knn_2.py

def prepare_image_Xy(df):
    train_image_X = []
    train_image_y = []
    for n in range(len(df) - 1):
        img = cv2.imread(str(df.iloc[n].path), cv2.IMREAD_GRAYSCALE)
        height, width = img.shape
        ratio = float(height/width)
        if ratio > 0.6:
            img_pred = cv2.resize(img, (81,81), interpolation=cv2.INTER_AREA)
            img_pred = img_to_array(img_pred)
            img_pred = img_pred / 255
            train_image_X.append(img_pred)
            train_image_y.append(df.iloc[n].label)
    X = np.array(train_image_X)
    y = np.array(train_image_y)
    
    return X,y
 
X_train, y_train = prepare_image_Xy(train)
X_test, y_test = prepare_image_Xy(test)