def processData(settings, file_list):
"""
The main function for loading data.
Load the batch, iterate all the images and labels in this batch.
file_list: the batch file list.
"""
with open(file_list, 'r') as fdata:
lines = [line.strip() for line in fdata]
random.shuffle(lines)
for file_name in lines:
with io.open(file_name.strip(), 'rb') as file:
data = cPickle.load(file)
indexes = list(range(len(data['images'])))
if settings.is_train:
random.shuffle(indexes)
for i in indexes:
if settings.use_jpeg == 1:
img = image_util.decode_jpeg(data['images'][i])
else:
img = data['images'][i]
img_feat = image_util.preprocess_img(
img, settings.img_mean, settings.img_size,
settings.is_train, settings.color)
label = data['labels'][i]
yield {
'image': img_feat.astype('float32'),
'label': int(label)
}
def processData(settings, file_name):
"""
加载数据
迭代每一批的所有图像和标签
file_name: 批文件名
"""
#使用pickle类来进行python对象的序列化，而cPickle提供了一个更快速简单的接口，如python文档所说的：“cPickle -- A faster pickle”
data = cPickle.load(io.open(file_name, 'rb'))
#list() 方法用于将元组转换为列表，元组与列表的区别在于元组的元素值不能修改，元组是放在括号中，列表是放于方括号中。
indexes = list(range(len(data['images'])))
if settings.is_train:
random.shuffle(indexes)
for i in indexes:
if settings.use_jpeg == 1:
img = image_util.decode_jpeg(data['images'][i])
else:
img = data['images'][i]
#如果不是训练, 裁剪图像中心区域.否则随机裁剪,
img_feat = image_util.preprocess_img(img, settings.img_mean,
settings.img_size,
settings.is_train, settings.color)
label = data['labels'][i]
'''
包含yield语句的函数会被特地编译成生成器。当函数被调用时，他们返回一个生成器对象
不像一般函数生成值后退出，生成器函数生成值后会自动挂起并暂停他们的执行和状态，他的本地变量将保存状态信息，这些信息在函数恢复时将再度有效
执行到 yield时，processData 函数就返回一个迭代值，下次迭代时，代码从 yield的下一条语句继续执行
'''
yield img_feat.tolist(), int(label)
def processData(settings, file_name):
"""
The main function for loading data.
Load the batch, iterate all the images and labels in this batch.
file_name: the batch file name.
"""
data = cPickle.load(io.open(file_name, 'rb'))
indexes = list(range(len(data['images'])))
if settings.is_train:
random.shuffle(indexes)
for i in indexes:
if settings.use_jpeg == 1:
img = image_util.decode_jpeg(data['images'][i])
else:
img = data['images'][i]
img_feat = image_util.preprocess_img(img, settings.img_mean,
settings.img_size,
settings.is_train, settings.color)
label = data['labels'][i]
yield img_feat.tolist(), int(label)