"""
Median Blur (OpenCV) + Histogram Equalization (scikit-image) on coloured images
Syntax: py *.py Input.png where *.py is the name of this file
--------------------------------------------------------------------------------
There are many filters that are designed to work with gray-scale images but not
with color images. To simplify the process of creating functions that can adapt
to RGB images, scikit-image provides the adapt_rgb decorator.

NOTE: A decorator in Python is any callable Python object that is used to modify 
a function or a class.      ---Reference: Sreenivas Bhattiprolu---
--------------------------------------------------------------------------------
"""
import cv2, sys, os
from skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
from skimage import filters
from skimage import io
import matplotlib.pyplot as plt
from skimage.color import rgb2gray

file_name = str(sys.argv[1])
im = os.getcwd() + "\\" + file_name
img = io.imread(im)

#Two ways to apply the filter on color images
#-------------------------------------------------------------------------------
#1)Separate R, G, B channels - apply filters - put channel back together
#2)Convert RGB to HSV - apply filter to V channel - convert RSV to RGB
#Too many lines of code needed for this, adapt_rgb decorator makes it easy

@adapt_rgb(each_channel)
def sobel_each(img):
    return filters.sobel(img)

@adapt_rgb(hsv_value)
def sobel_hsv(img):
    return filters.sobel(img)

each_channel_image = sobel_each(img)
hsv_value_image = sobel_hsv(img)

#Convert to grey if needed
#sobel_grey = rgb2gray(hsv_value_image)
#plt.imshow(sobel_grey)

#Decorators work on any function, including the ones using OpenCV filters
@adapt_rgb(each_channel)
def median_each(img, k):
    output_image = cv2.medianBlur(img, k)
    return (output_image)

median_using_cv2 = median_each(img, 3)
#plt.imshow(median_using_cv2)

#Histogram equalization on RGB channels may not yield desired results. Applying 
#it on V channel in HSV provides the best results
#-------------------------------------------------------------------------------
from skimage import exposure
@adapt_rgb(each_channel)
def eq_each(img):
    output_image = exposure.equalize_hist(img)
    return (output_image)

equ_RGB = eq_each(img)
#plt.imshow(equ_RGB)

@adapt_rgb(hsv_value)
def eq_hsv(img):
    output_image = exposure.equalize_hist(img)
    return (output_image)

equ_hsv = eq_hsv(img)
#plt.imshow(equ_hsv)

fig = plt.figure(figsize=(10, 10))

ax1 = fig.add_subplot(2,2,1); ax1.imshow(img)
ax1.title.set_text('Input Image')

ax2 = fig.add_subplot(2,2,2); ax2.imshow(equ_RGB)
ax2.title.set_text('Equalized using RGB channels')

ax3 = fig.add_subplot(2,2,3); ax3.imshow(equ_hsv)
ax3.title.set_text('Equalized using V channel in HSV')

plt.show()