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arloai.py
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arloai.py
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from influxdb import InfluxDBClient
import os
import re
import datetime
import calendar
import warnings
import logging
from imageai.Detection import VideoObjectDetection
import os
from arlo import Arlo
import pytz
import time
import cv2
from PIL import Image
import numpy as np
tz = pytz.timezone('Europe/Rome')
from datetime import timedelta, date
import sys
######### Variables ############
influxhost = "$INFLUXHOST" #CHANGEME
influxport = "$INFLUXPORT" #CHANGEME
influxuser = "$INFLUXUSER" #CHANGEME
influxpassword = "$INFLUXPASSWORD" #CHANGEME
influxdbname = "$DBARLO" #CHANGEME
USERNAME = '$ARLOUSERNAME' #CHANGEME
PASSWORD = '$ARLOPASSWORD' #CHANGEME
detectionspeed = 3 # 0 = normal .... 4 = flash
fluxdb = InfluxDBClient(influxhost, influxport, influxuser, influxpassword, influxdbname)
videoinfo = None
datevideo = datetime.datetime.now()
def detectionSpeedToString(value):
if(value==0):
return "normal"
elif(value==1):
return "fast"
elif(value==2):
return "faster"
elif(value==3):
return "fastest"
elif(value==4):
return "flash"
def forFrame(frame_number, output_array, output_count, returned_frame):
global datevideo
counter = 0
for eachItem in output_count:
if(eachItem=="person"):
try:
name = "people/" + datevideo
name += str(frame_number) + ".jpg"
cv2.imwrite(name, returned_frame)
except Exception as e:
print(e)
return
def forSeconds(output_arrays, count_arrays, average_output_count):
global datevideo
global videoinfo
global startProcTime
try:
timeProc = time.time() - startProcTime
count=0
for i in average_output_count:
count+=1
json_body = [
{
"measurement": "motion",
"time": datevideo,
"tags": { "camera": videoinfo['deviceId'], "objects": "yes" },
"fields": {
i: "1",
"videoid": videoinfo['uniqueId'],
"timeProc": timeProc,
"detectionspeed": detectionspeed
}
}]
fluxdb.write_points(json_body)
if(count==0):
json_body = [
{
"measurement": "motion",
"time": datevideo,
"tags": { "camera": videoinfo['deviceId'], "objects": "no" },
"fields": {
"videoid": videoinfo['uniqueId'],
"timeProc": timeProc,
"detectionspeed": detectionspeed
}
}]
fluxdb.write_points(json_body)
except Exception as e:
print(e)
execution_path = os.getcwd()
firstVideo = False
try:
# Instantiating the Arlo object automatically calls Login(), which returns an oAuth token that gets cached.
# Subsequent successful calls to login will update the oAuth token.
arlo = Arlo(USERNAME, PASSWORD)
# At this point you're logged into Arlo.
today = (date.today()-timedelta(days=0)).strftime("%Y%m%d")
seven_days_ago = (date.today()-timedelta(days=7)).strftime("%Y%m%d")
# Get all of the recordings for a date range.
library = arlo.GetLibrary(today, today)
# Iterate through the recordings in the library.
for recording in library:
videoinfo = recording
datevideo = datetime.datetime.fromtimestamp(int(recording['name'])//1000, pytz.timezone("UTC")).strftime('%Y-%m-%d %H:%M:%S')
videofilename = datetime.datetime.fromtimestamp(int(recording['name'])//1000).strftime('%Y-%m-%d %H-%M-%S') + ' ' + recording['uniqueId'] + '.mp4'
##
# The videos produced by Arlo are pretty small, even in their longest, best quality settings,
# but you should probably prefer the chunked stream (see below).
###
# # Download the whole video into memory as a single chunk.
# video = arlo.GetRecording(recording['presignedContentUrl'])
# with open('videos/'+videofilename, 'wb') as f:
# f.write(video)
# f.close()
# Or:
#
# Get video as a chunked stream; this function returns a generator.
if(os.path.isfile('videos/'+videofilename)==False):
#save cpu creating ImageAI only if necessary
if(firstVideo==False):
firstVideo=True
fluxdb.create_database(influxdbname)
detector = VideoObjectDetection()
#detector.setModelTypeAsRetinaNet()
detector.setModelTypeAsTinyYOLOv3()
#detector.setModelPath( os.path.join(execution_path , "resnet50_coco_best_v2.0.1.h5"))
detector.setModelPath( os.path.join(execution_path , "yolo-tiny.h5"))
detector.loadModel(detection_speed=detectionSpeedToString(detectionspeed))
stream = arlo.StreamRecording(recording['presignedContentUrl'])
with open('videos/'+videofilename, 'wb') as f:
for chunk in stream:
f.write(chunk)
f.close()
print('Downloaded video '+videofilename+' from '+recording['createdDate']+'.')
print(os.path.join(execution_path , 'videos/'+videofilename))
startProcTime = time.time()
detections = detector.detectObjectsFromVideo(input_file_path=os.path.join(execution_path , 'videos/'+videofilename), frames_per_second=25, frame_detection_interval=12, minimum_percentage_probability=72, save_detected_video=False, video_complete_function=forSeconds, per_frame_function=forFrame, return_detected_frame=True )
except Exception as e:
print(e)