#! /usr/bin/python3
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
import imutils
import numpy as np
import requests

protopath = "MobileNetSSD_deploy.prototxt"
modelpath = "MobileNetSSD_deploy.caffemodel"
detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
person_counter = 0

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
           "sofa", "train", "tvmonitor"]

# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
rawCapture = PiRGBArray(camera)
# allow the camera to warmup
time.sleep(0.1)
# grab an image from the camera
while True:
    camera.capture(rawCapture, format="bgr")
    image = rawCapture.array

    image = imutils.resize(image, width=1024, height=1024)
    (H, W) = image.shape[:2]

    blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)

    detector.setInput(blob)
    person_detections = detector.forward()

    for i in np.arange(0, person_detections.shape[2]):
        confidence = person_detections[0, 0, i, 2]
        if confidence > 0.2:
            idx = int(person_detections[0, 0, i, 1])

            if CLASSES[idx] == "person":
                person_counter += 1
    r = requests.post("http://127.0.0.1:8000/update_sensor", json={"name": "pocet ludi", "value": str(person_counter)})
    time.sleep(60)