在图像处理，尤其是医学图像处理的过程中，我们经常会遇到将灰度图映射成彩色图的情形，如将灰度图根据灰度的高低映射成彩虹色图。这个过程我们通常将之称为伪彩映射，伪彩映射的关键在于找到合适的彩色映射表，即colormap，也称color bar。
前段时间做了一个涉及到伪彩映射的项目，在找colormap的过程中，我发现Python的matplotlib模块中内嵌了一大批常用的colormaps，使用如下代码：
import numpy as np import matplotlib.pyplot as plt # Have colormaps separated into categories: # http://matplotlib.org/examples/color/colormaps_reference.html cmaps = [('Perceptually Uniform Sequential', ['viridis', 'inferno', 'plasma', 'magma']), ('Sequential', ['Blues', 'BuGn', 'BuPu', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']), ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool', 'copper', 'gist_heat', 'gray', 'hot', 'pink', 'spring', 'summer', 'winter']), ('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral', 'seismic']), ('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3']), ('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern', 'brg', 'CMRmap', 'cubehelix', 'gnuplot', 'gnuplot2', 'gist_ncar', 'nipy_spectral', 'jet', 'rainbow', 'gist_rainbow', 'hsv', 'flag', 'prism'])] nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps) gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) def plot_color_gradients(cmap_category, cmap_list): fig, axes = plt.subplots(nrows=nrows) fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99) axes[0].set_title(cmap_category + ' colormaps', fontsize=14) for ax, name in zip(axes, cmap_list): ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name)) pos = list(ax.get_position().bounds) x_text = pos[0] - 0.01 y_text = pos[1] + pos[3]/2. fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10) # Turn off *all* ticks & spines, not just the ones with colormaps. for ax in axes: ax.set_axis_off() for cmap_category, cmap_list in cmaps: plot_color_gradients(cmap_category, cmap_list) plt.show() 我们可以得到matplotlib中内嵌的colormaps（应该是全部，但不是很确定）：
如何获取colormap
如此众多的colormaps，应该能满足我们大部分的需求。当然，我们更关心的是如何将这些colormap中具体的数值导出来，这样使用起来会更加的灵活方便。当然，只要你想做到，是没有什么能够阻拦你的。
以获取最常用的jet映射表为例，我们可以使用如下代码分别获取整型和浮点型的jet map，并将其保存在txt文件中：
from matplotlib import cm def get_jet(): colormap_int = np.zeros((256, 3), np.uint8) colormap_float = np.zeros((256, 3), np.float) for i in range(0, 256, 1): colormap_float[i, 0] = cm.jet(i)[0] colormap_float[i, 1] = cm.jet(i)[1] colormap_float[i, 2] = cm.jet(i)[2] colormap_int[i, 0] = np.int_(np.round(cm.jet(i)[0] * 255.0)) colormap_int[i, 1] = np.int_(np.round(cm.jet(i)[1] * 255.0)) colormap_int[i, 2] = np.int_(np.round(cm.jet(i)[2] * 255.0)) np.savetxt("jet_float.txt", colormap_float, fmt = "%f", delimiter = ' ', newline = '\n') np.savetxt("jet_int.txt", colormap_int, fmt = "%d", delimiter = ' ', newline = '\n') print colormap_int return 获取其他种类的colormap与之类似：
def get_spectral(): colormap_int = np.zeros((256, 3), np.uint8) colormap_float = np.zeros((256, 3), np.float) for i in range(0, 256, 1): colormap_float[i, 0] = cm.spectral(i)[0] colormap_float[i, 1] = cm.spectral(i)[1] colormap_float[i, 2] = cm.spectral(i)[2] colormap_int[i, 0] = np.int_(np.round(cm.spectral(i)[0] * 255.0)) colormap_int[i, 1] = np.int_(np.round(cm.spectral(i)[1] * 255.0)) colormap_int[i, 2] = np.int_(np.round(cm.spectral(i)[2] * 255.0)) np.savetxt("spectral_float.txt", colormap_float, fmt = "%f", delimiter = ' ', newline = '\n') np.savetxt("spectral_int.txt", colormap_int, fmt = "%d", delimiter = ' ', newline = '\n') print colormap_int return 当然，我们也可以根据需要对获得的colormap中的值进行调整。当我们获得心仪的colormap之后，伪彩映射就成了水到渠成的事情了。
下面是用Python写的伪彩映射的代码：
def gray2color(gray_array, color_map): rows, cols = gray_array.shape color_array = np.zeros((rows, cols, 3), np.uint8) for i in range(0, rows): for j in range(0, cols): color_array[i, j] = color_map[gray_array[i, j]] #color_image = Image.fromarray(color_array) return color_array def test_gray2color(): gray_image = Image.open('Image.png').convert("L") gray_array = np.array(gray_image) figure() subplot(211) plt.imshow(gray_array, cmap = 'gray') jet_map = np.loadtxt('E:\\Development\\Thermal\\ColorMaps\\jet_int.txt', dtype = np.int) color_jet = gray2color(gray_array, jet_map) subplot(212) plt.imshow(color_jet) show() return
这一篇先就介绍到这里，后面一篇将向大伙介绍如何使用python生成自定义的colormap。
