Add SAP

_pages/about.md

@@ -1,6 +1,6 @@
 ---
 layout: about
-title: about
+title: bio
 permalink: /
 subtitle: Researcher in computer vision, machine learning and remote sensing, Ph.D.
 profile:
@@ -28,10 +28,10 @@ learning** on a very large scale! Before that, I had the chance to work
 as a postdoc on the [SIXP][sixp] project. I have worked on plant species
 and very high resolution multispectral imagery with **semantic
 segmentation** on a very a fine scale! Earlier, I did my [Ph.D.
-thesis][thesis] in [LETG Rennes][letg] and [IRISA's OBELIX
+thesis][thesis] in [LETG Rennes][letg], [IRISA's OBELIX team][obelix]
-team][obelix]. The topic was to propose new and efficient ways of
+and [Tellus Environment][tellus]. The topic was to propose new and
-processing **3D point clouds** from **LiDAR data**, using
+efficient ways of processing **3D point clouds** from **LiDAR data**,
-**morphological hierarchies** and deep learning.
+using **morphological hierarchies** and deep learning.
 
 I am currently looking for new adventures!
 
@@ -40,7 +40,7 @@ I am currently looking for new adventures!
 
 [thesis]: assets/pdf/Guiotte - 2021 - 2D3D discretization of Lidar point clouds Proces.pdf
 [obelix]: http://www-obelix.irisa.fr/
+[tellus]: https://tellus-environment.com/
 [letg]: https://letg.cnrs.fr/
 [aj]: https://lavionjaune.com/
 [sixp]: https://sixp.inria.fr/
-

_pages/projects.md

@@ -2,7 +2,7 @@
 layout: page
 title: projects
 permalink: /projects/
-description: A growing collection of your cool projects.
+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!
 nav: true
 nav_order: 2
 display_categories: [thesis, other]

_projects/1_project.md

@@ -3,8 +3,8 @@ layout: page
 title: project 1
 description: a project with a background image
 img: assets/img/12.jpg
-importance: 1
+importance: 9
-category: thesis
+category: demo
 ---
 
 Every project has a beautiful feature showcase page.

_projects/sap.md

@@ -0,0 +1,145 @@
+---
+layout: page
+title: SAP
+description: Python package to compute morphological hierarchies of images and more. 
+img: /assets/img/sap.svg
+importance: 1
+category: thesis
+---
+
+SAP (for Simple Attribute Profiles) is a Python package to easily
+compute attribute profiles of images. I have developed this package as
+part of my PhD thesis.
+
+The source code is available on [github][git]. I used this project to
+experiments CI/CD with gitlab pipelines (the project was initially
+hosted on the INRIA's gitlab) and lately with Github [actions].
+[Testing][test], [code coverage][cover], release publishing on
+[PyPI][pypi] and [online documentation][doc] are all automatically
+updated.
+
+[git]: https://github.com/fguiotte/sap
+[doc]: https://python-sap.rtfd.io
+[actions]: https://github.com/fguiotte/sap/actions
+[pypi]: https://pypi.org/project/sap/
+[test]: https://github.com/fguiotte/sap/tree/master/test
+[cover]: https://app.codecov.io/gh/fguiotte/sap/tree/master/sap
+
+
+## Installation 
+
+To start tinkering images with the package, you just have to:
+
+```bash
+pip install sap
+```
+
+## Quick start
+
+A small Python snippet to get you started quickly:
+
+```python
+import sap
+import numpy as np
+import matplotlib.pyplot as plt
+
+image = np.random.random((512, 512))
+
+t = sap.MaxTree(image)
+area = t.get_attribute('area')
+filtered_image = t.reconstruct(area < 100)
+
+plt.imshow(filtered_image)
+plt.show()
+```
+
+## Slower launch 
+
+This package is a combination of three submodules.
+
+### Trees
+
+The first submodule `sap.trees` is to build trees from images, to compute
+attributes, and to filter them.
+
+For example, we can build the max-tree of an image, compute the area
+attributes of the nodes and reconstruct a filtered image removing nodes
+with area less than 100 pixels:
+
+```python
+t = sap.MaxTree(image)
+area = t.get_attribute('area')
+filtered_image = t.reconstruct(area < 100)
+```
+
+
+
+### Profiles
+
+The second submodule `sap.profiles` is provided to compute *Attribute
+Profiles* (and other profiles) of images. The submodule contains the
+utils to easily concatenate the profiles (*Extended Attribute Profiles*)
+and to display them.
+
+```python
+import imageio.v3 as iio # Reads and writes images
+import sap
+
+image = iio.imread('image.png')
+
+ap = sap.attribute_profiles(image, {'area': [100, 1000]})
+sap.show_profiles(ap)
+```
+
+![Attribute profiles stacks connected component filtering of images (opening and closing) at several scales.](/assets/img/ap_area.png){.img-fluid .rounded .z-depth-1}
+
+### Spectra
+
+The third submodule is `sap.spectra`. We use it to compute Pattern
+Spectra of trees and to display them. Pattern Spectra can be useful to
+set thresholds of attribute filters and Attribute Profiles.
+
+
+```python
+import rasterio as rio # Reads and writes geospatial raster data
+from matplotlib import pyplot as plt # Display plots and images
+import sap
+
+dsm = rio.open('dsm.tif').read()[0]
+
+max_tree = sap.MaxTree(dsm)
+
+plt.imshow(max_tree.reconstruct())
+plt.show()
+```
+
+![](/assets/img/sap_1.png){.img-fluid .z-depth-1 .rounded}
+
+```python
+ps = sap.spectrum2d(max_tree, 'area', 'compactness', x_log=True)
+
+sap.show_spectrum(*ps)
+
+plt.xlabel('area')
+plt.ylabel('compactness')
+plt.colorbar()
+plt.title('SAP 2D spectrum')
+
+plt.show()
+```
+
+![](/assets/img/sap_2.png){.img-fluid .z-depth-1 .rounded}
+
+
+To go further, please have a look to the [online documentation][doc].
+
+
+This package has been used, amongst other projects, to perform an
+experimental comparison of the attribute profiles and their variations
+in a published paper [^1].
+
+[^1]: Deise Santana Maia, Minh-Tan Pham, Erchan Aptoula, Florent
+  Guiotte, et Sébastien Lefèvre, « *Classification of Remote Sensing
+  Data With Morphological Attribute Profiles: A decade of advances* »,
+  GRSM, vol. 9, nᵒ 3, p. 43‑71, sept. 2021, doi:
+  [10.1109/MGRS.2021.3051859](https://doi.org/10.1109/MGRS.2021.3051859).

_projects/spectra.md

@@ -1,7 +1,7 @@
 ---
 layout: page
 title: Spectra
-description: Application using the morphological hierarchies and LiDAR data
+description: Application using the morphological hierarchies and LiDAR data.
 img: /assets/img/spectra.png
 importance: 1
 category: thesis
@@ -21,6 +21,8 @@ represents their compactness (a shape-based
 ratio).](/assets/img/spectra.png){.figure-img .img-fluid .rounded
 .z-depth-1}
 
+## Interactive filtering
+
 We can use this spectrum to select the attribute thresholds. The current
 application allows us to do this in real time!
 
@@ -40,6 +42,8 @@ the square of its perimeter. It ranges from 0 for  non-compact shapes to
 (e.g. circular shapes) while the bottom of the spectrum represents
 linear shapes.
 
+## Example driven
+
 Selecting thresholds in the attribute space can still be difficult. We
 propose to drive the threshold selection by the example. The application
 allows to select in the LiDAR data structures and to highlight their
@@ -49,6 +53,8 @@ shapes and sizes in the spectrum.
 .img-fluid .rounded .z-depth-1 }
 
 
+## Wait, there's more!
+
 In the previous example we showed the use of two attributes, area and
 compactness. However, there are many more that we can use or even
 define, depending on the purpose of the application.
@@ -64,6 +70,8 @@ space exploration!
 ![3D spectrum (area, compactness, height).](/assets/vid/3D_axis2_trans_2x.mp4){.figure-img .img-fluid
 .rounded .z-depth-1 loop=true autoplay=true}
 
+## Notes
+
 The underlying data structure for processing LiDAR data are hierarchical
 morphologies, in particular component trees, which allow an efficient
 representation of nested connected components for the computation of
@@ -73,4 +81,15 @@ The application was developed in Python using the [SAP
 package](/projects/sap/) to build the trees, compute the spectra and
 filter the data.
 
-This application was developed as part of my Ph.D. thesis.
+This application was developed as part of my PhD thesis. Experiments
+and quantitative results have been published on use cases in a natural
+environment, related to illegal gold panning and dikes detection [^1].
+
+[^1]: F. Guiotte, G. Etaix, S. Lefèvre, et T. Corpetti, « *Interactive
+  Digital Terrain Model Analysis in Attribute Space* », International
+  Archives of the Photogrammetry, Remote Sensing and Spatial Information
+  Sciences, vol. XLIII-B2-2020, p. 1203‑1209, 2020, doi:
+  [10.5194/isprs-archives-XLIII-B2-2020-1203-2020][doi].
+
+[doi]: https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1203-2020
+

assets/img/sap.svg

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