import numpy as np
import cv2
import math
print("version openCV:"+str(  cv2.__version__ ))

img = cv2.imread('riz2.jpg',0)




# Appliquer le facteur 1/2 aux valeurs des pixels pour tester le seuil adaptatif
#img = (img / 2).astype(np.uint8)
#ou
#img = cv2.convertScaleAbs(img, alpha=0.5, beta=0)

ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)


# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

print("ret2:"+str(ret2))
print(img)
print(type(img))
print(img.shape)


kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(th2,kernel,iterations = 2)

#affichage difference
#cv2.imshow('image',th2-erosion)
dilation = cv2.dilate(erosion,kernel,iterations = 1)

cv2.imwrite('dilation.png',dilation)
#affichage difference
#cv2.imshow('image',th2-dilation)

imgcolor = cv2.cvtColor(dilation, cv2.COLOR_GRAY2BGR)
cpt=0
contours, hierarchy = cv2.findContours(dilation,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for contour in contours:
    print("The contours have this data " +str(contour))
    M = cv2.moments(contour)
    print(str(M))
    
    cx = int(M['m10']/M['m00'])
    cy = int(M['m01']/M['m00'])
    imgcolor[cy][cx]=[255,0,0]
    area = cv2.contourArea(contour)
    perimeter = cv2.arcLength(contour,True)
    hull = cv2.convexHull(contour)
    rect = cv2.minAreaRect(contour)
    aspectratio=max(rect[1])/min(rect[1])
    hullarea = cv2.contourArea(hull)
    #box = cv2.boxPoints(rect)
    #box = np.int0(box)
    solidity=float(area)/hullarea
    #if cpt%2==0:
    if area>20 and area<200 and aspectratio> 2 and  aspectratio<6 and solidity>0.8:
      cv2.drawContours(imgcolor,contour,-1,(0,255,0),1)
    else:
      cv2.drawContours(imgcolor,contour,-1,(0,0,255),1)
    cpt=cpt+1
    


cv2.imshow('image',imgcolor)

cv2.waitKey(0)
cv2.destroyAllWindows()