9is!.S/rSSKrSSKJr SrSSjrg) random_noiseN) img_as_floatcUS:Xa[R"U[S9$US:Xa[R"U[S9$UR U5U:*$)a Bernoulli trials at a given probability of a given size. This function is meant as a lower-memory alternative to calls such as `np.random.choice([True, False], size=image.shape, p=[p, 1-p])`. While `np.random.choice` can handle many classes, for the 2-class case (Bernoulli trials), this function is much more efficient. Parameters ---------- p : float The probability that any given trial returns `True`. shape : int or tuple of ints The shape of the ndarray to return. rng : `numpy.random.Generator` ``Generator`` instance, typically obtained via `np.random.default_rng()`. Returns ------- out : ndarray[bool] The results of Bernoulli trials in the given `size` where success occurs with probability `p`. r)dtyper)npzerosboolonesrandom)pshaperngs R/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/util/noise.py _bernoullirsH0 AvxxT**AvwwuD)) ::e  !!c UR5nUR5S:aSnOSn[U5n[RR U5nSSSSSSSS .nSS S S [R "U5S -S .nSS/S/S/SS//S.nUH"n XXa;dM[U SXU35e XUHn URXU 5 M US:Xa,URUSUSS -UR5n X -n GOUS:XaBUSS:*R5(a [S5eXRSUSS -5-n GO_US:Xa[[R"U55n S[R"[R"U 55-n US:XaUR!5nUS-US-- nUR#X -5[%U 5- n US:Xa U WS--S- n OUS:Xa['USX$SSS9n OUS:Xa['USX$SSS9n OUS:XaVUR)5n USnUSn[+XRUS9n[+UURUS9nU)nS U UU-'X\UU-'O3US!:Xa-URUSUSS -UR5n XU --n U(a[R,"W US5n W $)"a Function to add random noise of various types to a floating-point image. Parameters ---------- image : ndarray Input image data. Will be converted to float. mode : str, optional One of the following strings, selecting the type of noise to add: 'gaussian' (default) Gaussian-distributed additive noise. 'localvar' Gaussian-distributed additive noise, with specified local variance at each point of `image`. 'poisson' Poisson-distributed noise generated from the data. 'salt' Replaces random pixels with 1. 'pepper' Replaces random pixels with 0 (for unsigned images) or -1 (for signed images). 's&p' Replaces random pixels with either 1 or `low_val`, where `low_val` is 0 for unsigned images or -1 for signed images. 'speckle' Multiplicative noise using ``out = image + n * image``, where ``n`` is Gaussian noise with specified mean & variance. rng : {`numpy.random.Generator`, int}, optional Pseudo-random number generator. By default, a PCG64 generator is used (see :func:`numpy.random.default_rng`). If `rng` is an int, it is used to seed the generator. clip : bool, optional If True (default), the output will be clipped after noise applied for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is needed to maintain the proper image data range. If False, clipping is not applied, and the output may extend beyond the range [-1, 1]. mean : float, optional Mean of random distribution. Used in 'gaussian' and 'speckle'. Default : 0. var : float, optional Variance of random distribution. Used in 'gaussian' and 'speckle'. Note: variance = (standard deviation) ** 2. Default : 0.01 local_vars : ndarray, optional Array of positive floats, same shape as `image`, defining the local variance at every image point. Used in 'localvar'. amount : float, optional Proportion of image pixels to replace with noise on range [0, 1]. Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05 salt_vs_pepper : float, optional Proportion of salt vs. pepper noise for 's&p' on range [0, 1]. Higher values represent more salt. Default : 0.5 (equal amounts) Returns ------- out : ndarray Output floating-point image data on range [0, 1] or [-1, 1] if the input `image` was unsigned or signed, respectively. Notes ----- Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside the valid image range. The default is to clip (not alias) these values, but they may be preserved by setting `clip=False`. Note that in this case the output may contain values outside the ranges [0, 1] or [-1, 1]. Use this option with care. Because of the prevalence of exclusively positive floating-point images in intermediate calculations, it is not possible to intuit if an input is signed based on dtype alone. Instead, negative values are explicitly searched for. Only if found does this function assume signed input. Unexpected results only occur in rare, poorly exposes cases (e.g. if all values are above 50 percent gray in a signed `image`). In this event, manually scaling the input to the positive domain will solve the problem. The Poisson distribution is only defined for positive integers. To apply this noise type, the number of unique values in the image is found and the next round power of two is used to scale up the floating-point result, after which it is scaled back down to the floating-point image range. To generate Poisson noise against a signed image, the signed image is temporarily converted to an unsigned image in the floating point domain, Poisson noise is generated, then it is returned to the original range. rgggaussian_valueslocalvar_valuespoisson_values sp_values s&p_values)gaussianlocalvarpoissonsaltpeppers&pspeckleg{Gz?g?g?)meanvaramountsalt_vs_pepper local_varsr r!r$r"r#)rrrrrz! keyword not in allowed keywords rrz'All values of `local_vars` must be > 0.rg?rr)moderr"r#r)rrr)lowerminrrr default_rng zeros_like ValueError setdefaultnormalranylenuniqueceillog2maxrfloatrcopyrclip)imager&rr6kwargslow_clip allowedtypes kwdefaults allowedkwargskeykwnoiseoutvalsold_maxr qflippedsaltedpeppereds rrr's3l ::Z!12 M L$67 7%8 d!3457 ./"n-0 z 6&>6%=C+?Mm   < A % * * , ,FG GjjF<$8C$?@@  299U#$BGGBGGDM** t iikGS[Ws]3Ekk%,'%+5 t 3'#-C  3h/?PS    3h/?PS  jjl 8  # $Q 5Au{{47 !Gf "*Gh    6&>6%=C+?Mem# ggc8S) Jr)rNT)__all__numpyrrrrrrrrJs   ">Br