步骤：
(1)：计算测试数据与训练数据之间的距离；
(2)：按结果距离排序
(3)：选取距离最小的K个点
(4)：确定此K个样本中各个类别的频率
(5)：将频率最高的作为预测分类的结果

样本数据集：0、1、2、3。分别是黑夜、激光、CXK、山水
以上有三种方法，参考其他博主。

import datetimestarttime=datetime.datetime.now()import numpy as npimport osimport cv2 as cvfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrix,classification_reportfrom sklearn.neighbors import KNeighborsClassifier#from numpy import *# 1 导入数据X=[]Y=[]for i in range(0, 4):    # 遍历文件夹，读取图片    for f in os.listdir("D:/testimage/test/%s" % i):        # 获取图像名称        img = cv.imread("D:/testimage/test/" + str(i) + "/" + str(f))        image = cv.resize(img, (256, 256), interpolation=cv.INTER_CUBIC)        image=cv.cvtColor(image,cv.COLOR_BGR2GRAY)        hist = cv.calcHist([image], [0], None, [256], [0.0, 256.0])        X.append(((hist / 255).flatten()))        Y.append(i)X = np.array(X)Y = np.array(Y)#切分训练集和测试集x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=1)class KNN:    def __init__(self, train_data, train_label, test_data):        self.train_data = train_data        self.train_label = train_label        self.test_data = test_data    def knn_classify(self):        num_train = (self.train_data).shape[0]        num_test = (self.test_data).shape[0]        labels = []        for i in range(num_test):            y = []            for j in range(num_train):                dis = np.sum(np.square((self.train_data)[j] - (self.test_data)[i]))                y.append(dis)            labels.append(self.train_label[y.index(min(y))])        labels = np.array(labels)        return labelsknn = KNN(x_train, y_train, x_test)predictions_labels = knn.knn_classify()print(confusion_matrix(y_test, predictions_labels))print(classification_report(y_test, predictions_labels))endtime = datetime.datetime.now()print(endtime - starttime)#方法2 用KNN集成函数包clf0 = KNeighborsClassifier(n_neighbors=10).fit(x_train, y_train)predictions0 = clf0.predict(x_test)print(confusion_matrix(y_test, predictions0))print (classification_report(y_test, predictions0))#方法3 数学原理方法'''def KNN_Classify(newInput, dataSet, labels, k):    numSamples = dataSet.shape[0]  # shape[0]表示行数    # tile(A, reps): 构造一个矩阵，通过A重复reps次得到    # the following copy numSamples rows for dataSet    diff = np.tile(newInput, (numSamples, 1)) - dataSet  # 按元素求差值    squaredDiff = diff ** 2  # 将差值平方    squaredDist = np.sum(squaredDiff, axis=1)  # 按行累加    distance = squaredDist ** 0.5  # 将差值平方和求开方，即得距离    # # step 2: 对距离排序    # argsort() 返回排序后的索引值    sortedDistIndices = np.argsort(distance)    classCount = {}  # define a dictionary (can be append element)    for i in range(k):        # # step 3: 选择k个最近邻        voteLabel = labels[sortedDistIndices[i]]        # # step 4: 计算k个最近邻中各类别出现的次数        # when the key voteLabel is not in dictionary classCount, get()        # will return 0        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1    # # step 5: 返回出现次数最多的类别标签    maxCount = 0    for key, value in classCount.items():        if value > maxCount:            maxCount = value            maxIndex = key    return maxIndexpredictions = []for i in range(x_test.shape[0]):    predictions_labes = KNN_Classify(x_test[i], x_train, y_train, 10)    predictions.append(predictions_labes)print(confusion_matrix(y_test, predictions))# 打印预测结果混淆矩阵print(classification_report(y_test, predictions))# 打印精度、召回率、FI结果endtime = datetime.datetime.now()print(endtime - starttime)'''

由于样本很相似，所以准确率很高，样本的采集来源于视频帧率截取。

参考文献：[1] 机器学习之KNN算法实现图像分类

[2] [Python图像处理] 二十六.图像分类原理及基于KNN、朴素贝叶斯算法的图像分类案例

CSDN:@WitnesS