o &Õj=Nã@sÄdZddlmZmZmZddlmZmZddlZ ddl Z ddl Z ddl Z ddlmZddlmZddlmZd d lmZd d lmZe  e¡Gd d „d eƒƒZGdd„deƒZGdd„dejƒZdS)zû Augmenters that deal with edge detection. List of augmenters: * :class:`Canny` :class:`~imgaug.augmenters.convolutional.EdgeDetect` and :class:`~imgaug.augmenters.convolutional.DirectedEdgeDetect` are currently still in ``convolutional.py``. é)Úprint_functionÚdivisionÚabsolute_import)ÚABCMetaÚabstractmethodN)Ú_normalize_cv2_input_arr_é)Úmeta)Úblendé)Ú parameters)Údtypesc@seZdZdZedd„ƒZdS)ÚIBinaryImageColorizerz@Interface for classes that convert binary masks to color images.cCsdS)aè Convert a binary image to a colorized one. Parameters ---------- image_binary : ndarray Boolean ``(H,W)`` image. image_original : ndarray Original ``(H,W,C)`` input image. nth_image : int Index of the image in the batch. random_state : imgaug.random.RNG Random state to use. Returns ------- ndarray Colorized form of `image_binary`. N©)ÚselfÚ image_binaryÚimage_originalÚ nth_imageÚ random_staterrúX/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/imgaug/augmenters/edges.pyÚcolorize#szIBinaryImageColorizer.colorizeN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrrrrrsrc@s2eZdZdZd dd„Zdd„Zdd„Zd d „Zd S) Ú RandomColorsBinaryImageColorizera= Colorizer using two randomly sampled foreground/background colors. Parameters ---------- color_true : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional Color of the foreground, i.e. all pixels in binary images that are ``True``. This parameter will be queried once per image to generate ``(3,)`` samples denoting the color. (Note that even for grayscale images three values will be sampled and converted to grayscale according to ``0.299*R + 0.587*G + 0.114*B``. This is the same equation that is also used by OpenCV.) * If an int, exactly that value will always be used, i.e. every color will be ``(v, v, v)`` for value ``v``. * If a tuple ``(a, b)``, three random values from the range ``a <= x <= b`` will be sampled per image. * If a list, then three random values will be sampled from that list per image. * If a StochasticParameter, three values will be sampled from the parameter per image. color_false : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional Analogous to `color_true`, but denotes the color for all pixels that are ``False`` in the binary input image. ©réÿcCs4tj|dddddd|_tj|dddddd|_dS)NÚ color_truerTF©Ú value_rangeÚtuple_to_uniformÚlist_to_choiceZ allow_floatsÚ color_false)ÚiapÚhandle_discrete_paramrr#)rrr#rrrÚ__init__\s ú úz)RandomColorsBinaryImageColorizer.__init__cCs(|jjd|d}|jjd|d}||fS)N)é)r)rÚ draw_samplesr#)rrrr#rrrÚ _draw_samplesmsÿÿz.RandomColorsBinaryImageColorizer._draw_samplesc Cst|jdks Jd|jfƒ‚|jjdksJd|jjfƒ‚|jdks)Jd|jfƒ‚|jddvs:Jd |jdfƒ‚|jjd ksHJd |jjƒ‚| |¡\}}t|jddƒ}tj|jd |jd |f|jd}|d kr–d|d d|d d|d||<d|d d|d d|d||<n |||<|||<|jddkr¸t  ||dd…dd…dd…fg¡}|S)Nr zIExpected binary image to colorize to be 2-dimensional, got %d dimensions.ÚbzCExpected binary image to colorize to be boolean, got dtype kind %s.r'z?Expected original image to be 3-dimensional, got %d dimensions.éÿÿÿÿ©rr'ézDExpected original image to have 1, 3 or 4 channels. Got %d channels.Úuint8z:Expected original image to have dtype uint8, got dtype %s.rr)Údtypeg‰A`åÐ"Ó?gbX9´Èâ?gÉv¾Ÿ/½?r-) Úndimr/ÚkindÚshapeÚnamer)ÚminÚnpZzerosZdstack) rrrrrrr#Z nb_channelsZimage_colorizedrrrrtsf ÿÿÿÿ ÿÿ ÿÿÿÿþ  ÿ þÿ  ÿ þ ÿ ÿz)RandomColorsBinaryImageColorizer.colorizecCsd|j|jfS)Nz?RandomColorsBinaryImageColorizer(color_true=%s, color_false=%s))rr#©rrrrÚ__str__¡sÿÿz(RandomColorsBinaryImageColorizer.__str__N)rr)rrrrr&r)rr7rrrrr?s   -rcsNeZdZdZ      d‡fdd„ Zd d „Zd d „Zd d„Zdd„Z‡Z S)ÚCannyaŠ Apply a canny edge detector to input images. **Supported dtypes**: * ``uint8``: yes; fully tested * ``uint16``: no; not tested * ``uint32``: no; not tested * ``uint64``: no; not tested * ``int8``: no; not tested * ``int16``: no; not tested * ``int32``: no; not tested * ``int64``: no; not tested * ``float16``: no; not tested * ``float32``: no; not tested * ``float64``: no; not tested * ``float128``: no; not tested * ``bool``: no; not tested Parameters ---------- alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional Blending factor to use in alpha blending. A value close to 1.0 means that only the edge image is visible. A value close to 0.0 means that only the original image is visible. A value close to 0.5 means that the images are merged according to `0.5*image + 0.5*edge_image`. If a sample from this parameter is 0, no action will be performed for the corresponding image. * If an int or float, exactly that value will be used. * If a tuple ``(a, b)``, a random value from the range ``a <= x <= 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. hysteresis_thresholds : number or tuple of number or list of number or imgaug.parameters.StochasticParameter or tuple of tuple of number or tuple of list of number or tuple of imgaug.parameters.StochasticParameter, optional Min and max values to use in hysteresis thresholding. (This parameter seems to have not very much effect on the results.) Either a single parameter or a tuple of two parameters. If a single parameter is provided, the sampling happens once for all images with `(N,2)` samples being requested from the parameter, where each first value denotes the hysteresis minimum and each second the maximum. If a tuple of two parameters is provided, one sampling of `(N,)` values is independently performed per parameter (first parameter: hysteresis minimum, second: hysteresis maximum). * If this is a single number, both min and max value will always be exactly that value. * If this is a tuple of numbers ``(a, b)``, two random values from the range ``a <= x <= b`` will be sampled per image. * If this is a list, two random values will be sampled from that list per image. * If this is a StochasticParameter, two random values will be sampled from that parameter per image. * If this is a tuple ``(min, max)`` with ``min`` and ``max`` both *not* being numbers, they will be treated according to the rules above (i.e. may be a number, tuple, list or StochasticParameter). A single value will be sampled per image and parameter. sobel_kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional Kernel size of the sobel operator initially applied to each image. This corresponds to ``apertureSize`` in ``cv2.Canny()``. If a sample from this parameter is ``<=1``, no action will be performed for the corresponding image. The maximum for this parameter is ``7`` (inclusive). Higher values are not accepted by OpenCV. If an even value ``v`` is sampled, it is automatically changed to ``v-1``. * If this is a single integer, the kernel size always matches that value. * If this is a tuple of integers ``(a, b)``, a random discrete value will be sampled from the range ``a <= x <= b`` per image. * If this is a list, a random value will be sampled from that list per image. * If this is a StochasticParameter, a random value will be sampled from that parameter per image. colorizer : None or imgaug.augmenters.edges.IBinaryImageColorizer, optional A strategy to convert binary edge images to color images. If this is ``None``, an instance of ``RandomColorBinaryImageColorizer`` is created, which means that each edge image is converted into an ``uint8`` image, where edge and non-edge pixels each have a different color that was uniformly randomly sampled from the space of all ``uint8`` colors. 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.Canny() Create an augmenter that generates random blends between images and their canny edge representations. >>> aug = iaa.Canny(alpha=(0.0, 0.5)) Create a canny edge augmenter that generates edge images with a blending factor of max ``50%``, i.e. the original (non-edge) image is always at least partially visible. >>> aug = iaa.Canny( >>> alpha=(0.0, 0.5), >>> colorizer=iaa.RandomColorsBinaryImageColorizer( >>> color_true=255, >>> color_false=0 >>> ) >>> ) Same as in the previous example, but the edge image always uses the color white for edges and black for the background. >>> aug = iaa.Canny(alpha=(0.5, 1.0), sobel_kernel_size=[3, 7]) Create a canny edge augmenter that initially preprocesses images using a sobel filter with kernel size of either ``3x3`` or ``13x13`` and alpha-blends with result using a strength of ``50%`` (both images equally visible) to ``100%`` (only edge image visible). >>> aug = iaa.Alpha( >>> (0.0, 1.0), >>> iaa.Canny(alpha=1), >>> iaa.MedianBlur(13) >>> ) Create an augmenter that blends a canny edge image with a median-blurred version of the input image. The median blur uses a fixed kernel size of ``13x13`` pixels. ©gçð?©)é<éŒ)é éð©r'éNÚ deprecatedc sætt|ƒj||||dtj|ddddd|_t|tƒrLt|ƒdkrLt   |d¡sLt   |d¡sLtj |dd d dddd tj |dd d dddd f|_ n tj |d d dddd |_ tj |dddddd |_ |durm||_dStƒ|_dS)N)Úseedr3rÚ deterministicÚalpha)rr:T)r r!r"r rrzhysteresis_thresholds[0]rrzhysteresis_thresholds[1]Úhysteresis_thresholdsÚsobel_kernel_size)rrAF)Úsuperr8r&r$Zhandle_continuous_paramrEÚ isinstanceÚtupleÚlenÚiaZis_single_numberr%rFrGrÚ colorizer) rrErFrGrMrCr3rrD©Ú __class__rrr&@sh þþ   ÿ þúúøú ú ÿÿýzCanny.__init__c CsJt|ƒ}| d¡}|j |f|d¡}|j}t|tƒr;|d |f|d¡}|d |f|d¡}tj||gdd} n | |df|d¡} |j  |f|d¡} | dd…df| dd…dfk} t  | ¡rz| | dd…fdd…ddgf| | dd…f<t  | dk¡r…Jdƒ‚t  | d k¡rJd ƒ‚t  | d¡dk} | | d8<|| | fS) Nr-rrr r+)Zaxisr'zLSampled a sobel kernel size below 0 in Canny. Allowed value range is 0 to 7.rAzLSampled a sobel kernel size above 7 in Canny. Allowed value range is 0 to 7.) rKÚ duplicaterEr(rFrIrJr5ÚstackrGÚanyÚmod) rZ augmentablesrZ nb_imagesÚrssÚ alpha_samplesZhthreshZ min_valuesZ max_valuesÚhthresh_samplesÚ sobel_samplesÚinvalidZeven_idxrrrr)}s0   ÿ  ,ÿÿ zCanny._draw_samplescCs@|jdur|S|j}tj|dggd¢|d| t|ƒ¡}| ||d¡}|d}|d} |d} tt||| | ƒƒ} | D]a\} \} }}}| jddvsUJd | jdfƒ‚d| jdd…v}|dkr|dkr|st j t | dd…dd…dd …fƒ|d|d|d d }|dk}|j j || | || d }t || |¡|j| <q<|S)Nr.)ÚboolZuint16Zuint32Zuint64Zuint128Zuint256Zint8Zint16Zint32Zint64Zint128Zint256Zfloat32Zfloat64Zfloat96Zfloat128Zfloat256)ÚallowedZ disallowedZ augmenterr+rrr r,zeCanny edge detector can currently only handle images with channel numbers that are 1, 3 or 4. Got %d.r'T)Z threshold1Z threshold2Z apertureSizeZ L2gradient)rr)ÚimagesÚiadtZ gate_dtypesrPrKr)Ú enumerateÚzipr2Úcv2r8rrMrr Z blend_alpha)rÚbatchrÚparentsÚhooksr[rTZsamplesrUrVrWÚgenÚiÚimagerEZhthreshsZsobelZhas_zero_sized_axesZ image_cannyZimage_canny_colorrrrÚ_augment_batch_¢sP ö  ÿÿþû ÿ ÿ€zCanny._augment_batch_cCs|j|j|j|jgS)z=See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`.)rErFrGrMr6rrrÚget_parametersÖs ÿzCanny.get_parameterscCs d|j|j|j|j|j|jfS)NzhCanny(alpha=%s, hysteresis_thresholds=%s, sobel_kernel_size=%s, colorizer=%s, name=%s, deterministic=%s))rErFrGrMr3rDr6rrrr7Ûs ýúz Canny.__str__)r9r;r@NNNrBrB) rrrrr&r)rfrgr7Ú __classcell__rrrNrr8§sú=%4r8)rÚ __future__rrrÚabcrrÚnumpyr5r_ÚsixZimgaugrLZ imgaug.imgaugrÚr r r r$r r\Z add_metaclassÚobjectrrZ Augmenterr8rrrrÚs       h