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182 lines
669 KiB
Markdown
182 lines
669 KiB
Markdown
# 图像基础
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导入相应的包：
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In [1]:
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```py
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import matplotlib.pyplot as plt
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import matplotlib.image as mpimg
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import numpy as np
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%matplotlib inline
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```
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## 导入图像
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我们首先导入上面的图像，注意 `matplotlib` 默认只支持 `PNG` 格式的图像，我们可以使用 `mpimg.imread` 方法读入这幅图像：
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In [2]:
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```py
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img = mpimg.imread('stinkbug.png')
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```
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In [3]:
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```py
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img.shape
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```
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Out[3]:
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```py
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(375L, 500L, 3L)
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```
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这是一个 `375 x 500 x 3` 的 `RGB` 图像，并且每个像素使用 uint8 分别表示 `RGB` 三个通道的值。不过在处理的时候，`matplotlib` 将它们的值归一化到 `0.0~1.0` 之间：
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In [4]:
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```py
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img.dtype
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```
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Out[4]:
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```py
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dtype('float32')
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```
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## 显示图像
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使用 `plt.imshow()` 可以显示图像：
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In [5]:
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```py
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imgplot = plt.imshow(img)
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```
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## 伪彩色图像
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从单通道模拟彩色图像：
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In [6]:
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```py
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lum_img = img[:,:,0]
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imgplot = plt.imshow(lum_img)
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```
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## 改变 colormap
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In [7]:
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```py
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imgplot = plt.imshow(lum_img)
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imgplot.set_cmap('hot')
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```
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In [8]:
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```py
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imgplot = plt.imshow(lum_img)
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imgplot.set_cmap('spectral')
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```
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显示色度条：
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In [9]:
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```py
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imgplot = plt.imshow(lum_img)
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imgplot.set_cmap('spectral')
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plt.colorbar()
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plt.show()
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```
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## 限制显示范围
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先查看直方图：
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In [10]:
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```py
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plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')
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plt.show()
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```
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将显示范围设为 `0.0-0.7`：
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In [11]:
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```py
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imgplot = plt.imshow(lum_img)
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imgplot.set_clim(0.0,0.7)
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```
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## resize 操作
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In [12]:
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```py
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from PIL import Image
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img = Image.open('stinkbug.png')
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rsize = img.resize((img.size[0]/10,img.size[1]/10))
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rsizeArr = np.asarray(rsize)
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imgplot = plt.imshow(rsizeArr)
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```
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上面我们将这个图像使用 PIL 的 `Image` 对象导入，并将其 `resize` 为原来的 1/100，可以看到很多细节都丢失了。
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在画图时，由于画面的大小与实际像素的大小可能不一致，所以不一致的地方会进行插值处理，尝试一下不同的插值方法：
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In [13]:
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```py
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imgplot = plt.imshow(rsizeArr)
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imgplot.set_interpolation('nearest')
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```
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In [14]:
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```py
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imgplot = plt.imshow(rsizeArr)
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imgplot.set_interpolation('bicubic')
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```
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