Fixed the code that broke randomly

Main.py

+import os
+
 from sklearn.naive_bayes import GaussianNB
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.svm import SVC
@@ -63,11 +65,12 @@ def cross_validation_on_model(model, S):
 ## EXEMPLE D'EXECUTION DU PROJET
 
 #INITIALISATION
-S = compute_images_dictionnary(PATH_SEA, PATH_WITHOUT_SEA)
+"""S = compute_images_dictionnary(PATH_SEA, PATH_WITHOUT_SEA)
 svc_model = get_SVC_model_with_best_parameters()
 
 #TESTS
-cross_validation_on_model(svc_model, S)
+cross_validation_on_model(svc_model, S)"""
+os.listdir(PATH_SEA)
 
 #test_model_on_single_train(svc_model, S)
 

Main/MainProjet.py

@@ -2,9 +2,15 @@ from Pipeline.ImageFolderManager import buildSampleFromPath
 from Pipeline.Model import get_SVC_model_with_best_parameters
 from Pipeline.Evaluation import cross_validation_on_model
 
-PATH_SEA = "../Init/Mer"  # PATH TO THE IMAGES CONTAINING THE SEA
-PATH_WITHOUT_SEA = "../Init/Ailleurs"  # PATH TO THE IMAGE WITHOUT THE SEA
+PATH_SEA = "Init/Mer"  # PATH TO THE IMAGES CONTAINING THE SEA
+PATH_WITHOUT_SEA = "Init/Ailleurs"  # PATH TO THE IMAGE WITHOUT THE SEA
 
+#Exemple d'utilisation du projet
+print("Début de la création de la liste d'images")
 S = buildSampleFromPath(PATH_SEA, PATH_WITHOUT_SEA)
+print("Fin de la création de la liste d'images")
+print("Début de la récupération du modèle")
 svc_model = get_SVC_model_with_best_parameters()
+print("Fin de la récupération du modèle")
+print("Début de l'évaluation")
 cross_validation_on_model(svc_model, S)
\ No newline at end of file

Pipeline/AlgoTraining.py

 import numpy as np
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from ImageDictionaryManager import extract_relevant_data
+from Pipeline.ImageDictionaryManager import extract_relevant_data
 
 
 def fit_algorithm(S, algo):
@@ -25,7 +25,7 @@ def fit_algorithm(S, algo):
     return algo, S_test, y_test, S_train, y_train
 
 
-def predictFromHisto(S, model, list_dict=True):
+def predict(S, model, list_dict=True):
     """
     Use the given model to predict the values on the images. Update the sample S to display the
     predicted values.

Pipeline/Evaluation.py

@@ -2,8 +2,8 @@ import numpy as np
 import pandas as pd
 from sklearn.model_selection import cross_val_score
 from sklearn.metrics import accuracy_score
-from ImageDictionaryManager import extract_relevant_data
-from AlgoTraining import fit_algorithm
+from Pipeline.ImageDictionaryManager import extract_relevant_data
+from Pipeline.AlgoTraining import fit_algorithm
 
 def computeError(S):
     """

Pipeline/ImageDictionaryManager.py

 import os
 from PIL import Image
-from ImageResizing import resizeImage
-from Unsharp_Mask import apply_unsharp_mask
-from Histogram import computeHisto
-from GLCM import compute_glcm_caracteristics
-from Gabor import get_gabor_filters
+from Pipeline.ImageResizing import resizeImage
+from Pipeline.Unsharp_Mask import apply_unsharp_mask
+from Pipeline.Histogram import computeHisto
+from Pipeline.GLCM import compute_glcm_caracteristics
+from Pipeline.Gabor import get_gabor_filters
 from threading import Thread
 
 def computeDict(image_path, path, y_true_value, max_size: tuple):
@@ -26,15 +26,14 @@ def computeDict(image_path, path, y_true_value, max_size: tuple):
 
     resized = Image.fromarray(unsharp_resized)
 
-    rotated = [resized]#, resized.rotate(90), resized.rotate(180), resized.rotate(270)]
-    #rotated_gl = [im.convert("L") for im in rotated]
-    rotated_gl = [resized.convert('L')]
-
     histogram = computeHisto(resized)
 
     result = []
-    #create_dictionary_image(full_path, histogram, rotated_gl[0], y_true_value, result)
-    threaded_dictionary_creation(full_path, histogram, result, rotated_gl, y_true_value)
+
+
+    create_dictionary_image(full_path, histogram, resized.convert('L'), y_true_value, result)
+
+    #threaded_dictionary_creation(full_path, histogram, result, resized, y_true_value)
 
     return result
 
@@ -61,7 +60,7 @@ def extract_relevant_data(l: dict) -> list:
     return l["X_histo"] + l["gabor_features"] + l["X_glcm_data"]
 
 
-def threaded_dictionary_creation(full_path, histogram, result, rotated_gl, y_true_value):
+def threaded_dictionary_creation(full_path, histogram, result, image, y_true_value):
     """
     this function add to the list "result", the dictionary of the given images in "rotated_gl", this is a list
     of images in gray level, delegating the computation of the different criteria used to train the model.
@@ -73,6 +72,9 @@ def threaded_dictionary_creation(full_path, histogram, result, rotated_gl, y_tru
     :param y_true_value: int that represent if there is the sea in the image, used to train and assert the model accuracy
     """
     threads = []
+    rotated = [image , image.rotate(90), image.rotate(180), image.rotate(270)]
+    # rotated_gl = [im.convert("L") for im in rotated]
+    rotated_gl = [im.convert('L') for im in rotated]
     for image_gl in rotated_gl:
         thread = Thread(target=create_dictionary_image, args=(full_path, histogram, image_gl, y_true_value, result))
         threads.append(thread)

Pipeline/ImageFolderManager.py

 import os
-from ImageDictionaryManager import computeDict
+from Pipeline.ImageDictionaryManager import computeDict
 
 MAX_SIZE = (224, 224)