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Merge pull request #5485 from luotao1/image

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add doc for image.py
mobile_baidu
Tao Luo 8 years ago committed by GitHub
parent 502d7daff7 f4729a241f
commit d4d8f742a3
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5 changed files with 170 additions and 133 deletions
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113
doc/api/v2/data.rst
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@ -2,112 +2,9 @@
Data Reader Interface and DataSets
==================================
.. toctree::
:maxdepth: 1
DataTypes
=========
.. automodule:: paddle.v2.data_type
:members:
:noindex:
DataFeeder
==========
.. automodule:: paddle.v2.data_feeder
:members:
:noindex:
Reader
======
.. automodule:: paddle.v2.reader
:members:
:noindex:
.. automodule:: paddle.v2.reader.creator
:members:
:noindex:
minibatch
=========
.. automodule:: paddle.v2.minibatch
:members:
:noindex:
Dataset
=======
.. automodule:: paddle.v2.dataset
:members:
:noindex:
mnist
+++++
.. automodule:: paddle.v2.dataset.mnist
:members:
:noindex:
cifar
+++++
.. automodule:: paddle.v2.dataset.cifar
:members:
:noindex:
conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members: get_dict,get_embedding,test
:noindex:
imdb
++++
.. automodule:: paddle.v2.dataset.imdb
:members:
:noindex:
imikolov
++++++++
.. automodule:: paddle.v2.dataset.imikolov
:members:
:noindex:
movielens
+++++++++
.. automodule:: paddle.v2.dataset.movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
.. automodule:: paddle.v2.dataset.sentiment
:members:
:noindex:
uci_housing
+++++++++++
.. automodule:: paddle.v2.dataset.uci_housing
:members:
:noindex:
wmt14
+++++
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:
data/data_reader.rst
data/image.rst
data/dataset.rst

36
doc/api/v2/data/data_reader.rst
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@ -0,0 +1,36 @@
=====================
Data Reader Interface
=====================
DataTypes
=========
.. automodule:: paddle.v2.data_type
:members:
:noindex:
DataFeeder
==========
.. automodule:: paddle.v2.data_feeder
:members:
:noindex:
Reader
======
.. automodule:: paddle.v2.reader
:members:
:noindex:
.. automodule:: paddle.v2.reader.creator
:members:
:noindex:
minibatch
=========
.. automodule:: paddle.v2.minibatch
:members:
:noindex:

75
doc/api/v2/data/dataset.rst
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@ -0,0 +1,75 @@
Dataset
=======
.. automodule:: paddle.v2.dataset
:members:
:noindex:
mnist
+++++
.. automodule:: paddle.v2.dataset.mnist
:members:
:noindex:
cifar
+++++
.. automodule:: paddle.v2.dataset.cifar
:members:
:noindex:
conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members: get_dict,get_embedding,test
:noindex:
imdb
++++
.. automodule:: paddle.v2.dataset.imdb
:members:
:noindex:
imikolov
++++++++
.. automodule:: paddle.v2.dataset.imikolov
:members:
:noindex:
movielens
+++++++++
.. automodule:: paddle.v2.dataset.movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
.. automodule:: paddle.v2.dataset.sentiment
:members:
:noindex:
uci_housing
+++++++++++
.. automodule:: paddle.v2.dataset.uci_housing
:members:
:noindex:
wmt14
+++++
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:

5
doc/api/v2/data/image.rst
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@ -0,0 +1,5 @@
Image Interface
===============
.. automodule:: paddle.v2.image
:members:

74
python/paddle/v2/image.py
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@ -1,33 +1,35 @@
import numpy as np
try:
import cv2
except ImportError:
cv2 = None
import os
import tarfile
import cPickle
__all__ = [
"load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
"random_crop", "left_right_flip", "simple_transform", "load_and_transform",
"batch_images_from_tar"
]
"""
This file contains some common interfaces for image preprocess.
Many users are confused about the image layout. We introduce
the image layout as follows.
- CHW Layout
- The abbreviations: C=channel, H=Height, W=Width
- The default layout of image opened by cv2 or PIL is HWC.
PaddlePaddle only supports the CHW layout. And CHW is simply
a transpose of HWC. It must transpose the input image.
- Color format: RGB or BGR
OpenCV use BGR color format. PIL use RGB color format. Both
formats can be used for training. Noted that, the format should
be keep consistent between the training and inference peroid.
"""
import numpy as np
try:
import cv2
except ImportError:
cv2 = None
import os
import tarfile
import cPickle
__all__ = [
"load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
"random_crop", "left_right_flip", "simple_transform", "load_and_transform",
"batch_images_from_tar"
]
def batch_images_from_tar(data_file,
@ -36,17 +38,18 @@ def batch_images_from_tar(data_file,
num_per_batch=1024):
"""
Read images from tar file and batch them into batch file.
param data_file: path of image tar file
type data_file: string
param dataset_name: 'train','test' or 'valid'
type dataset_name: string
param img2label: a dic with image file name as key
:param data_file: path of image tar file
:type data_file: string
:param dataset_name: 'train','test' or 'valid'
:type dataset_name: string
:param img2label: a dic with image file name as key
and image's label as value
type img2label: dic
param num_per_batch: image number per batch file
type num_per_batch: int
return: path of list file containing paths of batch file
rtype: string
:type img2label: dic
:param num_per_batch: image number per batch file
:type num_per_batch: int
:return: path of list file containing paths of batch file
:rtype: string
"""
batch_dir = data_file + "_batch"
out_path = "%s/%s" % (batch_dir, dataset_name)
@ -99,14 +102,16 @@ def load_image_bytes(bytes, is_color=True):
Example usage:
.. code-block:: python
with open('cat.jpg') as f:
im = load_image_bytes(f.read())
:param bytes: the input image bytes array.
:type file: str
:type bytes: str
:param is_color: If set is_color True, it will load and
return a color image. Otherwise, it will
load and return a gray image.
:type is_color: bool
"""
flag = 1 if is_color else 0
file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
@ -121,6 +126,7 @@ def load_image(file, is_color=True):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
:param file: the input image path.
@ -128,6 +134,7 @@ def load_image(file, is_color=True):
:param is_color: If set is_color True, it will load and
return a color image. Otherwise, it will
load and return a gray image.
:type is_color: bool
"""
# cv2.IMAGE_COLOR for OpenCV3
# cv2.CV_LOAD_IMAGE_COLOR for older OpenCV Version
@ -147,6 +154,7 @@ def resize_short(im, size):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
@ -175,6 +183,7 @@ def to_chw(im, order=(2, 0, 1)):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
im = to_chw(im)
@ -196,6 +205,7 @@ def center_crop(im, size, is_color=True):
Example usage:
.. code-block:: python
im = center_crop(im, 224)
:param im: the input image with HWC layout.
@ -223,6 +233,7 @@ def random_crop(im, size, is_color=True):
Example usage:
.. code-block:: python
im = random_crop(im, 224)
:param im: the input image with HWC layout.
@ -251,6 +262,7 @@ def left_right_flip(im):
Example usage:
.. code-block:: python
im = left_right_flip(im)
:paam im: input image with HWC layout
@ -275,6 +287,7 @@ def simple_transform(im,
Example usage:
.. code-block:: python
im = simple_transform(im, 256, 224, True)
:param im: The input image with HWC layout.
@ -285,6 +298,11 @@ def simple_transform(im,
:type crop_size: int
:param is_train: Whether it is training or not.
:type is_train: bool
:param is_color: whether the image is color or not.
:type is_color: bool
:param mean: the mean values, which can be element-wise mean values or
mean values per channel.
:type mean: numpy array | list
"""
im = resize_short(im, resize_size)
if is_train:
@ -324,6 +342,7 @@ def load_and_transform(filename,
Example usage:
.. code-block:: python
im = load_and_transform('cat.jpg', 256, 224, True)
:param filename: The file name of input image.
@ -334,6 +353,11 @@ def load_and_transform(filename,
:type crop_size: int
:param is_train: Whether it is training or not.
:type is_train: bool
:param is_color: whether the image is color or not.
:type is_color: bool
:param mean: the mean values, which can be element-wise mean values or
mean values per channel.
:type mean: numpy array | list
"""
im = load_image(filename)
im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)

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