Solución:
Hay un código Python para convertir RGB a HSV (y viceversa) en el módulo colorsys de la biblioteca estándar. Mi primer intento usó
rgb_to_hsv=np.vectorize(colorsys.rgb_to_hsv)
hsv_to_rgb=np.vectorize(colorsys.hsv_to_rgb)
para vectorizar esas funciones. Desafortunadamente, usando np.vectorize
da como resultado un código bastante lento.
Pude obtener una velocidad de aproximadamente 5 veces al traducir colorsys.rgb_to_hsv
y colorsys.hsv_to_rgb
en operaciones nativas numpy.
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 shift_hue(arr,hout):
hsv=rgb_to_hsv(arr)
hsv[...,0]=hout
rgb=hsv_to_rgb(hsv)
return rgb
img = Image.open('tweeter.png').convert('RGBA')
arr = np.array(img)
if __name__=='__main__':
green_hue = (180-78)/360.0
red_hue = (180-180)/360.0
new_img = Image.fromarray(shift_hue(arr,red_hue), 'RGBA')
new_img.save('tweeter_red.png')
new_img = Image.fromarray(shift_hue(arr,green_hue), 'RGBA')
new_img.save('tweeter_green.png')
rendimientos
y
Con una copia reciente de Pillow, probablemente se debería usar Image.convert ():
def rgb2hsv(image: PIL.Image.Image):
return image.convert('HSV')
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