import numpy as np
import cv2

cap = cv2.VideoCapture(0)

kernel = np.ones((3,3),np.uint8)

img=cv2.imread('carita-match.jpg',1)
imgray1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray1,127,255,0)
contours1, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
for i in range(0,len(contours1)):
	cnt = contours1[i]
	M = cv2.moments(cnt)
	if M['m00'] > 1000 and M['m00'] < 5000:
		cv2.drawContours(img, [cnt], 0, (9,255,0), 2)
		print i,M['m00']

contorno= contours1[168]

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
	imgray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
	ret,thresh = cv2.threshold(imgray,127,255,0)
	contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
	for i in range(0 , len(contours)):
		cnt = contours[i]
		M = cv2.moments(cnt)
		if M['m00'] > 1500 and M['m00'] < 2000:
			cx = int(M['m10']/M['m00'])
			cy = int(M['m01']/M['m00'])
			perimeter = cv2.arcLength(cnt,False)
			hull = cv2.convexHull(cnt)
			hull = cv2.convexHull(cnt,returnPoints = False)
			defects = cv2.convexityDefects(cnt,hull)

			for i in range(defects.shape[0]):
			    s,e,f,d = defects[i,0]
			    start = tuple(cnt[s][0])
			    end = tuple(cnt[e][0])
			    far = tuple(cnt[f][0])
			    cv2.line(frame,start,end,[0,255,0],2)
			    cv2.circle(frame,far,5,[0,0,255],-1)
			dist = cv2.matchShapes(contorno,cnt,1,0.0)
			print dist

        cv2.imshow('Frame',frame)
        cv2.imshow('Bordes',thresh)
        cv2.imshow('Bordes imagen 1',img)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break

# Release everything if job is finished
cap.release()
cv2.destroyAllWindows()