o e@sBdZddlmZmZmZddlZddlZddlZddl Z ddl m Z ddl m Zddl mZdd l mZd d d Zd dZddZddZddZddZddZddZddZddZdd Zd!d"Zd#d$Z & dd'd(Z & dd)d*Z!d+d,Z"d-d.Z#d/d0Z$  1dd2d3Z%  1dd4d5Z&d6d7Z'd8d9Z(d:d;Z)dd?Z+d@dAZ,dBdCZ-ddEdFZ.ddGdHZ/dIdJZ0GdKdLdLe j1Z2GdMdNdNe j1Z3GdOdPdPe3Z4GdQdRdRe3Z5GdSdTdTe3Z6GdUdVdVe j1Z7GdWdXdXe j1Z8GdYdZdZe9Z:Gd[d\d\e j1Z;Gd]d^d^e8ZGdcdddde j1Z?Gdedfdfe j1Z@Gdgdhdhe j1ZAGdidjdjeAZBGdkdldleBZCGdmdndneAZDGdodpdpeAZEGdqdrdreAZFGdsdtdteAZGGdudvdveAZHGdwdxdxe j1ZIGdydzdze9ZJGd{d|d|eIZKe Ld} &   dddZMGddde j1ZNdS)a Augmenters that perform simple arithmetic changes. List of augmenters: * :class:`Add` * :class:`AddElementwise` * :class:`AdditiveGaussianNoise` * :class:`AdditiveLaplaceNoise` * :class:`AdditivePoissonNoise` * :class:`Multiply` * :class:`MultiplyElementwise` * :class:`Cutout` * :class:`Dropout` * :class:`CoarseDropout` * :class:`Dropout2d` * :class:`TotalDropout` * :class:`ReplaceElementwise` * :class:`ImpulseNoise` * :class:`SaltAndPepper` * :class:`CoarseSaltAndPepper` * :class:`Salt` * :class:`CoarseSalt` * :class:`Pepper` * :class:`CoarsePepper` * :class:`Invert` * :class:`Solarize` * :class:`ContrastNormalization` * :class:`JpegCompression` )print_functiondivisionabsolute_importN)meta) parameters)dtypes)random)imgaug.augmenters.arithmetic_fill_rectangle_constant_)r _fill_rectangle_gaussian_)constantZgaussiancCN|jdkr t|Stj|gdgddd|jjdkr"t||St||S)aAdd a single scalar value or one scalar value per channel to an image. This method ensures that ``uint8`` does not overflow during the addition. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: limited; tested (1) * ``uint32``: no * ``uint64``: no * ``int8``: limited; tested (1) * ``int16``: limited; tested (1) * ``int32``: no * ``int64``: no * ``float16``: limited; tested (1) * ``float32``: limited; tested (1) * ``float64``: no * ``float128``: no * ``bool``: limited; tested (1) - (1) Non-uint8 dtypes can overflow. For floats, this can result in +/-inf. Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. If `value` contains more than one value, the shape of the image is expected to be ``(H,W,C)``. value : number or ndarray The value to add to the image. Either a single value or an array containing exactly one component per channel, i.e. ``C`` components. Returns ------- ndarray Image with value added to it. rbooluint8uint16int8int16float16float32 uint32uint64uint128uint256int32int64int128int256float64float96float128float256NallowedZ disallowedZ augmenterr) sizenpcopyiadt gate_dtypesdtypename_add_scalar_to_uint8_add_scalar_to_non_uint8)imagevaluer2LD:\Projects\ConvertPro\env\Lib\site-packages\imgaug/augmenters/arithmetic.py add_scalar<s )   r4cCs8t|pt|pt|o|jdk}| }|jdkrdn|jd}tt |dd tj }tj ddtj d}|r|jdksKJd |j|jf|jd ksZJd |j|jf|jd|jksnJd |jd|jft |ddtjfd|f|tjddf}n||}t|dd |j}t||S) Nrrrr,zLExpected `value` to be 1-dimensional, got %d-dimensional data with shape %s.znExpected `image` to be 3-dimensional when adding one value per channel, got %d-dimensional data with shape %s.zjExpected number of channels in `image` and number of components in `value` to be identical. Got %d vs. %d.)iais_single_number is_np_scalar is_np_arrayr'ndimshaper(cliproundastyperarangetilenewaxisr, apply_lut)r0r1is_single_valueis_channelwise nb_channels value_rangetablesr2r2r3r.ysH      r.c Cs|j}t|pt|pt|o|jdk}| }|jdkr!dn|jd}dd|r,|ndf}t | |}|jj d}td|jj |f}t j||dd}t j||g|j|gdd\}}tj|||dd }t ||S) Nrrr5%s%dTvalidater Zincrease_itemsize_factornooutcasting)r,r;r<r=r>r'r?r@r(arrayreshapeitemsizekindr*clip_to_dtype_value_range_promote_array_dtypes_addrestore_dtypes_) r0r1 input_dtyperHrIrJr@rW dtype_targetr2r2r3r/s,    r/cCs:tj|gdgddd|jjdkrt||St||S)a.Add an array of values to an image. This method ensures that ``uint8`` does not overflow during the addition. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: limited; tested (1) * ``uint32``: no * ``uint64``: no * ``int8``: limited; tested (1) * ``int16``: limited; tested (1) * ``int32``: no * ``int64``: no * ``float16``: limited; tested (1) * ``float32``: limited; tested (1) * ``float64``: no * ``float128``: no * ``bool``: limited; tested (1) - (1) Non-uint8 dtypes can overflow. For floats, this can result in +/-inf. Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. values : ndarray The values to add to the image. Expected to have the same height and width as `image` and either no channels or one channel or the same number of channels as `image`. Returns ------- ndarray Image with values added to it. rrNr%r)r*r+r,r-_add_elementwise_to_uint8_add_elementwise_to_non_uint8)r0valuesr2r2r3add_elementwises(  rbcCsZ|jjdkr t|}|tj}t|ddtj}||}t|ddtj}|S)Nfr6r7r)r,rXr(rBrCrrAr)r0ra image_augr2r2r3r_s  r_cCs|j}|j}|jdkr|dtjf}|jdkr|dtjf}|jd}|jjd}td|jj|f}tj||dd}|jddkrLt |dd|f}tj ||g|j|gdd\}}tj |||d d }t ||}t |dkrt|d S|S) Nr.r5rMdrNrrPrQrR.r)r@r,r?r(rFrWrXr*rYrErZr[r\len)r0ra input_shaper]rJrWr^r2r2r3r`s0        r`cCr)aMultiply an image by a single scalar or one scalar per channel. This method ensures that ``uint8`` does not overflow during the multiplication. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: limited; tested (1) * ``uint32``: no * ``uint64``: no * ``int8``: limited; tested (1) * ``int16``: limited; tested (1) * ``int32``: no * ``int64``: no * ``float16``: limited; tested (1) * ``float32``: limited; tested (1) * ``float64``: no * ``float128``: no * ``bool``: limited; tested (1) - (1) Non-uint8 dtypes can overflow. For floats, this can result in +/-inf. note:: Tests were only conducted for rather small multipliers, around ``-10.0`` to ``+10.0``. In general, the multipliers sampled from `multiplier` must be in a value range that corresponds to the input image's dtype. E.g. if the input image has dtype ``uint16`` and the samples generated from `multiplier` are ``float64``, this function will still force all samples to be within the value range of ``float16``, as it has the same number of bytes (two) as ``uint16``. This is done to make overflows less likely to occur. Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. If `value` contains more than one value, the shape of the image is expected to be ``(H,W,C)``. multiplier : number or ndarray The multiplier to use. Either a single value or an array containing exactly one component per channel, i.e. ``C`` components. Returns ------- ndarray Image, multiplied by `multiplier`. rrrNr%r) r'r(r)r*r+r,r-_multiply_scalar_to_uint8_multiply_scalar_to_non_uint8)r0 multiplierr2r2r3multiply_scalarHs 7   rlcCs&t|pt|pt|o|jdk}| }|jdkrdn|jd}t|}tj ddtjd}|r~|jdksBJd|j|jf|jdksQJd |j|jf|jd|jkseJd |jd|jft |ddtj fd|f|tj ddf}n||}t |dd  |j}t||S) Nrrr5rr8r9zQExpected `multiplier` to be 1-dimensional, got %d-dimensional data with shape %s.r:zvExpected `image` to be 3-dimensional when multiplying by one value per channel, got %d-dimensional data with shape %s.zoExpected number of channels in `image` and number of components in `multiplier` to be identical. Got %d vs. %d.r7)r;r<r=r>r'r?r@r(rrDrErFrArCr,rG)r0rkrHrIrJrKrLr2r2r3risH       ric Cs|j}t|pt|pt|o|jdk}| }|jdkr!dn|jd}dd|r,|ndf}t | |}t |jj |jj dkrCdnd}td|jj |f}tj||dd}tj||g|j|gdd\}}tj|||d d }t||S) Nrrr5rcrMTrNrPrQrR)r,r;r<r=r>r'r?r@r(rUrVmaxrWrXr*rYrZmultiplyr\) r0rkr]rHrIrJr@rWr^r2r2r3rjs2   rjcCsRtj|gdgddd|jjdkr||9}|S|jjdkr$t||St||S)aMultiply an image with an array of values. This method ensures that ``uint8`` does not overflow during the addition. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: limited; tested (1) * ``uint32``: no * ``uint64``: no * ``int8``: limited; tested (1) * ``int16``: limited; tested (1) * ``int32``: no * ``int64``: no * ``float16``: limited; tested (1) * ``float32``: limited; tested (1) * ``float64``: no * ``float128``: no * ``bool``: limited; tested (1) - (1) Non-uint8 dtypes can overflow. For floats, this can result in +/-inf. note:: Tests were only conducted for rather small multipliers, around ``-10.0`` to ``+10.0``. In general, the multipliers sampled from `multipliers` must be in a value range that corresponds to the input image's dtype. E.g. if the input image has dtype ``uint16`` and the samples generated from `multipliers` are ``float64``, this function will still force all samples to be within the value range of ``float16``, as it has the same number of bytes (two) as ``uint16``. This is done to make overflows less likely to occur. Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. multipliers : ndarray The multipliers with which to multiply the image. Expected to have the same height and width as `image` and either no channels or one channel or the same number of channels as `image`. Returns ------- ndarray Image, multiplied by `multipliers`. rrNr%br)r*r+r,rXr-_multiply_elementwise_to_uint8"_multiply_elementwise_to_non_uint8)r0 multipliersr2r2r3multiply_elementwises5    rscCsj|jjdkr|jtjdd}|tj}n|jtjdd}|tj}tj||d|d}tj|tj ddS)NrcFr)rQ)rTrS)rB) r,rXrCr(rrrnr*r\r)r0rrrdr2r2r3rp8s  rpcCs|j}t|}t|}t|jj|jjdkrdnd}td|jj|f}tj||d||fd}|jddkrG|jd}t |dd|f}tj ||g||gdd\}}tj |||d d }t ||S) NrcrrrMT)rOZvalidate_valuesr5rPrQrR) r,r(minrmrWrXr*rYr@rErZrnr\)r0rrr]Zmul_minZmul_maxrWr^rJr2r2r3rqJs,      rqrFc Cstt||||||||| S)aFill a single area within an image using a fill mode. This cutout method uses the top-left and bottom-right corner coordinates of the cutout region given as absolute pixel values. .. note:: Gaussian fill mode will assume that float input images contain values in the interval ``[0.0, 1.0]`` and hence sample values from a gaussian within that interval, i.e. from ``N(0.5, std=0.5/3)``. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.cutout_`. Added in 0.4.0. Parameters ---------- image : ndarray Image to modify. x1 : number See :func:`~imgaug.augmenters.arithmetic.cutout_`. y1 : number See :func:`~imgaug.augmenters.arithmetic.cutout_`. x2 : number See :func:`~imgaug.augmenters.arithmetic.cutout_`. y2 : number See :func:`~imgaug.augmenters.arithmetic.cutout_`. fill_mode : {'constant', 'gaussian'}, optional See :func:`~imgaug.augmenters.arithmetic.cutout_`. cval : number or tuple of number, optional See :func:`~imgaug.augmenters.arithmetic.cutout_`. fill_per_channel : number or bool, optional See :func:`~imgaug.augmenters.arithmetic.cutout_`. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.arithmetic.cutout_`. Returns ------- ndarray Image with area filled in. )cutout_r(r)) r0x1y1x2y2 fill_modecvalfill_per_channelseedr2r2r3cutoutws 7rc  Csddl} |jdd\} } ttt|d| }ttt|d| }ttt|d| }ttt|d| }||krx||krx|tvsOJdttt|ft|\} } | | }t || }||||||||dkt |t j stt |n|d}|S)aFill a single area within an image using a fill mode (in-place). This cutout method uses the top-left and bottom-right corner coordinates of the cutout region given as absolute pixel values. .. note:: Gaussian fill mode will assume that float input images contain values in the interval ``[0.0, 1.0]`` and hence sample values from a gaussian within that interval, i.e. from ``N(0.5, std=0.5/3)``. Added in 0.4.0. **Supported dtypes**: minimum of ( :func:`~imgaug.augmenters.arithmetic._fill_rectangle_gaussian_`, :func:`~imgaug.augmenters.arithmetic._fill_rectangle_constant_` ) Parameters ---------- image : ndarray Image to modify. Might be modified in-place. x1 : number X-coordinate of the top-left corner of the cutout region. y1 : number Y-coordinate of the top-left corner of the cutout region. x2 : number X-coordinate of the bottom-right corner of the cutout region. y2 : number Y-coordinate of the bottom-right corner of the cutout region. fill_mode : {'constant', 'gaussian'}, optional Fill mode to use. cval : number or tuple of number, optional The constant value to use when filling with mode ``constant``. May be an intensity value or color tuple. fill_per_channel : number or bool, optional Whether to fill in a channelwise fashion. If number then a value ``>=0.5`` will be interpreted as ``True``. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional A random number generator to sample random values from. Usually an integer seed value or an ``RNG`` instance. See :class:`imgaug.random.RNG` for details. Returns ------- ndarray Image with area filled in. The input image might have been modified in-place. rNrz6Expected one of the following fill modes: %s. Got: %s.?)rwrxryrzr| per_channel random_state) importlibr@rurmint_CUTOUT_FILL_MODESstrlistkeys import_modulegetattr isinstanceiarandomZRNG)r0rwrxryrzr{r|r}r~rheightwidth module_namefnamemodulefuncr2r2r3rvs4?       rvcCs|jj}|dvrd} d} d} n t|j\} } } t| t| dd} ||} ||}| |f}|r?|jdkr?||jdf}|j| | |d }|jjd krR|dk}n t || |  |j}|jdkrtt ||||||d d f<|S||||||f<|S) aZFill a rectangular image area with samples from a gaussian. Added in 0.4.0. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: limited; tested (1) * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: limited; tested (1) * ``float16``: yes; tested (2) * ``float32``: yes; tested (2) * ``float64``: yes; tested (2) * ``float128``: limited; tested (1) (2) * ``bool``: yes; tested - (1) Possible loss of resolution due to gaussian values being sampled as ``float64`` s. - (2) Float input arrays are assumed to be in interval ``[0.0, 1.0]`` and all gaussian samples are within that interval too. )rcror?g@g@r:r)r'roN) r,rXr*get_value_range_of_dtypefloatr?r@normalr(rArCZ atleast_3d)r0rwrxryrzr|rrrX min_valueZ center_value max_valueZstddevrrr@rectZrect_vrr2r2r3r s.      r c Cst|rJ|rF|jdkrdn|jd}|dur|d}n.t||kr9tt|t|} t|| fd|}nt||krE|d|}n|d}tj ||j d|||||df<|S)aFill a rectangular area within an image with constant value(s). `cval` may be a single value or one per channel. If the number of items in `cval` does not match the number of channels in `image`, it may be tiled up to the number of channels. Added in 0.4.0. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: yes; tested * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: yes; tested * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: yes; tested * ``float128``: yes; tested * ``bool``: yes; tested rNr5rr9.) r; is_iterabler?r@rgrr(ceilrErUr,) r0rwrxryrzr|rrrJmulr2r2r3r Rs     "r cCstj|gdgddd|j}|jdkr|dtjf}|jdkr(|dtjf}|dk}|jddkrA|jd }t|dd|f}|jjd vrR|jjd krRt |}tj ||jd d }|j |jd d}|||<t |dkrq|dS|S)aReplace components in an image array with new values. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: no (1) * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: no (2) * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: yes; tested * ``float128``: no * ``bool``: yes; tested - (1) ``uint64`` is currently not supported, because :func:`~imgaug.dtypes.clip_to_dtype_value_range_()` does not support it, which again is because numpy.clip() seems to not support it. - (2) `int64` is disallowed due to being converted to `float64` by :func:`numpy.clip` since 1.17 (possibly also before?). Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. mask : ndarray Mask of shape ``(H,W,[C])`` denoting which components to replace. If ``C`` is provided, it must be ``1`` or match the ``C`` of `image`. May contain floats in the interval ``[0.0, 1.0]``. replacements : iterable Replacements to place in `image` at the locations defined by `mask`. This 1-dimensional iterable must contain exactly as many values as there are replaced components in `image`. Returns ------- ndarray Image with replaced components. ) rrrrrrrrrr!) rrrrrr r"r#r$Nr%r.rrr5)iurorcFrNrtrf) r*r+r@r?r(rFrEr,rXrBrYrCrg)r0maskZ replacementsrhZ mask_threshrJreplacement_samplesr2r2r3replace_elementwise_s2/$     rTcCstt|||||dS)aInvert an array. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.invert_`. Parameters ---------- image : ndarray See :func:`invert_`. min_value : None or number, optional See :func:`invert_`. max_value : None or number, optional See :func:`invert_`. threshold : None or number, optional See :func:`invert_`. invert_above_threshold : bool, optional See :func:`invert_`. Returns ------- ndarray Inverted image. rr thresholdinvert_above_threshold)invert_r(r))r0rrrrr2r2r3invertsrc Cs^hd}t|j\}}}|dur|n|}|dur|n|}||ks0Jdt|t||jjf||ksCJdt|t||jjf||ksSJdt|t|f||ks[||krn|jj|vsnJdd||jjf|jjdkr|t|||||Sttt t d } | |jj } |dur| |||S| t |||} |r||k} n||k} | | || <|S) a Invert an array in-place. Added in 0.4.0. **Supported dtypes**: if (min_value=None and max_value=None): * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: yes; tested * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: yes; tested * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: yes; tested * ``float128``: yes; tested * ``bool``: yes; tested if (min_value!=None or max_value!=None): * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: no (1) * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: no (2) * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: no (2) * ``float128``: no (3) * ``bool``: no (4) - (1) Not allowed due to numpy's clip converting from ``uint64`` to ``float64``. - (2) Not allowed as int/float have to be increased in resolution when using min/max values. - (3) Not tested. - (4) Makes no sense when using min/max values. Parameters ---------- image : ndarray Image array of shape ``(H,W,[C])``. The array *might* be modified in-place. min_value : None or number, optional Minimum of the value range of input images, e.g. ``0`` for ``uint8`` images. If set to ``None``, the value will be automatically derived from the image's dtype. max_value : None or number, optional Maximum of the value range of input images, e.g. ``255`` for ``uint8`` images. If set to ``None``, the value will be automatically derived from the image's dtype. threshold : None or number, optional A threshold to use in order to invert only numbers above or below the threshold. If ``None`` no thresholding will be used. invert_above_threshold : bool, optional If ``True``, only values ``>=threshold`` will be inverted. Otherwise, only values ``rrrrrrrrNzaExpected min_value to be above or equal to dtype's min value, got %s (vs. min possible %s for %s)zaExpected max_value to be below or equal to dtype's max value, got %s (vs. max possible %s for %s)z7Expected min_value to be below max_value, got %s and %szJCan use custom min/max values only with the following dtypes: %s. Got: %s., r)rorrrc)r*rr,rr-join_invert_uint8_ _invert_bool_invert_uint16_or_larger_ _invert_int_ _invert_floatrXr(r)) r0rrrrZallow_dtypes_custom_minmaxZ min_value_dt_Z max_value_dtZdtype_kind_to_invert_funcrarr_invrr2r2r3rslY           rcCs&|dkr|dksJd||f|S)NrrzKmin_value and max_value must be 0 and 1 for bool arrays. Got %.4f and %.4f.r2arrrrr2r2r3rs rcCst||||}t||}|SN) _generate_table_for_invert_uint8r;Z apply_lut_)rrrrrtabler2r2r3rs  rcCs<|dko |t|jjk}|r||Stt|||||SNr)r(Ziinfor,rm_invert_by_distancerA)rrrZ min_max_is_vrr2r2r3rs rcCs\|d|dkr#t|}||k}|||<d||d||<|Stt|||||S)Nr5r)r(r)rrA)rrrrrr2r2r3rs   rcCs2tj|d|ddrd|Stt|||||S)Nr5r)Zrtol)r(iscloserrArr2r2r3rs  rcCsz|}|jjdvrtt|gdd}t||}||}|jjdkr,t|||}|jjdvr;tj||jdd}|S)N)rrcrrrcF)rA) r,rXr*Zincrease_array_resolutions_r(r)absrAr\)rrrrdistance_from_minr2r2r3rs   rcCstdtj}|dko|dk}|r|ddd}n t||}||}t|||tj}|duri|tj}|rTtj|dt||t|dgdd}|Stj|dt||t|dgdd}|S)Nr8rr7r5)Zaxis) r(rDrCrrrArZ concatenater)rrrrrZfull_value_rangeZ table_invrr2r2r3rs0  rcCstt||dS)anInvert pixel values above a threshold. Added in 0.4.0. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.solarize_`. Parameters ---------- image : ndarray See :func:`solarize_`. threshold : None or number, optional See :func:`solarize_`. Returns ------- ndarray Inverted image. r) solarize_r(r)r0rr2r2r3solarize srcCs t||dS)aInvert pixel values above a threshold in-place. This function is a wrapper around :func:`invert`. This function performs the same transformation as :func:`PIL.ImageOps.solarize`. Added in 0.4.0. **Supported dtypes**: See ``~imgaug.augmenters.arithmetic.invert_(min_value=None and max_value=None)``. Parameters ---------- image : ndarray See :func:`invert_`. threshold : None or number, optional See :func:`invert_`. Note: The default threshold is optimized for ``uint8`` images. Returns ------- ndarray Inverted image. This *can* be the same array as input in `image`, modified in-place. r)rrr2r2r3r:s rc Csnddl}|jdkrt|Sd}d}|jjdks!Jd|jjfd|kr+dks3nJd|f|jdk}|jd koC|jd dk}|rJ|d }|s]|s]|jd d ks]Jd |jftt t |||d |d||}|j |}t jddd$} |j| |d| dd} |s|rd} tj| | dd}Wdn1swY|r|dtjf}|S)aDCompress an image using jpeg compression. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: ? * ``uint32``: ? * ``uint64``: ? * ``int8``: ? * ``int16``: ? * ``int32``: ? * ``int64``: ? * ``float16``: ? * ``float32``: ? * ``float64``: ? * ``float128``: ? * ``bool``: ? Parameters ---------- image : ndarray Image of dtype ``uint8`` and shape ``(H,W,[C])``. If ``C`` is provided, it must be ``1`` or ``3``. compression : int Strength of the compression in the interval ``[0, 100]``. Returns ------- ndarray Input image after applying jpeg compression to it and reloading the result into a new array. Same shape and dtype as the input. rNrerrzCJpeg compression can only be applied to uint8 images. Got dtype %s.z>Expected compression to be in the interval [0, 100], got %.4f.rr:r5rfzkExpected either a grayscale image of shape (H,W) or (H,W,1) or an RGB image of shape (H,W,3). Got shape %s.rezwb+z.jpg)modesuffix)qualityRGBLZjpeg)pilmodeformat.)Z PIL.Imager'r(r)r,r-r?r@rrArBZImageZ fromarraytempfileNamedTemporaryFilesaveseekimageioZimreadrF) r0 compressionZPILZmaximum_qualityZminimum_qualityZhas_no_channelsZis_single_channelrZ image_pilrcrr2r2r3 compress_jpeg\sb#        rc8eZdZdZ   d fdd Zdd Zd d ZZS) AddaK Add a value to all pixels in an image. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.add_scalar`. Parameters ---------- value : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Value to add to all pixels. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, then a value from the discrete interval ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a value will be sampled per image from that parameter. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Add(10) Always adds a value of 10 to all channels of all pixels of all input images. >>> aug = iaa.Add((-10, 10)) Adds a value from the discrete interval ``[-10..10]`` to all pixels of input images. The exact value is sampled per image. >>> aug = iaa.Add((-10, 10), per_channel=True) Adds a value from the discrete interval ``[-10..10]`` to all pixels of input images. The exact value is sampled per image *and* channel, i.e. to a red-channel it might add 5 while subtracting 7 from the blue channel of the same image. >>> aug = iaa.Add((-10, 10), per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. iFN deprecatedc@tt|j||||dtj|ddddd|_t|d|_dSNr~r-r deterministicr1TrKtuple_to_uniformlist_to_choicer)superr__init__iaphandle_continuous_paramr1handle_probability_paramrselfr1rr~r-rr __class__r2r3r   z Add.__init__cC|jdur|S|j}t|}t|}|d}|jj|f|dd} |jj||f|dd} tt || | } | D]*\} \} }}| j d}|dkrP|d|}n |j dkrY|dng}t | ||j| <q9|SNrrrrr) imagesrgrestimate_max_number_of_channels duplicater draw_samplesr1 enumeratezipr@r'r4)rbatchrparentshooksr nb_imagesnb_channels_maxrssper_channel_samplesZ value_samplesgenrr0Zvalue_samples_iper_channel_samples_irJr1r2r2r3_augment_batch_s(      zAdd._augment_batch_cC |j|jgSz=See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`.r1rrr2r2r3get_parametersI zAdd.get_parametersrFNNrr__name__ __module__ __qualname____doc__rrr __classcell__r2r2rr3rsK.rcr) AddElementwisea Add to the pixels of images values that are pixelwise randomly sampled. While the ``Add`` Augmenter samples one value to add *per image* (and optionally per channel), this augmenter samples different values per image and *per pixel* (and optionally per channel), i.e. intensities of neighbouring pixels may be increased/decreased by different amounts. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.add_elementwise`. Parameters ---------- value : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional Value to add to the pixels. * If an int, exactly that value will always be used. * If a tuple ``(a, b)``, then values from the discrete interval ``[a..b]`` will be sampled per image and pixel. * If a list of integers, a random value will be sampled from the list per image and pixel. * If a ``StochasticParameter``, then values will be sampled per image and pixel from that parameter. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AddElementwise(10) Always adds a value of 10 to all channels of all pixels of all input images. >>> aug = iaa.AddElementwise((-10, 10)) Samples per image and pixel a value from the discrete interval ``[-10..10]`` and adds that value to the respective pixel. >>> aug = iaa.AddElementwise((-10, 10), per_channel=True) Samples per image, pixel *and also channel* a value from the discrete interval ``[-10..10]`` and adds it to the respective pixel's channel value. Therefore, added values may differ between channels of the same pixel. >>> aug = iaa.AddElementwise((-10, 10), per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. rFNrcrr)rrrrrr1rrrrr2r3rrzAddElementwise.__init__cCs|jdur|S|j}t|}|d|}|jj|f|dd}tt|||dd} | D](\} \} } } | j\}}}||| dkrC|ndf}|jj|| d}t | ||j| <q.|S)Nrrrr) rrgrrrrrr@r1rb)rrrrrrrrrrrr0rrsrrrJ sample_shaperar2r2r3rs$   zAddElementwise._augment_batch_cCrrrrr2r2r3rrzAddElementwise.get_parametersrrr2r2rr3rOsOrc(eZdZdZ   d fdd ZZS) AdditiveGaussianNoiseas Add noise sampled from gaussian distributions elementwise to images. This augmenter samples and adds noise elementwise, i.e. it can add different noise values to neighbouring pixels and is comparable to ``AddElementwise``. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.AddElementwise`. Parameters ---------- loc : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Mean of the normal distribution from which the noise is sampled. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the interval ``[a, b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, a value will be sampled from the parameter per image. scale : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Standard deviation of the normal distribution that generates the noise. Must be ``>=0``. If ``0`` then `loc` will simply be added to all pixels. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the interval ``[a, b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, a value will be sampled from the parameter per image. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AdditiveGaussianNoise(scale=0.1*255) Adds gaussian noise from the distribution ``N(0, 0.1*255)`` to images. The samples are drawn per image and pixel. >>> aug = iaa.AdditiveGaussianNoise(scale=(0, 0.1*255)) Adds gaussian noise from the distribution ``N(0, s)`` to images, where ``s`` is sampled per image from the interval ``[0, 0.1*255]``. >>> aug = iaa.AdditiveGaussianNoise(scale=0.1*255, per_channel=True) Adds gaussian noise from the distribution ``N(0, 0.1*255)`` to images, where the noise value is different per image and pixel *and* channel (e.g. a different one for red, green and blue channels of the same pixel). This leads to "colorful" noise. >>> aug = iaa.AdditiveGaussianNoise(scale=0.1*255, per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. rrFNrc Vtj|ddddd}tj|ddddd} tj|| d} tt|j| |||||ddSNlocTrscalerN)rrrr~r-rr)rrZNormalrr r rrrrr~r-rrloc2Zscale2r1rr2r3r)s  zAdditiveGaussianNoise.__init__rr FNNrrrrrrrrr2r2rr3r s [r cr) AdditiveLaplaceNoisea Add noise sampled from laplace distributions elementwise to images. The laplace distribution is similar to the gaussian distribution, but puts more weight on the long tail. Hence, this noise will add more outliers (very high/low values). It is somewhere between gaussian noise and salt and pepper noise. Values of around ``255 * 0.05`` for `scale` lead to visible noise (for ``uint8``). Values of around ``255 * 0.10`` for `scale` lead to very visible noise (for ``uint8``). It is recommended to usually set `per_channel` to ``True``. This augmenter samples and adds noise elementwise, i.e. it can add different noise values to neighbouring pixels and is comparable to ``AddElementwise``. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.AddElementwise`. Parameters ---------- loc : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Mean of the laplace distribution that generates the noise. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the interval ``[a, b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, a value will be sampled from the parameter per image. scale : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Standard deviation of the laplace distribution that generates the noise. Must be ``>=0``. If ``0`` then only `loc` will be used. Recommended to be around ``255*0.05``. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the interval ``[a, b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, a value will be sampled from the parameter per image. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255) Adds laplace noise from the distribution ``Laplace(0, 0.1*255)`` to images. The samples are drawn per image and pixel. >>> aug = iaa.AdditiveLaplaceNoise(scale=(0, 0.1*255)) Adds laplace noise from the distribution ``Laplace(0, s)`` to images, where ``s`` is sampled per image from the interval ``[0, 0.1*255]``. >>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255, per_channel=True) Adds laplace noise from the distribution ``Laplace(0, 0.1*255)`` to images, where the noise value is different per image and pixel *and* channel (e.g. a different one for the red, green and blue channels of the same pixel). This leads to "colorful" noise. >>> aug = iaa.AdditiveLaplaceNoise(scale=0.1*255, per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. rr FNrc r r )rrZLaplacerrrrrr2r3rs  zAdditiveLaplaceNoise.__init__rrr2r2rr3r=s frc(eZdZdZ   dfdd ZZS) AdditivePoissonNoisea Add noise sampled from poisson distributions elementwise to images. Poisson noise is comparable to gaussian noise, as e.g. generated via ``AdditiveGaussianNoise``. As poisson distributions produce only positive numbers, the sign of the sampled values are here randomly flipped. Values of around ``10.0`` for `lam` lead to visible noise (for ``uint8``). Values of around ``20.0`` for `lam` lead to very visible noise (for ``uint8``). It is recommended to usually set `per_channel` to ``True``. This augmenter samples and adds noise elementwise, i.e. it can add different noise values to neighbouring pixels and is comparable to ``AddElementwise``. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.AddElementwise`. Parameters ---------- lam : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Lambda parameter of the poisson distribution. Must be ``>=0``. Recommended values are around ``0.0`` to ``10.0``. * If a number, exactly that value will always be used. * If a tuple ``(a, b)``, a random value from the interval ``[a, b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, a value will be sampled from the parameter per image. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AdditivePoissonNoise(lam=5.0) Adds poisson noise sampled from a poisson distribution with a ``lambda`` parameter of ``5.0`` to images. The samples are drawn per image and pixel. >>> aug = iaa.AdditivePoissonNoise(lam=(0.0, 15.0)) Adds poisson noise sampled from ``Poisson(x)`` to images, where ``x`` is randomly sampled per image from the interval ``[0.0, 15.0]``. >>> aug = iaa.AdditivePoissonNoise(lam=5.0, per_channel=True) Adds poisson noise sampled from ``Poisson(5.0)`` to images, where the values are different per image and pixel *and* channel (e.g. a different one for red, green and blue channels for the same pixel). >>> aug = iaa.AdditivePoissonNoise(lam=(0.0, 15.0), per_channel=True) Adds poisson noise sampled from ``Poisson(x)`` to images, with ``x`` being sampled from ``uniform(0.0, 15.0)`` per image and channel. This is the *recommended* configuration. >>> aug = iaa.AdditivePoissonNoise(lam=(0.0, 15.0), per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. rg.@FNrc sFtj|ddddd}ttj|d}tt|j||||||ddS)NlamrTr)rr)rrZ RandomSignZPoissonrrr) rrrr~r-rrZlam2r1rr2r3rs  zAdditivePoissonNoise.__init__)rFNNrrrr2r2rr3rs ^rcr) Multiplya Multiply all pixels in an image with a random value sampled once per image. This augmenter can be used to make images lighter or darker. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.multiply_scalar`. Parameters ---------- mul : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The value with which to multiply the pixel values in each image. * If a number, then that value will always be used. * If a tuple ``(a, b)``, then a value from the interval ``[a, b]`` will be sampled per image and used for all pixels. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then that parameter will be used to sample a new value per image. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Multiply(2.0) Multiplies all images by a factor of ``2``, making the images significantly brighter. >>> aug = iaa.Multiply((0.5, 1.5)) Multiplies images by a random value sampled uniformly from the interval ``[0.5, 1.5]``, making some images darker and others brighter. >>> aug = iaa.Multiply((0.5, 1.5), per_channel=True) Identical to the previous example, but the sampled multipliers differ by image *and* channel, instead of only by image. >>> aug = iaa.Multiply((0.5, 1.5), per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. g?g333333?FNrcrNrrTrr)rrrrrrrrrrrr~r-rrrr2r3rtrzMultiply.__init__cCrr) rrgrrrrrrrrr@r'rl)rrrrrrrrrrZ mul_samplesrrr0Z mul_samples_irrJrr2r2r3rs(      zMultiply._augment_batch_cCrrrrrr2r2r3rrzMultiply.get_parametersrFNNrrrr2r2rr3r(sK-rcr) MultiplyElementwisea Multiply image pixels with values that are pixelwise randomly sampled. While the ``Multiply`` Augmenter uses a constant multiplier *per image* (and optionally channel), this augmenter samples the multipliers to use per image and *per pixel* (and optionally per channel). **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.multiply_elementwise`. Parameters ---------- mul : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The value with which to multiply pixel values in the image. * If a number, then that value will always be used. * If a tuple ``(a, b)``, then a value from the interval ``[a, b]`` will be sampled per image and pixel. * If a list, then a random value will be sampled from that list per image and pixel. * If a ``StochasticParameter``, then that parameter will be used to sample a new value per image and pixel. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MultiplyElementwise(2.0) Multiply all images by a factor of ``2.0``, making them significantly bighter. >>> aug = iaa.MultiplyElementwise((0.5, 1.5)) Samples per image and pixel uniformly a value from the interval ``[0.5, 1.5]`` and multiplies the pixel with that value. >>> aug = iaa.MultiplyElementwise((0.5, 1.5), per_channel=True) Samples per image and pixel *and channel* uniformly a value from the interval ``[0.5, 1.5]`` and multiplies the pixel with that value. Therefore, used multipliers may differ between channels of the same pixel. >>> aug = iaa.MultiplyElementwise((0.5, 1.5), per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. rFNrcrr)rr!rrrrrrrrr2r3r s  zMultiplyElementwise.__init__cCs|jdur|S|j}t|}|d|}|jj|f|dd}t|jtjp5t|jtj o5t|jj tj} t t |||dd} | D]7\} \} } }| j \}}}||| dkrY|ndf}|jj||d}|jjdkrs| rs|jtdd}t| ||j| <qD|S)NrrrrroFrt)rrgrrrrrrBinomialFromLowerResolution other_paramrrr@r,rXrCrrs)rrrrrrrrrZis_mul_binomialrrr0rrrrrJrrr2r2r3r s0   z#MultiplyElementwise._augment_batch_cCrrrrr2r2r3r3 rz"MultiplyElementwise.get_parametersr rr2r2rr3r!sN!r!c@eZdZddZdS)_CutoutSamplesc Cs:||_||_||_||_||_||_||_||_| |_dSr nb_iterationspos_xpos_ysize_hsize_wsquaredr{r|r}) rr(r)r*r+r,r-r{r|r}r2r2r3r; s z_CutoutSamples.__init__Nrrrrr2r2r2r3r&9  r&csdeZdZdZ        dfd d Zed dZddZddZeddZ ddZ Z S)CutoutaFill one or more rectangular areas in an image using a fill mode. See paper "Improved Regularization of Convolutional Neural Networks with Cutout" by DeVries and Taylor. In contrast to the paper, this implementation also supports replacing image sub-areas with gaussian noise, random intensities or random RGB colors. It also supports non-squared areas. While the paper uses absolute pixel values for the size and position, this implementation uses relative values, which seems more appropriate for mixed-size datasets. The position parameter furthermore allows more flexibility, e.g. gaussian distributions around the center. .. note:: This augmenter affects only image data. Other datatypes (e.g. segmentation map pixels or keypoints within the filled areas) are not affected. .. note:: Gaussian fill mode will assume that float input images contain values in the interval ``[0.0, 1.0]`` and hence sample values from a gaussian within that interval, i.e. from ``N(0.5, std=0.5/3)``. Added in 0.4.0. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.cutout_`. Parameters ---------- nb_iterations : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional How many rectangular areas to fill. * If ``int``: Exactly that many areas will be filled on all images. * If ``tuple`` ``(a, b)``: A value from the interval ``[a, b]`` will be sampled per image. * If ``list``: A random value will be sampled from that ``list`` per image. * If ``StochasticParameter``: That parameter will be used to sample ``(B,)`` values per batch of ``B`` images. position : {'uniform', 'normal', 'center', 'left-top', 'left-center', 'left-bottom', 'center-top', 'center-center', 'center-bottom', 'right-top', 'right-center', 'right-bottom'} or tuple of float or StochasticParameter or tuple of StochasticParameter, optional Defines the position of each area to fill. Analogous to the definition in e.g. :class:`~imgaug.augmenters.size.CropToFixedSize`. Usually, ``uniform`` (anywhere in the image) or ``normal`` (anywhere in the image with preference around the center) are sane values. size : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The size of the rectangle to fill as a fraction of the corresponding image size, i.e. with value range ``[0.0, 1.0]``. The size is sampled independently per image axis. * If ``number``: Exactly that size is always used. * If ``tuple`` ``(a, b)``: A value from the interval ``[a, b]`` will be sampled per area and axis. * If ``list``: A random value will be sampled from that ``list`` per area and axis. * If ``StochasticParameter``: That parameter will be used to sample ``(N, 2)`` values per batch, where ``N`` is the total number of areas to fill within the whole batch. squared : bool or float or imgaug.parameters.StochasticParameter, optional Whether to generate only squared areas cutout areas or allow rectangular ones. If this evaluates to a true-like value, the first value from `size` will be converted to absolute pixels and used for both axes. If this value is a float ``p``, then for ``p`` percent of all areas to be filled `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). fill_mode : str or list of str or imgaug.parameters.StochasticParameter, optional Mode to use in order to fill areas. Corresponds to ``mode`` parameter in some other augmenters. Valid strings for the mode are: * ``contant``: Fill each area with a single value. * ``gaussian``: Fill each area with gaussian noise. Valid datatypes are: * If ``str``: Exactly that mode will alaways be used. * If ``list``: A random value will be sampled from that ``list`` per area. * If ``StochasticParameter``: That parameter will be used to sample ``(N,)`` values per batch, where ``N`` is the total number of areas to fill within the whole batch. cval : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional The value to use (i.e. the color) to fill areas if `fill_mode` is ```constant``. * If ``number``: Exactly that value is used for all areas and channels. * If ``tuple`` ``(a, b)``: A value from the interval ``[a, b]`` will be sampled per area (and channel if ``per_channel=True``). * If ``list``: A random value will be sampled from that ``list`` per area (and channel if ``per_channel=True``). * If ``StochasticParameter``: That parameter will be used to sample ``(N, Cmax)`` values per batch, where ``N`` is the total number of areas to fill within the whole batch and ``Cmax`` is the maximum number of channels in any image (usually ``3``). If ``per_channel=False``, only the first value of the second axis is used. fill_per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to fill each area in a channelwise fashion (``True``) or not (``False``). The behaviour per fill mode is: * ``constant``: Whether to fill all channels with the same value (i.e, grayscale) or different values (i.e. usually RGB color). * ``gaussian``: Whether to sample once from a gaussian and use the values for all channels (i.e. grayscale) or to sample channelwise (i.e. RGB colors) If this value is a float ``p``, then for ``p`` percent of all areas to be filled `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. deterministic : bool, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.bit_generator.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Cutout(nb_iterations=2) Fill per image two random areas, by default with grayish pixels. >>> aug = iaa.Cutout(nb_iterations=(1, 5), size=0.2, squared=False) Fill per image between one and five areas, each having ``20%`` of the corresponding size of the height and width (for non-square images this results in non-square areas to be filled). >>> aug = iaa.Cutout(fill_mode="constant", cval=255) Fill all areas with white pixels. >>> aug = iaa.Cutout(fill_mode="constant", cval=(0, 255), >>> fill_per_channel=0.5) Fill ``50%`` of all areas with a random intensity value between ``0`` and ``256``. Fill the other ``50%`` of all areas with random colors. >>> aug = iaa.Cutout(fill_mode="gaussian", fill_per_channel=True) Fill areas with gaussian channelwise noise (i.e. usually RGB). runiform皙?TrrFNrc sddlm} ddlm} tt|j|| | | dtj|dddddd |_ | ||_ tj |d d ddd |_t |d |_ |||_| ||_t |d|_dS)Nr)_handle_position_parameter)_handle_cval_argrr(rTF)rKrrZ allow_floatsr')rgqh?rr-r})r'r3Z geometricr4rr0rrZhandle_discrete_paramr(positionrrr-_handle_fill_mode_paramr{r|r})rr(r5r'r-r{r|r}r~r-rrr3r4rr2r3r s*      zCutout.__init__cCsnt|r|tvsJdttt|ft|St|tj r$|St |s2Jdt |j t |S)Nz.Expected 'fill_mode' to be one of: %s. Got %s.zYExpected 'fill_mode' to be a string, StochasticParameter or list of strings. Got type %s.)r;Z is_stringrrrrrZ DeterministicrStochasticParameterrtyperChoice)clsr{r2r2r3r6 s      zCutout._handle_fill_mode_paramcCs$|jdur|S||j|}|jd}|jd}|j|}|j|} |j|} |j|} d} tt|j|j} | D]V\}\}}| }||}|j dd\}}| |||||| |||| |||| ||||j |||j |||j |||j||| |j|<| |7} q9|S)Nrr)r _draw_samplesr+r,r)r*rrr(r@_augment_image_by_samplesr-r{r|r})rrrrrsamplesZcutout_height_halfZcutout_width_halfZx1_relZy1_relZx2_relZy2_relZnb_iterations_sumrrr0r(startendrrr2r2r3r s:            zCutout._augment_batch_c Cs^|d}t|}t|}|jj|f|dd}tt|}t |j t rA|j dj|f|dd}|j dj|f|dd} n|j j|df|dd} | dddf}| dddf} |j j|df|dd} |j j|f|dd} |jj|f|dd} |jj||f|d d}|jj|f|d d}t||| | dddf| dddf| | ||d S) Nrrrrr:r')rrgrrr(rrr(sumrr5tupler'r-r{r|r}r&)rrrZrngsnb_rowsrr(Znb_dropped_areasr)r*posr'r-r{r|r}r2r2r3r;F sZ          zCutout._draw_samplesc  Cst|D]?\} } || } || dkr,|| || d}| | | d}||} ||} t|| || | || || || | | | d }q|S)Nrr)rwrxryrzr{r|r}r~)rrv)r:r0rwrxryrzr-r{r|r}rrZx1_iZx2_iZheight_hZx_centerr2r2r3r<t s&  z Cutout._augment_image_by_samplescCs |j|j|j|j|j|j|jgSr)r(r5r'r-r{r|r}rr2r2r3r s zCutout.get_parameters) rr1r2TrrFNNrr) rrrrr classmethodr6rr;r<rrr2r2rr3r0H s() (. r0cr) Dropoutas Set a fraction of pixels in images to zero. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.MultiplyElementwise`. Parameters ---------- p : float or tuple of float or imgaug.parameters.StochasticParameter, optional The probability of any pixel being dropped (i.e. to set it to zero). * If a float, then that value will be used for all images. A value of ``1.0`` would mean that all pixels will be dropped and ``0.0`` that no pixels will be dropped. A value of ``0.05`` corresponds to ``5`` percent of all pixels being dropped. * If a tuple ``(a, b)``, then a value ``p`` will be sampled from the interval ``[a, b]`` per image and be used as the pixel's dropout probability. * If a list, then a value will be sampled from that list per batch and used as the probability. * If a ``StochasticParameter``, then this parameter will be used to determine per pixel whether it should be *kept* (sampled value of ``>0.5``) or shouldn't be kept (sampled value of ``<=0.5``). If you instead want to provide the probability as a stochastic parameter, you can usually do ``imgaug.parameters.Binomial(1-p)`` to convert parameter `p` to a 0/1 representation. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Dropout(0.02) Drops ``2`` percent of all pixels. >>> aug = iaa.Dropout((0.0, 0.05)) Drops in each image a random fraction of all pixels, where the fraction is uniformly sampled from the interval ``[0.0, 0.05]``. >>> aug = iaa.Dropout(0.02, per_channel=True) Drops ``2`` percent of all pixels in a channelwise fashion, i.e. it is unlikely for any pixel to have all channels set to zero (black pixels). >>> aug = iaa.Dropout(0.02, per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for ``50`` percent of all images. rg?FNrcs*t|d}tt|j||||||ddS)Npr)!_handle_dropout_probability_paramrrJr)rrLrr~r-rrp_paramrr2r3r s   zDropout.__init__rKFNNrrrr2r2rr3rJ s OrJcCst|rtd|}|St|trst|dks#Jd|t|f|d|dks8Jd||d|dfd|dkrDdkrSnn d|dkrRdks`nJd||d|dfttd|dd|d}|St|rt dd |DsJd |d d |Dft d d |DsJd |d dd |Dftdt |}|St|tj r|}|St d|t|jf)NrrzPExpected `%s` to be given as a tuple containing exactly 2 values, got %d values.rzfExpected `%s` to be given as a tuple containing exactly 2 values (a, b) with a < b. Got %.4f and %.4f.rzbExpected `%s` given as tuple to only contain values in the interval [0.0, 1.0], got %.4f and %.4f.cSg|]}t|qSr2)r;r<.0vr2r2r3  z5_handle_dropout_probability_param..zAExpected iterable parameter '%s' to only contain numbers, got %s.cSsg|]}t|qSr2)r8rQr2r2r3rT scSs$g|]}d|ko dknqS)rrr2rRZp_ir2r2r3rT s$ziExpected iterable parameter '%s' to only contain probabilities in the interval [0.0, 1.0], got values %s.rcSsg|]}d|fqS)z%.4fr2rVr2r2r3rT rUzeExpected `%s` to be float or int or tuple (, ) or StochasticParameter, got type '%s'.)r;r<rr"rrFrgZUniformrallrr9r7 Exceptionr8r)rLr-rNr2r2r3rM sX   8"   rMc*eZdZdZ    d fdd ZZS) CoarseDropouta Set rectangular areas within images to zero. In contrast to ``Dropout``, these areas can have larger sizes. (E.g. you might end up with three large black rectangles in an image.) Note that the current implementation leads to correlated sizes, so if e.g. there is any thin and high rectangle that is dropped, there is a high likelihood that all other dropped areas are also thin and high. This method is implemented by generating the dropout mask at a lower resolution (than the image has) and then upsampling the mask before dropping the pixels. This augmenter is similar to Cutout. Usually, cutout is defined as an operation that drops exactly one rectangle from an image, while here ``CoarseDropout`` can drop multiple rectangles (with some correlation between the sizes of these rectangles). **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.MultiplyElementwise`. Parameters ---------- p : float or tuple of float or imgaug.parameters.StochasticParameter, optional The probability of any pixel being dropped (i.e. set to zero) in the lower-resolution dropout mask. * If a float, then that value will be used for all pixels. A value of ``1.0`` would mean, that all pixels will be dropped. A value of ``0.0`` would lead to no pixels being dropped. * If a tuple ``(a, b)``, then a value ``p`` will be sampled from the interval ``[a, b]`` per image and be used as the dropout probability. * If a list, then a value will be sampled from that list per batch and used as the probability. * If a ``StochasticParameter``, then this parameter will be used to determine per pixel whether it should be *kept* (sampled value of ``>0.5``) or shouldn't be kept (sampled value of ``<=0.5``). If you instead want to provide the probability as a stochastic parameter, you can usually do ``imgaug.parameters.Binomial(1-p)`` to convert parameter `p` to a 0/1 representation. size_px : None or int or tuple of int or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the dropout mask in absolute pixel dimensions. Note that this means that *lower* values of this parameter lead to *larger* areas being dropped (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_percent` must be set. * If an integer, then that size will always be used for both height and width. E.g. a value of ``3`` would lead to a ``3x3`` mask, which is then upsampled to ``HxW``, where ``H`` is the image size and ``W`` the image width. * If a tuple ``(a, b)``, then two values ``M``, ``N`` will be sampled from the discrete interval ``[a..b]``. The dropout mask will then be generated at size ``MxN`` and upsampled to ``HxW``. * If a ``StochasticParameter``, then this parameter will be used to determine the sizes. It is expected to be discrete. size_percent : None or float or tuple of float or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the dropout mask *in percent* of the input image. Note that this means that *lower* values of this parameter lead to *larger* areas being dropped (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_px` must be set. * If a float, then that value will always be used as the percentage of the height and width (relative to the original size). E.g. for value ``p``, the mask will be sampled from ``(p*H)x(p*W)`` and later upsampled to ``HxW``. * If a tuple ``(a, b)``, then two values ``m``, ``n`` will be sampled from the interval ``(a, b)`` and used as the size fractions, i.e the mask size will be ``(m*H)x(n*W)``. * If a ``StochasticParameter``, then this parameter will be used to sample the percentage values. It is expected to be continuous. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). min_size : int, optional Minimum height and width of the low resolution mask. If `size_percent` or `size_px` leads to a lower value than this, `min_size` will be used instead. This should never have a value of less than ``2``, otherwise one may end up with a ``1x1`` low resolution mask, leading easily to the whole image being dropped. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.CoarseDropout(0.02, size_percent=0.5) Drops ``2`` percent of all pixels on a lower-resolution image that has ``50`` percent of the original image's size, leading to dropped areas that have roughly ``2x2`` pixels size. >>> aug = iaa.CoarseDropout((0.0, 0.05), size_percent=(0.05, 0.5)) Generates a dropout mask at ``5`` to ``50`` percent of each input image's size. In that mask, ``0`` to ``5`` percent of all pixels are marked as being dropped. The mask is afterwards projected to the input image's size to apply the actual dropout operation. >>> aug = iaa.CoarseDropout((0.0, 0.05), size_px=(2, 16)) Same as the previous example, but the lower resolution image has ``2`` to ``16`` pixels size. On images of e.g. ``224x224` pixels in size this would lead to fairly large areas being dropped (height/width of ``224/2`` to ``224/16``). >>> aug = iaa.CoarseDropout(0.02, size_percent=0.5, per_channel=True) Drops ``2`` percent of all pixels at ``50`` percent resolution (``2x2`` sizes) in a channel-wise fashion, i.e. it is unlikely for any pixel to have all channels set to zero (black pixels). >>> aug = iaa.CoarseDropout(0.02, size_percent=0.5, per_channel=0.5) Same as the previous example, but the `per_channel` feature is only active for ``50`` percent of all images. g{Gz?皙?NFr:rc snt|d} |durtj| ||d} n|durtj| ||d} ntj| d|d} tt|j| ||||| ddS)NrLr$size_pxmin_sizer$ size_percentr_r:r@r)rMrr#rrZr) rrLr^rarr_r~r-rrrNrr2r3r s*   zCoarseDropout.__init__ r[NNFr:NNrrrr2r2rr3rZ srZcs@eZdZdZ   dfdd Zdd Zd d Zd d ZZS) Dropout2da4Drop random channels from images. For image data, dropped channels will be filled with zeros. .. note:: This augmenter may also set the arrays of heatmaps and segmentation maps to zero and remove all coordinate-based data (e.g. it removes all bounding boxes on images that were filled with zeros). It does so if and only if *all* channels of an image are dropped. If ``nb_keep_channels >= 1`` then that never happens. Added in 0.4.0. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: yes; tested * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: yes; tested * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: yes; tested * ``float128``: yes; tested * ``bool``: yes; tested Parameters ---------- p : float or tuple of float or imgaug.parameters.StochasticParameter, optional The probability of any channel to be dropped (i.e. set to zero). * If a ``float``, then that value will be used for all channels. A value of ``1.0`` would mean, that all channels will be dropped. A value of ``0.0`` would lead to no channels being dropped. * If a tuple ``(a, b)``, then a value ``p`` will be sampled from the interval ``[a, b)`` per batch and be used as the dropout probability. * If a list, then a value will be sampled from that list per batch and used as the probability. * If a ``StochasticParameter``, then this parameter will be used to determine per channel whether it should be *kept* (sampled value of ``>=0.5``) or shouldn't be kept (sampled value of ``<0.5``). If you instead want to provide the probability as a stochastic parameter, you can usually do ``imgaug.parameters.Binomial(1-p)`` to convert parameter `p` to a 0/1 representation. nb_keep_channels : int Minimum number of channels to keep unaltered in all images. E.g. a value of ``1`` means that at least one channel in every image will not be dropped, even if ``p=1.0``. Set to ``0`` to allow dropping all channels. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Dropout2d(p=0.5) Create a dropout augmenter that drops on average half of all image channels. Dropped channels will be filled with zeros. At least one channel is kept unaltered in each image (default setting). >>> import imgaug.augmenters as iaa >>> aug = iaa.Dropout2d(p=0.5, nb_keep_channels=0) Create a dropout augmenter that drops on average half of all image channels *and* may drop *all* channels in an image (i.e. images may contain nothing but zeros). r\rNrcsjtt|j||||dt|d|_t|d|_d|_d|_d|_ d|_ d|_ d|_ d|_ d|_d|_dS)NrrLrTr)rrdrrMrLrmnb_keep_channels _drop_images_drop_heatmaps_drop_segmentation_maps_drop_keypoints_drop_bounding_boxes_drop_polygons_drop_line_strings_heatmaps_cval_segmentation_maps_cval)rrLrer~r-rrrr2r3r& s    zDropout2d.__init__cCs|||\}}|jdur#t|j|D]\}}d|dddd|f<qt|dkr+|S|jdurC|jrC|j} |D] } | |j| jd<q8|jdur[|j r[|j } |D] } | |j| j d<qPdD]"} t |d| f} t || } | dur| r|D] } t | | | gqtq]|SNr.)Z keypointsZbounding_boxesZpolygonsZ line_stringsz_drop_%s)r;rrrgheatmapsrgrmarr_0to1segmentation_mapsrhrnrrsetattr)rrrrrZimagewise_drop_channel_idsall_dropped_idsr0drop_idsr|drop_idx attr_namedo_drop attr_valuer2r2r3r; s4     zDropout2d._augment_batch_cCs|}dd|D}tjdd|Dtjd}|jjtt|f|d}g}g}d}t|D]D\} } |||| } t | dkd} t | |j d} t | | kr^| | | d| } || t | | k}|rp|| || 7}q0||fS)NcSs.g|]}t|dkr |ntt|dgqS)rr)rgrFrrRr@r2r2r3rTd s  z+Dropout2d._draw_samples..cSsg|]}|dqS)rr2rzr2r2r3rTh sr9rrr)Zget_rowwise_shapesr(rUrrLrrrErnonzerormrergshuffleappend)rrrZshapesZimagewise_channelsZ p_samplesZimagewise_channels_to_droprtZ channel_idxrrJZ p_samples_iruZ nb_dropableZ all_droppedr2r2r3r;` s8         zDropout2d._draw_samplescCrr)rLrerr2r2r3r rzDropout2d.get_parameters)r\rNNrr) rrrrrrr;rrr2r2rr3rd s\%,rdcsLeZdZdZ   dfdd ZddZd d Zed d Zd dZ Z S) TotalDropouta Drop all channels of a defined fraction of all images. For image data, all components of dropped images will be filled with zeros. .. note:: This augmenter also sets the arrays of heatmaps and segmentation maps to zero and removes all coordinate-based data (e.g. it removes all bounding boxes on images that were filled with zeros). Added in 0.4.0. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: yes; tested * ``uint32``: yes; tested * ``uint64``: yes; tested * ``int8``: yes; tested * ``int16``: yes; tested * ``int32``: yes; tested * ``int64``: yes; tested * ``float16``: yes; tested * ``float32``: yes; tested * ``float64``: yes; tested * ``float128``: yes; tested * ``bool``: yes; tested Parameters ---------- p : float or tuple of float or imgaug.parameters.StochasticParameter, optional The probability of an image to be filled with zeros. * If ``float``: The value will be used for all images. A value of ``1.0`` would mean that all images will be set to zero. A value of ``0.0`` would lead to no images being set to zero. * If ``tuple`` ``(a, b)``: A value ``p`` will be sampled from the interval ``[a, b)`` per batch and be used as the dropout probability. * If a list, then a value will be sampled from that list per batch and used as the probability. * If ``StochasticParameter``: The parameter will be used to determine per image whether it should be *kept* (sampled value of ``>=0.5``) or shouldn't be kept (sampled value of ``<0.5``). If you instead want to provide the probability as a stochastic parameter, you can usually do ``imgaug.parameters.Binomial(1-p)`` to convert parameter `p` to a 0/1 representation. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.TotalDropout(1.0) Create an augmenter that sets *all* components of all images to zero. >>> aug = iaa.TotalDropout(0.5) Create an augmenter that sets *all* components of ``50%`` of all images to zero. rNrcs^tt|j||||dt|d|_d|_d|_d|_d|_d|_ d|_ d|_ d|_ d|_ dS)NrrLTrr)rr~rrMrLrfrgrhrirjrkrlrmrnrrLr~r-rrrr2r3r s   zTotalDropout.__init__c Cs2|||}d}|jdur0|jr0t|jrd|j|df<n|||}|D] }d|j|d<q&|jdurN|jrN|||}|j}|D] }||j|j d<qC|j durl|j rl|||}|j }|D] }||j |j d<qadD](} t|d| f} t|| } | dur| r|||}|D] }t| || gqqn|Sro)r;rrfr;r>_generate_drop_ids_oncerprgrmrqrrrhrnrrrs) rrrrr drop_maskrurvr|rwrxryr2r2r3r s6        zTotalDropout._augment_batch_cCs |jj|jf|d}|dk}|S)Nrr)rLrrG)rrrrLrr2r2r3r; szTotalDropout._draw_samplescCs|dur t|d}|Sr)r(r{)r:rrur2r2r3r" sz$TotalDropout._generate_drop_ids_oncecC|jgSr)rLrr2r2r3r) zTotalDropout.get_parameters)rNNrr) rrrrrrr;rIrrrr2r2rr3r~ sP& r~c8eZdZdZ   d fdd ZddZd d ZZS) ReplaceElementwisea+ Replace pixels in an image with new values. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.replace_elementwise_`. Parameters ---------- mask : float or tuple of float or list of float or imgaug.parameters.StochasticParameter Mask that indicates the pixels that are supposed to be replaced. The mask will be binarized using a threshold of ``0.5``. A value of ``1`` then indicates a pixel that is supposed to be replaced. * If this is a float, then that value will be used as the probability of being a ``1`` in the mask (sampled per image and pixel) and hence being replaced. * If a tuple ``(a, b)``, then the probability will be uniformly sampled per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image and pixel. * If a ``StochasticParameter``, then this parameter will be used to sample a mask per image. replacement : number or tuple of number or list of number or imgaug.parameters.StochasticParameter The replacement to use at all locations that are marked as ``1`` in the mask. * If this is a number, then that value will always be used as the replacement. * If a tuple ``(a, b)``, then the replacement will be sampled uniformly per image and pixel from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image and pixel. * If a ``StochasticParameter``, then this parameter will be used sample replacement values per image and pixel. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = ReplaceElementwise(0.05, [0, 255]) Replaces ``5`` percent of all pixels in each image by either ``0`` or ``255``. >>> import imgaug.augmenters as iaa >>> aug = ReplaceElementwise(0.1, [0, 255], per_channel=0.5) For ``50%`` of all images, replace ``10%`` of all pixels with either the value ``0`` or the value ``255`` (same as in the previous example). For the other ``50%`` of all images, replace *channelwise* ``10%`` of all pixels with either the value ``0`` or the value ``255``. So, it will be very rare for each pixel to have all channels replaced by ``255`` or ``0``. >>> import imgaug.augmenters as iaa >>> import imgaug.parameters as iap >>> aug = ReplaceElementwise(0.1, iap.Normal(128, 0.4*128), per_channel=0.5) Replace ``10%`` of all pixels by gaussian noise centered around ``128``. Both the replacement mask and the gaussian noise are sampled channelwise for ``50%`` of all images. >>> import imgaug.augmenters as iaa >>> import imgaug.parameters as iap >>> aug = ReplaceElementwise( >>> iap.FromLowerResolution(iap.Binomial(0.1), size_px=8), >>> iap.Normal(128, 0.4*128), >>> per_channel=0.5) Replace ``10%`` of all pixels by gaussian noise centered around ``128``. Sample the replacement mask at a lower resolution (``8x8`` pixels) and upscale it to the image size, resulting in coarse areas being replaced by gaussian noise. FNrcsLtt|j||||dtj|dddd|_t|d|_t|d|_dS)NrrTrr replacementr) rrrrrrrrr)rrrrr~r-rrrr2r3r s  zReplaceElementwise.__init__c Cs2|jdur|S|j}t|}|dd|}|jj|f|dd}tt|||ddd|ddd} | D]_\} \} } } }| j\}}}||| dkrM|ndf}|jj|| d}| dkr| jd}|j jt t |dddddff|d}t ||}n|j jt t |f|d}t| |||j| <q7|S)Nrrrrrr5)rrgrrrrrr@rrrr(rErepeatr)rrrrrrrrrrrr0Z per_channel_iZrs_maskZrs_replacementrrrJZsampling_shapeZ mask_samplesrr2r2r3r sD  &     z"ReplaceElementwise._augment_batch_cCs|j|j|jgSr)rrrrr2r2r3r sz!ReplaceElementwise.get_parameters)FNNrrrr2r2rr3r. si,rcr) SaltAndPeppera: Replace pixels in images with salt/pepper noise (white/black-ish colors). **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of replacing a pixel to salt/pepper noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a image-sized mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will be replaced with salt and pepper noise. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.SaltAndPepper(0.05) Replace ``5%`` of all pixels with salt and pepper noise. >>> import imgaug.augmenters as iaa >>> aug = iaa.SaltAndPepper(0.05, per_channel=True) Replace *channelwise* ``5%`` of all pixels with salt and pepper noise. rgQ?FNrc s.tt|j|tddd|||||ddS)Nrr7rrrr~r-rr)rrrrBeta)rrLrr~r-rrrr2r3rs  zSaltAndPepper.__init__rFNNrrrr2r2rr3r s @rcs(eZdZdZ   dfdd ZZS) ImpulseNoisea Add impulse noise to images. This is identical to ``SaltAndPepper``, except that `per_channel` is always set to ``True``. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.SaltAndPepper`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of replacing a pixel to impulse noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a image-sized mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will be replaced with impulse noise noise. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.ImpulseNoise(0.1) Replace ``10%`` of all pixels with impulse noise. rNrcs tt|j|d||||ddS)NT)rLrr~r-rr)rrrrrr2r3rXs  zImpulseNoise.__init__)rNNrrrr2r2rr3r$s 3rcrY) CoarseSaltAndPeppera Replace rectangular areas in images with white/black-ish pixel noise. This adds salt and pepper noise (noisy white-ish and black-ish pixels) to rectangular areas within the image. Note that this means that within these rectangular areas the color varies instead of each rectangle having only one color. See also the similar ``CoarseDropout``. TODO replace dtype support with uint8 only, because replacement is geared towards that value range **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of changing a pixel to salt/pepper noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a lower-resolution mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will denote a spatial location that is to be replaced by salt and pepper noise. size_px : int or tuple of int or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask in absolute pixel dimensions. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_percent` must be set. * If an integer, then that size will always be used for both height and width. E.g. a value of ``3`` would lead to a ``3x3`` mask, which is then upsampled to ``HxW``, where ``H`` is the image size and ``W`` the image width. * If a tuple ``(a, b)``, then two values ``M``, ``N`` will be sampled from the discrete interval ``[a..b]``. The mask will then be generated at size ``MxN`` and upsampled to ``HxW``. * If a ``StochasticParameter``, then this parameter will be used to determine the sizes. It is expected to be discrete. size_percent : float or tuple of float or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask *in percent* of the input image. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_px` must be set. * If a float, then that value will always be used as the percentage of the height and width (relative to the original size). E.g. for value ``p``, the mask will be sampled from ``(p*H)x(p*W)`` and later upsampled to ``HxW``. * If a tuple ``(a, b)``, then two values ``m``, ``n`` will be sampled from the interval ``(a, b)`` and used as the size fractions, i.e the mask size will be ``(m*H)x(n*W)``. * If a ``StochasticParameter``, then this parameter will be used to sample the percentage values. It is expected to be continuous. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). min_size : int, optional Minimum height and width of the low resolution mask. If `size_percent` or `size_px` leads to a lower value than this, `min_size` will be used instead. This should never have a value of less than ``2``, otherwise one may end up with a ``1x1`` low resolution mask, leading easily to the whole image being replaced. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.CoarseSaltAndPepper(0.05, size_percent=(0.01, 0.1)) Marks ``5%`` of all pixels in a mask to be replaced by salt/pepper noise. The mask has ``1%`` to ``10%`` the size of the input image. The mask is then upscaled to the input image size, leading to large rectangular areas being marked as to be replaced. These areas are then replaced in the input image by salt/pepper noise. >>> aug = iaa.CoarseSaltAndPepper(0.05, size_px=(4, 16)) Same as in the previous example, but the replacement mask before upscaling has a size between ``4x4`` and ``16x16`` pixels (the axis sizes are sampled independently, i.e. the mask may be rectangular). >>> aug = iaa.CoarseSaltAndPepper( >>> 0.05, size_percent=(0.01, 0.1), per_channel=True) Same as in the first example, but mask and replacement are each sampled independently per image channel. r[NFr:rc stj|dddd} |durtj| ||d} n|dur#tj| ||d} ntj| d|d} tddd} tt|j| | ||||| d dS) NrLTrr]r`rbrr7r)rrr#rrrr) rrLr^rarr_r~r-rrrmask_lowrrr2r3rs,  zCoarseSaltAndPepper.__init__rcrr2r2rr3rbsrcr) Salta Replace pixels in images with salt noise, i.e. white-ish pixels. This augmenter is similar to ``SaltAndPepper``, but adds no pepper noise to images. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of replacing a pixel with salt noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a image-sized mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will be replaced with salt noise. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Salt(0.05) Replace ``5%`` of all pixels with salt noise (white-ish colors). rFNrc Jtjtddddddd}|d}tt|j|||||||ddS)NrTrZpositiverr7r)r ForceSignrrrr rrLrr~r-rr replacement01rrr2r3r>s  z Salt.__init__rrr2r2rr3rs >rcrY) CoarseSaltav Replace rectangular areas in images with white-ish pixel noise. See also the similar ``CoarseSaltAndPepper``. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of changing a pixel to salt noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a lower-resolution mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will denote a spatial location that is to be replaced by salt noise. size_px : int or tuple of int or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask in absolute pixel dimensions. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_percent` must be set. * If an integer, then that size will always be used for both height and width. E.g. a value of ``3`` would lead to a ``3x3`` mask, which is then upsampled to ``HxW``, where ``H`` is the image size and ``W`` the image width. * If a tuple ``(a, b)``, then two values ``M``, ``N`` will be sampled from the discrete interval ``[a..b]``. The mask will then be generated at size ``MxN`` and upsampled to ``HxW``. * If a ``StochasticParameter``, then this parameter will be used to determine the sizes. It is expected to be discrete. size_percent : float or tuple of float or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask *in percent* of the input image. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_px` must be set. * If a float, then that value will always be used as the percentage of the height and width (relative to the original size). E.g. for value ``p``, the mask will be sampled from ``(p*H)x(p*W)`` and later upsampled to ``HxW``. * If a tuple ``(a, b)``, then two values ``m``, ``n`` will be sampled from the interval ``(a, b)`` and used as the size fractions, i.e the mask size will be ``(m*H)x(n*W)``. * If a ``StochasticParameter``, then this parameter will be used to sample the percentage values. It is expected to be continuous. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). min_size : int, optional Minimum height and width of the low resolution mask. If `size_percent` or `size_px` leads to a lower value than this, `min_size` will be used instead. This should never have a value of less than ``2``, otherwise one may end up with a ``1x1`` low resolution mask, leading easily to the whole image being replaced. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.CoarseSalt(0.05, size_percent=(0.01, 0.1)) Mark ``5%`` of all pixels in a mask to be replaced by salt noise. The mask has ``1%`` to ``10%`` the size of the input image. The mask is then upscaled to the input image size, leading to large rectangular areas being marked as to be replaced. These areas are then replaced in the input image by salt noise. r[NFr:rc  stj|dddd} |durtj| ||d} n|dur#tj| ||d} ntj| d|d} tjtdddddd d} | d } tt|j| | ||||| d dS) NrLTrr]r`rbrrrr7r)rrr#rrrrrrrLr^rarr_r~r-rrrrrrrr2r3r:  zCoarseSalt.__init__rcrr2r2rr3rQsmrcr) Peppera@ Replace pixels in images with pepper noise, i.e. black-ish pixels. This augmenter is similar to ``SaltAndPepper``, but adds no salt noise to images. This augmenter is similar to ``Dropout``, but slower and the black pixels are not uniformly black. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of replacing a pixel with pepper noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a image-sized mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will be replaced with pepper noise. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Pepper(0.05) Replace ``5%`` of all pixels with pepper noise (black-ish colors). rKFNrc r)NrFrrr7r)rrrrrrrrr2r3r!s  zPepper.__init__rOrr2r2rr3rs ArcrY) CoarsePepperaT Replace rectangular areas in images with black-ish pixel noise. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.ReplaceElementwise`. Parameters ---------- p : float or tuple of float or list of float or imgaug.parameters.StochasticParameter, optional Probability of changing a pixel to pepper noise. * If a float, then that value will always be used as the probability. * If a tuple ``(a, b)``, then a probability will be sampled uniformly per image from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image. * If a ``StochasticParameter``, then a lower-resolution mask will be sampled from that parameter per image. Any value ``>0.5`` in that mask will denote a spatial location that is to be replaced by pepper noise. size_px : int or tuple of int or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask in absolute pixel dimensions. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_percent` must be set. * If an integer, then that size will always be used for both height and width. E.g. a value of ``3`` would lead to a ``3x3`` mask, which is then upsampled to ``HxW``, where ``H`` is the image size and ``W`` the image width. * If a tuple ``(a, b)``, then two values ``M``, ``N`` will be sampled from the discrete interval ``[a..b]``. The mask will then be generated at size ``MxN`` and upsampled to ``HxW``. * If a ``StochasticParameter``, then this parameter will be used to determine the sizes. It is expected to be discrete. size_percent : float or tuple of float or imgaug.parameters.StochasticParameter, optional The size of the lower resolution image from which to sample the replacement mask *in percent* of the input image. Note that this means that *lower* values of this parameter lead to *larger* areas being replaced (as any pixel in the lower resolution image will correspond to a larger area at the original resolution). * If ``None`` then `size_px` must be set. * If a float, then that value will always be used as the percentage of the height and width (relative to the original size). E.g. for value ``p``, the mask will be sampled from ``(p*H)x(p*W)`` and later upsampled to ``HxW``. * If a tuple ``(a, b)``, then two values ``m``, ``n`` will be sampled from the interval ``(a, b)`` and used as the size fractions, i.e the mask size will be ``(m*H)x(n*W)``. * If a ``StochasticParameter``, then this parameter will be used to sample the percentage values. It is expected to be continuous. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). min_size : int, optional Minimum size of the low resolution mask, both width and height. If `size_percent` or `size_px` leads to a lower value than this, `min_size` will be used instead. This should never have a value of less than 2, otherwise one may end up with a ``1x1`` low resolution mask, leading easily to the whole image being replaced. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.CoarsePepper(0.05, size_percent=(0.01, 0.1)) Mark ``5%`` of all pixels in a mask to be replaced by pepper noise. The mask has ``1%`` to ``10%`` the size of the input image. The mask is then upscaled to the input image size, leading to large rectangular areas being marked as to be replaced. These areas are then replaced in the input image by pepper noise. r[NFr:rc  stj|dddd} |durtj| ||d} n|dur#tj| ||d} ntj| d|d} tjtddddd d d} | d } tt|j| | ||||| d dS) NrLTrr]r`rbrFrrr7r)rrr#rrrrrrrr2r3rrzCoarsePepper.__init__rcrr2r2rr3r3skrc speZdZdZddejejejejej ej ej ej fDZ    dfd d Zd d Zd dZddZZS)Inverta Invert all values in images, e.g. turn ``5`` into ``255-5=250``. For the standard value range of 0-255 it converts ``0`` to ``255``, ``255`` to ``0`` and ``10`` to ``(255-10)=245``. Let ``M`` be the maximum value possible, ``m`` the minimum value possible, ``v`` a value. Then the distance of ``v`` to ``m`` is ``d=abs(v-m)`` and the new value is given by ``v'=M-d``. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.invert_`. Parameters ---------- p : float or imgaug.parameters.StochasticParameter, optional The probability of an image to be inverted. * If a float, then that probability will be used for all images, i.e. `p` percent of all images will be inverted. * If a ``StochasticParameter``, then that parameter will be queried per image and is expected to return values in the interval ``[0.0, 1.0]``, where values ``>0.5`` mean that the image is supposed to be inverted. Recommended to be some form of ``imgaug.parameters.Binomial``. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). min_value : None or number, optional Minimum of the value range of input images, e.g. ``0`` for ``uint8`` images. If set to ``None``, the value will be automatically derived from the image's dtype. max_value : None or number, optional Maximum of the value range of input images, e.g. ``255`` for ``uint8`` images. If set to ``None``, the value will be automatically derived from the image's dtype. threshold : None or number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional A threshold to use in order to invert only numbers above or below the threshold. If ``None`` no thresholding will be used. * If ``None``: No thresholding will be used. * If ``number``: The value will be used for all images. * If ``tuple`` ``(a, b)``: A value will be uniformly sampled per image from the interval ``[a, b)``. * If ``list``: A random value will be picked from the list per image. * If ``StochasticParameter``: Per batch of size ``N``, the parameter will be queried once to return ``(N,)`` samples. invert_above_threshold : bool or float or imgaug.parameters.StochasticParameter, optional If ``True``, only values ``>=threshold`` will be inverted. Otherwise, only values ``0.5``. If `threshold` is ``None`` this parameter has no effect. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Invert(0.1) Inverts the colors in ``10`` percent of all images. >>> aug = iaa.Invert(0.1, per_channel=True) Inverts the colors in ``10`` percent of all image channels. This may or may not lead to multiple channels in an image being inverted. >>> aug = iaa.Invert(0.1, per_channel=0.5) Identical to the previous example, but the `per_channel` feature is only active for 50 percent of all images. cCrPr2)r(r,)rRdtr2r2r3rT2s zInvert.rFNrrc sxtt|j||| | dt|d|_t|d|_||_||_|dur(d|_ n tj |ddddd|_ t|d|_ dS)NrrLrrTrr) rrrrrrLrrrrrr rrLrrrrrr~r-rrrr2r3r:s&  zInvert.__init__c Cs|jdur|S|||}t|jD][\}}d|jvrq|j||j||j||j|d}|j|rS|jd} |j |d| f} t |d| ffi||d| f<q|j |dfrmt |fi||ddddddf<q|S)Nrrr.) rr;rr@rrrrrrLr) rrrrrr=rr0kwargsrJrr2r2r3rSs&     "&zInvert._augment_batch_c Cs|j}t|j}|jj||f|d}|dk}|jj|f|d}|dk}|jg|}|jg|}|j dur;dg|} n |j j|f|d} |j j|f|d} | dk} t ||||| | dS)NrrrLrrrrr) rGrrrrLrrrrrr_InvertSamples) rrrrrJrLrrrrrr2r2r3r;os:       zInvert._draw_samplescCs|j|j|j|j|j|jgSrrrr2r2r3rszInvert.get_parameters) rFNNNrNNrr)rrrrr(rrrrrrrrZALLOW_DTYPES_CUSTOM_MINMAXrrr;rrr2r2rr3rs r  rc@r%)rcCs(||_||_||_||_||_||_dSrr)rrLrrrrrr2r2r3rs  z_InvertSamples.__init__Nr.r2r2r2r3rr/rcs*eZdZdZ    d fdd ZZS) SolarizeaInvert all pixel values above a threshold. This is the same as :class:`Invert`, but sets a default threshold around ``128`` (+/- 64, decided per image) and default `invert_above_threshold` to ``True`` (i.e. only values above the threshold will be inverted). See :class:`Invert` for more details. Added in 0.4.0. **Supported dtypes**: See :class:`~imgaug.augmenters.arithmetic.Invert`. Parameters ---------- p : float or imgaug.parameters.StochasticParameter See :class:`Invert`. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional See :class:`Invert`. min_value : None or number, optional See :class:`Invert`. max_value : None or number, optional See :class:`Invert`. threshold : None or number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional See :class:`Invert`. invert_above_threshold : bool or float or imgaug.parameters.StochasticParameter, optional See :class:`Invert`. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.Solarize(0.5, threshold=(32, 128)) Invert the colors in ``50`` percent of all images for pixels with a value between ``32`` and ``128`` or more. The threshold is sampled once per image. The thresholding operation happens per channel. rFN@Trc s(tt|j||||||||| | d dS)N) rLrrrrrr~r-rr)rrrrrr2r3rs  zSolarize.__init__) rFNNrTNNrrrr2r2rr3rs=rzimgaug.contrast.LinearContrastrrcCs"ddlm}|j||||||dS)aZ Change the contrast of images. dtype support: See ``imgaug.augmenters.contrast.LinearContrast``. Deprecated since 0.3.0. Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Strength of the contrast normalization. Higher values than 1.0 lead to higher contrast, lower values decrease the contrast. * If a number, then that value will be used for all images. * If a tuple ``(a, b)``, then a value will be sampled per image uniformly from the interval ``[a, b]`` and be used as the alpha value. * If a list, then a random value will be picked per image from that list. * If a ``StochasticParameter``, then this parameter will be used to sample the alpha value per image. per_channel : bool or float or imgaug.parameters.StochasticParameter, optional Whether to use (imagewise) the same sample(s) for all channels (``False``) or to sample value(s) for each channel (``True``). Setting this to ``True`` will therefore lead to different transformations per image *and* channel, otherwise only per image. If this value is a float ``p``, then for ``p`` percent of all images `per_channel` will be treated as ``True``. If it is a ``StochasticParameter`` it is expected to produce samples with values between ``0.0`` and ``1.0``, where values ``>0.5`` will lead to per-channel behaviour (i.e. same as ``True``). seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> iaa.ContrastNormalization((0.5, 1.5)) Decreases oder improves contrast per image by a random factor between ``0.5`` and ``1.5``. The factor ``0.5`` means that any difference from the center value (i.e. 128) will be halved, leading to less contrast. >>> iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5) Same as before, but for 50 percent of all images the normalization is done independently per channel (i.e. factors can vary per channel for the same image). In the other 50 percent of all images, the factor is the same for all channels. r)contrast)alpharr~r-rr)rZLinearContrast)rrr~r-rrZ contrast_libr2r2r3ContrastNormalizations Lrcr) JpegCompressiona Degrade the quality of images by JPEG-compressing them. During JPEG compression, high frequency components (e.g. edges) are removed. With low compression (strength) only the highest frequency components are removed, while very high compression (strength) will lead to only the lowest frequency components "surviving". This lowers the image quality. For more details, see https://en.wikipedia.org/wiki/Compression_artifact. Note that this augmenter still returns images as numpy arrays (i.e. saves the images with JPEG compression and then reloads them into arrays). It does not return the raw JPEG file content. **Supported dtypes**: See :func:`~imgaug.augmenters.arithmetic.compress_jpeg`. Parameters ---------- compression : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Degree of compression used during JPEG compression within value range ``[0, 100]``. Higher values denote stronger compression and will cause low-frequency components to disappear. Note that JPEG's compression strength is also often set as a *quality*, which is the inverse of this parameter. Common choices for the *quality* setting are around 80 to 95, depending on the image. This translates here to a *compression* parameter of around 20 to 5. * If a single number, then that value always will be used as the compression. * If a tuple ``(a, b)``, then the compression will be a value sampled uniformly from the interval ``[a, b]``. * If a list, then a random value will be sampled from that list per image and used as the compression. * If a ``StochasticParameter``, then ``N`` samples will be drawn from that parameter per ``N`` input images, each representing the compression for the ``n``-th image. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.JpegCompression(compression=(70, 99)) Remove high frequency components in images via JPEG compression with a *compression strength* between ``70`` and ``99`` (randomly and uniformly sampled per image). This corresponds to a (very low) *quality* setting of ``1`` to ``30``. rreNrcs2tt|j||||dtj|ddddd|_dS)NrrrTr)rrrrrr)rrr~r-rrrr2r3rs  zJpegCompression.__init__c Csb|jdur|S|j}t|}|jj|f|d}tt||D]\}\} } t| t| |j|<q|S)Nr)rrgrrrrrr) rrrrrrrr=rr0sampler2r2r3rs  zJpegCompression._augment_batch_cCrr)rrr2r2r3rrzJpegCompression.get_parameters)rNNrrrr2r2rr3r?sCr)rrFN)NNNT)r)rFNNrr)Or __future__rrrrrnumpyr(Zimgaugr;rrrrr r*r rrr4r.r/rbr_r`rlrirjrsrprqrrvr r rrrrrrrrrrrrrZ Augmenterrrr rrrr!objectr&r0rJrMrZrdr~rrrrrrrrrrrrrrr2r2r2r3s     =++9*K*2I. = ]B.o $   (   "e~p|oO]%3I+L>RT W K R