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

cap = cv2.VideoCapture(0)

# Define the codec and create VideoWriter object
fourcc = cv2.cv.CV_FOURCC(*'XVID')  # cv2.VideoWriter_fourcc() does not exist
out = cv2.VideoWriter('personasConteo.avi',fourcc, 40.0, (640,480))

img1 = cv2.imread('imagen1.png',0)

orb = cv2.ORB()
kp1 = orb.detect(img1,None)
kp1, des1 = orb.compute(img1,kp1)
print type(des1),des1.shape
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary

flann = cv2.FlannBasedMatcher(index_params,search_params)

# BFMatcher with default params
bf = cv2.BFMatcher()

while(cap.isOpened()):
    ret, img = cap.read()

#    ret2, frame2 = cap2.read()
    if ret==True:

# find the keypoints with ORB
	kp = orb.detect(img,None)

# compute the descriptors with ORB
	kp, des2= orb.compute(img, kp)
	print "des 2",des2.shape
	matches = bf.knnMatch(des1,des2,k=2)

	# Apply ratio test
	good = []
	for m,n in matches:
	    if m.distance < 0.75*n.distance:
        	good.append([m])

	# cv2.drawMatchesKnn expects list of lists as matches.
#	img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,flags=2)
	img2 = cv2.drawKeypoints(img,kp,color=(0,255,0), flags=0)
	img1 = cv2.drawKeypoints(img1,kp1,color=(0,255,0), flags=0)

        cv2.imshow('matches ORB2',img2)
        cv2.imshow('matches ORB1',img1)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            cv2.imwrite('fast-true-features.png',img)
	    print kp1, kp2
            break
    else:
        break

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