o Me41@sdZddlmZddlZddlZejrcddlZddlZddl Z ej Z ej dkr3de vr3ej e jdZ eje ddgejejdZe\ZZeZedkrQdZn$zddlZWneybdZYnwzddlZWn eytdZYnwddl Z ddlZddlmmZgZd d Z d"d d Z d#ddZ!d#ddZ"ddZ#d$ddZ$d#ddZ%d#ddZ&d#ddZ'  d%ddZ(  d%d d!Z)dS)&at This file contains some common interfaces for image preprocess. Many users are confused about the image layout. We introduce the image layout as follows. - CHW Layout - The abbreviations: C=channel, H=Height, W=Width - The default layout of image opened by cv2 or PIL is HWC. PaddlePaddle only supports the CHW layout. And CHW is simply a transpose of HWC. It must transpose the input image. - Color format: RGB or BGR OpenCV use BGR color format. PIL use RGB color format. Both formats can be used for training. Noted that, the format should be keep consistent between the training and inference period. )print_functionNwin32z python.exez-cz import cv2)stdoutstderrcCs$tdurddl}|jddSdS)NrzWarning with paddle image module: opencv-python should be imported, or paddle image module could NOT work; please install opencv-python first.FT)cv2sysrwrite)rr DD:\Projects\ConvertPro\env\Lib\site-packages\paddle/dataset/image.py _check_cv2Hsr c Cs|d}d||tf}d||tf}tj|r|St|t|}|}g} g} d} |D]@} | j|vrt| | |  | || jt | |krti} | | d<| | d<t j| td|| fdd d | d 7} g} g} q4t | dkri} | | d<| | d<t j| td|| fdd d t|d  }t|D]}|tjd ||fdqWd|S1swY|S)a 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 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 Z_batchz%s/%s_%sz %s/%s_%s.txtrlabeldataz %s/batch_%dwb)protocolaz%s/%s N)osgetpidpathexistsmakedirstarfileopen getmembersnameappend extractfilereadlenpickledumplistdirrabspath)Z data_fileZ dataset_nameZ img2labelZ num_per_batchZ batch_dirZout_pathZ meta_filetfZmemsrlabelsZfile_idZmemoutputmetafiler r r batch_images_from_tarTsV         r+TcCs>tdusJ|r dnd}tjt|tjd}t||}|S)a Load an color or gray image from bytes array. 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 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 TrrZdtype)r npZasarray bytearrayZuint8rZimdecode)bytesis_colorflagZ file_bytesZimgr r r load_image_bytess   r2cCs*tdusJ|r dnd}t||}|S)a Load an color or gray image from the file path. Example usage: .. code-block:: python im = load_image('cat.jpg') :param file: the input image path. :type file: string :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 Trr)r rZimread)r*r0r1imr r r load_images  r4cCsftdusJ|jdd\}}||}}||kr |||}n|||}tj|||ftjd}|S)aW Resize an image so that the length of shorter edge is size. Example usage: .. code-block:: python im = load_image('cat.jpg') im = resize_short(im, 256) :param im: the input image with HWC layout. :type im: ndarray :param size: the shorter edge size of image after resizing. :type size: int TNr) interpolation)r shaperresizeZ INTER_CUBIC)r3sizehwZh_newZw_newr r r resize_shorts  r;rrrcCs$t|jt|ks J||}|S)a Transpose the input image order. The image layout is HWC format opened by cv2 or PIL. Transpose the input image to CHW layout according the order (2,0,1). Example usage: .. code-block:: python im = load_image('cat.jpg') im = resize_short(im, 256) im = to_chw(im) :param im: the input image with HWC layout. :type im: ndarray :param order: the transposed order. :type order: tuple|list )r!r6Z transpose)r3orderr r r to_chws r>c Csv|jdd\}}||d}||d}||||}}|r/|||||ddf}|S|||||f}|S)aP Crop the center of image with size. Example usage: .. code-block:: python im = center_crop(im, 224) :param im: the input image with HWC layout. :type im: ndarray :param size: the cropping size. :type size: int :param is_color: whether the image is color or not. :type is_color: bool Nr)r6 r3r8r0r9r:Zh_startZw_startZh_endZw_endr r r center_crops  r@c Cs|jdd\}}tjd||d}tjd||d}||||}}|r9|||||ddf}|S|||||f}|S)aQ Randomly crop input image with size. Example usage: .. code-block:: python im = random_crop(im, 224) :param im: the input image with HWC layout. :type im: ndarray :param size: the cropping size. :type size: int :param is_color: whether the image is color or not. :type is_color: bool Nrrr)r6r-randomrandintr?r r r random_cropsrCcCsDt|jdkr|r|dddddddfS|dddddfS)aW Flip an image along the horizontal direction. Return the flipped image. Example usage: .. code-block:: python im = left_right_flip(im) :param im: input image with HWC layout or HW layout for gray image :type im: ndarray :param is_color: whether input image is color or not :type is_color: bool N)r!r6)r3r0r r r left_right_flip5srFcCst||}|rt|||d}tjddkrt||}nt|||d}t|jdkr.t |}| d}|duritj |tj d}|j dkrR|rR|ddtjtjf}n|j dkrZ|}n t|jt|kseJ||8}|S) a Simply data argumentation for training. These operations include resizing, croping and flipping. Example usage: .. code-block:: python im = simple_transform(im, 256, 224, True) :param im: The input image with HWC layout. :type im: ndarray :param resize_size: The shorter edge length of the resized image. :type resize_size: int :param crop_size: The cropping size. :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 )r0rrrDfloat32Nr,r)r;rCr-rArBrFr@r!r6r>ZastypearrayrGndimZnewaxis)r3 resize_size crop_sizeis_trainr0meanr r r simple_transformKs&    rNcCs t||}t||||||}|S)aJ Load image from the input file `filename` and transform image for data argumentation. Please refer to the `simple_transform` interface for the transform operations. Example usage: .. code-block:: python im = load_and_transform('cat.jpg', 256, 224, True) :param filename: The file name of input image. :type filename: string :param resize_size: The shorter edge length of the resized image. :type resize_size: int :param crop_size: The cropping size. :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 )r4rN)filenamerJrKrLr0rMr3r r r load_and_transforms rP)r )T)r<)TN)*__doc__ __future__rsixnumpyr-PY3 subprocessrr executable interpreterplatform exec_prefixsepPopenPIPEZimport_cv2_proc communicateouterrpollretcoder ImportErrorrZsix.moves.cPicklemovescPickler"__all__r r+r2r4r;r>r@rCrFrNrPr r r r sb        =      <