输出:
<matplotlib.image.AxesImage at 0x7f9e6cd20048>def vertical_gradient_line(image, reverse=False):"""我们创建一个垂直梯度线 。形状 (1, image.shape[1], 3))如果reverse为False，则值从0增加到1，否则，值将从1递减到0 。"""number_of_columns = image.shape[1]if reverse:C = np.linspace(1, 0, number_of_columns)else:C = np.linspace(0, 1, number_of_columns)C = np.dstack((C, C, C))return Chorizontal_brush = vertical_gradient_line(windmills)tinted_windmills =windmills * horizontal_brushplt.axis("off")plt.imshow(tinted_windmills)输出:
<matplotlib.image.AxesImage at 0x7f9e6ccb3d68>
现在，我们将通过将Python函数的reverse参数设置为“True”来从右向左着色图像：
def vertical_gradient_line(image, reverse=False):"""我们创建一个水平梯度线 。形状 (1, image.shape[1], 3))如果reverse为False，则值从0增加到1，否则，值将从1递减到0 。"""number_of_columns = image.shape[1]if reverse:C = np.linspace(1, 0, number_of_columns)else:C = np.linspace(0, 1, number_of_columns)C = np.dstack((C, C, C))return Chorizontal_brush = vertical_gradient_line(windmills, reverse=True)tinted_windmills =windmills * horizontal_brushplt.axis("off")plt.imshow(tinted_windmills)输出:
<matplotlib.image.AxesImage at 0x7f9e6cbc82b0>def horizontal_gradient_line(image, reverse=False):"""我们创建一个垂直梯度线 。形状(image.shape[0], 1, 3))如果reverse为False，则值从0增加到1，否则，值将从1递减到0 。"""number_of_rows, number_of_columns = image.shape[:2]C = np.linspace(1, 0, number_of_rows)C = C[np.newaxis,:]C = np.concatenate((C, C, C)).transpose()C = C[:, np.newaxis]return Cvertical_brush = horizontal_gradient_line(windmills)tinted_windmills =windmills plt.imshow(tinted_windmills)输出:
<matplotlib.image.AxesImage at 0x7f9e6cb52390>
色调是由一种颜色与灰色的混合产生的，或由着色和阴影产生的 。
charlie = plt.imread('Chaplin.png')plt.gray()print(charlie)plt.imshow(charlie)[[ 0.164705890.168627460.17647059 ...,0.0.0.] [ 0.160784320.160784320.16470589 ...,0.0.0.] [ 0.156862750.156862750.16078432 ...,0.0.0.] ...,[ 0.0.0....,0.0.0.] [ 0.0.0....,0.0.0.] [ 0.0.0....,0.0.0.]]输出：
<matplotlib.image.AxesImage at 0x7f9e70047668>
给灰度图像着色
:http://scikit-image.org/docs/dev/auto_examples/plot_tinting_grayscale_images.html
在下面的示例中，我们将使用不同的颜色映射 。颜色映射可以在
matplotlib.pyplot.cm.datad中找到:
plt.cm.datad.keys()输出：
dict_keys(['afmhot', 'autumn', 'bone', 'binary', 'bwr', 'brg', 'CMRmap', 'cool', 'copper', 'cubehelix', 'flag', 'gnuplot', 'gnuplot2', 'gray', 'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow', 'seismic', 'spring', 'summer', 'terrain', 'winter', 'nipy_spectral', 'spectral', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PiYG', 'PRGn', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn', 'Reds', 'Spectral', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd', 'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow', 'gist_stern', 'gist_yarg', 'coolwarm', 'Wistia', 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c', 'Vega10', 'Vega20', 'Vega20b', 'Vega20c', 'afmhot_r', 'autumn_r', 'bone_r', 'binary_r', 'bwr_r', 'brg_r', 'CMRmap_r', 'cool_r', 'copper_r', 'cubehelix_r', 'flag_r', 'gnuplot_r', 'gnuplot2_r', 'gray_r', 'hot_r', 'hsv_r', 'jet_r', 'ocean_r', 'pink_r', 'prism_r', 'rainbow_r', 'seismic_r', 'spring_r', 'summer_r', 'terrain_r', 'winter_r', 'nipy_spectral_r', 'spectral_r', 'Blues_r', 'BrBG_r', 'BuGn_r', 'BuPu_r', 'GnBu_r', 'Greens_r', 'Greys_r', 'Oranges_r', 'OrRd_r', 'PiYG_r', 'PRGn_r', 'PuBu_r', 'PuBuGn_r', 'PuOr_r', 'PuRd_r', 'Purples_r', 'RdBu_r', 'RdGy_r', 'RdPu_r', 'RdYlBu_r', 'RdYlGn_r', 'Reds_r', 'Spectral_r', 'YlGn_r', 'YlGnBu_r', 'YlOrBr_r', 'YlOrRd_r', 'gist_earth_r', 'gist_gray_r', 'gist_heat_r', 'gist_ncar_r', 'gist_rainbow_r', 'gist_stern_r', 'gist_yarg_r', 'coolwarm_r', 'Wistia_r', 'Accent_r', 'Dark2_r', 'Paired_r', 'Pastel1_r', 'Pastel2_r', 'Set1_r', 'Set2_r', 'Set3_r', 'tab10_r', 'tab20_r', 'tab20b_r', 'tab20c_r', 'Vega10_r', 'Vega20_r', 'Vega20b_r', 'Vega20c_r'])import numpy as npimport matplotlib.pyplot as pltcharlie = plt.imread('Chaplin.png')#colormaps plt.cm.datad# cmaps = set(plt.cm.datad.keys())cmaps = {'afmhot', 'autumn', 'bone', 'binary', 'bwr', 'brg','CMRmap', 'cool', 'copper', 'cubehelix', 'Greens'}X = [(4,3,1, (1, 0, 0)), (4,3,2, (0.5, 0.5, 0)), (4,3,3, (0, 1, 0)),(4,3,4, (0, 0.5, 0.5)),(4,3,(5,8), (0, 0, 1)), (4,3,6, (1, 1, 0)),(4,3,7, (0.5, 1, 0) ),(4,3,9, (0, 0.5, 0.5)),(4,3,10, (0, 0.5, 1)), (4,3,11, (0, 1, 1)),(4,3,12, (0.5, 1, 1))]fig = plt.figure(figsize=(6, 5))#fig.subplots_adjust(bottom=0, left=0, top = 0.975, right=1)for nrows, ncols, plot_number, factor in X:sub = fig.add_subplot(nrows, ncols, plot_number)sub.set_xticks([])plt.colors()sub.imshow(charlie*0.0002, cmap=cmaps.pop())sub.set_yticks([])#fig.show()