Cleaned repository (removed old files from the original subject usele…

…ss for inpainting).

Translated all my code from french to english

README.md

@@ -1,5 +1,5 @@
 # Inpainting
-[fr] Image inpainting via dictionary learning and sparse representation
+Image inpainting via dictionary learning and sparse representation
 
 
 ## Examples
@@ -14,6 +14,6 @@
 * Find a better heuristic for patch approximation order.
 
 ## Sources
-*  Bin Shen and Wei Hu and Zhang, Yimin and Zhang, Yu-Jin, Image Inpainting via Sparse Representation Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ’09)
+* Bin Shen and Wei Hu and Zhang, Yimin and Zhang, Yu-Jin, Image Inpainting via Sparse Representation Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ’09)
 * Julien Mairal Sparse coding and Dictionnary Learning for Image Analysis INRIA Visual Recognition and Machine Learning Summer School, 2010
-*  A. Criminisi, P. Perez, K. Toyama Region Filling and Object Removal by Exemplar-Based Image Inpainting IEEE Transaction on Image Processing (Vol 13-9), 2004
+* A. Criminisi, P. Perez, K. Toyama Region Filling and Object Removal by Exemplar-Based Image Inpainting IEEE Transaction on Image Processing (Vol 13-9), 2004

main.py

@@ -1,55 +1,47 @@
-from src.picture_tools.examples import CAMERAMAN, HOUSE, JETPLANE, LAKE, LENA_COLOR_256, LENA_COLOR_512, LENA_GRAY_256, \
-    LENA_GRAY_512, LIVINGROOM, MANDRIL_COLOR, MANDRIL_GRAY, PEPPERS_COLOR, PEPPERS_GRAY, PIRATE, WALKBRIDGE, \
-    WOMAN_BLONDE, WOMAN_DARKHAIR, CASTLE, OUTDOOR
-from src.picture_tools.codage import Codage
-from src.picture_tools.picture import Picture, show_patch, flatten, unflatten, VALUE_MISSING_PIXEL, VALUE_OUT_OF_BOUNDS, get_patch
-from src.usps_tools import test_all_usps_1_vs_all, test_all_usps, test_all_usps_sklearn, test_all_usps_1_vs_all_sklearn, \
-    load_usps_1_vs_all, load_usps
-from src.linear.linear_regression import LinearRegression, identite, mse_g, l1, l1_g, l2, l2_g, DescenteDeGradient
+from src.picture_tools.colormodel import ColorModel
+from src.picture_tools.picture import Picture, VALUE_MISSING_PIXEL, VALUE_OUT_OF_BOUNDS
 from src.inpainting import InPainting
 from src.common.matplotlib import show_pictures
-
-import matplotlib.pyplot as plt
-import numpy as np
+from src.picture_tools.examples import CAMERAMAN, HOUSE, JETPLANE, LAKE, LENA_COLOR_256, LENA_COLOR_512,\
+    LENA_GRAY_256, LENA_GRAY_512, LIVINGROOM, MANDRIL_COLOR, MANDRIL_GRAY, PEPPERS_COLOR, PEPPERS_GRAY, PIRATE,\
+    WALKBRIDGE, WOMAN_BLONDE, WOMAN_DARKHAIR, CASTLE, OUTDOOR
 
 
 PATCH_SIZE = 101
 STEP = PATCH_SIZE // 4
-ALPHA = 0.00001
-MAX_ITERATIONS = 100000
-TOLERANCE = 0.0001
-VALUE_MISSING_PIXEL = VALUE_MISSING_PIXEL
-VALUE_OUT_OF_BOUNDS = VALUE_OUT_OF_BOUNDS
-CODAGE = Codage.RGB
+ALPHA = 1e-5
+MAX_ITERATIONS = 1e+5
+TOLERANCE = 1e-4
+COLOR_MODEL = ColorModel.RGB
 
 
 def main_lena():
-    # Chargement de l'image
-    original_picture = Picture(picture_path=LENA_COLOR_512, codage=CODAGE)
+    # Load picture
+    original_picture = Picture(picture_path=LENA_COLOR_512, color_model=COLOR_MODEL)
 
-    # Ajout du bruit
+    # Add noise
     noisy_picture = original_picture.copy()
     noisy_picture.add_noise(0.005)
 
     main_inpainting(original_picture, noisy_picture)
 
 
 def main_castle():
-    # Chargement de l'image
-    original_picture = Picture(picture_path=CASTLE, codage=CODAGE)
+    # Load picture
+    original_picture = Picture(picture_path=CASTLE, color_model=COLOR_MODEL)
 
-    # Ajout du bruit
-    noisy_picture = original_picture.copy()  # Ajout du bruit
+    # Add a rectangle of noise
+    noisy_picture = original_picture.copy()
     noisy_picture.add_rectangle(400, 380, 50, 20)
 
     main_inpainting(original_picture, noisy_picture)
 
 
 def main_outdoor():
-    # Chargement de l'image
-    original_picture = Picture(picture_path=OUTDOOR, codage=CODAGE)
+    # Load picture
+    original_picture = Picture(picture_path=OUTDOOR, color_model=COLOR_MODEL)
 
-    # Ajout du bruit
+    # Add a rectangle of noise
     noisy_picture = original_picture.copy()
     noisy_picture.add_rectangle(288, 497, 190, 80)
 
@@ -64,44 +56,12 @@ def main_inpainting(original_picture, noisy_picture, patch_size=PATCH_SIZE, step
                             value_out_of_bounds=value_out_of_bounds)
     inpainted_picture = inpainting.inpaint(noisy_picture)
 
-    # Affichage des résultats
+    # Show resulting pictures
     show_pictures(pictures=[original_picture._get_showable_picture(), noisy_picture._get_showable_picture(),
                             inpainted_picture._get_showable_picture()],
                   titles=["Original picture", "Noisy picture", "InPainted picture"],
                   save_path=save_path)
 
 
-def main_all_vs_all():
-    print("TEST ALL VS ALL")
-    test_all_usps(classifieur=LinearRegression,
-                  loss_g=mse_g,
-                  type_descente=DescenteDeGradient.BATCH,
-                  alpha=0)
-    for loss_g in (l2_g, l1_g):
-        for alpha in (0, 0.25, 0.5, 0.75, 1):
-            print(loss_g.__name__, "alpha=" + str(alpha))
-            test_all_usps(classifieur=LinearRegression,
-                          loss_g=loss_g,
-                          type_descente=DescenteDeGradient.BATCH,
-                          alpha=alpha)
-
-    test_all_usps_sklearn(alpha=1.0)
-
-
-def main_1_vs_all():
-    print("TEST 1 VS ALL")
-    test_all_usps_1_vs_all(classifieur=LinearRegression,
-                           loss_g=mse_g,
-                           type_descente=DescenteDeGradient.BATCH,
-                           alpha=0)
-    for loss_g in (l1_g, l2_g):
-        for alpha in (0, 0.25, 0.5, 0.75, 1):
-            print(loss_g.__name__, "alpha=" + str(alpha))
-            test_all_usps_1_vs_all(classifieur=LinearRegression,
-                                   loss_g=loss_g,
-                                   type_descente=DescenteDeGradient.BATCH,
-                                   alpha=alpha)
-
-
 if __name__ == "__main__":
     main_outdoor()