RЦi#SrSSKJs Jr SSKrSrSr\RRR5 SSjr Sr Sr Sr S r\S 4S jr\S 4S jrS S \S 4Sjr"SS5rg)zTensorflow Preprocessing Adapter Allows use of Tensorflow preprocessing pipeline in PyTorch Transform Copyright of original Tensorflow code below. Hacked together by / Copyright 2020 Ross Wightman N c [R"USX/5 [RRU5n[RR UUUUUUSS9nUupn [R "U 5upn [R "U 5upn [R "XX/5n[RRUUSS9nUsSSS5 $!,(df  g=f)aGenerates cropped_image using one of the bboxes randomly distorted. See `tf.image.sample_distorted_bounding_box` for more documentation. Args: image_bytes: `Tensor` of binary image data. bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]` where each coordinate is [0, 1) and the coordinates are arranged as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole image. min_object_covered: An optional `float`. Defaults to `0.1`. The cropped area of the image must contain at least this fraction of any bounding box supplied. aspect_ratio_range: An optional list of `float`s. The cropped area of the image must have an aspect ratio = width / height within this range. area_range: An optional list of `float`s. The cropped area of the image must contain a fraction of the supplied image within in this range. max_attempts: An optional `int`. Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the entire image. scope: Optional `str` for name scope. Returns: cropped image `Tensor` distorted_bounding_box_cropT)bounding_boxesmin_object_coveredaspect_ratio_range area_range max_attemptsuse_image_if_no_bounding_boxeschannelsN)tf name_scopeimageextract_jpeg_shapesample_distorted_bounding_boxunstackstackdecode_and_crop_jpeg) image_bytesbboxrr r r scopeshaper bbox_begin bbox_size_offset_yoffset_x target_height target_width crop_windowrs Y/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/data/tf_preprocessing.pyrr!s> u;k=P Q++K8(*(N(N 11!%+/)O)1%$A q!# : 6A)+I)>& QhhMPQ --k;QR-S% R Q Qs B2C C$c[R"X5n[R"U[R5n[R"[R "U5U5$)z0At least `x` of `a` and `b` `Tensors` are equal.)requalcastint32 greater_equal reduce_sum)abxmatchs r$_at_least_x_are_equalr/Us? HHQNE GGE288 $E  BMM%0! 44c B^^^^[R"/SQ[R/SQS9n[TUSSSSSS 9m[RR T5n[ U[R"T5S 5n[R"UUU4S jUUU4S j5mT$) z!Make a random crop of image_size.)r2?r3)r4)dtyper皙?)?gUUUUUU?)g{Gz?r3 N)rr r r rr c>[TT5$)N)_decode_and_center_crop)r image_sizesr$)_decode_and_random_crop..ls ' Z@r0cR>[RRT/TT/T5S$Nr)rrresize)rr< resize_methodsr$r=r>ms#*j)A=QRSTr0) rconstantfloat32rrrr/rcond)rr<rBroriginal_shapebadrs``` @r$_decode_and_random_croprH\s ;;+2::Y OD ' , EXX00=N  CC GG @T VE Lr0c [RRU5nUSnUSn[R"X[-- [R"[R "XE5[R 5-[R5nXF- S-S-nXV- S-S-n[R"XxXf/5n [RRX SS9n [RRU /X/U5Sn U $)z=Crops to center of image with padding then scales image_size.rr4r r) rrrr' CROP_PADDINGminimumrDr(rrrA) rr<rBr image_height image_widthpadded_center_crop_size offset_height offset_widthr#rs r$r;r;rs HH ' ' 4E8L(K gg \1 2 L6 C D  #<AaGM :a?AEL((M3NOK HH ) )+Q ) OE HHOOUGj%=} Ma PE Lr0cD[RRU5nU$)zRandom horizontal image flip.)rrrandom_flip_left_right)rs r$_fliprTs HH + +E 2E Lr0bicubiccUS:Xa$[RRRO#[RRRn[ XU5n[ U5n[R"XRUS/5n[RRXQ(a[RO[RS9nU$a!Preprocesses the given image for evaluation. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. use_bfloat16: `bool` for whether to use bfloat16. image_size: image size. interpolation: image interpolation method Returns: A preprocessed image `Tensor`. rUr )r6) rr ResizeMethodBICUBICBILINEARrHrTreshapeconvert_image_dtypebfloat16rDr use_bfloat16r< interpolationrBrs r$preprocess_for_trainras6Ci5OBHH))11UWU]U]UjUjUsUsM #K] KE %LE JJu:q9 :E HH ( ( LR[[bjj ) BE Lr0cpUS:Xa$[RRRO#[RRRn[ XU5n[R "XRUS/5n[RRXQ(a[RO[RS9nU$rW) rrrXrYrZr;r[r\r]rDr^s r$preprocess_for_evalrcs6Ci5OBHH))11UWU]U]UjUjUsUsM #K] KE JJu:q9 :E HH ( ( LR[[bjj ) BE Lr0Fc@U(a [XX45$[XX45$)atPreprocesses the given image. Args: image_bytes: `Tensor` representing an image binary of arbitrary size. is_training: `bool` for whether the preprocessing is for training. use_bfloat16: `bool` for whether to use bfloat16. image_size: image size. interpolation: image interpolation method Returns: A preprocessed image `Tensor` with value range of [0, 255]. )rarc)r is_trainingr_r<r`s r$preprocess_imagerfs!"#KzYY";jXXr0c*\rSrSrSSjrSrSrSrg)TfPreprocessTransformcXl[U[5(aUSOUUlX0lSUlUR 5UlSUlgr@) re isinstancetuplesizer` _image_bytes_build_tf_graph process_imagesess)selfrermr`s r$__init__TfPreprocessTransform.__init__sG&)$66DGD * !113 r0c&[R"S5 [R"/[RS9Ul[ URUR SURUR5nSSS5 U$!,(df  W$=f)Nz/cpu:0)rr6F) rdevice placeholderstringrnrfrermr`)rrimgs r$ro%TfPreprocessTransform._build_tf_graphss YYx "ii!D #!!4#3#3UDIItGYGY[C ! !  s A B BcURc[R"5UlURRURUR U0S9nUR 5RSS5R[R5nURS:a[R"USS9n[R"US5nU$)N) feed_dictrr )axisrJ)rqrSessionrunrprnroundclipastypenpuint8ndim expand_dimsrollaxis)rrrrys r$__call__TfPreprocessTransform.__call__s 99  DIiimmD..4;L;Lk:Zm[iikq#&--bhh7 88a<..2.Ckk#q! r0)rnr`rerprqrmN)FrrU)__name__ __module__ __qualname____firstlineno__rsror__static_attributes__r0r$rhrhsr0rh)r7)r8gHzG?)g?r3dN)__doc__tensorflow.compat.v1compatv1rnumpyr IMAGE_SIZErKdisable_eager_executionrr/rHr;rTrarcrfrhrr0r$rs.*!!    $$&473?+6-0&* 1h5,* @JYb*?IXa*"'"' *#, Y.r0