import numpy as np
import cv2
from matplotlib import pyplot as plt

im = cv2.imread('riz.jpg')


imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)

#imgray=imgray*0.5
#imgray=imgray.astype(np.uint8)

# affichage histogramme:
#https://opencv24-python-tutorials.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_histograms/py_histogram_equalization/py_histogram_equalization.html
hist,bins = np.histogram(imgray.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()
plt.plot(cdf_normalized, color = 'b')
plt.hist(imgray.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()

#autre methode pour l'histogramme:
#cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])
hist = cv2.calcHist([imgray],[0],None,[256],[0,256])
#hist = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
#plt.imshow(hist,interpolation = 'nearest')
#plt.show()
#print(str(hist))

#seuillage brut ou manuel
ret,thresh1 = cv2.threshold(imgray,150,255,cv2.THRESH_BINARY)

#seuillage Otsu's thresholding
ret2,th2 = cv2.threshold(imgray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print("ret2: "+str(ret2))

cv2.imwrite('riz_out.png',im)
cv2.imwrite('riz_gray.png',imgray)
cv2.imwrite('riz_otsu.png',th2)

kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(th2,kernel,iterations = 1)
cv2.imwrite('riz_otsu_erosion.png',erosion)
dilation = cv2.dilate(erosion,kernel,iterations = 1)
cv2.imwrite('riz_otsu_erosion_dilatation.png',dilation)

cv2.imshow('image',th2)
cv2.waitKey(0)
cv2.destroyAllWindows()