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import os
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import random
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import json
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import numpy as np
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from PIL import Image
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from 口罩检测.util import train_parameters
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def get_data_list(target_path,train_list_path,eval_list_path):
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#存放的类别信息
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class_detail=[]
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class_dirs = os.listdir(target_path)
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all_class_images=0 #数据集中总的图像数量
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class_label=0 #存放类别标签
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class_dim=0 #存放类别数目
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train_list=[] #训练集
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eval_list=[] #测试集
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for class_dir in class_dirs:
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class_dim+=1
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class_detail_list={}
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eval_sum=0
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trainer_sum=0
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class_sum=0 #每个类别有多少张图片
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path = target_path+"/"+class_dir
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img_paths = os.listdir(path)
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for img_path in img_paths:
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name_path = path+"/"+img_path
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if class_sum%10==0:
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eval_sum+=1
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eval_list.append(name_path+"\t%d"%class_label+"\n")
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else:
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trainer_sum+=1
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train_list.append(name_path+"\t%d"%class_label+"\n")
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class_sum+=1
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all_class_images+=1
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class_detail_list['class_name']=class_dir
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class_detail_list['class_label']=class_label
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class_detail_list['class_eval_images']=eval_sum
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class_detail_list['class_train_images']=trainer_sum
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class_detail.append(class_detail_list)
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train_parameters['label_dict'][str(class_label)]= class_dir
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class_label+=1
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train_parameters['class_dim']=class_dim
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random.shuffle(eval_list)
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with open(eval_list_path,'a') as eval_file:
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for eval_image in eval_list:
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eval_file.write(eval_image)
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random.shuffle(train_list)
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with open(train_list_path,'a') as train_file:
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for train_item in train_list:
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train_file.write(train_item)
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#说明json的文件信息
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readJson={}
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readJson['all_class_name']=target_path
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readJson['all_class_images']= all_class_images
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readJson['class_detail']=class_detail
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jsons = json.dumps(readJson, sort_keys=True, indent=4, separators=(',', ': '))
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with open(train_parameters['readme_path'],'w') as f:
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f.write(jsons)
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print ('生成数据列表完成！')
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def custom_reader(file_list):
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def reader():
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with open(file_list,'r') as f:
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lines=[line.strip() for line in f]
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for line in lines:
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img_path,label=line.strip().split('\t')
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img =Image.open(img_path)
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if img.mode!='RGB':
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img = img.convert('RGB')
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img = img.resize((244,244),Image.BILINEAR)
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img = np.array(img).astype('float32')
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img = img.transpose((2,0,1))
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img = img/255.0
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yield img,int(label)
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return reader
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target_path=train_parameters['target_path']
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train_list_path=train_parameters['train_list_path']
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eval_list_path=train_parameters['eval_list_path']
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batch_size=train_parameters['train_batch_size']
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#每次生成数据列表前，首先清空train.txt和eval.txt
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with open(train_list_path, 'w') as f:
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f.seek(0)
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f.truncate()
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with open(eval_list_path, 'w') as f:
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f.seek(0)
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f.truncate()
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#生成数据列表
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get_data_list(target_path,train_list_path,eval_list_path)
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