U h2 %@s ddlmZddlmZddlmZmZmZddlm Z ddl Z ddl Z ddl Z ddlmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm Z ddl!m"m#m$Z%ddl&m'Z'ddl(m)Z)m*Z*m+Z+dd l,m-Z-m.Z.dd l/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5m6Z6m7Z7d d d ddddddddddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/g%Z8d0d0d0d0d0d1d2d3Z9d0d0d0d0d0d1d4d5Z:ed0d0d0d0d0d1d6d&Z;edad0d8d0d9d:d'Zd0d?d@dAZ>dcd0d>d0d?dBdCZ?d0d>dDdEdFdGdHZ@ddd>dId>dJdKdLZAe eded0d>dOdDd0dPdQdZBe ed0dRdRd0dSdTd#ZCed0d0d0dUdVd"ZDe ed0dWdXd0dYdZdZEeed0d0d0d[d\dZFe ed0d8d]d0d^d_dZGe d0dWdWd0d`dadZHe d0dWdWd0d`dbdZIe ed0d8d8d8dcd8dWddd0de dfd ZJe eed0dWdWdddgd0dhdid ZKe ed0djd8dkdld0dmdndZLe ed0dXd8dcdld0dmdodZMe ed0dpd0d0dqdrd ZNe eed0dld0dsdtdZOd0d0dudvd ZPd0d0d0dwdxdZQed0dWd0dydzdZRdfd0dWdWdDd0d}d~dZSeedgd0dWdWdd0ddd!ZTd0d0duddZUe ed0d0duddZVed0d0duddZWd0d0duddZXd0d0duddZYed0d0duddZZd0d8dd0ddd*Z[dhd0d8d0dddZ\ee j]e j^fd0dWd8d8d0dddZ_dddddd$Z`did0d0dd0ddd)Zaeeed0d0d0dddZbed0d0ddddZced0dWd0dddZdeedjd0dWdWd0dddZed0dWd0dddZfd0dWd0dddZged0d8ddId8d0ddd(Zheeedkd0d8dWdWd8d0ddd+Zied0d0dd0dddZjeeed0d>d>d>dd0dddZkd0ddddZldlddddddZme ed0dWdWdWdWd8d0dœdd,Znd0d0d0d8d8d8d0dĜddƄZoed0d0d0d[dd-Zped0d0d0d[dd.Zqd0d0dd0dʜdd̄Zre*d0d0ddXd0d͜ddτZsdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgdddgddd gd dd gd dd gdddgdddgdddgddddgdddgdddgddd gd!d"d#gdd$dgd%d&d'gd(d)d*gd+d,d-gd.d/d0gd1d2d3gd4d5d6gd7d8d9gd:d;ddgd?d@dAgdBdCdDgdEdCdFgdGdCdHgdIdCdJgdKdCdLgdMdCdNgdOdCdPgdQdRZteed0d8dSd0dTdUdVZudmdXdWdWdWd0dYdZd/Zved0d0d0d[d\d]Zwd0d0d0d0d^d_d`ZxdS(n) annotations) defaultdict)AnyLiteralSequence)warnN)MAX_VALUES_BY_DTYPEadd add_array add_weightedclipclipped float32_io from_floatget_num_channelsis_grayscale_image is_rgb_imagemaybe_process_in_chunksmultiply multiply_addnormalize_per_imagepreserve_channel_dimto_floatuint8_io) random_utils)PCAhandle_empty_array non_rgb_error)bboxes_from_masksmasks_from_bboxes)EIGHTMONO_CHANNEL_DIMENSIONSNUM_MULTI_CHANNEL_DIMENSIONSNUM_RGB_CHANNELS ColorType ImageModePlanckianJitterMode SpatterModeadd_fogadd_rain add_shadow add_graveladd_snow_bleachadd_snow_textureadd_sun_flare_overlayadd_sun_flare_physics_basedadjust_brightness_torchvisionadjust_contrast_torchvisionadjust_hue_torchvisionadjust_saturation_torchvisionbrightness_contrast_adjustchannel_shuffleclaheconvolve downscaleequalize fancy_pcagamma_transformimage_compressioninvert iso_noiselinear_transformation_rgbmove_tone_curvenoop posterize shift_hsvsolarize superpixelsswap_tiles_on_imageto_gray unsharp_maskchromatic_aberrationerodedilategenerate_approx_gaussian_noise np.ndarray)img hue_shift sat_shift val_shiftreturnc Cs|j}t|tj}t|\}}}|dkr`tjddtjd}t||d |}t ||}t ||}t ||}t |||f |}t|tj S)Nrdtype)rUcv2cvtColor COLOR_RGB2HSVsplitnparangeint16modastypeLUTr merge COLOR_HSV2RGB) rNrOrPrQrUhuesatvalZlut_huerfK/tmp/pip-unpacked-wheel-e8onvpoz/albumentations/augmentations/functional.py_shift_hsv_uint8[s   rhcCspt|tj}t|\}}}|dkr>t||}t|d}t||}t||}t|||f}t|tjS)Nrh) rWrXrYrZr r[r^rarb)rNrOrPrQrcrdrerfrfrg_shift_hsv_non_uint8qs    rjcCs|dkr|dkr|dkr|St|}|rZ|dks8|dkrLd}d}tdddt|tj}|jtjkrvt||||}nt ||||}|rt|tj S|S)NrzqHueSaturationValue: hue_shift and sat_shift are not applicable to grayscale image. Set them to 0 or use RGB image stacklevel) rrrWrXCOLOR_GRAY2RGBrUr[uint8rhrjCOLOR_RGB2GRAY)rNrOrPrQZis_grayrfrfrgrCs  int)rN thresholdrRcs|j}t||tjkrvfddttdD}|j}t|tj ||d}t |t |jkrrt |d}|S| }|k}||||<|S)zInvert all pixel values above a threshold. Args: img: The image to solarize. threshold: All pixels above this grayscale level are inverted. Returns: Solarized image. cs g|]}|kr|n|qSrfrf.0iZmax_valrsrfrg szsolarize..rT) rUrr[rorangerrshaperWr`arraylen expand_dimscopy)rNrsrUlutZ prev_shape result_imgZcondrfrwrgrDs    z$Literal[(0, 1, 2, 3, 4, 5, 6, 7, 8)])rNbitsrRcCs,t|}|jrt|dkrx|dkr.t|S|tkr:|Stjddtjd}tdd|d}||M}t||St |}t |D]\}}|dkrt|d|f|d|f<q|tkr|d|f |d|f<qtjddtjd}tdd|d}||M}t|d|f||d|f<q|S)zReduce the number of bits for each color channel. Args: img: image to posterize. bits: number of high bits. Must be in range [0, 8] Returns: Image with reduced color channels. ryrrSrTrk.) r[ror|r~ zeros_liker r\rWr` empty_like enumerater)rNrZ bits_arrayrmaskrrvZ channel_bitsrfrfrgrBs*    znp.ndarray | None)rNrrRcCst|gdg|dgd}dd|D}t|dkr>|St|ddd}|s`|Stjdtjd }|d }t dD]"}t ||d||<|||7}qt |t |S) NrrSrrScSsg|] }|r|qSrfrf)ru_frfrfrgrxsz!_equalize_pil..ryrzrTrk) rWcalcHistravelr~rr[sumemptyror{minr`r})rNr histogramhsteprnrvrfrfrg _equalize_pils  rc Cs|dkrt|St|gdg|dgd}d}|D]}|dkrFqP|d7}q6t|d}t|}|||kr|t||Sd|||}d}tjdtj d}t |dt |D](} ||| 7}t t ||tj || <qt||S)NrrSrryrgo@rT)rWZ equalizeHistrrrr[r full_likezerosror{r~r roundr`) rNrrrvretotalscaleZ_sumridxrfrfrg _equalize_cvs&       rboolNone)rNr by_channelsrRcCsT|dk rPt|r0t|r0td|jd|j|sPt|sPd|j}t|dS)NzWrong mask shape. Image shape: z. Mask shape: zAWhen by_channels=False only 1-channel mask supports. Mask shape: )rr ValueErrorr|)rNrrmsgrfrfrg_check_preconditionss   rz int | None)rrvrRcCs8|dkr dS|tj}t|s(|dkr,|S|d|fSN.)r_r[ror)rrvrfrfrg _handle_mask"s  rcvTr%)rNrmoderrRcCst||||dkrtnt}t|r2||t|S|sht|tj}||dt||d<t|tjSt |}t t D](}t||}||d|f||d|f<qz|S)aaApply histogram equalization to the input image. This function enhances the contrast of the input image by equalizing its histogram. It supports both grayscale and color images, and can operate on individual channels or on the luminance channel of the image. Args: img (np.ndarray): Input image. Can be grayscale (2D array) or RGB (3D array). mask (np.ndarray | None): Optional mask to apply the equalization selectively. If provided, must have the same shape as the input image. Default: None. mode (ImageMode): The backend to use for equalization. Can be either "cv" for OpenCV or "pil" for Pillow-style equalization. Default: "cv". by_channels (bool): If True, applies equalization to each channel independently. If False, converts the image to YCrCb color space and equalizes only the luminance channel. Only applicable to color images. Default: True. Returns: np.ndarray: Equalized image. The output has the same dtype as the input. Raises: ValueError: If the input image or mask have invalid shapes or types. Note: - If the input image is not uint8, it will be temporarily converted to uint8 for processing and then converted back to its original dtype. - For color images, when by_channels=False, the image is converted to YCrCb color space, equalized on the Y channel, and then converted back to RGB. - The function preserves the original number of channels in the image. Example: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> equalized = A.equalize(image, mode="cv", by_channels=True) >>> assert equalized.shape == image.shape >>> assert equalized.dtype == image.dtype Zpil.r.) rrrrrrWrXZCOLOR_RGB2YCrCbZCOLOR_YCrCb2RGBr[rr{r#)rNrrrfunctionrrvZ_maskrfrfrgr9/s-    float | np.ndarray)rNlow_yhigh_yrRcstddd}ddddddd}t}t|rdt|rdtt||||tj}t|St |tj rt |tj rtt||d d tj f||j tjt fd d t|DStd t|d t|d S)aRescales the relationship between bright and dark areas of the image by manipulating its tone curve. Args: img: np.ndarray. Any number of channels low_y: per-channel or single y-position of a Bezier control point used to adjust the tone curve, must be in range [0, 1] high_y: per-channel or single y-position of a Bezier control point used to adjust image tone curve, must be in range [0, 1] ?rSrMr)trrrRcSs<d|}d|d||d||d||ddS)Nryrkrrf)rrrZ one_minus_trfrfrg evaluate_bezsz%move_tone_curve..evaluate_bezNcs.g|]&}tdddd|f|qSN)rWr`rtrNZlutsrfrgrxsz#move_tone_curve..z?low_y and high_y must both be of type float or np.ndarray. Got z and )r[linspacerZisscalarr ZrintrorWr` isinstanceZndarraynewaxisTrar{ TypeErrortype)rNrrrr num_channelsrrfrrgr@ps *)rNtransformation_matrixrRcCs t||Sr)rWZ transform)rNrrfrfrgr?sfloatztuple[int, int])rN clip_limittile_grid_sizerRcCsr|}tj||d}t|r(||St|tj}||dddddf|dddddf<t|tjS)a,Apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input image. This function enhances the contrast of the input image using CLAHE. For color images, it converts the image to the LAB color space, applies CLAHE to the L channel, and then converts the image back to RGB. Args: img (np.ndarray): Input image. Can be grayscale (2D array) or RGB (3D array). clip_limit (float): Threshold for contrast limiting. Higher values give more contrast. tile_grid_size (tuple[int, int]): Size of grid for histogram equalization. Width and height of the grid. Returns: np.ndarray: Image with CLAHE applied. The output has the same dtype as the input. Note: - If the input image is float32, it's temporarily converted to uint8 for processing and then converted back to float32. - For color images, CLAHE is applied only to the luminance channel in the LAB color space. Raises: ValueError: If the input image is not 2D or 3D. Example: >>> import numpy as np >>> img = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8) >>> result = clahe(img, clip_limit=2.0, tile_grid_size=(8, 8)) >>> assert result.shape == img.shape >>> assert result.dtype == img.dtype )Z clipLimitZ tileGridSizeNr)rrWZ createCLAHErapplyrX COLOR_RGB2LABZ COLOR_LAB2RGB)rNrrZ clahe_matrfrfrgr6s! .)rNkernelrRcCsttjd|d}||S)Nrz)Zddepthr)rrWZfilter2D)rNrZconv_fnrfrfrgr7szLiteral[('.jpg', '.webp')])rNquality image_typerRcCsP|dkrtj}n|dkr tj}ntdt||t||f\}}t|tjS)Nz.jpgz.webpz@Only '.jpg' and '.webp' compression transforms are implemented. )rWZIMWRITE_JPEG_QUALITYZIMWRITE_WEBP_QUALITYNotImplementedErrorZimencoderrZimdecodeZIMREAD_UNCHANGED)rNrrZ quality_flag_Z encoded_imgrfrfrgr<s)rN snow_pointbrightness_coeffrRcCsttj}||d9}||d7}t|tj}tj|tjd}|dddddf|dddddf|k|9<t|dddddftj|dddddf<tj|tjd}t|tj S)aAdds a simple snow effect to the image by bleaching out pixels. This function simulates a basic snow effect by increasing the brightness of pixels that are above a certain threshold (snow_point). It operates in the HLS color space to modify the lightness channel. Args: img (np.ndarray): Input image. Can be either RGB uint8 or float32. snow_point (float): A float in the range [0, 1], scaled and adjusted to determine the threshold for pixel modification. Higher values result in less snow effect. brightness_coeff (float): Coefficient applied to increase the brightness of pixels below the snow_point threshold. Larger values lead to more pronounced snow effects. Should be greater than 1.0 for a visible effect. Returns: np.ndarray: Image with simulated snow effect. The output has the same dtype as the input. Note: - This function converts the image to the HLS color space to modify the lightness channel. - The snow effect is created by selectively increasing the brightness of pixels. - This method tends to create a 'bleached' look, which may not be as realistic as more advanced snow simulation techniques. - The function automatically handles both uint8 and float32 input images. The snow effect is created through the following steps: 1. Convert the image from RGB to HLS color space. 2. Adjust the snow_point threshold. 3. Increase the lightness of pixels below the threshold. 4. Convert the image back to RGB. Mathematical Formulation: Let L be the lightness channel in HLS space. For each pixel (i, j): If L[i, j] < snow_point: L[i, j] = L[i, j] * brightness_coeff Examples: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8) >>> snowy_image = A.functional.add_snow_v1(image, snow_point=0.5, brightness_coeff=1.5) References: - HLS Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV - Original implementation: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library rkrrTNry) rr[rorWrX COLOR_RGB2HLSr}float32r COLOR_HLS2RGB)rNrr max_value image_hlsrfrfrgr,s0   80c CsLttj}t|tjtj}t|dddddfd||d||dddddf<t j |j ddddd}tj |dddd }|j d}t dd |ddtjf}||9}t|gd ||tj}t|d|dd} t| d } t| d | d|d} t| tjtj} t |j dddk} |||g| | <| S)a# Add a realistic snow effect to the input image. This function simulates snowfall by applying multiple visual effects to the image, including brightness adjustment, snow texture overlay, depth simulation, and color tinting. The result is a more natural-looking snow effect compared to simple pixel bleaching methods. Args: img (np.ndarray): Input image in RGB format. snow_point (float): Coefficient that controls the amount and intensity of snow. Should be in the range [0, 1], where 0 means no snow and 1 means maximum snow effect. brightness_coeff (float): Coefficient for brightness adjustment to simulate the reflective nature of snow. Should be in the range [0, 1], where higher values result in a brighter image. Returns: np.ndarray: Image with added snow effect. The output has the same dtype as the input. Note: - The function first converts the image to HSV color space for better control over brightness and color adjustments. - A snow texture is generated using Gaussian noise and then filtered for a more natural appearance. - A depth effect is simulated, with more snow at the top of the image and less at the bottom. - A slight blue tint is added to simulate the cool color of snow. - Random sparkle effects are added to simulate light reflecting off snow crystals. The snow effect is created through the following steps: 1. Brightness adjustment in HSV space 2. Generation of a snow texture using Gaussian noise 3. Application of a depth effect to the snow texture 4. Blending of the snow texture with the original image 5. Addition of a cool blue tint 6. Addition of sparkle effects Examples: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8) >>> snowy_image = A.functional.add_snow_v2(image, snow_coeff=0.5, brightness_coeff=0.2) Note: This function works with both uint8 and float32 image types, automatically handling the conversion between them. References: - Perlin Noise: https://en.wikipedia.org/wiki/Perlin_noise - HSV Color Space: https://en.wikipedia.org/wiki/HSL_and_HSV Nrkryr?g333333?)sizelocrrrsigmaXZsigmaY皙?r)g333333?g?ryg333333?g333333?gGz?)rr[rorWrXrYr_rr rnormalr| GaussianBlurrrdstack addWeightedrrbrandom) rNrrrZimg_hsvZ snow_textureZrowsZ depth_effectZ snow_layerZ img_with_snowZ blue_tintZsparklerfrfrgr-s 2 >   ztuple[int, int, int]zlist[tuple[int, int]]) rNslant drop_length drop_width drop_color blur_valuebrightness_coefficient rain_dropsrRc Cs|D]2\}} ||} | |} t||| f| | f||qt|||f}t|tjtj} | dddddf|9<t| tjtj S)adAdds rain drops to the image. Args: img (np.ndarray): Input image. slant (int): The angle of the rain drops. drop_length (int): The length of each rain drop. drop_width (int): The width of each rain drop. drop_color (tuple[int, int, int]): The color of the rain drops in RGB format. blur_value (int): The size of the kernel used to blur the image. Rainy views are blurry. brightness_coefficient (float): Coefficient to adjust the brightness of the image. Rainy days are usually shady. rain_drops (list[tuple[int, int]]): A list of tuples where each tuple represents the (x, y) coordinates of the starting point of a rain drop. Returns: np.ndarray: Image with rain effect added. Reference: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library Nrk) rWlineblurrXrYr_r[rrorb) rNrrrrrrrZ rain_drop_x0Z rain_drop_y0Z rain_drop_x1Z rain_drop_y1Z image_hsvrfrfrgr)ys znp.random.RandomState)rN fog_intensity alpha_coeffog_particle_positions random_staterRcCs|jdd\}}t|}tj|||ftjd}ttj} tdtt||d|} |D]T\} } td| d} t j | | |d}|dkr| n| f|}t j || | f||ddqZt |d d }tj|dd d | ||}|d|||}|tjS) aAdd fog to the input image. Args: img (np.ndarray): Input image. fog_intensity (float): Intensity of the fog effect, between 0 and 1. alpha_coef (float): Base alpha (transparency) value for fog particles. fog_particle_positions (list[tuple[int, int]]): List of (x, y) coordinates for fog particles. random_state (np.random.RandomState): Random state used Returns: np.ndarray: Image with added fog effect. NrkrTg?ryrrz)centerradiuscolorZ thickness)rrT)axisZkeepdims)r|rr[rrormaxrrrrrandintrWcirclermeanr_)rNrrrrheightwidthrZ fog_layerrZmax_fog_radiusxyZ min_radiusrralphaZ fog_imagerfrfrgr(s2   ztuple[float, float]r$z list[Any])rN flare_center src_radius src_colorcirclesrRc Cs|}|}|D]D\}\}} } \} } } t||| f| | | | fdt|||d|}qdd|D}|}|d}tjdd|d}tjd||d}t|D]`}t||t|||d|||d|||d|||d}t|||d|}q|S)a Add a sun flare effect to an image using a simple overlay technique. This function creates a basic sun flare effect by overlaying multiple semi-transparent circles of varying sizes and intensities on the input image. The effect simulates a simple lens flare caused by bright light sources. Args: img (np.ndarray): The input image. flare_center (tuple[float, float]): (x, y) coordinates of the flare center in pixel coordinates. src_radius (int): The radius of the main sun circle in pixels. src_color (ColorType): The color of the sun, represented as a tuple of RGB values. circles (list[Any]): A list of tuples, each representing a circle that contributes to the flare effect. Each tuple contains: - alpha (float): The transparency of the circle (0.0 to 1.0). - center (tuple[int, int]): (x, y) coordinates of the circle center. - radius (int): The radius of the circle. - color (tuple[int, int, int]): RGB color of the circle. Returns: np.ndarray: The output image with the sun flare effect added. Note: - This function uses a simple alpha blending technique to overlay flare elements. - The main sun is created as a gradient circle, fading from the center outwards. - Additional flare circles are added along an imaginary line from the sun's position. - This method is computationally efficient but may produce less realistic results compared to more advanced techniques. The flare effect is created through the following steps: 1. Create an overlay image and output image as copies of the input. 2. Add smaller flare circles to the overlay. 3. Blend the overlay with the output image using alpha compositing. 4. Add the main sun circle with a radial gradient. Examples: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8) >>> flare_center = (50, 50) >>> src_radius = 20 >>> src_color = (255, 255, 200) >>> circles = [ ... (0.1, (60, 60), 5, (255, 200, 200)), ... (0.2, (70, 70), 3, (200, 255, 200)) ... ] >>> flared_image = A.functional.add_sun_flare_overlay( ... image, flare_center, src_radius, src_color, circles ... ) References: - Alpha compositing: https://en.wikipedia.org/wiki/Alpha_compositing - Lens flare: https://en.wikipedia.org/wiki/Lens_flare rzrycSsg|] }t|qSrf)rr)rurrfrfrgrx*sz)add_sun_flare_overlay.. r)num)rrWrr r[rr{rr)rNrrrroverlayoutputrrrZrad3Zr_colorZg_colorZb_colorZpointZ num_timesZradrvZalprfrfrgr.s? 0c Cs|}|jdd\}}tj|tjd}t||||ddD]b} t|dtt | t ||t|dt t | t ||f} t ||| |dq@|D]&\} } } }t|| t| d|dqtj |d d d d }tjd|d|f\}}t||dd||dd}dt|t ||d dd}t|gd }||9}tt|}tj |dd d d d |d<tj |dd ddd |d<t|}dd|d|dS)a Add a more realistic sun flare effect to the image. This function creates a complex sun flare effect by simulating various optical phenomena that occur in real camera lenses when capturing bright light sources. The result is a more realistic and physically plausible lens flare effect. Args: img (np.ndarray): Input image. flare_center (tuple[int, int]): (x, y) coordinates of the sun's center in pixels. src_radius (int): Radius of the main sun circle in pixels. src_color (tuple[int, int, int]): Color of the sun in RGB format. circles (list[Any]): List of tuples, each representing a flare circle with parameters: (alpha, center, size, color) - alpha (float): Transparency of the circle (0.0 to 1.0). - center (tuple[int, int]): (x, y) coordinates of the circle center. - size (float): Size factor for the circle radius. - color (tuple[int, int, int]): RGB color of the circle. Returns: np.ndarray: Image with added sun flare effect. Note: This function implements several techniques to create a more realistic flare: 1. Separate flare layer: Allows for complex manipulations of the flare effect. 2. Lens diffraction spikes: Simulates light diffraction in camera aperture. 3. Radial gradient mask: Creates natural fading of the flare from the center. 4. Gaussian blur: Softens the flare for a more natural glow effect. 5. Chromatic aberration: Simulates color fringing often seen in real lens flares. 6. Screen blending: Provides a more realistic blending of the flare with the image. The flare effect is created through the following steps: 1. Create a separate flare layer. 2. Add the main sun circle and diffraction spikes to the flare layer. 3. Add additional flare circles based on the input parameters. 4. Apply Gaussian blur to soften the flare. 5. Create and apply a radial gradient mask for natural fading. 6. Simulate chromatic aberration by applying different blurs to color channels. 7. Blend the flare with the original image using screen blending mode. Examples: >>> import numpy as np >>> import albumentations as A >>> image = np.random.randint(0, 256, [1000, 1000, 3], dtype=np.uint8) >>> flare_center = (500, 500) >>> src_radius = 50 >>> src_color = (255, 255, 200) >>> circles = [ ... (0.1, (550, 550), 10, (255, 200, 200)), ... (0.2, (600, 600), 5, (200, 255, 200)) ... ] >>> flared_image = A.functional.add_sun_flare_physics_based( ... image, flare_center, src_radius, src_color, circles ... ) References: - Lens flare: https://en.wikipedia.org/wiki/Lens_flare - Diffraction: https://en.wikipedia.org/wiki/Diffraction - Chromatic aberration: https://en.wikipedia.org/wiki/Chromatic_aberration - Screen blending: https://en.wikipedia.org/wiki/Blend_modes#Screen NrkrTrz)r-ZrrygQ?rrgffffff?rr)rr|r[rrrWrrrcosradiansrsinrrZogridsqrtr rlistrZra)rNrrrrrrrZ flare_layerZangleZ end_pointrrrrrrrZchannelsrfrfrgr/8s,E$$&  zlist[np.ndarray])rN vertices_list intensitiesrRc Cst|}ttj}|}t||D]~\}}tj|jd|jddftjd}t ||g|ftj ||dd}|dddddf|k} t || |tj|| <q$|S)aAdd shadows to the image by reducing the intensity of the pixel values in specified regions. Args: img (np.ndarray): Input image. Multichannel images are supported. vertices_list (list[np.ndarray]): List of vertices for shadow polygons. intensities (np.ndarray): Array of shadow intensities. Range is [0, 1]. Returns: np.ndarray: Image with shadows added. Reference: https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library rryrTrkrN) rrr[rorziprr|rWZfillPolyrepeatr ) rNrrrrZ img_shadowedZverticesZshadow_intensityrZshadowed_indicesrfrfrgr*s "  )rNgravelsrRc CsRt|t|tj}|D](}|\}}}}}||||||df<qt|tjS)Nry)rrWrXrr) rNr rZgravelZmin_yZmax_yZmin_xZmax_xrdrfrfrgr+s )rNrRcCst|j|Sr)rrUrNrfrfrgr=s)rNchannels_shuffledrRcCs |d|fSrrf)rNr rfrfrgr5s)rNgammarRcCsB|jtjkr6tddd|d}t||tjSt||S)Nrg?gp?r)rUr[ror\rWr`r_power)rNrtablerfrfrgr;s ryF)rNrbeta beta_by_maxrRcCs2|rt|j}||}n|t|}t|||Sr)rrUr[rr)rNrrrrvaluerfrfrgr4s   皙?rznp.random.RandomState | None)image color_shift intensityrrRc Cst|tj}t|\}}tj|d|d|jdd|d}tjd|d||jdd|d}|d} | |7} | d;} |d } | |dd | 7} t|tjS) aApply poisson noise to an image to simulate camera sensor noise. Args: image (np.ndarray): Input image. Currently, only RGB images are supported. color_shift (float): The amount of color shift to apply. Default is 0.05. intensity (float): Multiplication factor for noise values. Values of ~0.5 produce a noticeable, yet acceptable level of noise. Default is 0.5. random_state (np.random.RandomState | None): If specified, this will be random state used for noise generation. Returns: np.ndarray: The noised image. Image types: uint8, float32 Number of channels: 3 ryrNrk)rrrrir.ryr) rWrXrZ meanStdDevrZpoissonr|rr) rrrrZhlsrstddevZluminance_noiseZ color_noisercZ luminancerfrfrgr>s&$cCst|tjS)aConvert an RGB image to grayscale using the weighted average method. This function uses OpenCV's cvtColor function with COLOR_RGB2GRAY conversion, which applies the following formula: Y = 0.299*R + 0.587*G + 0.114*B Args: img (np.ndarray): Input RGB image as a numpy array. Returns: np.ndarray: Grayscale image as a 2D numpy array. Image types: uint8, float32 Number of channels: 3 )rWrXrpr rfrfrgto_gray_weighted_average+srcCst|tjdS)aConvert an RGB image to grayscale using the L channel from the LAB color space. This function converts the RGB image to the LAB color space and extracts the L channel. The LAB color space is designed to approximate human vision, where L represents lightness. Key aspects of this method: 1. The L channel represents the lightness of each pixel, ranging from 0 (black) to 100 (white). 2. It's more perceptually uniform than RGB, meaning equal changes in L values correspond to roughly equal changes in perceived lightness. 3. The L channel is independent of the color information (A and B channels), making it suitable for grayscale conversion. This method can be particularly useful when you want a grayscale image that closely matches human perception of lightness, potentially preserving more perceived contrast than simple RGB-based methods. Args: img (np.ndarray): Input RGB image as a numpy array. Returns: np.ndarray: Grayscale image as a 2D numpy array, representing the L (lightness) channel. Values are scaled to match the input image's data type range. Image types: uint8, float32 Number of channels: 3 r)rWrXrr rfrfrgto_gray_from_labAs rcCs,|tj}tj|ddtj|dddS)aConvert an image to grayscale using the desaturation method. Args: img (np.ndarray): Input image as a numpy array. Returns: np.ndarray: Grayscale image as a 2D numpy array. Image types: uint8, float32 Number of channels: any rzrrk)r_r[rrr)rNZ float_imagerfrfrgto_gray_desaturationds rcCstj|dd|jS)aConvert an image to grayscale using the average method. This function computes the arithmetic mean across all channels for each pixel, resulting in a grayscale representation of the image. Key aspects of this method: 1. It treats all channels equally, regardless of their perceptual importance. 2. Works with any number of channels, making it versatile for various image types. 3. Simple and fast to compute, but may not accurately represent perceived brightness. 4. For RGB images, the formula is: Gray = (R + G + B) / 3 Note: This method may produce different results compared to weighted methods (like RGB weighted average) which account for human perception of color brightness. It may also produce unexpected results for images with alpha channels or non-color data in additional channels. Args: img (np.ndarray): Input image as a numpy array. Can be any number of channels. Returns: np.ndarray: Grayscale image as a 2D numpy array. The output data type matches the input data type. Image types: uint8, float32 Number of channels: any rzr)r[rr_rUr rfrfrgto_gray_averagexsrcCstj|ddS)aConvert an image to grayscale using the maximum channel value method. This function takes the maximum value across all channels for each pixel, resulting in a grayscale image that preserves the brightest parts of the original image. Key aspects of this method: 1. Works with any number of channels, making it versatile for various image types. 2. For 3-channel (e.g., RGB) images, this method is equivalent to extracting the V (Value) channel from the HSV color space. 3. Preserves the brightest parts of the image but may lose some color contrast information. 4. Simple and fast to compute. Note: - This method tends to produce brighter grayscale images compared to other conversion methods, as it always selects the highest intensity value from the channels. - For RGB images, it may not accurately represent perceived brightness as it doesn't account for human color perception. Args: img (np.ndarray): Input image as a numpy array. Can be any number of channels. Returns: np.ndarray: Grayscale image as a 2D numpy array. The output data type matches the input data type. Image types: uint8, float32 Number of channels: any rzr)r[rr rfrfrg to_gray_maxs rcCsd|j}|d|jd}tdd}||}||jdd}t|d}|tjkr`t||dS|S)aConvert an image to grayscale using Principal Component Analysis (PCA). This function applies PCA to reduce a multi-channel image to a single channel, effectively creating a grayscale representation that captures the maximum variance in the color data. Args: img (np.ndarray): Input image as a numpy array with shape (height, width, channels). Returns: np.ndarray: Grayscale image as a 2D numpy array with shape (height, width). If input is uint8, output is uint8 in range [0, 255]. If input is float32, output is float32 in range [0, 1]. Note: This method can potentially preserve more information from the original image compared to standard weighted average methods, as it accounts for the correlations between color channels. Image types: uint8, float32 Number of channels: any rzrkry)Z n_componentsNZmin_maxZ target_dtype) rUreshaper|rZ fit_transformrr[ror)rNrUZpixelspcaZ pca_resultZ grayscalerfrfrg to_gray_pcas   r"zRLiteral[('weighted_average', 'from_lab', 'desaturation', 'average', 'max', 'pca')])rNnum_output_channelsmethodrRcCs|dkrt|}nh|dkr$t|}nV|dkr6t|}nD|dkrHt|}n2|dkrZt|}n |dkrlt|}ntd|t||S)NZweighted_averageZfrom_labZ desaturationZaveragerr!zUnsupported method: )rrrrrr"rgrayscale_to_multichannel)rNr#r$resultrfrfrgrGs      r)grayscale_imager#rRcCs |}tj|g|ddS)aConvert a grayscale image to a multi-channel image. This function takes a 2D grayscale image or a 3D image with a single channel and converts it to a multi-channel image by repeating the grayscale data across the specified number of channels. Args: grayscale_image (np.ndarray): Input grayscale image. Can be 2D (height, width) or 3D (height, width, 1). num_output_channels (int, optional): Number of channels in the output image. Defaults to 3. Returns: np.ndarray: Multi-channel image with shape (height, width, num_channels). Note: If the input is already a multi-channel image with the desired number of channels, it will be returned unchanged. rzr)rsqueezer[stack)r'r#rfrfrgr%s r%)rNrdown_interpolationup_interpolationrRc Cs||jdd\}}|tjks&|tjko0|jtjk}|r>t|}tj|d|||d}tj|||f|d}|rxt|tjdS|S)Nrk)ZfxZfy interpolationr,r) r|rWZ INTER_NEARESTrUr[rorresizer) rNrr*r+rrZ need_castZ downscaledZupscaledrfrfrgr8s r) input_objparamsrRcKs|Srrf)r/r0rfrfrgrA*szlist[int] | None)rtilesmappingrRcCs~|jdks|dkr|St|}t|D]L\}}||\}}}} ||\} } } } || | | | f||||| f<q,|S)a@Swap tiles on the image according to the new format. Args: image: Input image. tiles: Array of tiles with each tile as [start_y, start_x, end_y, end_x]. mapping: list of new tile indices. Returns: np.ndarray: Output image with tiles swapped according to the random shuffle. rN)rrr[rr)rr1r2Z new_imagerZ new_indexstart_ystart_xend_yend_xZ start_y_origZ start_x_origZ end_y_origZ end_x_origrfrfrgrF.s  &)rN alpha_vectorrRc Cs|j}t|}|d|}tj|dd}||}|dkrTt|}|d||}njtj|dd} tj| \} } | ddd } | | } | dd| f} t t | t || |j j }||} | |} t | ddS)aPerform 'Fancy PCA' augmentation on an image with any number of channels. Args: img (np.ndarray): Input image alpha_vector (np.ndarray): Vector of scale factors for each principal component. Should have the same length as the number of channels in the image. Returns: np.ndarray: Augmented image of the same shape, type, and range as the input. Image types: uint8, float32 Number of channels: Any Note: - This function generalizes the Fancy PCA augmentation to work with any number of channels. - It preserves the original range of the image ([0, 255] for uint8, [0, 1] for float32). - For single-channel images, the augmentation is applied as a simple scaling of pixel intensity variation. - For multi-channel images, PCA is performed on the entire image, treating each pixel as a point in N-dimensional space (where N is the number of channels). - The augmentation preserves the correlation between channels while adding controlled noise. - Computation time may increase significantly for images with a large number of channels. Reference: Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). rzrrryF)ZrowvarN)r|rr r[rZstdZcovZlinalgZeighZargsortdotZdiagrr )rNr7 orig_shaperZ img_reshapedZimg_meanZ img_centeredZstd_devnoiseZimg_covZeig_valsZeig_vecsZ sort_permZimg_pcarfrfrgr:Hs""  " r[)rNfactorrRcCs(|dkrt|S|dkr|St||S)Nrry)r[rrrNr;rfrfrgr0s  cCst|dkr |St|r|nt|tj}|dkr`|jtjkrNt|d}tj |||jdSt |||d|S)NryrrrT) rrrWrXrprUr[rrrrr)rNr;rrfrfrgr1s"  )rNr;rrRcCsX|dkr |St|r|St|tj}t|tj}|dkr@|Stj|||d||dS)Nryr)r)rrWrXrprnr)rNr;rZgrayrfrfrgr3scCs^t|tj}tjddtjd}t|d|dtj}t |d||d<t|tj S)NrrSrTrVr) rWrXrYr[r\r]r^r_ror`rb)rNr;rrfrfrg_adjust_hue_torchvision_uint8s r=cCsbt|s|dkr|S|jtjkr*t||St|tj}t|d|dd|d<t|tj S)Nrrri) rrUr[ror=rWrXrYr^rbr<rfrfrgr2s  zSequence[bool])r n_segmentsreplace_samplesmax_sizer,rRcCst|s|S|j}|dk r|t|jdd}||kr|||}|jdd\}} t||t| |} } t|| | f|}tjj ||d|j t krdndd} d} t |j }t|}|j t krtj|dd}t|}t|D]}tjj| d|d|fd }t|D]l\}}||t|r|j}|d|f}|j jd kr`tt|}tt|| |}n|}||| |k<qq||jkrt||dd|S|S) Nrkrrz)r>Z compactnessZ channel_axisrrry.)Zintensity_image)rvub)r[anyr|rrr fgeometricr.skimageZ segmentationZslicndimr!rrUrrrr{ZmeasureZ regionpropsrr~mean_intensitykindrr)rr>r?r@r,r9rrrrZ new_heightZ new_widthsegmentsZ min_valuerrcZregionsZ region_idxZregionrGZ image_sp_crrfrfrgrEsD      rrr)rksizesigmarrsrRc Csttj||f|d}|jtkr8t|dkr8tj|dd}||}||}t|d|k}| tj }|||} t | dd} ||} t t | | t |d| S)N)rKrryrzrrr)rrWrrFr"rr[r(absr_rr r r) rrKrLrrsZblur_fnrZresidualrZsharpZ soft_maskrfrfrgrHs   zfloat | Sequence[float])r drop_mask drop_valuerRcCs<t|ttfr"|dkr"t|}n t||}t|||S)Nr)rrrrr[rrwhere)rrNrOZ drop_valuesrfrfrg pixel_dropout.s  rQr')rNnon_mudmudrainrrRcCsr|dkr$|dkrd}t|||S|dkr`|dkr@d}t||dkrTd}t||||Std|dS)NrTzRain spatter requires rain maskrSzMud spatter requires mud maskz!Mud spatter requires non_mud maskzUnsupported spatter mode: )r)rNrRrSrTrrrfrfrgspatter7s  rUz dict[tuple[int, int], list[int]])r1rRcCsDtt}t|D].\}\}}}}||||f}|||q|S)zBGroups tiles by their shape and stores the indices for each shape.)rrrappend)r1 shape_groupsindexr3r4r5r6r|rfrfrgcreate_shape_groupsTs rYz list[int])rWrrRcCs`tdd|D}dg|}|D]2}tj||d}t||D]\}}|||<qHq(|S)aShuffles indices within each group of similar shapes and creates a list where each index points to the index of the tile it should be mapped to. Args: shape_groups (dict[tuple[int, int], list[int]]): Groups of tile indices categorized by shape. random_state (Optional[np.random.RandomState]): Seed for the random number generator for reproducibility. Returns: list[int]: A list where each index is mapped to the new index of the tile after shuffling. css|]}t|VqdSr)r~)ruindicesrfrfrg lsz4shuffle_tiles_within_shape_groups..rzr)rvaluesrshufflerr )rWrZ num_tilesr2rZZshuffled_indicesoldnewrfrfrg!shuffle_tiles_within_shape_groups]s   r`)rNprimary_distortion_redsecondary_distortion_redprimary_distortion_bluesecondary_distortion_bluer,rRc Cs|jdd\}}tjdtjd}||d<||d<|d|d<|d|d<tj||d d gtjd} tj||d d gtjd} t|d || |||} t|d || |||} t| |d | gS) NrkrrTr)ryryg@)rrk)ryrkrr).rkr)r|r[Zeyerr}_distort_channelr) rNrarbrcrdr,rr camera_matZdistortion_coeffs_redZdistortion_coeffs_blueZ red_distortedZblue_distortedrfrfrgrIys2    )channelrfdistortion_coeffsrrr,rRcCs6tj||d|||ftjd\}}tj||||tjdS)N)Z cameraMatrixZ distCoeffsRZnewCameraMatrixrZm1type)r,Z borderMode)rWZinitUndistortRectifyMapZCV_32FC1ZremapZBORDER_REPLICATE)rgrfrhrrr,Zmap_xZmap_yrfrfrgres recCstj||ddSNry)Z iterations)rWrJrNrrfrfrgrJscCstj||ddSrj)rWrKrkrfrfrgrKsz Literal[('dilation', 'erosion')])rNr operationrRcCs6|dkrt||S|dkr$t||Std|dS)NZdilationZerosionzUnsupported operation: )rKrJr)rNrrlrfrfrg morphologys   rm)bboxesrrl image_shaperRcCs:|}t||}t|||}t||ddddf<|S)N)rrrmr)rnrrlromasksrfrfrgbboxes_morphologys   rrgk+ݓ?gӼ?g_LU?ga+e?gMSt$?gZd;O?gD?gs?g_L?g?g%䃞?gOjM?gm?glV}?g F%u?g{Pk?g:#J{/?)i i |pdXL@4!(#%')*,.02468:g,Ԛ?gAc]K?g QI?goʡ?gZӼ?gY?gDio?g QI?g46W[?g]Fx ?gX2ı.?g:H?gjM?g|a2U0?gHP?g-1?g"~j?g[B>٬?g߾3?g?ܵ?gʡE?g`"?gۊe?gH}8?gK7?g58EGr?gJ4?g' ?g+e?g?gǘ?g/n?)rsrtrurvrwrxryrzr{r|r}r~rrrrrrrrrrr)Z blackbodyZciedr&)rN temperaturerrRc Cs6|}tt|}tt|}t|||}d}t||||}t||d||}||kr~tt||}nD||||} d| } | tt||| tt||}|dddddf|d|d|dddddf<|dddddf|d|d|dddddf<|S)Niryrrk)rrPLANCKIAN_COEFFSkeysrr[r r}) rNrrZmin_tempZmax_temprZt_leftZt_rightZcoeffsZw_rightZw_leftrfrfrgplanckian_jitters,88r?ztuple[int, ...])r|rrLrrRcCst|d|}t|d|}t|tkrFt|||||df}nt||||f}tj||d|dftjd}||S)Nrryrzr-) rrr~r"rrrWr. INTER_LINEARr )r|rrLrZdownscaled_heightZdownsaled_widthZ low_res_noiser&rfrfrgrL5s )rNr:rRcCs t||Sr)r )rNr:rfrfrg add_noiseIsr) keypointsr1r2rRcCsH|js |S|dddfddtjf}|dddfddtjf}|j\}}}}||k||k@||k@||k@} tj| dd} tj| dd} t| rtdtddt|| } || df} || df}|| df}|| df}| }|dddf| ||dddf<|dddf|||dddf<|| || <|S)aSwap the positions of keypoints based on a tile mapping. This function takes a set of keypoints and repositions them according to a mapping of tile swaps. Keypoints are moved from their original tiles to new positions in the swapped tiles. Args: keypoints (np.ndarray): A 2D numpy array of shape (N, 2) where N is the number of keypoints. Each row represents a keypoint's (x, y) coordinates. tiles (np.ndarray): A 2D numpy array of shape (M, 4) where M is the number of tiles. Each row represents a tile's (start_y, start_x, end_y, end_x) coordinates. mapping (np.ndarray): A 1D numpy array of shape (M,) where M is the number of tiles. Each element i contains the index of the tile that tile i should be swapped with. Returns: np.ndarray: A 2D numpy array of the same shape as the input keypoints, containing the new positions of the keypoints after the tile swap. Raises: RuntimeWarning: If any keypoint is not found within any tile. Notes: - Keypoints that do not fall within any tile will remain unchanged. - The function assumes that the tiles do not overlap and cover the entire image space. NrryrzsSome keypoints are not in any tile. They will be returned unchanged. This is unexpected and should be investigated.rkrl) rr[rrZargmaxrCrRuntimeWarningr}r)rr1r2Zkp_xZkp_yr3r4r5r6Zin_tileZ tile_indicesZnot_in_any_tileZnew_tile_indicesZ old_start_xZ old_start_yZ new_start_xZ new_start_yZ new_keypointsrfrfrgswap_tiles_on_keypointsNs0      $$ r)rq)N)N)N)NrT)ryrF)rrN)r)N)r)rrr)N)rryr)y __future__r collectionsrtypingrrrwarningsrrWZnumpyr[rEZalbucorerr r r r r rrrrrrrrrrrrZ1albumentations.augmentations.geometric.functionalZ augmentationsZ geometricZ functionalrDZalbumentationsrZ"albumentations.augmentations.utilsrrrZalbumentations.core.bbox_utilsrrZalbumentations.core.typesr r!r"r#r$r%r&r'__all__rhrjrCrDrBrrrrr9r@r?r6r7r<r,r-r)r(r.r/r*r+r=r5r;r4r>rrrrrr"rGr%Z INTER_AREArr8rArFr:r0r1r3r=r2rErHrQrUrYr`rIrerJrKrmrrrrrLrrrfrfrfrgs   P ( ))  ?#, @Z"06Rn'  )!!#)F   <  *     8