python-opencv3 kmeans图像分类

参考：http://blog.csdn.net/willduan1/article/details/70335146

异常：

    reshape_feature = des.reshape(-1, 1)

AttributeError: 'tuple' object has no attribute 'reshape'

原因：

错误写法：

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, codecsimport cv2import numpy as npfrom 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_list    sift = 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)        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()