""" Adapted code from "Contrast Limited Adaptive Histogram Equalization" by Karel Zuiderveld , Graphics Gems IV, Academic Press, 1994. http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi The Graphics Gems code is copyright-protected. In other words, you cannot claim the text of the code as your own and resell it. Using the code is permitted in any program, product, or library, non-commercial or commercial. Giving credit is not required, though is a nice gesture. The code comes as-is, and if there are any flaws or problems with any Gems code, nobody involved with Gems - authors, editors, publishers, or webmasters - are to be held responsible. Basically, don't be a jerk, and remember that anything free comes with no guarantee. """ from __future__ import division import numbers import numpy as np from .. import img_as_float, img_as_uint from ..color.adapt_rgb import adapt_rgb, hsv_value from ..exposure import rescale_intensity from .._shared.utils import skimage_deprecation, warnings NR_OF_GREY = 2 ** 14 # number of grayscale levels to use in CLAHE algorithm @adapt_rgb(hsv_value) def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01, nbins=256, kernel_size=None): """Contrast Limited Adaptive Histogram Equalization (CLAHE). An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image. Parameters ---------- image : array-like Input image. kernel_size: integer or 2-tuple Defines the shape of contextual regions used in the algorithm. If an integer is given, the shape will be a square of sidelength given by this value. ntiles_x : int, optional (deprecated in favor of ``kernel_size``) Number of tile regions in the X direction (horizontal). ntiles_y : int, optional (deprecated in favor of ``kernel_size``) Number of tile regions in the Y direction (vertical). clip_limit : float: optional Clipping limit, normalized between 0 and 1 (higher values give more contrast). nbins : int, optional Number of gray bins for histogram ("dynamic range"). Returns ------- out : ndarray Equalized image. See Also -------- equalize_hist, rescale_intensity Notes ----- * For color images, the following steps are performed: - The image is converted to HSV color space - The CLAHE algorithm is run on the V (Value) channel - The image is converted back to RGB space and returned * For RGBA images, the original alpha channel is removed. References ---------- .. [1] http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE """ image = img_as_uint(image) image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1)) if kernel_size is None: warnings.warn('`ntiles_*` have been deprecated in favor of ' '`kernel_size`. The `ntiles_*` keyword arguments ' 'will be removed in v0.14', skimage_deprecation) ntiles_x = ntiles_x or 8 ntiles_y = ntiles_y or 8 kernel_size = (np.round(image.shape[0] / ntiles_y), np.round(image.shape[1] / ntiles_x)) if isinstance(kernel_size, numbers.Number): kernel_size = (kernel_size, kernel_size) kernel_size = [int(k) for k in kernel_size] image = _clahe(image, kernel_size, clip_limit * nbins, nbins) image = img_as_float(image) return rescale_intensity(image) def _clahe(image, kernel_size, clip_limit, nbins=128): """Contrast Limited Adaptive Histogram Equalization. Parameters ---------- image : array-like Input image. kernel_size: 2-tuple Defines the shape of contextual regions used in the algorithm. clip_limit : float, optional Normalized clipping limit (higher values give more contrast). nbins : int, optional Number of gray bins for histogram ("dynamic range"). Returns ------- out : ndarray Equalized image. The number of "effective" greylevels in the output image is set by `nbins`; selecting a small value (eg. 128) speeds up processing and still produce an output image of good quality. The output image will have the same minimum and maximum value as the input image. A clip limit smaller than 1 results in standard (non-contrast limited) AHE. """ if clip_limit == 1.0: return image # is OK, immediately returns original image. nr = int(np.ceil(image.shape[0] / kernel_size[0])) nc = int(np.ceil(image.shape[1] / kernel_size[1])) row_step = int(np.floor(image.shape[0] / nr)) col_step = int(np.floor(image.shape[1] / nc)) bin_size = 1 + NR_OF_GREY // nbins lut = np.arange(NR_OF_GREY) lut //= bin_size map_array = np.zeros((nr, nc, nbins), dtype=int) # Calculate greylevel mappings for each contextual region for r in range(nr): for c in range(nc): sub_img = image[r * row_step: (r + 1) * row_step, c * col_step: (c + 1) * col_step] if clip_limit > 0.0: # Calculate actual cliplimit clim = int(clip_limit * sub_img.size / nbins) if clim < 1: clim = 1 else: clim = NR_OF_GREY # Large value, do not clip (AHE) hist = lut[sub_img.ravel()] hist = np.bincount(hist) hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int)) hist = clip_histogram(hist, clim) hist = map_histogram(hist, 0, NR_OF_GREY - 1, sub_img.size) map_array[r, c] = hist # Interpolate greylevel mappings to get CLAHE image rstart = 0 for r in range(nr + 1): cstart = 0 if r == 0: # special case: top row r_offset = row_step / 2.0 rU = 0 rB = 0 elif r == nr: # special case: bottom row r_offset = row_step / 2.0 rU = nr - 1 rB = rU else: # default values r_offset = row_step rU = r - 1 rB = rB + 1 for c in range(nc + 1): if c == 0: # special case: left column c_offset = col_step / 2.0 cL = 0 cR = 0 elif c == nc: # special case: right column c_offset = col_step / 2.0 cL = nc - 1 cR = cL else: # default values c_offset = col_step cL = c - 1 cR = cL + 1 mapLU = map_array[rU, cL] mapRU = map_array[rU, cR] mapLB = map_array[rB, cL] mapRB = map_array[rB, cR] cslice = np.arange(cstart, cstart + c_offset) rslice = np.arange(rstart, rstart + r_offset) interpolate(image, cslice, rslice, mapLU, mapRU, mapLB, mapRB, lut) cstart += c_offset # set pointer on next matrix */ rstart += r_offset return image def clip_histogram(hist, clip_limit): """Perform clipping of the histogram and redistribution of bins. The histogram is clipped and the number of excess pixels is counted. Afterwards the excess pixels are equally redistributed across the whole histogram (providing the bin count is smaller than the cliplimit). Parameters ---------- hist : ndarray Histogram array. clip_limit : int Maximum allowed bin count. Returns ------- hist : ndarray Clipped histogram. """ # calculate total number of excess pixels excess_mask = hist > clip_limit excess = hist[excess_mask] n_excess = excess.sum() - excess.size * clip_limit # Second part: clip histogram and redistribute excess pixels in each bin bin_incr = int(n_excess / hist.size) # average binincrement upper = clip_limit - bin_incr # Bins larger than upper set to cliplimit hist[excess_mask] = clip_limit low_mask = hist < upper n_excess -= hist[low_mask].size * bin_incr hist[low_mask] += bin_incr mid_mask = (hist >= upper) & (hist < clip_limit) mid = hist[mid_mask] n_excess -= mid.size * clip_limit - mid.sum() hist[mid_mask] = clip_limit while n_excess > 0: # Redistribute remaining excess index = 0 while n_excess > 0 and index < hist.size: step_size = int(hist[hist < clip_limit].size / n_excess) step_size = max(step_size, 1) indices = np.arange(index, hist.size, step_size) under = hist[indices] < clip_limit hist[under] += 1 n_excess -= hist[under].size index += 1 return hist def map_histogram(hist, min_val, max_val, n_pixels): """Calculate the equalized lookup table (mapping). It does so by cumulating the input histogram. Parameters ---------- hist : ndarray Clipped histogram. min_val : int Minimum value for mapping. max_val : int Maximum value for mapping. n_pixels : int Number of pixels in the region. Returns ------- out : ndarray Mapped intensity LUT. """ out = np.cumsum(hist).astype(float) scale = ((float)(max_val - min_val)) / n_pixels out *= scale out += min_val out[out > max_val] = max_val return out.astype(int) def interpolate(image, xslice, yslice, mapLU, mapRU, mapLB, mapRB, lut): """Find the new grayscale level for a region using bilinear interpolation. Parameters ---------- image : ndarray Full image. xslice, yslice : array-like Indices of the region. map* : ndarray Mappings of greylevels from histograms. lut : ndarray Maps grayscale levels in image to histogram levels. Returns ------- out : ndarray Original image with the subregion replaced. Notes ----- This function calculates the new greylevel assignments of pixels within a submatrix of the image. This is done by a bilinear interpolation between four different mappings in order to eliminate boundary artifacts. """ norm = xslice.size * yslice.size # Normalization factor # interpolation weight matrices x_coef, y_coef = np.meshgrid(np.arange(xslice.size), np.arange(yslice.size)) x_inv_coef, y_inv_coef = x_coef[:, ::-1] + 1, y_coef[::-1] + 1 view = image[int(yslice[0]):int(yslice[-1] + 1), int(xslice[0]):int(xslice[-1] + 1)] im_slice = lut[view] new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice] + x_coef * mapRU[im_slice]) + y_coef * (x_inv_coef * mapLB[im_slice] + x_coef * mapRB[im_slice])) / norm) view[:, :] = new return image