python_OpenCV_mlpTrain.rar_opencv_opencv 神经网络_opencv 训练_python 神

# -*- coding: cp936 -*- import cv2.cv as cv import cv2 import cut import numpy import os import time from shareFunction import * def isBmp(fileName): return os.path.splitext(fileName)[1]=='.bmp' def ReadSample(): print('开始载入样本数据......') start = time.clock() srcPath='获取样本\\num' first=0 outData=[] for path in os.walk(srcPath): print(path[0]) for fileName in path[2]: if isBmp(fileName): array=ConvertToArray(cv.LoadImage(os.path.join(path[0],fileName),cv.CV_LOAD_IMAGE_GRAYSCALE)) if first==0: inData=array first=1 else: inData = numpy.append(inData,array) outData.append(int( os.path.split(path[0])[1] )) print('载入样本数据成功!') print('样本数量：') inData.shape=[-1,array.shape[0]] print('图像样本个数：'+str(inData.shape[0]) ) outData=numpy.float32(outData) print('标签样本个数：'+str(outData.shape[0]) ) sampleCount=inData.shape[0] sampleSize=inData[0].shape[0] print('载入样本用时：'+str( (time.clock()-start)) +'s' ) return [inData,outData,sampleCount,sampleSize] if __name__=='__main__': print(time.ctime()) print('创建神经网络......') mlp=cv2.ANN_MLP() [inData,outData,sampleCount,sampleSize]=ReadSample() layer_sizes = numpy.int32([ sampleSize , 100, 1]) mlp.create(layer_sizes) trainTime=input('请输入学习次数：') params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, trainTime , 0.001), train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP, bp_dw_scale = 0.001, bp_moment_scale = 0.0 ) print('创建神经网络成功！') print('神经网络开始学习......') start = time.clock() mlp.train(inData, outData, None, params = params) print('神经网络学习完成！') print('学习用时：'+str( (time.clock()-start)) +'s' ) print('学习次数：'+str(trainTime) ) print('平均学习一次用时：'+str( (time.clock()-start)/trainTime) +'s' ) mlp.save('mlp.xml') mlp.predict( inData, outData ) ## fileName=filter(isBmp,fileName)