Python编程对彩色图像滤波处理

一般的滤波器都是针对灰度图像的，scikit-image 库提供了针对彩色图像滤波的decorator：adapt_rgb，adapt_rgb 提供两种形式的滤波，一种是对rgb三个通道分别进行处理，另外一种方式是将rgb转为hsv颜色模型，然后针对v通道进行处理，最后再转回rgb颜色模型。

针对模式一，称为 each_channel
@adapt_rgb(each_channel)
def sobel_each(image):
return filters.sobel(image)

模式二称为 hsv_value
@adapt_rgb(hsv_value)
def sobel_hsv(image):
return filters.sobel(image)

利用上述两种模式，可以对彩色图像滤波，下面是完整的用例代码;
from skimage import data
from skimage.exposure import rescale_intensity
import matplotlib.pyplot as plt

from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
from skimage import filters

@adapt_rgb(each_channel)
def sobel_each(image):
return filters.sobel(image)

@adapt_rgb(hsv_value)
def sobel_hsv(image):
return filters.sobel(image)

image = data.astronaut()

# display the original image
plt.imshow(image)

fig = plt.figure(figsize=(16, 9))
ax_each = fig.add_subplot(121, adjustable='box-forced')
ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each, adjustable='box-forced')

# We use 1 - sobel_each(image)
# but this will not work if image is not normalized
ax_each.imshow(rescale_intensity(1 - sobel_each(image)))
ax_each.set_xticks([]), ax_each.set_yticks([])
ax_each.set_title("Sobel filter computed on individual RGB channels")

# We use 1 - sobel_hsv(image) but this will not work if image is not normalized
ax_hsv.imshow(rescale_intensity(1 - sobel_hsv(image)))
ax_hsv.set_xticks([]), ax_hsv.set_yticks([])
ax_hsv.set_title("Sobel filter computed on Value converted image (HSV)")

plt.show()

原图：

效果图: