import numpy as np
import scipy.signal  # pip3 install scipy
from skimage import color, io  # pip3 install scikit-image

full = color.rgba2rgb(io.imread("lena.png"))

h, w, c = full.shape

# Global contrast, RGB
i = full * 2 - 0.5
i = i.clip(0.0, 1.0)
rgb_result = full.copy()
rgb_result[:, :w//2, :] = i[:, :w//2, :]

io.imshow(rgb_result)
io.show()

# Lab global contrast
lab = color.rgb2lab(full)
# Numpy Indexing for images:
# http://scikit-image.org/docs/dev/user_guide/numpy_images.html
lab[:, :, 0] *= 2
lab[:, :, 0] -= 50
lab[:, :, 0] = lab[:, :, 0].clip(0.0)
# Warning: turns back the image to RGB, but using 0.0 - 1.0 float
# encoding (unlike the source image).

i = color.lab2rgb(lab)

lab_contrast = full.copy()
lab_contrast[:, :w//2, :] = i[:, :w//2, :]

io.imshow(lab_contrast)
io.show()


# Lab local contrast with simple convolution matrix
lab = color.rgb2lab(full)
amount = .05
convmat = np.ones([5, 5]) * -amount
convmat[2, 2] = 25*amount + 1
print(convmat)
convoluted = scipy.signal.convolve2d(lab[:, :, 0],
                                     convmat,
                                     mode='same')
lab[:, :, 0] = convoluted.clip(0.0)
print("lab2rgb")
i = color.lab2rgb(lab)

lab_result = full.copy()
lab_result[:, :w//2, :] = i[:, :w//2, :]

io.imshow(lab_result)
io.show()