o #jw@sjddlZddlZddlZddlZddlmmZddl m Z gZ ddZ ddZ dd Zd d Zd d ZddZddZddZddZddZddZddZddZdJdd ZdKd"d#Zd$d%Zd&d'ZdLd)d*Z ( +   dMd,d-Zd.d/Z dLd0d1Z dJd2d3Z!dJd4d5Z"dJd6d7Z#dNd8d9Z$dJd:d;Z%dOd=d>Z&dPd@dAZ'dBdCZ(dDdEZ)dFdGZ*dHdIZ+dS)QN)VariablecCsLt|tjtfr|jdks|jdks|dvr$tdt||j|dS)Nr)chwhwcz;not support [type={}, ndim={}, data_format={}] paddle image) isinstancepaddleZTensorrndimlower RuntimeErrorformattypeimg data_formatrk/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/vision/transforms/functional_tensor.py_assert_image_tensors    rcC$|dkrdS|dkrdSdS)Nrrr rrrr_get_image_h_axis*  rcCr)Nrrrrrrrr_get_image_w_axis1rrcCr)Nrrrrrrrrr_get_image_c_axis8rrcCs$t|dkrdSt|dkrdSdS)Nrrr)lenrrrr_get_image_n_axis?rrcC t|dkS)Nrrrrrr_is_channel_lastF r"cCr )Nrr!rrrr_is_channel_firstJr#r$cCst|r |jt|SdSN)rshaperrrr_get_image_num_batchesNsr'cCs|jt|Sr%)r&rrrrr_get_image_num_channelsTsr(cCs|jt||jt|fSr%)r&rrrrrr_get_image_sizeXs  r)cCs |jdd}|jdd}t||}t|}||}|t|||}|jdd\}}} t|||} ||| } ||| } || | } ||k|j| | }||k||k@|j| | d}||k||k@|j| | d}|||dd}|| }tj |||gddS)zConvert a image Tensor from RGB to HSV. This implementation is based on Pillow ( https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Convert.c) raxis@g@@?) maxminrequal ones_likewhereunbindastypedtypetruncstack)rmaxcmincZis_equalZ one_divisorZc_deltasrgbZc_delta_divisorrcgcbchrhgZhbhrrr _rgb_to_hsv_s"       $$ rEc Cs|jdd\}}}|d}t|}||}|tjd}t|d|dd}t|d||dd}t|d|d|dd}t|jddtjd|j d d|j } tj tj ||||||gddtj ||||||gddtj ||||||gddgd d} t d | | S) z'Convert a image Tensor from HSV to RGB.rr*r-r.r6rrJz...ijk, ...xijk -> ...xjk) r4rfloorr5int32clipr1 unsqueezeZaranger6reshaper8Zeinsum) rrDr;vfipqtmaskmatrixrrr _hsv_to_rgb{s,  rYcCs4t|rdnd}t||t|d||jS)Nr.o@r)ris_floating_pointZlerpfloatrNr5r6)Zimg1Zimg2ratioZ max_valuerrr _blend_imagess r^CHWcCsZt||tj||jd}tj||jd}t|r'|gd}|gd}|||S)aNormalizes a tensor image given mean and standard deviation. Args: img (paddle.Tensor): input data to be normalized. mean (list|tuple): Sequence of means for each channel. std (list|tuple): Sequence of standard deviations for each channel. data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: Tensor: Normalized mage. placerI)rr to_tensorrar$rP)rmeanZstdrrrr normalizes  rdrJcCszt|||dvr tdtjgd|jd|j}t|r$|d}t |}||j |dd}|j }|||<| |S)aConverts image to grayscale version of image. Args: img (paddel.Tensor): Image to be converted to grayscale. num_output_channels (int, optionl[1, 3]): if num_output_channels = 1 : returned image is single channel if num_output_channels = 3 : returned image is 3 channel data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Grayscale version of the image. rJrz+num_output_channels should be either 1 or 3)gŏ1w-!?gbX9?gv/?r`rITr+Zkeepdim) r ValueErrorrrbrar5r6r$rPrsumr&expand)rZnum_output_channelsrZ rgb_weightsZ_c_index_shaperrr to_grayscales    rkc Cs>d}tjd||df|jd}t| d||d|d|}trMt| d||d|d|d}||d<||d<td|d|g} n5t| d||d|d|d}tj |d|}tj |d|}t t j d|d|gdd} | d | } |d||df| } | d||d fS) N?rJrrHr.r.rJfloat32rrJrq)ronesr6linspacein_dynamic_mode unsqueeze_rbrOstaticsetitemassignnparray transposerPbmm) thetawrDowohd base_gridx_gridy_gridtmpZ scaled_theta output_gridrrr _affine_grids*$$ rcCs|jddkr|j|jd|jd|jd|jdgd}|dur?tj|jdd|jd|jdf|jd}tj||fdd}tj|||dd d }|dur|ddd dddddf}|dddd ddddf}|d|jdddg}t |t t frt |nd}t rt|d|ddf|j|}nt||d }t|t|d|ddg}|d krt|dk|j}|d|||}|S||d||}|S)NrrJrqr)r&rHr*zerosF)mode padding_modeZ align_cornersrronearestrlr.)r&rirrrr6concatFZ grid_sampletilertuplelistrrtrbrPr5Z expand_asryrzr2rxcast)rgridrfilldummyrWZlen_fillZfill_imgrrr_grid_transformsB"     rrc Cst|j}|dkr|d}|dkr|n|d}tj||jd}|d}|j}t ||d|d|d|dd }t |t rHt |gd}t ||||d }|dkrX|n|d }|dkrh|d}|S|}|S) aAffine to the image by matrix. Args: img (paddle.Tensor): Image to be rotated. matrix (float or int): Affine matrix. interpolation (str, optional): Interpolation method. If omitted, or if the image has only one channel, it is set NEAREST . when use pil backend, support method are as following: - "nearest" - "bilinear" - "bicubic" fill (3-tuple or int): RGB pixel fill value for area outside the rotated image. If int, it is used for all channels respectively. data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Affined image. rrrrrrJrqr`rJrqrrr)r~rDrrrrrrqrrJ)rr&rOr r{rrbrarPrrintrrsqueeze) rrX interpolationrrr r&routrrraffine&s$    rFc Cs| d}|d}t||d\}}|dkr|n|d}ddg} |dur,ddg} n ddt|||gD} trt|}t |t |d t | t |d g} tj | |j d } | d | d| d | d| d | d  | d | d7<| d | d| d | d| d| d  | d | d 7<nw|dtj }tt |t |td gt | t |td gg} tj| d | d | d| d | d| d | d  | d | d} tj| d | d | d| d | d| d| d  | d | d } | d} |rtrXtj d|d|dgd|d|dgd|d|dgd|d|dgg| j d | j} n)td|d|dgd|d|dgd|d|dgd|d|dgg| j} | d| dd} | jdd}| jdd}||}|dd}|dd }trt|t|}}n|d|d}}n||}}t| ||||}t||||d}|dkr|n|d}|dS)aRotates the image by angle. Args: img (paddle.Tensor): Image to be rotated. angle (float or int): In degrees degrees counter clockwise order. interpolation (str, optional): Interpolation method. If omitted, or if the image has only one channel, it is set NEAREST . when use pil backend, support method are as following: - "nearest" - "bilinear" - "bicubic" expand (bool, optional): Optional expansion flag. If true, expands the output image to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation. center (2-tuple, optional): Optional center of rotation. Origin is the upper left corner. Default is the center of the image. fill (3-tuple or int): RGB pixel fill value for area outside the rotated image. If int, it is used for all channels respectively. Returns: paddle.Tensor: Rotated image. ihrrrrNcSsg|] \}}||dqS)rlr).0rTr;rrr szrotate..rGr`rqrJrrrr.rl)rJrrrp)rJrrqrr*rMrr)rOr)r r{ziprrtmathradianscossinrbrapirrrvrwrPr5r6rxr|r0rLr/ceilrrrr)rZanglerricenterrrr~rDZ post_transZ rotn_centerrXZcorners_pos_min_maxZnposnwZnhrrrrrrrrotateTs #                 rcCs<|ddgd}tj|ddddgddgd}tjd|d}tj||fddd }d } tjd||d f|d} t| |d | d |} | | d <t| |d | d |d} | | d<|dt d |d |g} | d||d f | }| d||d f |d}||d }|d||dfS)NrFrrJrq)Z repeat_times)rqrJrHr*rrlrr.rmrrnrp) rPrrrrrrOrsrur{rbr|)rcoeffsrrr6Ztheta1rrZtheta2rrrrZ scaled_theta1Z output_grid1Z output_grid2rrrr_perspective_grids&$   rc Cst|j}|dkr|d}|dkr|n|d}|jd|jd}}t|r.|jntj}tj ||j d}t |||||d} t || ||d } |dkrR| n| d } |dkrb| d} | S| } | S) aPerspective the image. Args: img (paddle.Tensor): Image to be rotated. coeffs (list[float]): coefficients (a, b, c, d, e, f, g, h) of the perspective transforms. interpolation (str, optional): Interpolation method. If omitted, or if the image has only one channel, it is set NEAREST. When use pil backend, support method are as following: - "nearest" - "bilinear" - "bicubic" fill (3-tuple or int): RGB pixel fill value for area outside the rotated image. If int, it is used for all channels respectively. Returns: paddle.Tensor: Perspectived image. rrrrrrr`)rrr6rr)rr&rOr r{rr[r6rorbrarrr) rrrrrr rrr6rrrrr perspectives  rcC t||t|}|j|gdS)aVertically flips the given paddle tensor. Args: img (paddle.Tensor): Image to be flipped. data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Vertically flipped image. r*)rrflip)rrZh_axisrrrvflip0 rcCr)a$Horizontally flips the given paddle.Tensor Image. Args: img (paddle.Tensor): Image to be flipped. data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Horizontall flipped image. r*)rrr)rrZw_axisrrrhflipCrrcCsVt||t|r|dd||||||fS|||||||ddfS)a>Crops the given paddle.Tensor Image. Args: img (paddle.Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. top (int): Vertical component of the top left corner of the crop box. left (int): Horizontal component of the top left corner of the crop box. height (int): Height of the crop box. width (int): Width of the crop box. data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Cropped image. N)rr$)rtopleftheightwidthrrrrcropVs ""rc Csnt|d|s |}tr ||d||||||f<|Stj|dt|||t|||f|}|S)aQErase the pixels of selected area in input Tensor image with given value. Args: img (paddle.Tensor): input Tensor image. i (int): y coordinate of the top-left point of erased region. j (int): x coordinate of the top-left point of erased region. h (int): Height of the erased region. w (int): Width of the erased region. v (paddle.Tensor): value used to replace the pixels in erased region. inplace (bool, optional): Whether this transform is inplace. Default: False. Returns: paddle.Tensor: Erased image. r_.)rclonerrtrvrwslice)rrSjrDr~rQZinplacerrrerasens  rc Csxt||t|tjrt|t|f}t||\}}|\}}tt||d}tt||d}t||||||dS)aCrops the given paddle.Tensor Image and resize it to desired size. Args: img (paddle.Tensor): Image to be cropped. (0,0) denotes the top left corner of the image. output_size (sequence or int): (height, width) of the crop box. If int, it is used for both directions data_format (str, optional): Data format of img, should be 'HWC' or 'CHW'. Default: 'CHW'. Returns: paddle.Tensor: Cropped image. r,r)rrnumbersNumberrr)roundr) rZ output_sizerZ image_widthZ image_heightZ crop_heightZ crop_widthZcrop_topZ crop_leftrrr center_crops rconstantc CsNt||t|tjttfstdt|tjtttfs tdt|ts)tdt|ttfrAt|dvrAt dt|d|dvsIJdt|t rW|}}}}n#t|d krj|d }}|d }}n|d }|d }|d }|d }||||g}|d krd}n|dkrt d| d }t j |||t|d|d}|d S)a= Pads the given paddle.Tensor on all sides with specified padding mode and fill value. Args: img (paddle.Tensor): Image to be padded. padding (int|list|tuple): Padding on each border. If a single int is provided this is used to pad all borders. If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively. If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively. fill (float, optional): Pixel fill value for constant fill. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Default: 0. padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default: 'constant'. - constant: pads with a constant value, this value is specified with fill - edge: pads with the last value on the edge of the image - reflect: pads with reflection of image (without repeating the last value on the edge) padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2] - symmetric: pads with reflection of image (repeating the last value on the edge) padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3] Returns: paddle.Tensor: Padded image. zGot inappropriate padding argzGot inappropriate fill argz"Got inappropriate padding_mode arg)rqrz9Padding must be an int or a 2, or 4 element tuple, not a z element tuple)redgeZreflect symmetriczBPadding mode should be either constant, edge, reflect or symmetricrqrrJrrZ replicaterzDo not support symmetric modeN)padrvaluer)rrrrrr TypeErrorstrrrgrrOrrr\r) rpaddingrrrpad_left pad_rightpad_topZ pad_bottomrrrrsN "         rbilinearcCst||t|tst|ttfrt|dkstd|t|trht||\}}|dks2|dkrs  rcCst|d|dks Jdt|d}t|r|jntj}|dkr-tj||ddd}n|d kr?tjt||ddd}nt d t |||S) aAdjusts contrast of an image. Args: img (paddle.Tensor): Image to be adjusted. contrast_factor (float): How much to adjust the contrast. Can be any non negative number. 0 gives a solid gray image, 1 gives the original image while 2 increases the contrast by a factor of 2. Returns: paddle.Tensor: Contrast adjusted image. rrz'contrast_factor should be non-negative.r_rJ)rrrTrfrr) rr(rr[r6rorcr5rkrgr^)rZcontrast_factorchannelsr6rrrradjust_contrastVs    rcCsVt|d|dks Jdt|d}|dkr|S|dkr!t|}ntdt|||S)aAdjusts color saturation of an image. Args: img (paddle.Tensor): Image to be adjusted. saturation_factor (float): How much to adjust the saturation. 0 will give a black and white image, 1 will give the original image while 2 will enhance the saturation by a factor of 2. Returns: paddle.Tensor: Saturation adjusted image. r_rz)saturation_factor should be non-negative.rJrr)rr(rkrgr^)rZsaturation_factorrrrrradjust_saturationvs    rc Cst|d|dkr |dksJdt|d}|dkr|S|dkra|j}|tjkr0|tjd}t|}|jdd \}}}||}|| }t tj |||gdd }|tjkr_|d|}|St d ) a2Adjusts hue of an image. The image hue is adjusted by converting the image to HSV and cyclically shifting the intensities in the hue channel (H). The image is then converted back to original image mode. `hue_factor` is the amount of shift in H channel and must be in the interval `[-0.5, 0.5]`. Args: img (paddle.Tensor): Image to be adjusted. hue_factor (float): How much to shift the hue channel. Should be in [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative direction respectively. 0 means no shift. Therefore, both -0.5 and 0.5 will give an image with complementary colors while 0 gives the original image. Returns: paddle.Tensor: Hue adjusted image. r_rrlz)hue_factor should be in range [-0.5, 0.5]rJrrZrr*r) rr(r6rZuint8r5rorEr4rLrYr8rg) rZ hue_factorrr6Zimg_hsvrDr;rQZ img_adjustedrrr adjust_hues(     r)r_)rJr_)rNr_)rFNNr_)F)rrr_)rr_),rrnumpyryrZpaddle.nn.functionalnnZ functionalrZbase.frameworkr__all__rrrrrr"r$r'r(r)rErYr^rdrkrrrrrrrrrrrrrrrrrrrrrs\  # -1  (     W<