\;jMvdZddlZddlZddlmZddlmZmZddlm Z m Z ddl m Z m Z ddlmZgZd Zd Zd Zd Zd ZdZdZdZdZdZej6edZej6edZ ddZe ddddedddfdZe ddddedddfdZ e ddddeddfdZ!dZ"y) a This module will download dataset from http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html and parse train/test dataset into paddle reader creators. This set contains images of flowers belonging to 102 different categories. The images were acquired by searching the web and taking pictures. There are a minimum of 40 images for each category. The database was used in: Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes.Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing (2008) http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}. N) cpu_count)load_image_bytessimple_transform) map_readers xmap_readers) deprecated try_import)downloadz8http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgzz9http://paddlemodels.bj.bcebos.com/flowers/imagelabels.matz3http://paddlemodels.bj.bcebos.com/flowers/setid.mat 52808999861908f626f3c1f4e79d11fa e0620be6f572b9609742df49c70aed4d a5357ecc9cb78c4bef273ce3793fc85ctstidtrnidvalidc|\}}t|}t|dd|gd}|jjd|fS)zB map image bytes data to type needed by model input layer )g\(Y@gR1]@gQ^@)meanfloat32)rrflattenastype)is_trainsampleimglabels _G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/dataset/flowers.pydefault_mapperr:sKJC 3 C  S#x&> C ;;=   *E 11TFc vfd}|r t||tdt|St||S)a 1. read images from tar file and merge images into batch files in 102flowers.tgz_batch/ 2. get a reader to read sample from batch file :param data_file: downloaded data file :type data_file: string :param label_file: downloaded label file :type label_file: string :param setid_file: downloaded setid file containing information about how to split dataset :type setid_file: string :param dataset_name: data set name (tstid|trnid|valid) :type dataset_name: string :param mapper: a function to map image bytes data to type needed by model input layer :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: data reader :rtype: callable c3Ktd}|jdd}|j d}i}|D]}d|z}||dz ||<tj }|j }d}|D]Q} | j |vs|j | j} || j } | t| dz fSyw)Nzscipy.iolabelsrzjpg/image_%05d.jpgr ) r loadmattarfileopen getmembersname extractfilereadint)scior#indexes img2labelirtfmemsfile_idmemimager data_file dataset_name label_file setid_files rreaderzreader_creator..readerms*%j)(3A6,,z*<8; A&*C#AE]IcN\\) $}}Cxx9$s+002!#((+SZ!^++ s B CAC)rminrr) r5r7r8r6mapper buffered_sizeuse_xmapcycler9s ```` rreader_creatorr@Js5F,(FFC9;,?OO66**rz2.0.0zpaddle.vision.datasets.Flowersz>Please use new dataset API which supports paddle.io.DataLoader)since update_tolevelreasonc tttdtttdt tt dtt||||S)a8 Create flowers training set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: train data reader :rtype: callable flowersr?) r@r DATA_URLDATA_MD5 LABEL_URL LABEL_MD5 SETID_URL SETID_MD5 TRAIN_FLAGr<r=r>r?s rtrainrPsD0 9h/Iy1Iy1  rc tttdtttdt tt dtt||||S)a3 Create flowers test set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :param cycle: whether to cycle through the dataset :type cycle: bool :return: test data reader :rtype: callable rFrG) r@r rHrIrJrKrLrM TEST_FLAGrOs rtestrSsD0 9h/Iy1Iy1  rc tttdtttdt tt dtt|||S)a Create flowers validation set reader. It returns a reader, each sample in the reader is image pixels in [0, 1] and label in [1, 102] translated from original color image by steps: 1. resize to 256*256 2. random crop to 224*224 3. flatten :param mapper: a function to map sample. :type mapper: callable :param buffered_size: the size of buffer used to process images :type buffered_size: int :return: test data reader :rtype: callable rF) r@r rHrIrJrKrLrM VALID_FLAG)r<r=r>s rrrsA, 9h/Iy1Iy1 rcttdtttdttt dt y)NrF)r rHrIrJrKrLrMrrfetchrXs& Xy(+ Y 9- Y 9-r)r TF)#__doc__ functoolsr%multiprocessingrpaddle.dataset.imagerr paddle.readerrr paddle.utilsrr commonr __all__rHrJrLrIrKrMrNrRrUrpartial train_mapper test_mapperr@rPrSrrXrWrrrds($%C3/  E G A - . .     2!y  6 i6   :+z . K  TD  < . K  4$e  < . K  D4  6.r