updated the Readme and the architecture of the project

README.md

@@ -4,7 +4,7 @@
 
 As a final exam, this project aims to evaluate 3A SICOM students. 
 
-Students will work individually, and each student must submit its work (report and code) on Chamilo by **Tuesday, June 6**.
+Students will work individually, and each student must submit its work (report and code) as a marge request on Gitlab by **Tuesday, June 6**.
 
 ## 2. Presentation
 
@@ -18,7 +18,7 @@ Because of the filters the pixels on the sensor will only acquire one color (bei
 
 The goal of this project is to perform *demosaicking*: recover all the missing colors for each pixel. This way we will recover the full RGB image.
 
-We propose you to reconstruct 4 images (you can find them in the `images` folder of this project). These images are from the open dataset of the **National Gallery of Art**, USA, which can be found [here](https://github.com/NationalGalleryOfArt/opendata). To reconstruct these images we provide you the forward operator, modeling the effect of a CFA camera (either Bayer of Quad Bayer pattern). This operation is described in the file `src/forward_operator.py`.
+We propose you to reconstruct 4 images (you can find them in the `images` folder of this project). These images are from the open dataset of the **National Gallery of Art**, USA, which can be found [here](https://github.com/NationalGalleryOfArt/opendata). To reconstruct these images we provide you the forward operator, modeling the effect of a CFA camera with 2 different CFA patterns:either Bayer of Quad Bayer pattern. This operation is described in `src/forward_operator.py`.
 
 ## 3. How to proceed?
 
@@ -26,7 +26,7 @@ All of the images to reconstruct have the same size (1024x1024) and are RGB. You
 
 ## 4. Some hints to start
 
-We provide in `src/demo_reconstruct` a basic interpolation image which can help you to start. You can also find in the academic litterature techniques (interpolation, inverse problem, machine learning, etc) that solves demosaicking.
+We provide in `src/methods/baseline` a basic interpolation image which can help you to start. These methods are described in the PhD Thesis _Model Based Signal Processing Techniques for Nonconventional Optical Imaging Systems_ and the user's manual _Pyxalis Image Viewer_ (see references). You can also find in the academic litterature techniques (interpolation, inverse problem, machine learning, etc) that solves demosaicking.
 
 *The time of computations might be very long depending of your machine. To verify that your method works, try to work with smaller test images.*
 
@@ -38,40 +38,38 @@ The project has the following organization:
 sicom_image_analysis_project/
 ├─.gitignore                   # Git's ignore file
 ├─images/                      # All images you need to reconstruct
-│ ├─img_1.png
-│ ├─img_2.png
-│ ├─img_3.png
-│ └─img_4.png
-├─main.ipynb                   # The notebook to run all of your computations
-├─output/                      # The output folder where you can save your reconstruction (ignore .gitkeep)
-│ └─.gitkeep
+├─main.ipynb                   # A notebook to experiment with the code
+├─output/                      # The output folder where you can save your reconstruction
 ├─README.md                    # Readme, contains all information about the project
-├─readme_imgs/                 # Images for the Readme, ignore it
-│ ├─example.png
-│ └─patterns.png
+├─readme_imgs/                 # Images for the Readme
 ├─requirements.txt             # Requirement file for packages installation
-└─src/                         # All of the source files for the project, your source file must appear here!
-  ├─demo_reconstructions.py    # Example of demosaicking | **Do not modify**
+└─src/                         # All of the source files for the project
   ├─reconstruct.py             # File containing the main reconstruction fonction
-  ├─checks.py                  # File containing some sanity checks | **Do not modify**
-  ├─forward_model.py           # File containing the CFA operator | **Do not modify**
-  └─utils.py                   # Some utilities | **Do not modify**
-  ~~~
+  ├─checks.py                  # File containing some sanity checks
+  ├─forward_model.py           # File containing the CFA operator
+  ├─utils.py                   # Some utilities
+  └─methods/
+    ├─baseline/                # Example of demosaicking
+    └─template/                # Template of your project (to be copied)
+~~~
 
-You are expected to write your own code in new files of `src`, and call it in the `src/reconstruct.py` file.
+Copy `src/methods/template` and rename it with your name. You are expected to write your own code in new files inside `src/methods/your_name`, and call them in the `src/methods/your_name/reconstruct.py` file. You are **not allowed to modify any file outside of `src/methods/your_name`** apart from `main.ipynb`, see instructions bellow.
 
-## 6. Instructions: 
+Please have a look in `src/methods/baseline`, your projet should work in the same way.
 
-Each student will submit its work in Chamilo, inside the work **Image_analysis_projects_2023**. It must contain:
+## 6. Instructions:
 
-- A report **as a pdf file** with the name **name.pdf**.
-- Your code as a archive with the name **name.zip**.
+Each student will fork this Git repository on its own account. You will then work in your own version of this repository.
+
+To submit your work you just need to create a merge request in Gitlab (see [here](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html#when-you-work-in-a-fork) for a detailed explanation). This merge request will **only encompass the changes made to `src/methods/your_name`, without `main.ipynb`**. Because the merge request will only create `src/methods/your_name` there will not be any conflicts between each student's project.
+
+Along with your code you must submit a report **as a pdf file** with the name **name.pdf** inside the folder `src/methods/your_name`.
 
 The code and the report must be **written in English**.
 
 ### 6.1. The report:
 
-Your report **must be a pdf file** written in English. In this file you are asked to explain:
+Your report **must be a pdf file** written in English and should not be longer than 5 pages. In this file you are asked to explain:
 
 1. The problem statement, show us that you've understood the project.
 2. The solution that you've chosen, explaining the theory.
@@ -81,10 +79,20 @@ Your report **must be a pdf file** written in English. In this file you are aske
 
 ### 6.2. The code:
 
-- Your code must be operational.
-- In the notebook, feel free to add cells to show us how your code works and to show us the tools you've developped to evaluate your work.
+- You should first **copy** the folder `src/methods/template` and rename it with your name. It is in this folder that you will work, **nothing else should be modified** apart from `main.ipynb` for experimenting with the code.
+- You will add as many files you want in this folder but the only interface to run your code is `run_reconstruction` in `src/methods/your_name`. You can modify this function as you please, as long as it takes in argument the image to reconstruct (`y`), the name of the CFA (`cfa`) and returns a demosaicked image.
+- You can use all the functions defined in the project, even the ones that you should not modify (`utils.py`, `forward_model.py`, etc).
+- Your code must be operational through `run_reconstruction`, as we will test it on new and private images.
+- The notebook provides a working bench. It should **not be included in the merge request**, it is just a work document for you.
 - Comment your code when needed.
 
 ## 7. Supervisors
+
 - Mauro Dalla Mura: mauro.dalla-mura@gipsa-lab.grenoble-inp.fr
 - Matthieu Muller: matthieu.muller@gipsa-lab.grenoble-inp.fr
+
+## 8. References
+
+- NGA: [website](https://www.nga.gov/)
+- Model Based Signal Processing Techniques for Nonconventional Optical Imaging Systems: [website](https://theses.hal.science/tel-03596486)
+- Pyxalis: [website](https://pyxalis.com/wp-content/uploads/2021/12/PYX-ImageViewer-User_Guide.pdf)

src/methods/baseline/reconstruct.py

+"""The main file for the baseline reconstruction.
+This file should NOT be modified.
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.baseline.demo_reconstruction import naive_interpolation
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """Performs demosaicking on y.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    input_shape = (y.shape[0], y.shape[1], 3)
+    op = CFA(cfa, input_shape)
+
+    res = naive_interpolation(op, y)
+
+    return res
+
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+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller

src/reconstruct.py → src/methods/template/reconstruct.py

 """The main file for the reconstruction.
 This file should NOT be modified except the body of the 'run_reconstruction' function.
-Students should only call their functions (declared in others files of src/) in it.
+Students can call their functions (declared in others files of src/methods/your_name).
 """
 
 
@@ -8,32 +8,23 @@ import numpy as np
 
 from src.forward_model import CFA
 
-# Import the files needed for the reconstruction like:
-# from src.file import function_1
-from demo_reconstruction import naive_interpolation
-
 
 def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
     """Performs demosaicking on y.
 
     Args:
         y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA. Can be bayer or quad_bayer.
 
     Returns:
         np.ndarray: Demosaicked image.
     """
     # Performing the reconstruction.
     # TODO
-
-    ######################################## Demo code ########################################
     input_shape = (y.shape[0], y.shape[1], 3)
     op = CFA(cfa, input_shape)
 
-
-    res = naive_interpolation(op, y)
-    ###########################################################################################
-
-    return res
+    return np.zeros(op.input_shape)
 
 
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