#此模塊主要用來讀取數(shù)據(jù)集，返回一個(gè)數(shù)據(jù)可迭代對象
#重點(diǎn)是，此模塊分批次的把圖像讀入內(nèi)存的，而不是一次全讀入，有效的減少了內(nèi)存溢出
import os
import cv2
import numpy as np
import keras
from random import shuffle

#目標(biāo)圖像大小
image_size= (229, 229, 3)
#類別編碼
class_dict=dict(zip(['neg','pos','neu'],[0,1,2]))
#處理.txt文件,并加載圖片文件夾里的圖片名
#txt_path，txt文件路徑，data_path，圖片文件夾路徑

def read_txt(txt_path,data_path):
 # 中間數(shù)組
 labels_class = []
 labels_score = []
 with open(txt_path) as f:
 lines_list = f.readlines()
 for lines in lines_list:
  line = lines.split(' ')
  labels_class.append(line[0].rstrip(".jpg"))
  labels_score.append(line[2])
 labels_dict=dict(zip(labels_class,labels_score))
 #處理圖片數(shù)據(jù)集
 all_picture_name = os.listdir(data_path)
 #打亂數(shù)據(jù)集
 shuffle(all_picture_name)
 all_picture_path=[os.path.join(data_path,one)for one in all_picture_name]
 return all_picture_name,all_picture_path,labels_dict

class data_generate:
 def __init__(self,all_piture_name,all_picture_path,labels_dict,batch_size):
 self.index=0
 self.all_picture_name=all_piture_name
 self.all_picture_path=all_picture_path
 self.labels_dict=labels_dict
 self.batch_size = batch_size
 def get_mini_batch(self):
  while True:
  batch_images=[]
  batch_labels=[]
  batch_class=[]
  batch_score=[]
  for i in range(self.batch_size):
  if(self.index==len(self.all_picture_name)):
   self.index=0

  bgr_image = cv2.imread(self.all_picture_path[self.index])
  if len(bgr_image.shape) == 2: # 若是灰度圖則轉(zhuǎn)為三通道
   bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)
  rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
  rgb_image=cv2.resize(rgb_image,(image_size[0], image_size[1]))
  img = np.array(rgb_image)
  img=keras.applications.inception_v3.preprocess_input(img)
  batch_images.append(img)
  #label=[]
  label1=self.all_picture_name[self.index].rstrip(".jpg")
  batch_class.append(keras.utils.to_categorical(class_dict[label1[:3]], 3))
  batch_score.append(np.array(self.labels_dict[label1]))
  #batch_labels.append(label)
  self.index+=1
  batch_images=np.array(batch_images)
  batch_class = np.array(batch_class)
  batch_score = np.array(batch_score)
  #注意label的生成batch_class，一個(gè)單獨(dú)數(shù)組，batch_score一個(gè)單獨(dú)的數(shù)組
  '''
  注釋掉的這段代碼生成的label是錯(cuò)誤的。
  batch_images=[]
  batch_labels=[]
  for i in range(self.batch_size):
  if(self.index==len(self.images)):
   self.index=0
  batch_images.append(self.images[self.index])
  batch_labels.append(self.labels[self.index])
  self.index+=1
  batch_images=np.array(batch_images)
  batch_labels=np.array(batch_labels)
  yield batch_images,batch_labels
  '''
  yield batch_images,[batch_class,batch_score]

history=model.fit_generator(generator=train_data.get_mini_batch(),
   steps_per_epoch=146,
   epochs=300,
   validation_data=test_data.get_mini_batch(),
   validation_steps=34,
   )