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import os
import numpy as np
import cv2
import mrcnn.config
import mrcnn.utils
from mrcnn.model import MaskRCNN
from pathlib import Path


# Configuration that will be used by the Mask-RCNN library

bigsnarfdude

import os
import cv2
import time
import argparse
import multiprocessing
import numpy as np
import tensorflow as tf

from cam_utils import FPS, WebcamVideoStream
from multiprocessing import Queue, Pool

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# coding: utf-8

# In[3]:


import sys
sys.path.append("..")

import pandas as pd

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from azureCreds import *
import os, uuid, sys
from azure.storage.file import FileService
from azure.storage.file import ContentSettings



# generator = file_service.list_directories_and_files(share_name=CONTAINER_NAME, 
#                                                     directory_name=DIRECTORY_NAME)

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# https://scipy-cookbook.readthedocs.io/items/KalmanFiltering.html
# https://github.com/pykalman/pykalman

import pandas as pd
from pykalman import KalmanFilter
import numpy as np

def rolling_window(a, step):
    shape   = a.shape[:-1] + (a.shape[-1] - step + 1, step)
    strides = a.strides + (a.strides[-1],)

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# coding: utf-8

# In[38]:


from keras.layers import Input, Concatenate # symbolic version
from keras.layers import Input, Conv2D, Flatten, Dense
from keras.models import Model

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#face_counter.py

 
import numpy as np
import cv2
import requests

cv2.namedWindow('frame')
cv2.namedWindow('dist')

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import cv2
from os import system
import cognitive_face as CF
from utils import *
import config as CFG 


CF.Key.set(CFG.KEY)

BASE_URL = 'https://canadacentral.api.cognitive.microsoft.com/face/v1.0'

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def grab(cam, queue, width, height, fps):
    global running
    capture = cv2.VideoCapture(cam)
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
    capture.set(cv2.CAP_PROP_FPS, fps)

    while(running):
        frame = {}
        capture.grab()