k近邻算法-手写数字识别系统

构造一个能识别数字 0 到 9 的基于 KNN 分类器的手写数字识别系统。

需要识别的数字是存储在文本文件中的具有相同的色彩和大小: 宽高是 32 像素 * 32 像素的黑白图像。

在控制台输入 mspaint，打开画图工具把像素调至32 x 32

选择单色位图

def getImageArr(path):
    imageArr = cv2.imread(path)
    imageArr = imageArr[:, :, 0] / 255
    imageArr = imageArr.astype(np.int32)
    where_0 = np.where(imageArr == 0)
    where_1 = np.where(imageArr == 1)
    imageArr[where_0] = 1
    imageArr[where_1] = 0
    return imageArr

解析得到01010的格式

解析题上给的数据集

def img2vector(filename):
    returnVect = np.zeros((1,1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0,32*i+j] = int(lineStr[j])
    return returnVect

写上k近邻算法

def classifyFun(inX, dataList, dataLabels, k):
    dataListLen = dataList.shape[0]
    difMat = np.tile(inX, (dataListLen, 1)) - dataList
    sqDifMat = difMat ** 2
    sqDifMatSum = sqDifMat.sum(axis = 1)
    DifDist = sqDifMatSum ** 0.5
    # 排序
    DifDistSortIdx = DifDist.argsort()
    classCount = {}
    for i in range(k):
        getLabel = dataLabels[DifDistSortIdx[i]]
        classCount[getLabel] = classCount.get(getLabel, 0) + 1
    classifyAns = sorted(classCount.items(), key = operator.itemgetter(1),reverse = True)
    print(classifyAns)
    return classifyAns[0][0]

def ClassTrain():
    digLabels = []
    trainingFileList = os.listdir("trainingDigits")
    digCount = len(trainingFileList)
    trainingMat = np.zeros((digCount, 32 * 32))
    for i in range(digCount):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNum = int(fileStr.split('_')[0])
        digLabels.append(classNum)
        trainingMat[i, :] = img2vector('trainingDigits/'+fileNameStr)
    return trainingMat, digLabels

最后进行一个调用

if __name__ == "__main__":
    imageIn = getImageArr("5.bmp")
    trainingMat, digLabels = ClassTrain()
    classifierResult = classifyFun(imageIn.flatten(), trainingMat, digLabels, 10)
    plt.imshow(imageIn)
    print(classifierResult)