爱站程序员基地以前照相没有像现在这样那么容易的，而在现在你只需要一部手机，就可以免费拍照，而在上一代人之前，业余艺术家和真正的艺术家拍照的费用非常昂贵，并且每张照片的成本也不是免费的。
我们拍照是为了及时地保存美好的瞬间，被保存的记忆可以随时在未来被"打开"。
这个过程就像腌制东西一样，所以我们要注意正确的防腐剂。虽然现在手机为我们提供了一系列的图像处理软件，但是如果我们需要处理大量的照片时，我们就需要其他的工具了，此时编程和Python就派上用场了。Python及其模块如Numpy、Scipy、Matplotlib和其他特殊模块提供了各种各样的函数，能够处理大量图片。
为了向你介绍必要的知识，本文Python教程将教你如何进行基本的图像处理和操作，为此我们使用模块NumPy、Matplotlib和SciPy。
我们从scipy包的misc工具开始。
# 以下行仅在Python notebook中需要加上:%matplotlib inlinefrom scipy import miscascent = misc.ascent()import matplotlib.pyplot as pltplt.gray()plt.imshow(ascent)plt.show()
除了显示图像之外，我们还可以看到带有刻度的轴。如果你需要一些关于大小和像素位置的方向时，这是很有用的，但在大多数情况下，你并不需要这些信息，则我们可以通过添加命令plt.axis("off")来去掉刻度和轴:
from scipy import miscascent = misc.ascent()import matplotlib.pyplot as pltplt.axis(\"off\") # 删除轴和刻度plt.gray()plt.imshow(ascent)plt.show()我们可以看到这个图像的类型是一个整数数组:ascent.dtype输出:dtype(\'int64\')
我们也可以检查图像的大小:
ascent.shape
输出:
(512,512)
misc包里还有一张浣熊的图片:
import scipy.miscface = scipy.misc.face()print(face.shape)print(face.max)print(face.dtype)plt.axis(\"off\")plt.gray()plt.imshow(face)plt.show()(768, 1024, 3)<built-in method max of numpy.ndarray object at 0x7f9e70102800>uint8import matplotlib.pyplot as plt
matplotlib只支持png图像
img = plt.imread(\'frankfurt.png\')print(img[:3])[[[ 0.41176471 0.56862748 0.80000001][ 0.40392157 0.56078434 0.79215688][ 0.40392157 0.56862748 0.79607844]...,[ 0.48235294 0.62352943 0.81960785][ 0.47843137 0.627451 0.81960785][ 0.47843137 0.62352943 0.82745099]][[ 0.40784314 0.56470591 0.79607844][ 0.40392157 0.56078434 0.79215688][ 0.40392157 0.56862748 0.79607844]...,[ 0.48235294 0.62352943 0.81960785][ 0.47843137 0.627451 0.81960785][ 0.48235294 0.627451 0.83137256]][[ 0.40392157 0.56862748 0.79607844][ 0.40392157 0.56862748 0.79607844][ 0.40392157 0.56862748 0.79607844]...,[ 0.48235294 0.62352943 0.81960785][ 0.48235294 0.62352943 0.81960785][ 0.48627451 0.627451 0.83137256]]]plt.axis(\"off\")imgplot = plt.imshow(img)lum_img = img[:,:,1]print(lum_img)[[ 0.56862748 0.56078434 0.56862748 ..., 0.62352943 0.6274510.62352943][ 0.56470591 0.56078434 0.56862748 ..., 0.62352943 0.627451 0.627451 ][ 0.56862748 0.56862748 0.56862748 ..., 0.62352943 0.623529430.627451 ]...,[ 0.31764707 0.32941177 0.32941177 ..., 0.30588236 0.31372550.31764707][ 0.31764707 0.3137255 0.32941177 ..., 0.3019608 0.321568640.33725491][ 0.31764707 0.3019608 0.33333334 ..., 0.30588236 0.321568640.33333334]]plt.axis(\"off\")imgplot = plt.imshow(lum_img)
色彩、色度和色调
现在，我们将展示如何给图像着色。色彩是色彩理论的一种表达，是画家常用的一种技法。想到画家就不得不提荷兰，所以在下一个例子中，我们使用荷兰风车的图片来演示。
windmills = plt.imread(\'windmills.png\')plt.axis(\"off\")plt.imshow(windmills)
输出:
<matplotlib.image.AxesImage at 0x7f9e77f02f98>
我们现在给图像着色，用白色来增加图像的亮度，为此，我们编写了一个Python函数，它接受一个图像和一个百分比值作为参数。设置"百分比"为0不会改变图像，设置为1表示图像将完全变白:
import numpy as npimport matplotlib.pyplot as pltdef tint(imag, percent):\"\"\"imag: 图像percent: 0，图像将保持不变，1，图像将完全变白色，值应该在0~1\"\"\"tinted_imag = imag + (np.ones(imag.shape) - imag) * percentreturn tinted_imagwindmills = plt.imread(\'windmills.png\')tinted_windmills = tint(windmills, 0.8)plt.axis(\"off\")plt.imshow(tinted_windmills)
输出:
<matplotlib.image.AxesImage at 0x7f9e6cd99978>阴影是一种颜色与黑色的混合，它减少了亮度。import numpy as npimport matplotlib.pyplot as pltdef shade(imag, percent):\"\"\"imag: 图像percent: 0，图像将保持不变，1，图像将完全变黑，值应该在0~1\"\"\"tinted_imag = imag * (1 - percent)return tinted_imagwindmills = plt.imread(\'windmills.png\')tinted_windmills = shade(windmills, 0.7)plt.imshow(tinted_windmills)
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，形状为(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，形为(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，形状为(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 = windmillsplt.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)[[ 0.16470589 0.16862746 0.17647059 ..., 0. 0. 0. ][ 0.16078432 0.16078432 0.16470589 ..., 0. 0. 0. ][ 0.15686275 0.15686275 0.16078432 ..., 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()
参考链接：Python开发/
