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SAFT/main.py

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+from util import *
+from algo import *
+"""
+The A value of the algorithm is the sound speed divided by the frequency or 0.059 mm, which gives about 17 samples per mm. 
+The B value considered for this results 0,35 mm which corresponds the resolution of our transducer x-y scanning driving system.
+Dans le cas de notre etude avec le dataset Shear_transform, nous avons des fréquences nominales de 55 kHz (ondes de cisaillement)
+measurement was performed using a sampling rate of 2 MHz
+"""
+if __name__ == "__main__":
+    dossier = "Dataset/Shear_transform"
+    fichiers_selectionnes = ['Shear_x001-x101_y{:03d}_Rot00_transform.csv'.format(i) for i in range(10, 62)]
+
+    pre_volume = np.array(lire_fichier_csv(dossier, fichiers_selectionnes))
+    volume = pre_volume[:, ::4, :]
+
+    image = volume[0, :, :]
+
+    win = 10
+    A = 1
+    B = 1
+
+    # Application de l'algorithme SAFT
+    result = saft_algorithm(image, win, A, B)

SAFT/util.py

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+import csv
+import os
+
+def lire_un_fichier_csv(data):
+    tableau_2D = []
+    tab = []
+    with open(data, "r") as csv_file:
+        csv_reader = csv.reader(csv_file, delimiter=',')
+        for ligne in csv_reader:
+            tableau_2D.append(ligne)
+    for i in range(len(tableau_2D[0])):
+        ligne = []
+        for j in range(len(tableau_2D)):
+            ligne.append(float(tableau_2D[j][i]))
+        tab.append(ligne)
+    return tab
+
+def lire_fichier_csv(data,fichier):
+    resultat=[]
+    tableau=[]
+    for i in fichier:
+        tableau=lire_un_fichier_csv(os.path.join(data,i))
+        resultat.append(tableau)
+    return resultat