LIVE1_train_test.zip_SIQA_卷积神经网络_图像质量评价_神经网络评价_立体图像质量

# -*- coding: utf-8 -*- ''' 本程序用于live2 视频数据库的 train-test 实验，每次实验80%训练，20%测试 inputdata:360个图片，每个图片分成220个32*32的图像块 traindata=360*0.8=292 testdata=360*0.2=73 ''' from __future__ import print_function import scipy.io as sc import numpy as np import matplotlib.pyplot as plt import os import h5py #import sys #reload(sys) #sys.setdefaultencoding('utf-8') np.random.seed(1337) # for reproducibility #from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K from keras.layers import Merge CurrentPath=os.getcwd() path1=CurrentPath+'/dataset/LIVE1/diff' path2=CurrentPath+'/dataset/LIVE1/left' path3=CurrentPath+'/dataset/LIVE1/right' files= os.listdir(path1)#读取文件夹文件列表 files.sort() #print(files) files_l= os.listdir(path2)#读取文件夹文件列表 files_l.sort() files_r= os.listdir(path3)#读取文件夹文件列表 files_r.sort() #print(files) X_train_diff=np.empty((292*220,32,32,1),dtype='float32') y_train=np.empty((292*220,1),dtype='float32') X_test_diff=np.empty((73*220,32,32,1),dtype='float32') y_test=np.empty((73*220,1),dtype='float32') #astype('float32') X_train_left=np.empty((292*220,32,32,1),dtype='float32') X_test_left=np.empty((73*220,32,32,1),dtype='float32') X_train_right=np.empty((292*220,32,32,1),dtype='float32') X_test_right=np.empty((73*220,32,32,1),dtype='float32') MosFile='DMOS_LIVE1.mat' MosDic=sc.loadmat(MosFile) MosData=MosDic['LIVE1'] MosData.astype('float32') MosData=((MosData-min(MosData))/(max(MosData)-min(MosData)))*2-1 ExpNum=100#实验（train-test）的次数 for NumExp in xrange(ExpNum): x=range(365)#qifeng库共450个视频 np.random.shuffle(x)#打乱视频顺序 for i in xrange(292):#取80%作为训练数据 dataFile=path1+'/'+files[x[i]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['diff'] SingleData=np.rollaxis(temp,2,0) X_train_diff[i*220:(i+1)*220,:,:,0]=SingleData# y_train[i*220:(i+1)*220,:]=MosData[x[i]]#*np.ones(5100,1) print(files[x[i]]) dataFile=path2+'/'+files_l[x[i]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['left'] SingleData=np.rollaxis(temp,2,0) X_train_left[i*220:(i+1)*220,:,:,0]=SingleData# print(files_l[x[i]]) dataFile=path3+'/'+files_r[x[i]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['right'] SingleData=np.rollaxis(temp,2,0) X_train_right[i*220:(i+1)*220,:,:,0]=SingleData# print(files_r[x[i]]) print('i:',i) for i in xrange(73): dataFile=path1+'/'+files[x[i+292]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['diff'] SingleData=np.rollaxis(temp,2,0) X_test_diff[i*220:(i+1)*220,:,:,0]=SingleData y_test[i*220:(i+1)*220,:]=MosData[x[i+292]] dataFile=path2+'/'+files_l[x[i+292]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['left'] SingleData=np.rollaxis(temp,2,0) X_test_left[i*220:(i+1)*220,:,:,0]=SingleData dataFile=path3+'/'+files_r[x[i+292]] tempLoad=sc.loadmat(dataFile) temp=tempLoad['right'] SingleData=np.rollaxis(temp,2,0) X_test_right[i*220:(i+1)*220,:,:,0]=SingleData print('i:',i) X_train_diff/=255#归一 X_test_diff/=255 X_train_left/=255#归一 X_test_left/=255 X_train_right/=255#归一 X_test_right/=255 batch_size = 128 nb_classes = 1 nb_epoch = 70 # input image dimensions img_rows, img_cols = 32, 32 # number of convolutional filters to use nb_filters = 64 # size of pooling area for max pooling pool_size = (3, 3) # convolution kernel size kernel_size = (3, 3) # the data, shuffled and split between train and test sets #(X_train, y_train), (X_test, y_test) = mnist.load_data() #if K.image_dim_ordering() == 'th': # X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) # X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) # input_shape = (1, img_rows, img_cols) #else: # X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) # X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) # input_shape = (img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) print('X_train shape:', X_train_diff.shape) print(X_train_diff[0], 'train samples') print(X_train_diff[0], 'test samples') diff_branch = Sequential() diff_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) diff_branch.add(Activation('relu')) diff_branch.add(MaxPooling2D(pool_size=pool_size)) diff_branch.add(Dropout(0.25)) diff_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) diff_branch.add(Activation('relu')) diff_branch.add(MaxPooling2D(pool_size=(8,8))) diff_branch.add(Dropout(0.25)) left_branch = Sequential() left_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) left_branch.add(Activation('relu')) left_branch.add(MaxPooling2D(pool_size=pool_size)) left_branch.add(Dropout(0.25)) left_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) left_branch.add(Activation('relu')) left_branch.add(MaxPooling2D(pool_size=(8,8))) left_branch.add(Dropout(0.25)) right_branch = Sequential() right_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) right_branch.add(Activation('relu')) right_branch.add(MaxPooling2D(pool_size=pool_size)) right_branch.add(Dropout(0.25)) right_branch.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) right_branch.add(Activation('relu')) right_branch.add(MaxPooling2D(pool_size=(8,8))) right_branch.add(Dropout(0.25)) merged = Merge([diff_branch, left_branch, right_branch], mode='concat') final_model = Sequential() final_model.add(merged) final_model.add(Flatten()) final_model.add(Dense(300)) final_model.add(Activation('relu')) final_model.add(Dropout(0.5)) final_model.add(Dense(nb_classes)) final_model.add(Activation('linear')) #sgd=SGD() final_model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['mae']) final_model.fit([X_train_diff,X_train_left,X_train_right],y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=([X_test_diff,X_test_left,X_test_right], y_test)) score = final_model.evaluate([X_test_diff,X_test_left,X_test_right], y_test,batch_size=batch_size) out=final_model.predict([X_test_diff,X_test_left,X_test_right],batch_size=batch_size) sc.savemat('LIVE1out_'+str(NumExp),{'out':out}) sc.savemat('LIVE1Dmos_'+str(NumExp),{'y_test':y_test}) out1=out.reshape(73,220) out_mean2=np.mean(out1,axis=1) y_test_re=y_test.reshape(73,220) y_test_mean=np.mean(y_test_re,axis=1) print('Test score:', score[0]) print('Test accuracy:', score[1]) # batch_size = 128 # nb_classes = 1 # nb_epoch = 200#迭代次数 # # # input image dimensions # img_rows,img_cols,img_time = 32,32,10 # # number of convolutional filters to use # nb_filters = (64,128) # # size of pooling area for max pooling #