Convnet在Windows下的使用

最简单的方法是找个能调试的python IDE，读取各个文件之后看里面的内容，这里有个我写的方法，仔细阅读之后就可以使用了。事先安装PIL包。有错写评论即可。

第93,94行是建立各个data_batch_1等等

最后一行建立batches.meta文件

这个code生成7个data_batch文件，分为两类，一类为nopeople，一类为exist_people。需要修改网络配置文件[fc10]层outputs个数为2。

14-1-9补充：这个程序最好在linux下使用，windows下生成的文件貌似在使用时会报错。

 import os
 import cPickle
 import pickle
 import numpy as np
 from numpy import array, append
 from PIL import Image
 import Image

 def makeBatch (load_path, save_path, data_size):
     data = []
     filenames = []
     class_list = []
     class_file = file('train-origin-pics-labels.txt', 'rb').readlines()
     file_list = os.listdir(load_path)
     num_sq = save_path[len(save_path)-1]
     for item in  file_list:
         if item.endswith(".jpg"):
             picture_number = item[0:len(item)-4]
             picture_num = int(picture_number)
             class_picture = class_file[picture_num-1][10:11]
             if int(picture_num)%100 == 0:
                 print picture_number
             n = os.path.join(load_path, item)
             inputImage = Image.open(n)
             (width,height) = inputImage.size
             #if  width > height:
             #    newwidth = width/height*128
             #    small_image = inputImage.resize((newwidth, 128),Image.ANTIALIAS)
             #else:
             #    newheight = height/width*128
             #    small_image = inputImage.resize((128, newheight),Image.ANTIALIAS)
             small_image = inputImage.resize((data_size, data_size),Image.ANTIALIAS)
             try:
                 r, g, b = small_image.split()
                 reseqImage = list(r.getdata()) + list(g.getdata()) + list(b.getdata())
                 data.append(reseqImage)
                 filenames.append(item)
                 class_list.append(class_picture)
             except:
                 print 'error' + picture_number
     data_array = np.array(data, dtype = np.uint8)
     T_data = data_array.T
     out_file = file(save_path, 'wb')
     dic = {'batch_label':'batch ' + num_sq + ' of 6', 'data':T_data, 'labels':class_list, 'filenames':filenames}
     pickle.dump(dic, out_file)
     out_file.close()

 def read_batch(batch_path, data_size):
     in_file = open(batch_path, 'r+')
     xx = cPickle.load(in_file)
     in_file.close()
     T_datas = xx['data']
     datas = T_datas.T
     c = np.zeros((1, data_size*data_size*3), dtype=np.float32)
     i  = 0
     for data in datas:
         i += 1
         c = c + data
     return i, c

 def add_all(data_size, path):
     count = 0
     totalc = np.zeros((1, data_size*data_size*3), dtype=np.float32)
     for idx in range(1, 7):
         print 'reading batch'+str(idx)
         path += '/data_batch_' + str(idx)
         curcount, curc = read_batch(path, data_size)
         count += curcount
         totalc = totalc + curc

     return count, totalc

 def write_data(data_size, path):
     cout, total = add_all(data_size)
     a  = []
     for i in range(0, len(total[0])):
         c = total[0][i] / cout
         a.append( [c])
     a_array = array(a, dtype = np.float32)
     return a_array

 def main(data_size, path):
     data_mean = write_data(data_size, path)
     label_names = ['nopeople', 'exist_people']
     num1 = 5000
     num2 = data_size*data_size*3
     dic = {'data_mean':data_mean, 'label_names':label_names, 'num_cases_per_batch':num1, 'num_vis':num2}
     out_file = open(path+'/batches.meta', 'w+')
     cPickle.dump(dic, out_file)
     out_file.close()

 data_size = 64
 for i in range(1, 7):
     makeBatch('./train-origin-pics-part'+str(i), 'baidu_data_size_'+str(data_size)+'/data_batch_'+str(i), data_size)
 main(data_size, 'baidu_data_size_'+str(data_size))