RGB for HSV Python, hue changing continuously
What I'm trying to do: Continuously change the hue value of the image, from 0 to 360, keeping one image for each hue.
How I am trying: I started by using the code I found on this link , then modified it to change the hue and keep the images.
What is the problem: The code from the above link does not seem to save the image as true HSV because when it merges the image it uses the RGB mode of the image. But I cannot find a way to do this HSV.
def hueChange(img, hue):
if isinstance(img, Image.Image):
img.load()
r, g, b = img.split()
h_data = []
s_data = []
v_data = []
for rd, gr, bl in zip(r.getdata(), g.getdata(), b.getdata()):
h, s, v = colorsys.rgb_to_hsv(rd / 255., bl / 255., gr / 255.)
h_data.append(int(hue))
s_data.append(int(s * 255.))
v_data.append(int(v * 255.))
r.putdata(h_data)
g.putdata(s_data)
b.putdata(v_data)
return toRGB(Image.merge('RGB',(r,g,b)))
else:
return None
# Don't care about the range indices, they are just for testing
for hue in range(1, 255, 30):
in_name = '/Users/cgois/Dropbox/Python/fred/fred' + str(hue) + '.jpg'
img = Image.open(in_name)
img = hueChange(img, hue)
out_name = '/Users/cgois/Dropbox/Python/fred/hue/fred_hue' + str(hue) + '.png'
img.save(out_name)
The last solution I tried was to do the conversion as above and then convert it back to RGB using similar code in hueChange (...). However, the only effect was that the output images had a * (single) * colored caption on top of them.
Any ideas? Thank you for your time (:
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Use colorsys.hsv_to_rgb
to convert tuples (H, S, V) back to RGB:
import os
import colorsys
import Image
def hueChange(img, hue):
# It better to raise an exception than silently return None if img is not
# an Image.
img.load()
r, g, b = img.split()
r_data = []
g_data = []
b_data = []
for rd, gr, bl in zip(r.getdata(), g.getdata(), b.getdata()):
h, s, v = colorsys.rgb_to_hsv(rd / 255., bl / 255., gr / 255.)
rgb = colorsys.hsv_to_rgb(hue/360., s, v)
rd, gr, bl = [int(x*255.) for x in rgb]
r_data.append(rd)
g_data.append(gr)
b_data.append(bl)
r.putdata(r_data)
g.putdata(g_data)
b.putdata(b_data)
return Image.merge('RGB',(r,g,b))
filename = 'image.png'
basename, ext = os.path.splitext(filename)
img = Image.open(filename).convert('RGB')
for hue in range(1, 360, 30):
img2 = hueChange(img, hue)
out_name = '{}_hue{:03d}.jpg'.format(basename, hue)
img2.save(out_name)
Changing pixel values by pixel can be very slow for large images. Use NumPy for better performance. (NumPy features were taken from here ):
import os
import Image
import numpy as np
def rgb_to_hsv(rgb):
# Translated from source of colorsys.rgb_to_hsv
# r,g,b should be a numpy arrays with values between 0 and 255
# rgb_to_hsv returns an array of floats between 0.0 and 1.0.
rgb = rgb.astype('float')
hsv = np.zeros_like(rgb)
# in case an RGBA array was passed, just copy the A channel
hsv[..., 3:] = rgb[..., 3:]
r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
maxc = np.max(rgb[..., :3], axis=-1)
minc = np.min(rgb[..., :3], axis=-1)
hsv[..., 2] = maxc
mask = maxc != minc
hsv[mask, 1] = (maxc - minc)[mask] / maxc[mask]
rc = np.zeros_like(r)
gc = np.zeros_like(g)
bc = np.zeros_like(b)
rc[mask] = (maxc - r)[mask] / (maxc - minc)[mask]
gc[mask] = (maxc - g)[mask] / (maxc - minc)[mask]
bc[mask] = (maxc - b)[mask] / (maxc - minc)[mask]
hsv[..., 0] = np.select(
[r == maxc, g == maxc], [bc - gc, 2.0 + rc - bc], default=4.0 + gc - rc)
hsv[..., 0] = (hsv[..., 0] / 6.0) % 1.0
return hsv
def hsv_to_rgb(hsv):
# Translated from source of colorsys.hsv_to_rgb
# h,s should be a numpy arrays with values between 0.0 and 1.0
# v should be a numpy array with values between 0.0 and 255.0
# hsv_to_rgb returns an array of uints between 0 and 255.
rgb = np.empty_like(hsv)
rgb[..., 3:] = hsv[..., 3:]
h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
i = (h * 6.0).astype('uint8')
f = (h * 6.0) - i
p = v * (1.0 - s)
q = v * (1.0 - s * f)
t = v * (1.0 - s * (1.0 - f))
i = i % 6
conditions = [s == 0.0, i == 1, i == 2, i == 3, i == 4, i == 5]
rgb[..., 0] = np.select(conditions, [v, q, p, p, t, v], default=v)
rgb[..., 1] = np.select(conditions, [v, v, v, q, p, p], default=t)
rgb[..., 2] = np.select(conditions, [v, p, t, v, v, q], default=p)
return rgb.astype('uint8')
def hueChange(img, hue):
arr = np.array(img)
hsv = rgb_to_hsv(arr)
hsv[..., 0] = hue
rgb = hsv_to_rgb(hsv)
return Image.fromarray(rgb, 'RGB')
filename = 'image.png'
basename, ext = os.path.splitext(filename)
img = Image.open(filename).convert('RGB')
for hue in np.linspace(0, 360, 8):
img2 = hueChange(img, hue/360.)
out_name = '{}_hue{:03d}.jpg'.format(basename, int(hue))
img2.save(out_name)
According to this page , when Photoshop Colorize is unchecked, the hue of each pixel is shifted by the same amount. When the Colorize check box is selected, the tint of each pixel is set to the same amount.
So, to change the hue by a fixed amount, use:
def hueShift(img, amount):
arr = np.array(img)
hsv = rgb_to_hsv(arr)
hsv[..., 0] = (hsv[..., 0]+amount) % 1.0
rgb = hsv_to_rgb(hsv)
return Image.fromarray(rgb, 'RGB')
filename = 'without_colorize.jpg'
basename, ext = os.path.splitext(filename)
img = Image.open(filename).convert('RGB')
for amount in (50, 133):
img2 = hueShift(img, amount/360.)
out_name = '{}_hue{:+03d}.jpg'.format(basename, int(amount))
img2.save(out_name)
without_colorize.jpg:
color tone + 50:
hue + 133:
Note. By changing the shade of a certain area from the hair and face, it became a different color with a clear, unnatural border. It looks like my code doesn't exactly reproduce what Photoshop does ...
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