prep.py

X_data = df['IMAGE PATH']
y_data = df['MAPPED LABELS']
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, shuffle=True, test_size=0.01,stratify=y_data)
#Creating numpy arrays of images
X = []
y = []
for i in X_train:
    X.append(cv2.imread(i))
for i in y_train:
    y.append(i)
X = np.array(X)
y = np.array(y)
# Converting the labels vector to one-hot format
y = keras.utils.to_categorical(y, 5)
print(f"Total number of Images: {len(X_data)}")
print(f"Number of Training Images: {len(X_train)}")
print(f"Number of Test Images: {len(X_test)}") # Saving a small number of images for model testing|
print(f"Shape of Images: {X[0].shape}") #Printing the shape of Images