a aDf @s ddlmZddlmZmZmZmZmZmZm Z ddl m Z ddl Z ddl ZddlZddlmZddlmZddlmZmZmZmZmZmZmZmZmZddlmZm Z m!Z!m"Z"gd Z#d Z$d Z%d Z&d Z'd Z(d Z)dZ*ej+ej+ej+ej+dddZ,ej+ej+ej+ej+dddZ-edej+eee.ej+dddZ/eej+edej+dddZ0ej+ej+ej+ej+ej+dddZ1ej+ej+ej+ej+ej+ddd Z2eej+ej+ej+ej+ej+dd!d"Z3dej+e4ej+d$d%d&Z5eej+e4ej+d'd(d)Z6dej+eej+ej+d*d+d,Z7dej+eej+ej+d*d-d.Z8ej+eej+e9dd/d0d1Z:deej+ej+e9ee4eej+d2d3d4Z;edej+eej+e e9ej+d7d8d9Zd?Z>ej+ej+ej+d@dAdBZ?eej+e!e!e!ej+d=dCdDZ@ej+e!e!e!ej+d=dEdFZAeej+ej+ej+dGdHdIZBedej+e.ee4e4fej+dLdMdNZCeej+ej+ej+dOdPdQZDeej+e4edRej+dSdTdUZEeej+e.e.ej+dVdWdXZFeej+e4e4e4ee4e4e4fe4e.eee4e4fej+dY dZd[ZGeej+e.e.eee4e4fej+d\d]d^ZHeej+e.e.e4eeeej+d_d`daZIeeej+eej+ej+dbdcddZJeeej+eeej+dedfdgZKej+ej+dhdidjZLej+ej+ej+dkdldmZMeej+e.ej+dndodpZNeej+ej+ej+dqdrdsZOedej+e.e.e9ej+dvdwdxZPedej+e.e.e9ej+dvdydzZQdej+e.e.e9ej+dvd{d|ZRedej+e.e.ee4eej+dddZSej+ej+dhddZTej+ej+dhddZUee jVe jWfej+e.e4e4ej+dddZXdej+ee.ej+dddZYdej+ejZee.ej+dddZ[eeedddZ\dej+ej+eee4ej+dddZ]eej+ej+ej+dddZ^eej+ej+ej+dddZ_eej+ej+ej+dddZ`ej+ej+ej+dddZaej+ee4e4e4e4fdddZbej+ee4e4e4e4fej+dddZcdej+e.ej+dddZdej+e.ej+dddZeeej+ej+edddZfej+e.ej+ej+dddZgeej+e.ej+dddZhedej+e.e.ej+dddZiej+e.ej+dddZjej+e.ej+dddZkeej+e4ee9ee4e4ej+dddZleeej+e.ej+e.ej+dddZmeedej+e4e.e.e4ej+dœddĄZneej+ej+e e.ee.fej+dŜddDŽZoeeej+eej+eej+eej+e"ej+dȜddʄZpe4e4ej+d˜dd̈́Zqde4e4eejrjsej+dΜddЄZtdee4e4fee4e4feejrjsej+dќddӄZuej+eee4e4fee4fdԜddքZvdeee4e4fee4feejrjsee4dלddلZwej+e.e.e.e.e4ej+dڜdd܄Zxej+ej+ej+e4e4e4ej+dݜdd߄Zyeej+ej+ej+dOddZzeej+ej+ej+dOddZ{ej+ej+e|ej+dddZ}dS)) defaultdict)AnyDictListOptionalSequenceTupleUnion)warnN)Literal) random_utils) MAX_VALUES_BY_DTYPE_maybe_process_in_chunksclipclippedensure_contiguousis_grayscale_image is_rgb_imagenon_rgb_warningpreserve_channel_dim) ColorType ImageMode ScalarType SpatterMode),add_fogadd_rain add_shadow add_graveladd_snow add_sun_flare add_weightedadjust_brightness_torchvisionadjust_contrast_torchvisionadjust_hue_torchvisionadjust_saturation_torchvisionbrightness_contrast_adjustchannel_shuffleclaheconvolve downscaleequalize fancy_pca from_floatgamma_transform gauss_noiseimage_compressioninvert iso_noiselinear_transformation_rgbmove_tone_curvemultiplynoop normalize posterize shift_hsv shift_rgbsolarize superpixelsswap_tiles_on_imageto_floatto_gray gray_to_rgb unsharp_maskr split_uniform_gridchromatic_aberrationerodedilateh)imgmean denominatorreturncCs|jrDt|tkrD|j|jkrDtj|dgdt|tjd}|jsftj|gdtjd}n>t|tkr|j|jkrtj|dgdt|tjd}t|d}t ||tj|t ||tj||S)NrrGdtypefloat32) shapelenFOURnparraytolistfloat64Zascontiguousarrayastypecv2subtractr4rJrKrLr]t/nfs/NAS7/SABIOD/METHODE/ermites/ermites_venv/lib/python3.9/site-packages/albumentations/augmentations/functional.py normalize_cv2Us&&r_cCs |tj}||8}||9}|SN)rYrUrQr\r]r]r^normalize_numpycs rao@)rJrKstdmax_pixel_valuerMcCs`tj|tjd}||9}tj|tjd}||9}tj|tjd}t|rTt|||St|||S)NrN)rUrVrQZ reciprocalrr_ra)rJrKrcrdZmean_npZstd_nprLr]r]r^r6js r6)imageimage_per_channelmin_maxmin_max_per_channel)rJ normalizationrMc Cs(|tj}|jtkr$tj|dd}|dkrZ|}|d}|||}|dd}n|dkr|jdd}|jddd}||d d d d f|d d d d f}|dd}np|d kr| }| }||||}nF|d kr|j dd d }|j dd d }||||}nt d||S)aApply per-image normalization based on the specified strategy. 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Returns: Solarized image. rcs g|]}|kr|n|qSr]r].0iZmax_valrr]r^ zsolarize..rPrNrj) rOr rUrangeintrRrZrrVrSrpcopy)rJrrOlutZ prev_shape result_imgZcondr]rr^r:s   r:)rJbitsrMc Cst|}|jtjkr"d}t|t|dk|tkBrDd}t||jrVt|dkr|dkrht |S|tkrx| Stj ddtjd}tdd|d}||M}t ||St|sd }t|t|}t|D]\}}|dkr t |d |f|d |f<q|tkr.|d |f |d |f<qtj ddtjd}tdd|d}||M}t |d |f||d |f<q|S) zReduce the number of bits for each color channel. Args: img: image to posterize. bits: number of high bits. Must be in range [0, 8] Returns: Image with reduced color channels. 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Mask shape: )rOrUrrrrrsrR)rJrrrr]r]r^_check_preconditionss   r)rrJrrrMcCs8|dur dS|tj}t|s(|dur,|S|d|fSN.)rYrUrr)rrJrrr]r]r^ _handle_masks  rcvT)rJrmoderrMcCst||||dkrtnt}t|r6||t|||S|spt|tj}||dt||||d<t|tjSt |}t dD],}t||||}||d|f||d|f<q|S)NZpil.rrF.) rrrrrrZr{ZCOLOR_RGB2YCrCbZCOLOR_YCrCb2RGBrUrr)rJrrrfunctionrrZ_maskr]r]r^r*s   r*)rJlow_yhigh_yrMc s|j}dkrdks(nd}t|dkr.evaluate_bezrzr) rOrsrUrlinspacendarrayZ vectorizeZrintrYrrZr) rJrr input_dtyperrrZ remappingZlut_fnr]rr^r3s   r3)rJr_shiftg_shiftb_shiftrMcCsb||kr|kr nn||St|}|||g}t|D] \}}|d|f||d|f<q<|Sr)rUrr)rJrrrrshiftsrshiftr]r]r^_shift_rgb_non_uint8s  r)rJvaluerMcCsJt|j}td|dd}||7}t|d||j}t||S)NrrPrQ)r rOrUr~rYrrZr)rJr max_valuerr]r]r^_shift_image_uint8s  rc Cs||kr|kr@nn(|j\}}}||||g}t||St|}|||g}t|D]"\} } t|d| f| |d| f<q\|Sr)rRreshaperrUrr) rJrrrheightwidthchannelsrrrrr]r]r^_shift_rgb_uint8s    rcCs(|jtjkrt||||St||||Sr`)rOrUrrr)rJrrrr]r]r^r9s r9)rJtransformation_matrixrMcCs t||Sr`)rZZ transform)rJrr]r]r^r2sr2@rHrH)rJ clip_limittile_grid_sizerMcCs|jtjkrd}t|tj|dd|Dd}t|rB||St|tj }||dddddf|dddddf<t|tj S)Nz clahe supports only uint8 inputscSsg|] }t|qSr])r)rxr]r]r^rrzclahe..)Z clipLimitZ tileGridSizer) rOrUrrrZZ createCLAHErapplyr{Z COLOR_RGB2LABZ COLOR_LAB2RGB)rJrrrZ clahe_matr]r]r^r's  .r')rJkernelrMcCsttjd|d}||S)Nrj)Zddepthr)rrZZfilter2D)rJrZconv_fnr]r]r^r(#sr().jpg.webp)rJquality image_typerMcCs|dkrtj}n|dkr tj}ntd|j}d}|tjkrftd|dtt |tdd}d }n |tj tjfvrt d |d t ||t ||f\}}t|tj}|rt|d d }|S)Nrrz@Only '.jpg' and '.webp' compression transforms are implemented. FzDImage compression augmentation is most effective with uint8 inputs, z is used as input.rrNTUnexpected dtype z for image augmentationrzr)rZZIMWRITE_JPEG_QUALITYZIMWRITE_WEBP_QUALITYNotImplementedErrorrOrUrQr UserWarningr,rrsZimencoderZimdecodeZIMREAD_UNCHANGEDr=)rJrrZ quality_flagr needs_float_Z encoded_imgr]r]r^r/)s.  r/)rJ snow_pointbrightness_coeffrMcCs t||j}d}|d9}|d7}|tjkrDt|tdd}d}n |tjtjfvrdtd|dt|tj }tj |tjd}|d d d d d f|d d d d d f|k|9<t |d d d d d ftjd |d d d d d f<tj |tjd}t|tj }|rt |d d }|S) aBleaches out pixels, imitation snow. From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Args: img: Image. snow_point: Number of show points. brightness_coeff: Brightness coefficient. Returns: Image. Fg_@UrrNTrz for RandomSnow augmentationNrPrzr)rrOrUrQr,rrsrZr{ COLOR_RGB2HLSrVr COLOR_HLS2RGBr=)rJrrrr image_hls image_rgbr]r]r^rIs& 82 r) rJslant drop_length drop_width drop_color blur_valuebrightness_coefficient rain_dropsrMcCst||j}d} |tjkr4t|tdd}d} n |tjtjfvrTtd|d|} |D]2\} } | |} | |}t | | | f| |f||q`t | ||f} t | tj  tj}|dddddf|9<t | tjtj}| rt|d d S|S) aRFrom https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Args: img: Image. slant: drop_length: drop_width: drop_color: blur_value: Rainy view are blurry. brightness_coefficient: Rainy days are usually shady. rain_drops: Returns: Image FrrNTrz for RandomRain augmentationNrErzr)rrOrUrQr,rrsrrZlineblurr{r|rYrr=)rJrrrrrrrrrreZ rain_drop_x0Z rain_drop_y0Z rain_drop_x1Z rain_drop_y1Z image_hsvrr]r]r^rws4   r)rJfog_coef alpha_coef haze_listrMc Cs"t||j}d}|tjkr4t|tdd}d}n |tjtjfvrTtd|d|jd}tt |d|d }|D]z}|\} } | } | } ||} |d }| |d | |d f}t | |t |d d t | | | d| d | | }qxt ||d |d f}|rt|dd}|S)aAdd fog to the image. From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Args: img: Image. fog_coef: Fog coefficient. alpha_coef: Alpha coefficient. haze_list: Returns: Image. FrrNTrz for RandomFog augmentationrPrF rE)rzrzrzrjrrzr)rrOrUrQr,rrsrRrrrrrZcircle addWeightedrr=)rJrrrrrrZhwZ haze_pointsryoverlayoutputalpharadpointrr]r]r^rs0    r)rJflare_center_xflare_center_y src_radius src_colorcirclesrMc Cs~t||j}d}|tjkr4t|tdd}d}n |tjtjfvrTtd|d|}|} |D]J\} \} } } \}}}t || | f| |||fdt || | d| d | qht |t |f}| }|d }tj d d|d } tj d||d }t |D]f}t ||t |||d| ||d| ||d| ||d}t ||| d|d | q| }|rzt|d d}|S)a>Add sun flare. From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Args: img (numpy.ndarray): flare_center_x (float): flare_center_y (float): src_radius: src_color (int, int, int): circles (list): Returns: numpy.ndarray: FrrNTrz for RandomSunFlareaugmentationrjrPrrr)numrzr)rrOrUrQr,rrsrrZrrrrrr=)rJrrrrr rrrrrrrZrad3Zr_colorZg_colorZb_colorrZ num_timesrrZalprr]r]r^rs4  0 r)rJ vertices_listrMcCst||j}d}|tjkr4t|tdd}d}n |tjtjfvrTtd|dt|tj }t |}t ||d|ddddd ft tdk}|ddddd f|d |ddddd f|<t|tj }|rt|dd S|S) zAdd shadows to the image. Args: img (numpy.ndarray): vertices_list (list[numpy.ndarray]): Returns: numpy.ndarray: Reference: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library FrrNTrz for RandomShadow augmentationrzNrrP?r)rrOrUrQr,rrsrZr{rrZfillPolyr rr=)rJr rrrrZred_max_value_indrr]r]r^r)s"  $4 r)rJgravelsrMc Cst||j}d}|tjkr4t|tdd}d}n |tjtjfvrTtd|dt|tj }|D](}|\}}}} } | ||| ||df<qft|tj } |rt | dd } | S) afAdd gravel to the image. From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Args: img (numpy.ndarray): image to add gravel to gravels (list): list of gravel parameters. (float, float, float, float): (top-left x, top-left y, bottom-right x, bottom right y) Returns: numpy.ndarray: FrrNTrz for AddGravel augmentationrPrzr) rrOrUrQr,rrsrZr{rrr=) rJr rrrZgravely1y2x1Zx2rrr]r]r^rTs   r)rJrMcCst|j|Sr`)r rOrJr]r]r^r0|sr0)rJchannels_shuffledrMcCs |d|fSrr])rJrr]r]r^r&sr&)rJgammarMcCsB|jtjkr6tddd|d}t||tjSt||S)Nrg?p?rz)rOrUrr~rZrrYpower)rJrtabler]r]r^r-s r-)regaussrMcCs|d}||S)NrQ)rY)rerr]r]r^r.s r.rPF)rJrbeta beta_by_maxrMcCsX|j}|d}|dkr ||9}|dkrT|rBt|}|||7}n||t|7}|S)NrQrPr)rOrYr rUrK)rJrrrrOrr]r]r^$_brightness_contrast_adjust_non_uints rcCstd}t|}td|dd}|dkr8||9}|dkrh|rR|||7}n|||t|7}t|d||}t||S)NrrrPrQ) rUrOr r~rYrKrrZr)rJrrrrOrrr]r]r^ _brightness_contrast_adjust_uints rcCs(|jtjkrt||||St||||Sr`)rOrUrrr)rJrrrr]r]r^r%s r%皙?r )re color_shift intensity random_statekwargsrMcKs|jtjkrd}t|t|s,d}t|td}tj||tjd}t |tj }t |\}} t j | d|d|jdd|d } t jd |d ||jdd|d } |d } | | 7} | d ;} |d } | | dd| 7} t |tjd}|tjS)aApply poisson noise to image to simulate camera sensor noise. Args: image (numpy.ndarray): Input image, currently, only RGB, uint8 images are supported. color_shift (float): intensity (float): Multiplication factor for noise values. Values of ~0.5 are produce noticeable, yet acceptable level of noise. random_state: **kwargs: Returns: numpy.ndarray: Noised image rzImage must be RGBrrNrPrzNrE)sizerrrIr.rPr)rOrUrrrfloatr4rQrZr{rZ meanStdDevr ZpoissonrRnormalrrY)rerrrr rZ one_over_255ZhlsrZstddevZluminance_noiseZ color_noiserZ luminancer]r]r^r1s& &$r1cCst|tj}t|tjSr`)rZr{rr)rJgrayr]r]r^r>sr>cCst|tjSr`)rZr{rrr]r]r^r?sr?)rJrdown_interpolationup_interpolationrMc Cs|jdd\}}|tjks&|tjko0|jtjk}|r>t|}tj|d|||d}tj|||f|d}|rtt |ddtddS|S)NrE)ZfxZfy interpolation)r(rrPrrN) rRrZZ INTER_NEARESTrOrUrr=resizer,r) rJrr&r'rrZ need_castZ downscaledZupscaledr]r]r^r)s r))rJrrMcCs>|dur.|jtvr$td|jdt|j}||tjS)NzUnsupported dtype z. 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Returns: np.ndarray: Output image with tiles swapped according to the random shuffle. rN)r!rrUrr)rer-r.Z new_imager Z new_indexstart_ystart_xend_yend_xZ start_y_origZ start_x_origZ end_y_origZ end_x_origr]r]r^r<5s  &r<)rJ multiplierrMcCs|tj}t||Sr`)rYrUrQr4rJr3r]r]r^_multiply_uint8Os r5cCst|st|dkr`|d}tjddtjd}||9}t|tjt|j}t t j |d}||S|j d}tjddtjdg|}tj |dd}||9}t|tjt|j}g}t|D]>}t t j |dd|fd}|||dddd|fqtj |ddS)NrPrrxrNrrjrk)rrSrUr~rQrrr rOrrZrrRstackrappend)rJr3rfuncrZimagesrr]r]r^_multiply_uint8_optimizedUs"  "r9cCs||Sr`r]r4r]r]r^_multiply_non_uint8msr:cCs8|jtjkr.t|jdkr$t||St||St||S)zArgs: img: Image. multiplier: Multiplier coefficient. Returns: Image multiplied by `multiplier` coefficient. rP)rOrUrrSrRr9r5r:r4r]r]r^r4rs   r4)rrMcCsptj|dd}|sdStj|dd}t|dddg\}}t|dddg\}}|||d|dfS)zCreate bounding box from binary mask (fast version) Args: mask (numpy.ndarray): binary mask. Returns: tuple: A bounding box tuple `(x_min, y_min, x_max, y_max)`. rPrk)rjrjrjrjrrj)rUrwhere)rrowscolsy_miny_maxx_minx_maxr]r]r^bbox_from_masks rB)rJbboxrMcCs>tj|jddtjd}|\}}}}d|||||f<|S)zCreate binary mask from bounding box Args: img: input image bbox: A bounding box tuple `(x_min, y_min, x_max, y_max)` Returns: mask: binary mask NrErNrP)rUrrRr)rJrCrr@r>rAr?r]r]r^mask_from_bboxs  rD皙?)rJrrMcCs.t|r|jtjkr d}t||t}|d}|dd}|tj |dd}tj |dd}tj |\}}|d d d } |d d d|d d | f}t|} td } ||d d | d d df<t| t| } tdD] } |d | f| | d 7<qt|d d}|tjS)aPerform 'Fancy PCA' augmentation from: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Args: img: numpy array with (h, w, rgb) shape, as ints between 0-255 alpha: how much to perturb/scale the eigen vecs and vals the paper used std=0.1 Returns: numpy image-like array as uint8 range(0, 255) z(Image must be RGB image in uint8 format.rbrjrFrrkF)ZrowvarN)rFrP.rzr)rrOrUrrrYr#rrrKZcovZlinalgZeighZargsortsortZ column_stackrrVrr)rJrrZorig_imgZimg_rsZ img_centeredZimg_covZeig_valsZeig_vecsZ sort_permm1m2Zadd_vectrr]r]r^r+s(     r+)rJfactorrMcCs2tdd|}t|ddtj}t||S)Nrrxrz)rUr~rrYrrZrrJrIrr]r]r^$_adjust_brightness_torchvision_uint8srKcCsL|dkrt|S|dkr|S|jtjkr4t||St|||jt|jS)NrrP)rUrrOrrKrr rJrIr]r]r^r!s   r!)rJrIrKrMcCs:tdd|}||d|}t||jd}t||S)NrrxrPrz)rUr~rrOrZr)rJrIrKrr]r]r^"_adjust_contrast_torchvision_uint8srMcCs|dkr |St|r|nt|tj}|dkr`|jtjkrNt|d}tj |||jdS|jtj krxt |||St | tj||d||jt|jS)NrPrr rN)rrKrZr{rrOrUrQrrrrMrrYr )rJrIrKr]r]r^r"s"    r")rJrIrrMcCs||dkr |St|r|St|tj}t|tj}|dkr@|Stj|||d||d}|jtjkrh|St ||jt |jS)NrPr)r) rrZr{rrrrOrUrrr )rJrIrr%resultr]r]r^r$s r$cCs^t|tj}tjddtjd}t|d|dtj}t |d||d<t|tj S)NrrxrNryr) rZr{r|rUr~rrrYrrrrJr]r]r^_adjust_hue_torchvision_uint82s rOcCsft|r |S|dkr|S|jtjkr.t||St|tj}t|d|dd|d<t|tj S)NrrrI) rrOrUrrOrZr{r|rrrLr]r]r^r#<s  r#)re n_segmentsreplace_samplesmax_sizer(rMcCst|s|S|j}|durt|jdd}||kr||}|jdd\}} t||t| |} } ttj| | f|d} | |}tj j ||d|j t krdndd} d}t |j}t|}|j t kr|jg|jdR}|jd}t|D]}tjj| d|d|fd }t|D]l\}}||t|r|j}|d|f}|jjd vrttt|}tt|||}n|}||| |k<qq||jkrttj|d|df|d} | |S|S) NrE)Zdsizer(rrj)rPZ compactnessZ channel_axisrrP.)Zintensity_image)rub)rUrrRrrrrrZr)skimageZ segmentationZslicrnTWOr rOrrrZmeasureZ regionpropsrrSmean_intensitykindrrq)rerPrQrRr(Z orig_shaper!rrrZ new_heightZ new_widthZ resize_fnsegmentsZ min_valuerZ nb_channelscZregionsZridxZregionrWZ image_sp_crr]r]r^r;KsT        r;)img1rimg2rrMcCs&||j|j}t||||dSNr)rrRrYrOrZr)r[rr\rr]r]r^r sr r皙?r)reksizesigmarrrMc Csttj||f|d}|j}|tjkr.t|}n |tjtjfvrNtd|d||}||}t |d|k} | d} |||} t | dd} || } | | d| |} t | |dS) N)r_ZsigmaXrz for UnsharpMask augmentationrzrQrrPrN) rrZZ GaussianBlurrOrUrr=rQrsabsrYrr,) rer_r`rrZblur_fnrrZresidualrZsharpZ soft_maskrr]r]r^r@s     r@)re drop_mask drop_valuerMcCs<t|ttfr"|dkr"t|}n t||}t|||Sr]) isinstancerr#rUrrr;)rerbrcZ drop_valuesr]r]r^ pixel_dropouts  re)rJnon_mudmudrainrrMcCst|t|j}|tjd|}|dkrL|durBd}t|||7}nN|dkr|durhd}t||dur|d}t||||}ntdt||dS) NrPrhzRain spatter requires rain maskrgzMud spatter requires mud maskz!Mud spatter requires non_mud maskzUnsupported spatter mode: rz)rr rOrYrUrQrsstr)rJrfrgrhrZcoefrr]r]r^spatters$   rj)rpartsrMcs,t||\tfddt|DS)aGenerates an array of nearly equal integer intervals that sum up to `n`. This function divides the number `n` into `parts` nearly equal parts. It ensures that the sum of all parts equals `n`, and the difference between any two parts is at most one. This is useful for distributing a total amount into nearly equal discrete parts. Args: n (int): The total value to be split. parts (int): The number of parts to split into. Returns: np.ndarray: An array of integers where each integer represents the size of a part. Example: >>> almost_equal_intervals(20, 3) array([7, 7, 6]) # Splits 20 into three parts: 7, 7, and 6 >>> almost_equal_intervals(16, 4) array([4, 4, 4, 4]) # Splits 16 into four equal parts cs g|]}|krdnqSrPr]rZ part_size remainderr]r^rrz*almost_equal_intervals..)divmodrUrVr)rrkr]rmr^almost_equal_intervalssrp)r! divisionsrrMcCs,t||}tj||d}tt|ddS)aGenerate shuffled splits for a given dimension size and number of divisions. Args: size (int): Total size of the dimension (height or width). divisions (int): Number of divisions (rows or columns). random_state (Optional[np.random.RandomState]): Seed for the random number generator for reproducibility. Returns: np.ndarray: Cumulative edges of the shuffled intervals. rr)rpr shufflerUinsertZcumsum)r!rqrZ intervalsr]r]r^generate_shuffled_splitss ru) image_shapegridrrMcsT|\}t|d|d|t|d|d|fddt|D}t|S)alSplits an image shape into a uniform grid specified by the grid dimensions. Args: image_shape (Tuple[int, int]): The shape of the image as (height, width). grid (Tuple[int, int]): The grid size as (rows, columns). Returns: np.ndarray: An array containing the tiles' coordinates in the format (start_y, start_x, end_y, end_x). rrPc s>g|]6}tD](}|||d|dfqqSrl)r)rrjZ height_splitsZn_colsZ width_splitsr]r^r#s z&split_uniform_grid..)rurrUrV)rvrwrZn_rowsr-r]ryr^rAsrA)r-rMcCsDtt}t|D].\}\}}}}||||f}|||q|S)zBGroups tiles by their shape and stores the indices for each shape.)rlistrr7)r- shape_groupsindexr/r0r1r2rRr]r]r^create_shape_groups,s r})r{rrMcCs`tdd|D}dg|}|D]2}tj||d}t||D]\}}|||<qHq(|S)aShuffles indices within each group of similar shapes and creates a list where each index points to the index of the tile it should be mapped to. Args: shape_groups (Dict[Tuple[int, int], List[int]]): Groups of tile indices categorized by shape. random_state (Optional[np.random.RandomState]): Seed for the random number generator for reproducibility. Returns: List[int]: A list where each index is mapped to the new index of the tile after shuffling. css|]}t|VqdSr`)rS)rindicesr]r]r^ Drz4shuffle_tiles_within_shape_groups..rjrr)rvaluesr rsrzip)r{rZ num_tilesr.r~Zshuffled_indicesoldnewr]r]r^!shuffle_tiles_within_shape_groups5s   r)rJprimary_distortion_redsecondary_distortion_redprimary_distortion_bluesecondary_distortion_bluer(rMc Cst||jdd\}}tjdtjd}||d<||d<|d|d<|d|d<tj||d d gtjd} tj||d d gtjd} t|d || |||} t|d || |||} t| |d | gS) NrErFrN)rr)rPrPr)rrE)rPrErr).rEr")rrRrUZeyerQrV_distort_channelZdstack) rJrrrrr(rr camera_matZdistortion_coeffs_redZdistortion_coeffs_blueZ red_distortedZblue_distortedr]r]r^rBQs4   rB)channelrdistortion_coeffsrrr(rMcCs6tj||d|||ftjd\}}tj||||tjdS)N)Z cameraMatrixZ distCoeffsRZnewCameraMatrixr!Zm1type)r(Z borderMode)rZZinitUndistortRectifyMapZCV_32FC1ZremapZBORDER_REPLICATE)rrrrrr(Zmap_xZmap_yr]r]r^r}s rcCstj||ddSNrP)Z iterations)rZrCrJrr]r]r^rCsrCcCstj||ddSr)rZrDrr]r]r^rDsrD)rJr operationrMcCs6|dkrt||S|dkr$t||Std|dS)NZdilationZerosionzUnsupported operation: )rDrCrs)rJrrr]r]r^ morphologys   r)rb)r)N)N)N)NrT)rr)rPrF)rPrF)rPrF)rr N)N)N)N)rE)r)rr^r)N)N)N)~ collectionsrtypingrrrrrrr warningsr rZnumpyrUrUZtyping_extensionsr Zalbumentationsr Z"albumentations.augmentations.utilsr rrrrrrrrZalbumentations.core.typesrrrr__all__rVZTHREEZNUM_RGB_CHANNELSrorTrZ THREE_SIXTYrr_rar#r6rtrrr8rr:r7rrboolrrr*r3rrrr9r2r'r(r/rrrrrrr0r&r-r.rrr%r1r>r?Z INTER_AREAZ INTER_LINEARr)r=rOr,r5r<r5r9r:r4rBrDr+rKr!rMr"r$rOr#r;r r@rerjrprandomZ RandomStaterurAr}rrBrrCrDrirr]r]r]r^s& $   , /7  "4 $  $- ?(2> )& / $"B  B (!      &    -