h5SrSSKJr SSKrSSKJr SSKJr SSKJ r SSK J r SSK J r SSKrSSKrSS KJrJrJrJrJr SS KJr SS KJr SS KJr SS KJr /SQr\SSj5r \\SSj55r!\SSj5r"SSjr#S Sjr$\\S!Sj55r%S"Sjr&S#S$Sjjr'S%S&Sjjr(S'Sjr)S(Sjr*S%S)Sjjr+S%S)Sjjr,S*Sjr-g)+aOFunctional implementations of various blur operations for image processing. This module provides a collection of low-level functions for applying different blur effects to images, including standard blur, median blur, glass blur, defocus, and zoom effects. These functions form the foundation for the corresponding transform classes. ) annotationsN)Sequence)product)ceil)Literal)warn)clipped float32_iomaybe_process_in_chunkspreserve_channel_dimuint8_io)ValidationInfo)scale)convolve)EIGHT)box_blur central_zoomdefocus glass_blur median_blur zoom_blurcD[[RX4S9nU"U5$)zBlur an image. This function applies a blur to an image. Args: img (np.ndarray): Input image. ksize (int): Kernel size. Returns: np.ndarray: Blurred image. ksize)r cv2blurimgrblur_fns f/var/www/fran/franai/venv/lib/python3.13/site-packages/albumentations/augmentations/blur/functional.pyrrs &chhunEG 3<cB[[RUS9nU"U5$)zMedian blur an image. This function applies a median blur to an image. Args: img (np.ndarray): Input image. ksize (int): Kernel size. Returns: np.ndarray: Median blurred image. r)r r medianBlurrs r rr/s&cnnEBG 3<r!c [R"[R"U5USS9nUS:Xa[R"UR SU- US5n[R"UR SU- US5n[R "XxR S5n [R"XR S5n [U5H6n USS2U S4n USS2U S4n XiU -X-4XiU 4sXiU 4'XiU -X-4'M8 OUS:Xa[[[U5[UR SU- US5[UR SU- US555HUunupn U[U5:aUO U[U5-nXOU S4n XOU S4n XiU -X-4XiU 4sXiU 4'XiU -X-4'MW O[S US 35e[R"XaSS9$) aPGlass blur an image. This function applies a glass blur to an image. Args: img (np.ndarray): Input image. sigma (float): Sigma. max_delta (int): Maximum delta. iterations (int): Number of iterations. dxy (np.ndarray): Dxy. mode (Literal["fast", "exact"]): Mode. Returns: np.ndarray: Glass blurred image. )rr)sigmaXrfastrNexactzUnsupported mode `z$`. Supports only `fast` and `exact`.) r GaussianBlurnparrayarangeshapetilerepeatrange enumeraterlen ValueError)rsigma max_delta iterationsdxymodexhswshwidydxindidxs r rrBs2 #uFCA v~ YYsyy|i/B ? YYsyy|i/B ? ggb((1+6 iiHHQK8z"AQ1WBQ1WB)*r616>):AdG &AdGQ2vqv~&# ' j!ciilY. 2>ciilY. 2>  NC!s3x#S3s8^C!QYB!QYB)*r616>):AdG &AdGQ2vqv~& -dV3WXYY   A6 ::r!c[R"[SU5*[SU5S-5nU[::aSOSn[R"X35upV[R "US-US--US-:*[R S9nU[R"U5-n[R"XtU4US9n[XS9$) zDefocus an image. This function defocuses an image. Args: img (np.ndarray): Input image. radius (int): Radius. alias_blur (float): Alias blur. Returns: np.ndarray: Defocused image. r(dtype)r%)kernel) r+r-maxrmeshgridr,float32sumrr*r) rradius alias_blurlengthrr:y aliased_diskrKs r rrzsYYAvAv(: ;F5AaE ;;v &DA88QTAqD[VQY6bjjILBFF<((L   lEN: NF C ''r!c,URSSup#[X!- 5[X1- 5pTX$- S-X5- S-pv[XXd-2XwU-24U[R5nURSU- S-URSU- S-pXX-2XU-24$)zCentral zoom an image. This function zooms an image. Args: img (np.ndarray): Input image. zoom_factor (int): Zoom factor. Returns: np.ndarray: Zoomed image. NrHrr()r.rrr INTER_LINEAR) r zoom_factorheightwidthh_chw_chh_topw_top h_trim_top w_trim_tops r rrsIIbqMMFf*+T%2E-F$Ma'%,1)<5 EL(%$,*>>?cN^N^ _C!iilV39CIIaL5P1PP QQr!c[R"U[RS9nUHnU[X5- nM X-[ U5S-- $)zZoom blur an image. This function zooms and blurs an image. Args: img (np.ndarray): Input image. zoom_factors (np.ndarray | Sequence[int]): Zoom factors. Returns: np.ndarray: Zoomed and blurred image. rIr()r+ zeros_likerNrr3)r zoom_factorsoutrWs r rrsJ --2:: .C#  |C--$ I#l+a/ 00r!c USU:d USU:a9[XS5[XS54n[USUSUSUS3[SS9 U$U$) Nrr(z: Invalid kernel size range z. Values less than z2 are not allowed. Range automatically adjusted to .rH stacklevel)rLr UserWarning)result min_value field_name new_results r _ensure_min_valuermsy ay9q I 5)AY/Yq 1JK  l6vh? ){+//9l! =    Mr!cUSS:Xd USS-S:XaUSOUSS-USS:Xd USS-S:XaUSOUSS-4nX :wa[USUSUS3[SS9 U$)NrrHr(z+: Non-zero kernel sizes must be odd. Range z automatically adjusted to rerf)rrh)rirkrls r _ensure_odd_valuesrosAY!^vay1}'9q vay1}AY!^vay1}'9q vay1}J lEfXMhishttu v  r!c[U[5(a7[U5S:wa [S5e[ US5[ US54nO U[ U54n[ X2UR 5n[X1R 5nUSUS:a.USUS4n[UR SUSUS3[SS9 U$U$) z0Process blur limit to ensure valid kernel sizes.rHz(Sequence must contain exactly 2 elementsrr(z: Invalid range z. (min > max). Range automatically adjusted to rerf) isinstancerr3r4intrmrkrorrh)valueinforjri final_results r process_blur_limitrvs%"" u:?GH HeAh-U1X/SZ( v$// BF  8F ay6!9q 6!9-  /x7efresst u   Mr!cT[R"USS5n[R"X4[RS9nUS-n[R"U5n[R "U5n[R "U5n US-n US:a [U5n [U *5n U SU - -n O4US:aUn U *SU - -n [U 5n O[U *5n [U 5n [R"XU5nXhU--nXiU--nU(a:URSS5U -S- nURSS5U -S- nUU- nUU- n[R"[R"U5SUS- 5R[5n[R"[R"U5SUS- 5R[5n[R"[R"UU/5SS9nSUUS S 2S4US S 2S44'UR!5(dSXVU4'U$) aCreate a motion blur kernel. Args: kernel_size (int): Size of the kernel (must be odd) angle (float): Angle in degrees (counter-clockwise) direction (float): Blur direction (-1.0 to 1.0) allow_shifted (bool): Allow kernel to be randomly shifted from center random_state (random.Random): Python's random.Random instance Returns: np.ndarray: Motion blur kernel gg?rIrHrr(r')axisN)r+clipzerosrNdeg2radcossinabsfloatlinspaceuniformroundastyperrunique column_stackany) kernel_sizeangle direction allow_shifted random_staterKcenter angle_radrAr@ line_length bias_factort_startt_endtr:rSshift_xshift_ypointss r create_motion_kernelrs* 4-I XX{0 CF A F 5!I  B  B"K1})n  %q;/ Q ,!k/2k" %k" GK0A aAaA&&r1- ;a?&&r1- ;a? W  W   Q a077= low rFrHrr()rLrandint)rlowhighnum_odd_valuesrand_idxs r sample_odd_from_rangerIs& aa1 % &C q$(a-( )D t>D { jQ&*N##A~'9:H !h, r!cVUS:Xa[US-5S-S-OUnUS-S:XaUS-OUn[R"US-*US-U5n[R"SX0- S--5nXDR 5- nUSS2[R 4U[R SS24-$)aOCreate a Gaussian kernel following PIL's approach. Args: sigma (float): Standard deviation for Gaussian kernel. ksize (int): Kernel size. If 0, size is computed as int(sigma * 3.5) * 2 + 1 to match PIL's implementation. Otherwise, must be positive and odd. Returns: np.ndarray: 2D normalized Gaussian kernel. r @rHr(N)rrr+rexprOnewaxisr5rsizer: kernel_1ds r create_gaussian_kernelrms(-z3us{ a ! #uDax1}4!8$D daiL$!)T2AtqyQ../IMMO+I Q ] #i A &> >>r!cUS:Xa[US-5S-S-OUnUS-S:XaUS-OUn[US9n[R"SX0- S--5nXDR 5- $)aRCreate a 1D Gaussian kernel following PIL's approach. Args: sigma (float): Standard deviation for Gaussian kernel. ksize (int): Kernel size. If 0, size is computed as int(sigma * 3.5) * 2 + 1 to match PIL's implementation. Otherwise, must be positive and odd. Returns: np.ndarray: 1D normalized Gaussian kernel. rrrHr(rr)rr"create_gaussian_kernel_input_arrayr+rrOrs r create_gaussian_kernel_1drsr(-z3us{ a ! #uDax1}4!8$D +5AtqyQ../I }} &&r!c US:a4[R"[[US-*US-S-S555$[R"US-*US-U5$)aCreates a 1-D array which will create an array of x-coordinates which will be input for the gaussian function (values from -size/2 to size/2 with step size of 1) Piecewise function is needed as equivalent python list comprehension is faster than np.linspace for values of size < 100 Args: size (int): kernel size Returns: np.ndarray: x-coordinate array which will be input for gaussian function that will be used for separable gaussian blur drHr()r+r,listr1rrs r rrsS czxxUTQY<$!)q!DEFF ;;|TQY 55r!)r np.ndarrayrrrreturnr)rrr5rr6rrr7rrr8rr9zLiteral['fast', 'exact']rr)rrrPrrrQrrr)rrrWrrrr)rrrbznp.ndarray | Sequence[int]rr)rituple[int, int]rjrrrk str | Nonerr)N)rirrkrrr)r)rszint | tuple[int, int]rtrrjrrrr) rrrrrrrrboolr random.Randomrr)rrrrrrrrrrr)r5rrrrrr)rrrrr).__doc__ __future__rrandomcollections.abcr itertoolsrmathrtypingrwarningsrrnumpyr+albucorer r r r r pydanticr1albumentations.augmentations.geometric.functionalr-albumentations.augmentations.pixel.functionalr$albumentations.core.type_definitionsr__all__rrrrrrrmrorvrrrrrr!r rsU# $ aa#CB6 ["  "4; 4; 4;4; 4;  4; # 4;4;4;n(4R, 1  1*  2PP PP P  P  Pf! 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