import numpy as np
import matplotlib.pyplot as plt
from scipy import misc as mc

img = mc.imread('plik.png')




def rgb2gray(img):
	r = img[:, :, 0]
	g = img[:, :, 1]
	b = img[:, :, 2]
	s1 = 0.2126 * r + 0.7152 * g + 0.0722 * b
	return s1

def histogram(count,image):
	offset=255.0/count
	histogramValueArray = []
	i = 0
	while (i < 255.0):
		i=i+offset
		histogramValueArray.append(np.sum(((image >= (i-offset)) & (image <= i))))
	return histogramValueArray


def linear_expansion(image):
	# linear expansion
	minVal = np.amin(image)
	maxVal = np.amax(image)
	test = 255
	height = img.shape[0]
	width = img.shape[1]
	s = (height,width)
	output = np.zeros(s)
	for i in range(0,height):
		for j in range(0,width):
			if (image[i][j] < 0):
				output[i][j] = 0
			elif (image[i][j] > test):
				output[i][j]=test
			else:
				output[i][j]=((image[i][j]-minVal)*(test/(maxVal-minVal)))
	return output

def nonlinear_expansion(image):
	# nonlinear expansion
	height = img.shape[0]
	width = img.shape[1]
	size = (height,width)
	output = np.zeros(size)

	test1 = image/255
	test2 = np.zeros(size)
	#power
	test1 = np.power(test1,2)
	#sqrt
	#test1 = np.power(test1,0.5)
	for i in range(0,height):
		for j in range(0,width):
			#log10
			#pw.gda.pl -> czynnik normalizujacy
			#test1[i][j] = np.log(test1[i][j]+1)*1.4428
			#exp
			#test1[i][j] = np.exp(test1[i][j]-1)/1.7183
			if ((test1[i][j] > 0) & (test1[i][j] < 1)):
				test2[i][j]=test1[i][j]
			elif (test1[i][j] < 0):
				test2[i][j]=0
			elif (test1[i][j] > 1):
				test2[i][j]=1
	test2=test2*255
	return test2

def balance(image):
	# returns balanced image
	height = img.shape[0]
	width = img.shape[1]
	size = (height,width)
	output = np.zeros(size)

	D0 = 0
	D = np.zeros(256)
	LUT = np.zeros(256)

	D[0] = (np.sum(image)/(height*width))
	if( D[0] != 0 ):
		D0 = D[0]
	for i in range(0,255):
		D[i+1] = D[i] + (np.sum(image)/(height*width))
		if (D0.astype('int') != 0 & D[i+1].astype('int') != 0 ):
			D0 = D[i+1]
	LUT = (D - D0) / (1 - D0)
	for i in range(0,height):
		for j in range(0,width):
			output[i][j] = LUT[image[i][j].astype('int') + 1]
	output = output * -1
	return output

imggray = rgb2gray(img)

# first histogram

histogramArr = (histogram(1000,imggray))
tab = np.linspace(0,255,len( histogramArr) )
plt.bar(tab,  histogramArr)
plt.show();

# test the histogram function
#A = np.array([[1, 0, 0.25], [1, 0.75, 0.5], [1, 0.5, 0.25]]) * 255.0
#print (A)
#print (histogram( 3, A )) # [3 2 4]
#print (histogram( 5, A )) # [1 2 2 1 3]

# linear_expansion
#linearExpansionArr = histogram(1000,linear_expansion(imggray))
#tab = np.linspace(0,255,len(linearExpansionArr) )
#plt.bar(tab, linearExpansionArr)
#plt.show()

#nonlinear_expansion
#nonlinearExpansionArr = histogram(1000,nonlinear_expansion(imggray))
#tab = np.linspace(0,255,len(nonlinearExpansionArr) )
#plt.bar(tab, nonlinearExpansionArr)
#plt.show()
#plt.imshow(nonlinear_expansion(imggray), cmap=plt.cm.gray , vmin=0 , vmax=255)
#plt.show();
#potegowanie przyciemnia
#pierwiastkowanie rozjasnia
#log rozjasnia
#ex przycieminia

# balance
#balanceArr = histogram(1000,balance(imggray))
#tab = np.linspace(0,255,len(balanceArr) )
#plt.bar(tab, balanceArr)
#plt.show()
#plt.imshow(balance(imggray), cmap=plt.cm.gray , vmin=0 , vmax=255)
#plt.show();
#rownomierny rozklad swiatla