关于libsvm libsvm是台湾大学林智仁(Chih-Jen Lin)教授等开发，它主要用于分类(支持二分类和多分类)和回归，主页主页，下载

import keras from keras.layers import Input,Dense,Conv2D from keras.layers import MaxPooling2D,Flatten,Convolution2D from keras.models import Model import os import numpy as np from PIL import Image from keras.optimizers import SGD from scipy import misc root_path = os.getcwd() train_names = ['bear','blackswan','bus','camel','car','cows','dance','dog','hike','hoc','kite','lucia','mallerd','pigs','soapbox','stro','surf','swing','train','walking'] test_names = ['boat','dance-jump','drift-turn','elephant','libby'] def load_data(seq_names,data_number,seq_len): #生成图片对 print('loading data.....') frame_num = 51 train_data1 = [] train_data2 = [] train_lab = [] count = 0 while count < data_number:

import keras from keras.models import Sequential from keras.layers import Dense,MaxPooling2D,Input,Flatten,Convolution2D,Dropout,GlobalAveragePooling2D from keras.optimizers import SGD from keras.callbacks import TensorBoard,ModelCheckpoint from PIL import Image import os import numpy as np from scipy import misc root_path = os.getcwd() def load_data(): tran_imags = [] labels = [] seq_names = ['cat','dog'] for seq_name in seq_names: frames = sorted(os.listdir(os.path.join(root_path,'data','train_data', seq_name))) for frame in frames: imgs = [os.path.join(root_path, 'data', 'train_data', seq_name, frame)] imgs = np.array(Image.open(imgs[0])) tran_imags.append(imgs) if

PSNR（Peak Signal to Noise Ratio）：峰值信噪比 使用局部均值误差来判断差异，对于两个H*W*C的图像，I1,I2 其中n为采样值的比特数，比如

由于最近做实验用到二值图像连通区域（八连通）标记，刚开始的时候为了验证算法有效性，用了递归的方法（太慢了，而且图像一大就容易栈溢出），最后查