent
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Guiotte, et Sébastien Lefèvre, « *Classification of Remote Sensing
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Data With Morphological Attribute Profiles: A decade of advances* »,
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GRSM, vol. 9, nᵒ 3, p. 43‑71, sept. 2021, doi:
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[10.1109/MGRS.2021.3051859](https://doi.org/10.1109/MGRS.2021.3051859).
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---
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layout: page
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title: Spectra
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description: Application using the morphological hierarchies and LiDAR data
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description: Application using the morphological hierarchies and LiDAR data.
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img: /assets/img/spectra.png
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importance: 1
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category: thesis
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@ -21,6 +21,8 @@ represents their compactness (a shape-based
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ratio).](/assets/img/spectra.png){.figure-img .img-fluid .rounded
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.z-depth-1}
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## Interactive filtering
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We can use this spectrum to select the attribute thresholds. The current
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application allows us to do this in real time!
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@ -40,6 +42,8 @@ the square of its perimeter. It ranges from 0 for non-compact shapes to
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(e.g. circular shapes) while the bottom of the spectrum represents
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linear shapes.
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## Example driven
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Selecting thresholds in the attribute space can still be difficult. We
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propose to drive the threshold selection by the example. The application
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allows to select in the LiDAR data structures and to highlight their
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@ -49,6 +53,8 @@ shapes and sizes in the spectrum.
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.img-fluid .rounded .z-depth-1 }
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## Wait, there's more!
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In the previous example we showed the use of two attributes, area and
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compactness. However, there are many more that we can use or even
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define, depending on the purpose of the application.
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@ -64,6 +70,8 @@ space exploration!
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{.figure-img .img-fluid
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.rounded .z-depth-1 loop=true autoplay=true}
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## Notes
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The underlying data structure for processing LiDAR data are hierarchical
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morphologies, in particular component trees, which allow an efficient
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representation of nested connected components for the computation of
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@ -73,4 +81,15 @@ The application was developed in Python using the [SAP
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package](/projects/sap/) to build the trees, compute the spectra and
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filter the data.
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This application was developed as part of my Ph.D. thesis.
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This application was developed as part of my PhD thesis. Experiments
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and quantitative results have been published on use cases in a natural
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environment, related to illegal gold panning and dikes detection [^1].
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[^1]: F. Guiotte, G. Etaix, S. Lefèvre, et T. Corpetti, « *Interactive
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Digital Terrain Model Analysis in Attribute Space* », International
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Archives of the Photogrammetry, Remote Sensing and Spatial Information
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Sciences, vol. XLIII-B2-2020, p. 1203‑1209, 2020, doi:
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[10.5194/isprs-archives-XLIII-B2-2020-1203-2020][doi].
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[doi]: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1203-2020
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