def parse_function(data, config):
features = tf.parse_single_example(data,
features={
'image':
tf.FixedLenFeature([], tf.string),
'shape':
tf.FixedLenFeature([], tf.string),
'label':
tf.FixedLenFeature([], tf.int64)
})
shape = tf.decode_raw(features['shape'], tf.int32)
label = tf.cast(features['label'], tf.int64)
shape = tf.reshape(shape, [3])
images = tf.image.decode_jpeg(features['image'], channels=3)
images = tf.cast(tf.reshape(images, shape), tf.float32)
images = image_augmentor(image=images, input_shape=shape, **config)
return images, label, # shape
def parse_function(data, config):
features = tf.parse_single_example(data,
features={
'image':
tf.FixedLenFeature([], tf.string),
'shape':
tf.FixedLenFeature([], tf.string),
'ground_truth':
tf.FixedLenFeature([], tf.string)
})
shape = tf.decode_raw(features['shape'], tf.int32)
ground_truth = tf.decode_raw(features['ground_truth'], tf.float32)
shape = tf.reshape(shape, [3])
ground_truth = tf.reshape(ground_truth, [-1, 5])
images = tf.image.decode_jpeg(features['image'], channels=3)
images = tf.cast(tf.reshape(images, shape), tf.float32)
images, ground_truth = image_augmentor(image=images,
input_shape=shape,
ground_truth=ground_truth,
**config)
return images, ground_truth
def parse_function(data, config):
features = tf.io.parse_single_example(
data,
features={ # 产生一个实例
'image': tf.io.FixedLenFeature([], tf.string),
'shape': tf.io.FixedLenFeature([], tf.string),
'ground_truth': tf.io.FixedLenFeature([], tf.string)
})
shape = tf.decode_raw(features['shape'],
tf.int32) # tf.decode_raw:主要用于将原来编码为字符串的类型变回来
ground_truth = tf.decode_raw(features['ground_truth'], tf.float32)
shape = tf.reshape(shape, [3])
ground_truth = tf.reshape(ground_truth, [-1, 5]) # 这个框的标注对应于5个内容
images = tf.image.decode_jpeg(features['image'],
channels=3) # channel=3为RGB图片
images = tf.cast(tf.reshape(images, shape), tf.float32)
images, ground_truth = image_augmentor(image=images,
input_shape=shape,
ground_truth=ground_truth,
**config)
return images, ground_truth