o Qe ã@s~ddlZddlmZddlmZgZdd„Zdd„Z dd d „Z d d „Z dd d„Z ddd„Z ddd„Zdd„ZGdd„dƒZdS)éN)ÚImage)Ú cStringIOcCs\|tt|jd|jdƒƒ}tt|jd|ƒƒtt|jd|ƒƒf}| |tj¡}|S)z¢ Resize an image so that the shorter edge has length target_size. img: the input image to be resized. target_size: the target resized image size. ré)ÚfloatÚminÚsizeÚintÚroundÚresizerZ ANTIALIAS)ÚimgZ target_sizeÚpercentZ resized_size©r úGD:\Projects\ConvertPro\env\Lib\site-packages\paddle/utils/image_util.pyÚ resize_images ÿrcCs@t|jƒdkr|dd…dd…ddd…fS|dd…ddd…fS)z Return the flipped image. Flip an image along the horizontal direction. im: input image, (K x H x W) ndarrays éNéÿÿÿÿ)ÚlenÚshape)Úimr r rÚflip#srTc CsÔ|rJt||jdƒt||jdƒ}}t d||f¡}||jdd}||jdd}||jd||jd} } ||dd…|| …|| …f<nH| d¡}t||jdƒt||jdƒ}}t ||f¡}||jdd}||jdd}||jd||jd} } |||| …|| …f<|r¡||d}||d}ntj d||d¡}tj d||d¡}||||} } |rÐ|dd…|| …|| …f} n ||| …|| …f} |sètj d¡dkrèt| ƒ} | S)aE Return cropped image. The size of the cropped image is inner_size * inner_size. im: (K x H x W) ndarrays inner_size: the cropped image size. color: whether it is color image. test: whether in test mode. If False, does random cropping and flipping. If True, crop the center of images. rérNÚfloat32r)ÚmaxrÚnpZzerosÚastypeÚrandomÚrandintr) rZ inner_sizeÚcolorÚtestÚheightÚwidthZ padded_imZstartYZstartXZendYZendXÚpicr r rÚcrop_img/sD ÿÿ ÿÿ r"cCs2t t t|ƒ¡¡}t|jƒdkrt |d¡}|S)Nr)rrr)rÚarrayrÚopenÚStringIOrrÚ transpose)Z jpeg_stringZnp_arrayr r rÚ decode_jpeg[s r'cCs.| d¡}| }t||||ƒ}||8}| ¡S)zì Does data augmentation for images. If is_train is false, cropping the center region from the image. If is_train is true, randomly crop a region from the image, and random does flipping. im: (K x H x W) ndarrays r)rr"Úflatten)rZimg_meanÚ crop_sizeZis_trainrrr!r r rÚpreprocess_imgbs r*cCsºt |¡d}||d}|r9||d|jdksJ‚| d||¡}|dd…|||…|||…f d¡}|S|||jdksDJ‚| ||¡}||||…|||…f d¡}|S)zâ Return the loaded meta file. Load the meta image, which is the mean of the images in the dataset. The mean image is subtracted from every input image so that the expected mean of each input image is zero. Z data_meanrrrNr)rÚloadrZreshaper)Ú meta_pathZ mean_img_sizer)rÚmeanÚborderr r rÚ load_metaqs&  ÿÿü   ÿÿr/cCst |¡}| ¡|S)z_ Load image and return. img_path: image path. is_color: is color image or not. )rr$r+)Zimg_pathÚis_colorr r r rÚ load_image‡s r1cCs„t |dj¡}t |¡}|dd…d}d|d|df}d|d|df}tjdtd}d}|D]}|D]} || ||d| |df||<|d7}q8q4t |d¡t | d|dg¡|d <t |d ¡}tjd t|ƒ|d|d|d ftjd} d} |D];} |D]} | | d| d…| d| d …dd…f| | <| d7} qˆ| | d| …dd…ddd …dd…f| | d| …<q„| S)zý image : iterable of (H x W x K) ndarrays crop_dims: (height, width) tuple for the crops. Returned data contains ten crops of input image, namely, four corner patches and the center patch as well as their horizontal reflections. rNrg@r)éé)Zdtype)rrr3)rré rrr2) rr#rÚemptyrZtileZ concatenaterr)r Z crop_dimsZim_shapeZ im_centerZ h_indicesZ w_indicesZcrops_ixÚcurrÚiÚjZcropsZixrÚcropr r rÚ oversample’s4   þ ÿ "ÿ. 4r:c@s>eZdZ    d dd„Zdd„Zdd„Zd d „Zd d „ZdS)ÚImageTransformerNTcCs(||_| |¡| |¡| |¡dS©N)r0Ú set_transposeÚset_channel_swapÚset_mean)Úselfr&Ú channel_swapr-r0r r rÚ__init__ºs  zImageTransformer.__init__cCó(|dur|jrdt|ƒksJ‚||_dS©Nr)r0rr&©r@Úorderr r rr=Äó zImageTransformer.set_transposecCrCrD)r0rrArEr r rr>ÊrGz!ImageTransformer.set_channel_swapcCsL|dur!|jdkr|dd…tjtjf}n |jr!t|jƒdks!J‚||_dS)Nrr)ÚndimrZnewaxisr0rrr-)r@r-r r rr?Ðs   zImageTransformer.set_meancCsP|jdur | |j¡}|jdur||jdd…dd…f}|jdur&||j8}|Sr<)r&rAr-)r@Údatar r rÚ transformerÛs     zImageTransformer.transformer)NNNT)Ú__name__Ú __module__Ú __qualname__rBr=r>r?rJr r r rr;¸s ü  r;)TT)T)ÚnumpyrZPILrZ six.movesrr%Ú__all__rrr"r'r*r/r1r:r;r r r rÚs   ,    &