本文整理汇总了Python中im2txt.ops.image_processing.process_image方法的典型用法代码示例。如果您正苦于以下问题：Python image_processing.process_image方法的具体用法？Python image_processing.process_image怎么用？Python image_processing.process_image使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类im2txt.ops.image_processing的用法示例。
在下文中一共展示了image_processing.process_image方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: process_image
# 需要导入模块: from im2txt.ops import image_processing [as 别名]
# 或者: from im2txt.ops.image_processing import process_image [as 别名]
def process_image(self, encoded_image, thread_id=0):
"""Decodes and processes an image string.
Args:
encoded_image: A scalar string Tensor; the encoded image.
thread_id: Preprocessing thread id used to select the ordering of color
distortions.
Returns:
A float32 Tensor of shape [height, width, 3]; the processed image.
"""
return image_processing.process_image(encoded_image,
is_training=self.is_training(),
height=self.config.image_height,
width=self.config.image_width,
thread_id=thread_id,
image_format=self.config.image_format) 示例2: process_image
# 需要导入模块: from im2txt.ops import image_processing [as 别名]
# 或者: from im2txt.ops.image_processing import process_image [as 别名]
def process_image(self, encoded_image, thread_id=0):
"""Decodes and processes an image string.
Args:
encoded_image: A scalar string Tensor; the encoded image.
thread_id: Preprocessing thread id used to select the ordering of color
distortions.
Returns:
A float32 Tensor of shape [height, width, 3]; the processed image.
"""
return image_processing.process_image(encoded_image,
is_training=self.is_training(),
height=self.config.image_height,
width=self.config.image_width,
thread_id=thread_id,
image_format=self.config.image_format) 示例3: build_inputs
# 需要导入模块: from im2txt.ops import image_processing [as 别名]
# 或者: from im2txt.ops.image_processing import process_image [as 别名]
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_feed")
input_feed = tf.placeholder(dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(input_feed, 1)
# No target sequences or input mask in inference mode.
target_seqs = None
input_mask = None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, caption = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
caption_feature=self.config.caption_feature_name)
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_captions.append([image, caption])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, input_seqs, target_seqs, input_mask = (
input_ops.batch_with_dynamic_pad(images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
self.images = images
self.input_seqs = input_seqs
self.target_seqs = target_seqs
self.input_mask = input_mask