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

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