import numpy as np
import cv2

cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture("siluetas.avi")

fgbg = cv2.BackgroundSubtractorMOG()
kernel = np.ones((5,5),np.uint8)
# leer el primer frame
ret, primerframe = cap.read()
primerframe = cv2.cvtColor(primerframe,cv2.COLOR_BGR2GRAY)
th3 = cv2.adaptiveThreshold(primerframe,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
primerfgmask = fgbg.apply(th3)

while(1):
    ret, frame = cap.read()
    grises = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    th3 = cv2.adaptiveThreshold(grises,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
    
    fgmask = fgbg.apply(th3)
    diff = primerfgmask - fgmask
    #copiafgmask = fgmask
    #erosion = cv2.dilate(copiafgmask,kernel,iterations = 3)
    # opcion 1 encontrar contornos
    contours, hierarchy = cv2.findContours(diff,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    for cont in contours:
	if cv2.contourArea(cont) > 1000:
		cv2.drawContours(frame, cont, -1, (255,255,255), 5) 
		x,y,w,h = cv2.boundingRect(cont)
		cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
		# obtener el centro de masa de esos contornos
    #		cv2.drawContours(erosion, cont, -1, (0,0,0), 1) 

    cv2.imshow('umbral adapatativo',primerfgmask)
    cv2.imshow('frame',fgmask)
    cv2.imshow('frame o',frame)
    cv2.imshow('diferencia ',diff)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

cap.release()
cv2.destroyAllWindows()