一般的濾波器都是針對灰度圖像的，scikit-image 庫提供了針對彩色圖像濾波的decorator：adapt_rgb，adapt_rgb 提供兩種形式的濾波，一種是對rgb三個通道分別進行處理，另外一種方式是將rgb轉為hsv顏色模型，然后針對v通道進行處理，最后再轉回rgb顏色模型。

針對模式一，稱為 each_channel
@adapt_rgb(each_channel)
def sobel_each(image):
return filters.sobel(image)

模式二稱為 hsv_value
@adapt_rgb(hsv_value)
def sobel_hsv(image):
return filters.sobel(image)

利用上述兩種模式，可以對彩色圖像濾波，下面是完整的用例代碼;
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()

原圖：

效果圖: