9ilSSKrSSKJrJr SSKJr /SQr\R"5r \ RS\R"555 \ RSSS \\ \\RS .5 S rS rS!S jrSrSrSr\R("SS9S"SS.Sjj5rSrS#SjrS$SjrS%SjrSrS&SjrSrSrS'SjrS(SjrS)Sjr S*S jr!g)+N) dtype_range dtype_limits)utils) histogramcumulative_distribution equalize_histrescale_intensity adjust_gamma adjust_logadjust_sigmoidc#B# UHupURU4v M g7fN)__name__).0dlimitss Y/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/exposure/exposure.py rsM9LIAAJJ'9Ls)ri)ri)ri?)uint10uint12uint14boolfloatcUS:ajUnX!- n[R"[R"U5[R"U55nURU:waUR U5nX- nU$)z:Offset the array to get the lowest value at 0 if negative.r)np promote_typesmin_scalar_typedtypeastype)arr low_boundary high_boundaryoffset dyn_range offset_dtypes r _offset_arrayr'sja!0 ''   y )2+=+=l+K  99 $**\*Cl JclUS;a[SU35eUS:Xam[UR5R[R 55n[UR 5R[R 55nOUS:Xa [USS9up#[R"WWS-5nU$)z1Compute bin centers for bincount-based histogram.imager-Incorrect value for `source_range` argument: r+rF clip_negative) ValueErrorintminr rint64maxrarange)r+ source_range image_min image_max bin_centerss r_bincount_histogram_centersr:/s--HWXXw **28845  **28845  +EG ))Iy1}5K r(cUc [X5nUSUSpC[XU5n[R"UR 5U[ US5- S-S9nUS:Xa[ US5nXVSnXR4$)a Efficient histogram calculation for an image of integers. This function is significantly more efficient than np.histogram but works only on images of integers. It is based on np.bincount. Parameters ---------- image : array Input image. source_range : string 'image' determines the range from the input image. 'dtype' determines the range from the expected range of the images of that data type. Returns ------- hist : array The values of the histogram. bin_centers : array The values at the center of the bins. Nrr/) minlengthr+)r:r'rbincountravelr2r4)r+r6r9r7r8histidxs r_bincount_histogramrB<s}.1%F &q>;r?y %I 6E ;;u{{} C 1>(E2 uqy4xI r(cVUS:XaSnU$US:Xa [USS9nU$[SU35e)Nr+rFr-r,)rr0)r+r6rGs r_get_numpy_hist_rangerVsJw   !%u=  HWXXr(F)multichannel_output) channel_axisc*URn[U5S:Xa"USS:aUc[R"S5 UbUSn/n[R "UR [R5(a [X5nO[X5n [XU 5n[U5H(n [USU 4XU5upURU 5 M* [R"W 5n [R"USS9nX}4$[XX#5up}X}4$)aReturn histogram of image. Unlike `numpy.histogram`, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution. If `channel_axis` is not set, the histogram is computed on the flattened image. For color or multichannel images, set ``channel_axis`` to use a common binning for all channels. Alternatively, one may apply the function separately on each channel to obtain a histogram for each color channel with separate binning. Parameters ---------- image : array Input image. nbins : int, optional Number of bins used to calculate histogram. This value is ignored for integer arrays. source_range : string, optional 'image' (default) determines the range from the input image. 'dtype' determines the range from the expected range of the images of that data type. normalize : bool, optional If True, normalize the histogram by the sum of its values. channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. Returns ------- hist : array The values of the histogram. When ``channel_axis`` is not None, hist will be a 2D array where the first axis corresponds to channels. bin_centers : array The values at the center of the bins. See Also -------- cumulative_distribution Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.camera()) >>> np.histogram(image, bins=2) (array([ 93585, 168559]), array([0. , 0.5, 1. ])) >>> exposure.histogram(image, nbins=2) (array([ 93585, 168559]), array([0.25, 0.75])) r<zThis might be a color image. The histogram will be computed on the flattened image. You can instead apply this function to each color channel, or set channel_axis..r)axis)shapelenrwarnrrNrrOr:rVrTrange _histogramappendasarraystack)r+rQr6 normalizerXshchannelsr@binsrGchanhbcr9s rrrsn B 2w!|2 |';   b6 ==bjj 1 1.uCD/uCJ!% ;D(ODuS$Y/YOEA KKN$jjn xx1%  'u\M  r(cUR5n[R"UR[R5(a2[ U[R 5(aUOSn[XU5upTO0[X5n[R"XUS9upWUSSUSS-S- nU(aU[R"U5- nXT4$)ax Parameters ---------- image : ndarray Image for which the histogram is to be computed. bins : int or ndarray The number of histogram bins. For images with integer dtype, an array containing the bin centers can also be provided. For images with floating point dtype, this can be an array of bin_edges for use by ``np.histogram``. source_range : string, optional 'image' (default) determines the range from the input image. 'dtype' determines the range from the expected range of the images of that data type. normalize : bool, optional If True, normalize the histogram by the sum of its values. N)rhr`r<r/g@) flattenrrNrrO isinstancendarrayrBrVrsum)r+rhr6rer9r@rGrSs rraras( MMOE }}U[["**--(rzz::d /[Qk*5? ,,uzJ "~ !" 5< bffTl"  r(c[X5up#UR5nU[US5- n[R"UR 5nUR USS9nXC4$)aReturn cumulative distribution function (cdf) for the given image. Parameters ---------- image : array Image array. nbins : int, optional Number of bins for image histogram. Returns ------- img_cdf : array Values of cumulative distribution function. bin_centers : array Centers of bins. See Also -------- histogram References ---------- .. [1] https://en.wikipedia.org/wiki/Cumulative_distribution_function Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.camera()) >>> hi = exposure.histogram(image) >>> cdf = exposure.cumulative_distribution(image) >>> all(cdf[0] == np.cumsum(hi[0])/float(image.size)) True r<Fcopy)rcumsumrr_supported_float_typerr )r+rQr@r9img_cdf cdf_dtypes rrr:saD"%/DkkmGgbk**G++EKK8InnYUn3G  r(cJUb*[R"U[S9n[XU5up4O [X5up4[R"UR XC5nUR UR5nUR[R"UR5SS9$)aReturn image after histogram equalization. Parameters ---------- image : array Image array. nbins : int, optional Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin. mask : ndarray of bools or 0s and 1s, optional Array of same shape as `image`. Only points at which mask == True are used for the equalization, which is applied to the whole image. Returns ------- out : float array Image array after histogram equalization. Notes ----- This function is adapted from [1]_ with the author's permission. References ---------- .. [1] http://www.janeriksolem.net/histogram-equalization-with-python-and.html .. [2] https://en.wikipedia.org/wiki/Histogram_equalization )rFrr) rarrayrrinterpflatreshaper]r rrur)r+rQmaskcdfr9outs rr r gs< xxD)25;F[25@ ))EJJ 1C ++ekk "C ::e11%++>U: KKr(cUS:XaURRnUS:Xa/[R"U5n[R"U5nX44$U[ ;a[ Uup4U(aSnX44$Uup4X44$)a Return image intensity range (min, max) based on desired value type. Parameters ---------- image : array Input image. range_values : str or 2-tuple, optional The image intensity range is configured by this parameter. The possible values for this parameter are enumerated below. 'image' Return image min/max as the range. 'dtype' Return min/max of the image's dtype as the range. dtype-name Return intensity range based on desired `dtype`. Must be valid key in `DTYPE_RANGE`. Note: `image` is ignored for this range type. 2-tuple Return `range_values` as min/max intensities. Note that there's no reason to use this function if you just want to specify the intensity range explicitly. This option is included for functions that use `intensity_range` to support all desired range types. clip_negative : bool, optional If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values. rr+r)rtyperr2r4 DTYPE_RANGE)r+ range_valuesr.i_mini_maxs rintensity_rangers8w{{'' wu u  <  $"<0  E <$  <r(c\[U5[[[R4;a[ R "U5$[U5[:XaU$U[;a![R"U5R$[SUS35e![a [Rs$f=f)aDetermine the output dtype for rescale_intensity. The dtype is determined according to the following rules: - if ``dtype_or_range`` is a dtype, that is the output dtype. - if ``dtype_or_range`` is a dtype string, that is the dtype used, unless it is not a NumPy data type (e.g. 'uint12' for 12-bit unsigned integers), in which case the data type that can contain it will be used (e.g. uint16 in this case). - if ``dtype_or_range`` is a pair of values, the output data type will be ``_supported_float_type(image_dtype)``. This preserves float32 output for float32 inputs. Parameters ---------- dtype_or_range : type, string, or 2-tuple of int/float The desired range for the output, expressed as either a NumPy dtype or as a (min, max) pair of numbers. image_dtype : np.dtype The input image dtype. Returns ------- out_dtype : type The data type appropriate for the desired output. zZIncorrect value for out_range, should be a valid image data type or a pair of values, got .) rlisttuplerrorrurr TypeErroruint16r0)dtype_or_range image_dtypes r _output_dtypers4 NeRZZ88**;77 Nt#$ 88N+00 0  --;,>> image = np.array([51, 102, 153], dtype=np.uint8) >>> rescale_intensity(image) array([ 0, 127, 255], dtype=uint8) It's easy to accidentally convert an image dtype from uint8 to float: >>> 1.0 * image array([ 51., 102., 153.]) Use `rescale_intensity` to rescale to the proper range for float dtypes: >>> image_float = 1.0 * image >>> rescale_intensity(image_float) array([0. , 0.5, 1. ]) To maintain the low contrast of the original, use the `in_range` parameter: >>> rescale_intensity(image_float, in_range=(0, 255)) array([0.2, 0.4, 0.6]) If the min/max value of `in_range` is more/less than the min/max image intensity, then the intensity levels are clipped: >>> rescale_intensity(image_float, in_range=(0, 102)) array([0.5, 1. , 1. ]) If you have an image with signed integers but want to rescale the image to just the positive range, use the `out_range` parameter. In that case, the output dtype will be float: >>> image = np.array([-10, 0, 10], dtype=np.int8) >>> rescale_intensity(image, out_range=(0, 127)) array([ 0. , 63.5, 127. ]) To get the desired range with a specific dtype, use ``.astype()``: >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8) array([ 0, 63, 127], dtype=int8) If the input image is constant, the output will be clipped directly to the output range: >>> image = np.array([130, 130, 130], dtype=np.int32) >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32) array([127, 127, 127], dtype=int32) )rr+rr-zOne or more intensity levels are NaN. Rescaling will broadcast NaN to the full image. Provide intensity levels yourself to avoid this. E.g. with np.nanmin(image), np.nanmax(image).r) stacklevel) rrrmaprrranyisnanrr_clipr )r+in_range out_range out_dtypeiminimaxominomaxs rr r sx&&!%++"2"2EKK@ !)[[9 UOE<=JD u KJD vvbhhD/011  H   GGE &E |$+.%,44Y??wwuD)00;;r(cV[R"US:5(a [S5eg)NrzxImage Correction methods work correctly only on images with non-negative values. Use skimage.exposure.rescale_intensity.)rrr0)r+s r_assert_non_negativerbs, vveai 2  r(cSU-[R"SSS5U--n[R"[R"U5S5R S5nX0$)z-LUT based implementation of gamma adjustment.rr/uint8)rrPminimumrintr )r+gammagainluts r_adjust_gamma_u8rksM * Aq#.%7 8C **RWWS\3 ' . .w 7C :r(c4US:a [S5eURRnU[RLa[ XU5nU$[ U5 [[US5S[US5S- 5nX- U-U-U-RU5nU$)aNPerforms Gamma Correction on the input image. Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1. Parameters ---------- image : ndarray Input image. gamma : float, optional Non negative real number. Default value is 1. gain : float, optional The constant multiplier. Default value is 1. Returns ------- out : ndarray Gamma corrected output image. See Also -------- adjust_log Notes ----- For gamma greater than 1, the histogram will shift towards left and the output image will be darker than the input image. For gamma less than 1, the histogram will shift towards right and the output image will be brighter than the input image. References ---------- .. [1] https://en.wikipedia.org/wiki/Gamma_correction Examples -------- >>> from skimage import data, exposure, img_as_float >>> image = img_as_float(data.moon()) >>> gamma_corrected = exposure.adjust_gamma(image, 2) >>> # Output is darker for gamma > 1 >>> image.mean() > gamma_corrected.mean() True rz+Gamma should be a non-negative real number.Tr/) r0rrrrrrrrr )r+rrrrscales rr r rs\ qyFGG KK  E uT2 J U#l5$/2\%5Nq5QQR5(E1D8@@G Jr(c6[U5 URRn[[ US5S[ US5S- 5nU(aSX- -S- U-U-nU"U5$[ R "SX- -5U-U-nURU5$)aPerforms Logarithmic correction on the input image. This function transforms the input image pixelwise according to the equation ``O = gain*log(1 + I)`` after scaling each pixel to the range 0 to 1. For inverse logarithmic correction, the equation is ``O = gain*(2**I - 1)``. Parameters ---------- image : ndarray Input image. gain : float, optional The constant multiplier. Default value is 1. inv : float, optional If True, it performs inverse logarithmic correction, else correction will be logarithmic. Defaults to False. Returns ------- out : ndarray Logarithm corrected output image. See Also -------- adjust_gamma References ---------- .. [1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf Tr/rr)rrrrrrlog2r )r+rinvrrrs rr r s@ KK  E ,ud+A.eT1J11MM NE U]#a'5047Sz ''!em# $u ,t 3C ::e r(c r[U5 URRn[[ US5S[ US5S- 5nU(a1SSS[ R "X!X- - -5-- - U-nU"U5$SS[ R "X!X- - -5-- U-nURU5$)aPerforms Sigmoid Correction on the input image. Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation ``O = 1/(1 + exp*(gain*(cutoff - I)))`` after scaling each pixel to the range 0 to 1. Parameters ---------- image : ndarray Input image. cutoff : float, optional Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Default value is 0.5. gain : float, optional The constant multiplier in exponential's power of sigmoid function. Default value is 10. inv : bool, optional If True, returns the negative sigmoid correction. Defaults to False. Returns ------- out : ndarray Sigmoid corrected output image. See Also -------- adjust_gamma References ---------- .. [1] Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions", http://markfairchild.org/PDFs/PAP07.pdf Tr/r)rrrrrrexpr )r+cutoffrrrrrs rr r sJ KK  E ,ud+A.eT1J11MM NE 1BFF4EM+A#BCCDDMSz BFF4EM#9:;; < EC ::e r(c[R"U5nUR[:Xa2UR 5S:H=(a UR 5S:H(+$UR S:Xa>SSKJnJ n URSS:XaU"U5nURSS:XaU"U5n[USS9n[R"XU/5nUSUS- USUS- - n X:$) aDetermine if an image is low contrast. Parameters ---------- image : array-like The image under test. fraction_threshold : float, optional The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type's full range. [1]_ lower_percentile : float, optional Disregard values below this percentile when computing image contrast. upper_percentile : float, optional Disregard values above this percentile when computing image contrast. method : str, optional The contrast determination method. Right now the only available option is "linear". Returns ------- out : bool True when the image is determined to be low contrast. Notes ----- For boolean images, this function returns False only if all values are the same (the method, threshold, and percentile arguments are ignored). References ---------- .. [1] https://scikit-image.org/docs/dev/user_guide/data_types.html Examples -------- >>> image = np.linspace(0, 0.04, 100) >>> is_low_contrast(image) True >>> image[-1] = 1 >>> is_low_contrast(image) True >>> is_low_contrast(image, upper_percentile=100) False r/rrZr)rgb2grayrgba2rgbr[Fr-) r asanyarrayrrr4r2ndimcolorrrr]r percentile) r+fraction_thresholdlower_percentileupper_percentilemethodrrdlimitsrratios ris_low_contrastrsd MM% E {{dYY[A%=EIIK1,<>> zzQ. ;;q>Q UOE ;;q>Q UOE56G ]]55E"F GF AY "wqzGAJ'> ?E  %%r(r)rr+F)r)rN)r+Fr*)r/r/)r/F)rD F)g?r/clinear)"numpyr util.dtyperr_sharedr__all__rsrupdateitemsrfloat64r'r:rBrJrTrVchannel_as_last_axisrrarr rrr rrr r r rr(rrs2  M9J9J9LMM    D!RZZ(   D.!b"J66;UMQU7Up F* Z'LT(V, ^t