\;j{ d dl Z d dlZd dlZd dlmZ d dlZd dlmZ d dl m
Z
d dl m Z g Z
dZdZdZd Zd
Zd Zd d
ddZ G d de
Zy) N)Image)_check_exists_and_download)Dataset)
try_importz8http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgzz9http://paddlemodels.bj.bcebos.com/flowers/imagelabels.matz3http://paddlemodels.bj.bcebos.com/flowers/setid.mat 52808999861908f626f3c1f4e79d11fa e0620be6f572b9609742df49c70aed4d a5357ecc9cb78c4bef273ce3793fc85ctstidtrnidvalid)traintestr c 2 e Zd ZdZ ddZd Zd Zy)Flowersa
Implementation of `Flowers102 `_
dataset.
Args:
data_file (str, optional): Path to data file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
label_file (str, optional): Path to label file, can be set None if
:attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
setid_file (str, optional): Path to subset index file, can be set
None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
mode (str, optional): Either train or test mode. Default 'train'.
transform (Callable, optional): transform to perform on image, None for no transform. Default: None.
download (bool, optional): download dataset automatically if :attr:`data_file` is None. Default: True.
backend (str, optional): Specifies which type of image to be returned:
PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend `,
default backend is 'pil'. Default: None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of Flowers dataset.
Examples:
.. code-block:: python
>>> # doctest: +TIMEOUT(60)
>>> import itertools
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import Flowers
>>> flowers = Flowers()
>>> print(len(flowers))
6149
>>> for i in range(5): # only show first 5 images
... img, label = flowers[i]
... # do something with img and label
... print(type(img), img.size, label)
... # (523, 500) [1]
>>> transform = T.Compose(
... [
... T.Resize(64),
... T.ToTensor(),
... T.Normalize(
... mean=[0.5, 0.5, 0.5],
... std=[0.5, 0.5, 0.5],
... to_rgb=True,
... ),
... ]
... )
>>> flowers_test = Flowers(
... mode="test",
... transform=transform, # apply transform to every image
... backend="cv2", # use OpenCV as image transform backend
... )
>>> print(len(flowers_test))
1020
>>> for img, label in itertools.islice(iter(flowers_test), 5): # only show first 5 images
... # do something with img and label
... print(type(img), img.shape, label)
... # [3, 64, 96] [1]
Nc Z |j dv s
J d| | t j j }|dvrt d| || _ t |j }|s |sJ d t |t t d| }|s |sJ d t |t t d| }|s |sJ d t |t t d| }|| _ t j | } |j# d d
| _ t& j( j+ | j$ s t' j, | j$ | j/ | j$ t1 d }
|
j3 | d d
| _ |
j3 | | d
| _ y )N)r
r r z3mode should be 'train', 'valid' or 'test', but got )pilcv2z4Expected backend are one of ['pil', 'cv2'], but got z>data_file is not set and downloading automatically is disabledflowersz?label_file is not set and downloading automatically is disabledz?setid_file is not set and downloading automatically is disabledz.tgz/zscipy.iolabelsr ) lowerpaddlevisionget_image_backend
ValueErrorbackend
MODE_FLAG_MAPr DATA_URLDATA_MD5 LABEL_URL LABEL_MD5 SETID_URL SETID_MD5 transformtarfileopenreplace data_pathospathexistsmkdir
extractallr loadmatr indexes) self data_file
label_file
setid_filemoder$ downloadr flagdata_tarscios gG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/vision/datasets/flowers.py__init__zFlowers.__init__l s zz|
H A G H
? mm557G . ( FwiP
TZZ\*
PO
P28Xy(I
QP
Q3Iy)XJ
QP
Q3Iy)XJ #<< *"**637ww~~dnn- HHT^^ $DNN+*%ll:.x8; ||J/5a8 c h | j | }t j | j |dz
g }d|z }t j
j
| j | }| j dk( rt j | }n7| j dk( r(t j t j | }| j | j | }| j dk( r||j d fS |j t j |j d fS )N zjpg/image_%05d.jpgr r int64)r/ nparrayr r) r* joinr( r r r& r$ astyper get_default_dtype)r0 idxindexlabelimg_nameimages r9 __getitem__zFlowers.__getitem__ s S!$++eai012'%/ T^^X6 <<5 JJu%E
\\U
"HHUZZ ./E >> %NN5)E <<5 %,,w// /||F 4 4 67 g9NNNr; c , t | j S )N)lenr/ )r0 s r9 __len__zFlowers.__len__ s 4<< r; )NNNr
NTN)__name__
__module__ __qualname____doc__r: rI rL r; r9 r r ) s1 @H
<9|O$!r; r )r) r% numpyr? PILr r paddle.dataset.commonr paddle.ior paddle.utilsr __all__r r r" r r! r# r r rQ r; r9 rX s^
< #
E G A - . .
"7WE
T!g T!r;