# loop over the images in each sub-folder
for file in images:
# get the image file name
file_path = os.path.join(current_dir, file)
# read the image and resize it to a fixed-size
image = cv2.imread(file_path)
image = cv2.resize(image, fixed_size)
####################################
# Global Feature extraction
####################################
fv_histogram = fd_histogram(image)
fv_4 = fd_4(image)
fv_haralick = fd_haralick(image)
fv_hu_moments = fd_hu_moments(image)
# new features
fv_Fast = fd_Fast(image)
fv_kaze = fd_Kaze(image)
###################################
# Concatenate global features
###################################
# global_feature = np.hstack([fv_histogram, fv_4, fv_haralick, fv_hu_moments])
# global_feature = np.hstack([fv_histogram, fv_kaze])
global_feature = np.hstack([fv_histogram, fv_Fast])
# fit the training data to the model
clf.fit(trainDataGlobal, trainLabelsGlobal)
# [f for f in os.listdir(test_path) if os.path.isfile(os.path.join(test_path, f))]
# loop through the test images
for idx, file in enumerate(glob.glob(test_path + "/*.jpg")):
# read the image
image = cv2.imread(file)
# resize the image
image = cv2.resize(image, fixed_size)
####################################
# Global Feature extraction
####################################
fv_histogram = fd_haralick(image)
fv_4 = fd_4(image)
###################################
# Concatenate global features
###################################
global_feature = np.hstack([fv_histogram, fv_4])
# scale features in the range (0-1)
# scaler = MinMaxScaler(feature_range=(0, 1))
# reshaped_features = np.reshape(global_feature, (1, len(global_feature)))
# rescaled_feature = scaler.fit_transform(reshaped_features)
rescaled_feature = global_feature
# predict label of test image
prediction = clf.predict(rescaled_feature.reshape(1, -1))[0]