Created
October 18, 2012 01:40
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Mock up of decorator to handle RGB images in grayscale images
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import numpy as np | |
import matplotlib.pyplot as plt | |
from skimage import data, color, filter, exposure | |
from skimage.util import dtype | |
from scipy.ndimage import gaussian_filter | |
lab_range = (0, 100) | |
def adapt_rgb(image_filter): | |
def image_filter_adapted(image, *args, **kwargs): | |
rgb_behavior = kwargs.pop('rgb_behavior', 'lightness') | |
if color.is_rgb(image): | |
if rgb_behavior == 'lightness': | |
lab = color.rgb2lab(image) | |
lightness = lab[:, :, 0] | |
lightness = exposure.rescale_intensity(lightness, | |
in_range=lab_range, | |
out_range=(0, 1)) | |
lightness = image_filter(lightness) | |
in_range = dtype.dtype_range[lightness.dtype.type] | |
if np.all(lightness > 0): | |
in_range = (0, in_range[1]) | |
lightness = exposure.rescale_intensity(lightness, | |
in_range=in_range, | |
out_range=lab_range) | |
lab[:, :, 0] = lightness | |
out = color.lab2rgb(lab) | |
elif rgb_behavior == 'hsv': | |
hsv = color.rgb2hsv(image) | |
value = hsv[:, :, 2] | |
value = image_filter(value) | |
hsv[:, :, 2] = value | |
out = color.hsv2rgb(hsv) | |
elif rgb_behavior == 'each channel': | |
c_new = [image_filter(c) for c in image.T] | |
out = np.array(c_new).T | |
else: | |
out = image_filter(image) | |
return out | |
return image_filter_adapted | |
@adapt_rgb | |
def edges(image): | |
return filter.sobel(image) | |
@adapt_rgb | |
def smooth(image): | |
# return filter.tv_denoise(image) # slow | |
return gaussian_filter(image, 10) | |
fig, axes = plt.subplots(ncols=3, nrows=2) | |
axes[0, 0].imshow(smooth(data.lena(), rgb_behavior='each channel')) | |
axes[1, 0].imshow(edges(data.lena(), rgb_behavior='each channel')) | |
axes[0, 1].imshow(smooth(data.lena(), rgb_behavior='hsv')) | |
axes[1, 1].imshow(edges(data.lena(), rgb_behavior='hsv')) | |
axes[0, 2].imshow(smooth(data.lena(), rgb_behavior='lightness')) | |
axes[1, 2].imshow(edges(data.lena(), rgb_behavior='lightness')) | |
plt.show() |
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