# -*- coding: utf-8 -*-
# @File : rgb2hls.py
# @Info : @ TSMC
# @Desc :
import colorsys
import numpy as np
import scipy.misc
import tensorflow as tf
from PIL import Image
def rgb2hls(img):
""" note: elements in img is a float number less than 1.0 and greater than 0.
:param img: an numpy ndarray with shape NHWC
:return:
"""
assert len(img.shape) == 3
hue = np.zeros_like(img[:, :, 0])
luminance = np.zeros_like(img[:, :, 0])
saturation = np.zeros_like(img[:, :, 0])
for x in range(height):
for y in range(width):
r, g, b = img[x, y]
h, l, s = colorsys.rgb_to_hls(r, g, b)
hue[x, y] = h
luminance[x, y] = l
saturation[x, y] = s
return hue, luminance, saturation
def np_rgb2hls(img):
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
maxc = np.max(img, -1)
minc = np.min(img, -1)
l = (minc + maxc) / 2.0
if np.array_equal(minc, maxc):
return np.zeros_like(l), l, np.zeros_like(l)
smask = np.greater(l, 0.5).astype(np.float32)
s = (1.0 - smask) * ((maxc - minc) / (maxc + minc)) + smask * ((maxc - minc) / (2.001 - maxc - minc))
rc = (maxc - r) / (maxc - minc + 0.001)
gc = (maxc - g) / (maxc - minc + 0.001)
bc = (maxc - b) / (maxc - minc + 0.001)
rmask = np.equal(r, maxc).astype(np.float32)
gmask = np.equal(g, maxc).astype(np.float32)
rgmask = np.logical_or(rmask, gmask).astype(np.float32)
h = rmask * (bc - gc) + gmask * (2.0 + rc - bc) + (1.0 - rgmask) * (4.0 + gc - rc)
h = np.remainder(h / 6.0, 1.0)
return h, l, s
def tf_rgb2hls(img):
""" note: elements in img all in [0,1]
:param img: a tensor with shape NHWC
:return:
"""
assert img.get_shape()[-1] == 3
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
maxc = tf.reduce_max(img, -1)
minc = tf.reduce_min(img, -1)
l = (minc + maxc) / 2.0
# if tf.reduce_all(tf.equal(minc, maxc)):
# return tf.zeros_like(l), l, tf.zeros_like(l)
smask = tf.cast(tf.greater(l, 0.5), tf.float32)
s = (1.0 - smask) * ((maxc - minc) / (maxc + minc)) + smask * ((maxc - minc) / (2.001 - maxc - minc))
rc = (maxc - r) / (maxc - minc + 0.001)
gc = (maxc - g) / (maxc - minc + 0.001)
bc = (maxc - b) / (maxc - minc + 0.001)
rmask = tf.equal(r, maxc)
gmask = tf.equal(g, maxc)
rgmask = tf.cast(tf.logical_or(rmask, gmask), tf.float32)
rmask = tf.cast(rmask, tf.float32)
gmask = tf.cast(gmask, tf.float32)
h = rmask * (bc - gc) + gmask * (2.0 + rc - bc) + (1.0 - rgmask) * (4.0 + gc - rc)
h = tf.mod(h / 6.0, 1.0)
h = tf.expand_dims(h, -1)
l = tf.expand_dims(l, -1)
s = tf.expand_dims(s, -1)
x = tf.concat([tf.zeros_like(l), l, tf.zeros_like(l)], -1)
y = tf.concat([h, l, s], -1)
return tf.where(condition=tf.reduce_all(tf.equal(minc, maxc)), x=x, y=y)
if __name__ == '__main__':
"""
HLS: Hue, Luminance, Saturation
H: position in the spectrum
L: color lightness
S: color saturation
"""
avatar = Image.open("hue.jpg")
width, height = avatar.size
print("width: {}, height: {}".format(width, height))
img = np.array(avatar)
img = img / 255.0
print(img.shape)
# # hue, luminance, saturation = rgb2hls(img)
# hue, luminance, saturation = np_rgb2hls(img)
img_tensor = tf.convert_to_tensor(img, tf.float32)
hls = tf_rgb2hls(img_tensor)
h, l, s = hls[:, :, 0], hls[:, :, 1], hls[:, :, 2]
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
hue, luminance, saturation = sess.run([h, l, s])
scipy.misc.imsave("hls_h_.jpg", hue)
scipy.misc.imsave("hls_l_.jpg", luminance)
scipy.misc.imsave("hls_s_.jpg", saturation)