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import dlib # dlib for accurate face detection
import cv2 # opencv
import imutils # helper functions from pyimagesearch.com

# Grab video from your webcam
stream = cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)24/1 ! nvvidconv flip-method=2 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink")

# Face detector
detector = dlib.get_frontal_face_detector()

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import cv2

cap = cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)24/1 ! nvvidconv flip-method=2 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink")

# Create the haar cascade
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
cv2.namedWindow('demo', cv2.WINDOW_AUTOSIZE)


while(True):

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var hist_height = 100;
var heat_height = 400;
var heat_width = 500;
var hist_width = 100;
var schem_width = 200;
var bar_padding = 2;
var margin = {top: 20, right: 90, bottom: 80, left: 60, center: 10, center_right: 10},
    width = heat_width + 2*hist_width + margin.center_right + schem_width,
    height = heat_height + hist_height + margin.center;
// margin.center is the margin between histogram and heatmap, center_right for the other histogram