des = sift.detectAndCompute(gray, None)  # 检测并计算描述符

正确写法：
Kp,des=sift.detectAndCompute(gray,None)#检测并计算描述符

注意：detect和Compute可以一起写，也可以分开写，

import os, codecs
import cv2
import numpy as np
from sklearn.cluster import KMeansdef get_file_name(path):'''''
    Args: path to list;  Returns: path with filenames
    '''
    filenames = os.listdir(path)path_filenames = []filename_list = []for file in filenames:if not file.startswith('.'):path_filenames.append(os.path.join(path, file))filename_list.append(file)return path_filenamesdef knn_detect(file_list, cluster_nums, randomState=None):features = []files = file_listsift = cv2.xfeatures2d.SIFT_create()for file in files:print(file)img = cv2.imread(file)img = cv2.resize(img, (32, 32), interpolation=cv2.INTER_CUBIC)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)print(gray.dtype)kp,des = sift.detectAndCompute(gray, None)  # 检测并计算描述符
        # Kp,des=sift.detectAndCompute(gray,None)#检测并计算描述符
        # des =sift.detect(gray, None)# sift.detectAndCompute(gray, None)
        # # # 找到后可以计算关键点的描述符
        # Kp, des = sift.compute(gray, des)
        if des is None:file_list.remove(file)continue

        reshape_feature = des.reshape(-1, 1)features.append(reshape_feature[0].tolist())input_x = np.array(features)kmeans = KMeans(n_clusters=cluster_nums, random_state=randomState).fit(input_x)return kmeans.labels_, kmeans.cluster_centers_def res_fit(filenames, labels):files = [file.split('/')[-1] for file in filenames]return dict(zip(files, labels))def save(path, filename, data):file = os.path.join(path, filename)with codecs.open(file, 'w', encoding='utf-8') as fw:for f, l in data.items():fw.write("{}\t{}\n".format(f, l))def main():path_filenames = get_file_name("./picture/")labels, cluster_centers = knn_detect(path_filenames, 2)res_dict = res_fit(path_filenames, labels)save('./', 'knn_res.txt', res_dict)if __name__ == "__main__":main()