python 图像归一化代码代编程辅导图python

from PIL import Imageimport osimport sysimport numpy as npimport timefrom sklearn import svm# 获取指定路径下的所有 .png 文件def get_file_list(path):return [os.path.join(path, f) for f in os.listdir(path) if f.endswith(".png")]# 解析出 .png 图件文件的名称def get_img_name_str(imgPath):return imgPath.split(os.path.sep)[-1]# 将 20px * 20px 的图像数据转换成 1*400 的 numpy 向量# 参数：imgFile--图像名 如：0_1.png# 返回：1*400 的 numpy 向量def img2vector(imgFile):#print("in img2vector func--para:{}".format(imgFile))img = Image.open(imgFile).convert('L')img_arr = np.array(img, 'i') # 20px * 20px 灰度图像img_normalization = np.round(img_arr/255) # 对灰度值进行归一化img_arr2 = np.reshape(img_normalization, (1,-1)) # 1 * 400 矩阵return img_arr2# 读取一个类别的所有数据并转换成矩阵 # 参数：# basePath: 图像数据所在的基本路径# Mnist-image/train/# Mnist-image/test/# cla：类别名称# 0,1,2,...,9# 返回：某一类别的所有数据----[样本数量*(图像宽x图像高)] 矩阵def read_and_convert(imgFileList):dataLabel = [] # 存放类标签dataNum = len(imgFileList)dataMat = np.zeros((dataNum, 400)) # dataNum * 400 的矩阵for i in range(dataNum):imgNameStr = imgFileList[i]imgName = get_img_name_str(imgNameStr) # 得到 数字_实例编号.png#print("imgName: {}".format(imgName))classTag = imgName.split(".")[0].split("_")[0] # 得到 类标签(数字)#print("classTag: {}".format(classTag))dataLabel.append(classTag)dataMat[i,:] = img2vector(imgNameStr)return dataMat, dataLabel# 读取训练数据def read_all_data():cName = ['1', '2', '3', '4', '5', '6', '7', '8', '9']train_data_path = "Mnist-image\\train\\0"flist = get_file_list(train_data_path)dataMat, dataLabel = read_and_convert(flist)for c in cName:train_data_path_ = "Mnist-image\\train\\" + cflist_ = get_file_list(train_data_path_)dataMat_, dataLabel_ = read_and_convert(flist_)dataMat = np.concatenate((dataMat, dataMat_), axis=0)dataLabel = np.concatenate((dataLabel, dataLabel_), axis=0)#print(dataMat.shape)#print(len(dataLabel))return dataMat, dataLabel# create modeldef create_svm(dataMat, dataLabel, decision='ovr'):clf = svm.SVC(decision_function_shape=decision)clf.fit(dataMat, dataLabel)return clf#clf = svm.SVC(decision_function_shape='ovr')st = time.clock()clf = create_svm(dataMat, dataLabel, decision='ovr')et = time.clock()print("Training spent {:.4f}s.".format((et-st)))# 对10个数字进行分类测试def main():tbasePath = "Mnist-image\\test\\"tcName = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']tst = time.clock()allErrCount = 0allErrorRate = 0.0allScore = 0.0for tcn in tcName:testPath = "Mnist-image\\test\\" + tcn#print("class " + tcn + " path is: {}.".format(testPath))tflist = get_file_list(testPath)#tflisttdataMat, tdataLabel = read_and_convert(tflist)print("test dataMat shape: {0}, test dataLabel len: {1} ".format(tdataMat.shape, len(tdataLabel)))#print("test dataLabel: {}".format(len(tdataLabel)))pre_st = time.clock()preResult = clf.predict(tdataMat)pre_et = time.clock()print("Recognition " + tcn + " spent {:.4f}s.".format((pre_et-pre_st)))#print("predict result: {}".format(len(preResult)))errCount = len([x for x in preResult if x!=tcn])print("errorCount: {}.".format(errCount))allErrCount += errCountscore_st = time.clock()score = clf.score(tdataMat, tdataLabel)score_et = time.clock()print("computing score spent {:.6f}s.".format(score_et-score_st))allScore += scoreprint("score: {:.6f}.".format(score))print("error rate is {:.6f}.".format((1-score)))print("---------------------------------------------------------")tet = time.clock()print("Testing All class total spent {:.6f}s.".format(tet-tst))print("All error Count is: {}.".format(allErrCount))avgAccuracy = allScore/10.0print("Average accuracy is: {:.6f}.".format(avgAccuracy))print("Average error rate is: {:.6f}.".format(1-avgScore))