Color rotation (hue)

[Tony Yu]

Hi Jean-Patrick,

There are a few possible issues. One is simply a difference in data type.
The angles you describe go from 0-to-180, but in scikit-image's
representation of these images, it goes from 0-to-1. Also, you're only
passing the red channel to the rgb image. I'm not sure what effect that has
on the final result.

In any case, I think the example below might be closer to what you're after.

Best,
-Tony

#---

import numpy as np
import matplotlib.pyplot as plt
from skimage import color
from skimage import data


def colorize(image, hue):
    """Return image tinted by the given hue based on a grayscale image."""
    hsv = color.rgb2hsv(color.gray2rgb(image))
    hsv[:, :, 0] = hue
    hsv[:, :, 1] = 1  # Turn up the saturation; we want the color to pop!
    return color.hsv2rgb(hsv)


image = data.camera()[::2, ::2]

hue_rotations = np.linspace(0, 1, 6)  # 0--1 is equivalent to 0--180
colorful_images = [colorize(image, hue) for hue in hue_rotations]

fig, axes = plt.subplots(nrows=2, ncols=3)

for ax, array in zip(axes.flat, colorful_images):
    ax.imshow(array, vmin=0, vmax=1)
    ax.set_axis_off()

plt.show()



On Wed, Mar 19, 2014 at 4:20 AM, Jean-Patrick Pommier <
jeanpatrick.pommier at gmail.com> wrote:

>
>
> Le mercredi 19 mars 2014 10:16:45 UTC+1, Jean-Patrick Pommier a écrit :
>
>> Hi,
>> I 'd like to modify continuously a pure red image to get an orange,
>> yellow, green, light blue, blue, purple image.
>> The idea is to convert a rgb image, to convert it into hsv color space,
>> to modify the hue value then to back convert into rgb color space.
>> The problem is that only green and blue can be obtained but no
>> intermediate color:
>>
>>
>> <https://lh4.googleusercontent.com/-s_Acsx0EmRY/UylefCuSGnI/AAAAAAAABsQ/T7pAI2f-D6w/s1600/color_rotation.png>
>>
>> According to adobe<http://dba.med.sc.edu/price/irf/Adobe_tg/models/hsb.html>,
>> adding a hue equal to 36 should yield a yellow image.
>>
>> *Here's the code:*
>>
>> from skimage import io
>> from skimage import color
>> from scipy import ndimage as nd
>> import numpy as np
>> from matplotlib import pyplot as plt
>> import os
>>
>> cy55 = io.imread('/home/jeanpat/MFISH/PSI/P07/01/Cy5/P070109C.tif')
>>
>> zero = np.zeros(cy55.shape,dtype=np.uint8)
>> rgb0 = np.dstack([cy55, zero,zero])
>>
>> hue_rotations = [18, 36,72,90,108]
>> images = {}
>> images[0] = rgb0
>> hsv0 = color.rgb2hsv(rgb0)
>> for hue in hue_rotations:
>>     hsv = np.copy(hsv0)
>>     hsv[:,:,0] = hsv[:,:,0]+ hue
>>     rgb = color.hsv2rgb(hsv)
>>     images[hue] = rgb
>>
>> The code to display the images is:
>
> i = 1
> plt.figure(num=None, figsize=(15, 5),  facecolor='w', edgecolor='k')
> for hue in np.sort(images.keys()):
>     plt.subplot(1,6,i,xticks=[],yticks=[])
>     plt.title('hue='+str(hue))
>     plt.imshow(images[hue])
>     i = i +1
> plt.show()
>
> Any greyscaled image should be ok but the image used here is:
>>
>>
>> <https://lh5.googleusercontent.com/-whwEGZWWhIE/UylgLDhf0VI/AAAAAAAABsc/02kl9UCvkAo/s1600/P070109C.tif>
>>
>>
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