基于Keras图像相似度计算孪生网络

基于Keras图像相似度计算孪生网络

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:
Keras 猫狗二分类

Keras 猫狗二分类

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
图像联通区域标记

图像联通区域标记

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