Spaces:
Sleeping
Sleeping
| import random | |
| import colorsys | |
| import numpy as np | |
| import matplotlib | |
| matplotlib.use('agg') | |
| import matplotlib.pyplot as plt | |
| from matplotlib.colors import LinearSegmentedColormap | |
| def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False): | |
| # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib | |
| """ | |
| Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks | |
| :param nlabels: Number of labels (size of colormap) | |
| :param type: 'bright' for strong colors, 'soft' for pastel colors | |
| :param first_color_black: Option to use first color as black, True or False | |
| :param last_color_black: Option to use last color as black, True or False | |
| :param verbose: Prints the number of labels and shows the colormap. True or False | |
| :return: colormap for matplotlib | |
| """ | |
| if type not in ('bright', 'soft'): | |
| print ('Please choose "bright" or "soft" for type') | |
| return | |
| if verbose: | |
| print('Number of labels: ' + str(nlabels)) | |
| # Generate color map for bright colors, based on hsv | |
| if type == 'bright': | |
| randHSVcolors = [(np.random.uniform(low=0.0, high=1), | |
| np.random.uniform(low=0.2, high=1), | |
| np.random.uniform(low=0.9, high=1)) for i in range(nlabels)] | |
| # Convert HSV list to RGB | |
| randRGBcolors = [] | |
| for HSVcolor in randHSVcolors: | |
| randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2])) | |
| if first_color_black: | |
| randRGBcolors[0] = [0, 0, 0] | |
| if last_color_black: | |
| randRGBcolors[-1] = [0, 0, 0] | |
| random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) | |
| # Generate soft pastel colors, by limiting the RGB spectrum | |
| if type == 'soft': | |
| low = 0.6 | |
| high = 0.95 | |
| randRGBcolors = [(np.random.uniform(low=low, high=high), | |
| np.random.uniform(low=low, high=high), | |
| np.random.uniform(low=low, high=high)) for i in range(nlabels)] | |
| if first_color_black: | |
| randRGBcolors[0] = [0, 0, 0] | |
| if last_color_black: | |
| randRGBcolors[-1] = [0, 0, 0] | |
| random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels) | |
| # Display colorbar | |
| if verbose: | |
| from matplotlib import colors, colorbar | |
| from matplotlib import pyplot as plt | |
| fig, ax = plt.subplots(1, 1, figsize=(15, 0.5)) | |
| bounds = np.linspace(0, nlabels, nlabels + 1) | |
| norm = colors.BoundaryNorm(bounds, nlabels) | |
| cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None, | |
| boundaries=bounds, format='%1i', orientation=u'horizontal') | |
| return randRGBcolors, random_colormap | |