OK So I"m getting there. I tried again using the Stable branch and everything seems to be running fine. I tried without hooks and am able to get notifications. With Hooks I'm still having issues. Not sure why but I setup a test zone to analyze traffic. Event Notifications don't get sent. Here are my logs:
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CONSOLE INF:2020-05-18,23:57:29 PARENT: New event 110 reported for Monitor:1 (Name:Driveway) Motion Traffic[last processed eid:109]
05/18/20 23:57:29.998836 zmeventnotification[5722].INF [main:887] [PARENT: New event 110 reported for Monitor:1 (Name:Driveway) Motion Traffic[last processed eid:109]]
CONSOLE DBG-2:2020-05-18,23:57:29 PARENT: checkEvents() new events found=1
CONSOLE DBG-2:2020-05-18,23:57:29 PARENT: There are 1 new Events to process
CONSOLE DBG-2:2020-05-18,23:57:29 PARENT: ---------->Tick END<--------------
CONSOLE DBG-1:2020-05-18,23:57:30 PARENT: Forked process:43386 to handle alarm eid:110
CONSOLE DBG-2:2020-05-18,23:57:30 |----> FORK:Driveway (1), eid:110 Adding event path:/home/camera/Amcrest/events/1/2020-05-18/110 to hook for image storage
CONSOLE DBG-1:2020-05-18,23:57:30 |----> FORK:Driveway (1), eid:110 Invoking hook on event start:'/var/lib/zmeventnotification/bin/zm_event_start.sh' 110 1 "Driveway" "Motion Traffic" "/home/camera/Amcrest/events/1/2020-05-18/110"
CONSOLE DBG-2:2020-05-18,23:57:30 |----> FORK:Driveway (1), eid:110 parse of hook: and []
CONSOLE DBG-1:2020-05-18,23:57:30 |----> FORK:Driveway (1), eid:110 hook start returned with text: json:[] exit:1
CONSOLE DBG-1:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 Matching alarm to connection rules...
CONSOLE DBG-1:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 Checking alarm rules for token ending in:...zVyEcaPsZT
CONSOLE DBG-1:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 Monitor 1 event: last time not found, so should send
CONSOLE DBG-1:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 shouldSendEventToConn returned true, so calling sendEvent
CONSOLE DBG-2:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 isAllowedChannel: got type:event_start resCode:1
CONSOLE INF:2020-05-18,23:57:32 |----> FORK:Driveway (1), eid:110 Not sending over FCM as notify filters are on_success:all and on_fail:none
05/18/20 23:57:32.758691 zmeventnotification[43386].INF [main:887] [|----> FORK:Driveway (1), eid:110 Not sending over FCM as notify filters are on_success:all and on_fail:none]
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: ---------->Tick START<--------------
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: RAW TEXT-->timestamp--TYPE--1589860447.542--SPLIT--1--SPLIT--1589860652.75839
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: Job: Update last sent timestamp of monitor:1 to 1589860652.75839 for id:1589860447.542
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: There are 1 active child forks...
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: We've already worked on Monitor:1, Event:110, not doing anything more
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: checkEvents() new events found=0
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: There are 0 new Events to process
CONSOLE DBG-2:2020-05-18,23:57:34 PARENT: ---------->Tick END<--------------
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: ---------->Tick START<--------------
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: There are 1 active child forks...
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: We've already worked on Monitor:1, Event:110, not doing anything more
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: checkEvents() new events found=0
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: There are 0 new Events to process
CONSOLE DBG-2:2020-05-18,23:57:39 PARENT: ---------->Tick END<--------------
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: ---------->Tick START<--------------
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: There are 1 active child forks...
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: We've already worked on Monitor:1, Event:110, not doing anything more
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: checkEvents() new events found=0
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: There are 0 new Events to process
CONSOLE DBG-2:2020-05-18,23:57:44 PARENT: ---------->Tick END<--------------
CONSOLE INF:2020-05-18,23:57:45 |----> FORK:Driveway (1), eid:110 Event 110 for Monitor 1 has finished
05/18/20 23:57:45.249422 zmeventnotification[43386].INF [main:887] [|----> FORK:Driveway (1), eid:110 Event 110 for Monitor 1 has finished]
CONSOLE DBG-2:2020-05-18,23:57:47 |----> FORK:Driveway (1), eid:110 Adding event path:/home/camera/Amcrest/events/1/2020-05-18/110 to hook for image storage
CONSOLE DBG-1:2020-05-18,23:57:47 |----> FORK:Driveway (1), eid:110 Invoking hook on event end:'/var/lib/zmeventnotification/bin/zm_event_end.sh' 110 1 "Driveway" "Motion: Traffic" "/home/camera/Amcrest/events/1/2020-05-18/110"
CONSOLE DBG-2:2020-05-18,23:57:47 |----> FORK:Driveway (1), eid:110 parse of hook:Motion: Traffic and []
CONSOLE DBG-1:2020-05-18,23:57:47 |----> FORK:Driveway (1), eid:110 hook end returned with text:Motion: Traffic json:[] exit:0
CONSOLE INF:2020-05-18,23:57:49 |----> FORK:Driveway (1), eid:110 Not sending event end alarm, as we did not send a start alarm for this, or start hook processing failed
05/18/20 23:57:49.254258 zmeventnotification[43386].INF [main:887] [|----> FORK:Driveway (1), eid:110 Not sending event end alarm, as we did not send a start alarm for this, or start hook processing failed]
CONSOLE DBG-2:2020-05-18,23:57:49 PARENT: ---------->Tick START<--------------
CONSOLE DBG-2:2020-05-18,23:57:49 PARENT: There are 1 active child forks...
CONSOLE DBG-2:2020-05-18,23:57:49 PARENT: checkEvents() new events found=0
CONSOLE DBG-2:2020-05-18,23:57:49 PARENT: There are 0 new Events to process
CONSOLE DBG-2:2020-05-18,23:57:49 PARENT: ---------->Tick END<--------------
CONSOLE DBG-1:2020-05-18,23:57:53 |----> FORK:Driveway (1), eid:110 exiting
CONSOLE DBG-1:2020-05-18,23:57:53 |----> FORK:Driveway (1), eid:110 Ending process:43386 to handle alarms
CONSOLE DBG-2:2020-05-18,23:57:54 PARENT: ---------->Tick START<--------------
CONSOLE DBG-2:2020-05-18,23:57:54 PARENT: RAW TEXT-->active_event_delete--TYPE--1--SPLIT--110
CONSOLE DBG-2:2020-05-18,23:57:54 PARENT: Job: Deleting active_event eid:110, mid:1
objectconfig.ini:
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# Configuration file for object detection
# NOTE: ALL parameters here can be overriden
# on a per monitor basis if you want. Just
# duplicate it inside the correct [monitor-<num>] section
[general]
# This is an optional file
# If specified, you can specify tokens with secret values in that file
# and onlt refer to the tokens in your main config file
secrets = /etc/zm/secrets.ini
# base data path for various files the ES+OD needs
# we support in config variable substitution as well
base_data_path=/var/lib/zmeventnotification
# It seems certain systems don't follow regular
# ZM conventions on install paths. This may cause
# problems with pyzm that the hooks use to do logging
# Look at https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ZMLog.init for parameters. Default is "{}"
# You can also use this to control logging irrespective of ZM log settings
#pyzm_overrides = {'conf_path':'/etc/zm'}
pyzm_overrides={'log_level_debug':2}
# base path where ZM config files reside
# this is needed by pyzm especially if your paths are different
# default is /etc/zm
base_zm_conf_path=/etc/zm
# portal/user/password are needed if you plan on using ZM's legacy
# auth mechanism to get images
portal=!ZM_PORTAL
user=!ZM_USER
password=!ZM_PASSWORD
# api portal is needed if you plan to use tokens to get images
# requires ZM 1.33 or above
api_portal=!ZM_API_PORTAL
allow_self_signed=yes
# if yes, last detection will be stored for monitors
# and bounding boxes that match, along with labels
# will be discarded for new detections. This may be helpful
# in getting rid of static objects that get detected
# due to some motion.
match_past_detections=yes
# The max difference in area between the objects if match_past_detection is on
# can also be specified in px like 300px. Default is 5%. Basically, bounding boxes of the same
# object can slightly differ ever so slightly between detection. Contributor @neillbell put in this PR
# to calculate the difference in areas and based on his tests, 5% worked well. YMMV. Change it if needed.
past_det_max_diff_area=5%
# sequence of models to run for detection
models=yolo
# if all, then we will loop through all models
# if first then the first success will break out
detection_mode=all
# If you need basic auth to access ZM
basic_user=
basic_password=
# this is the global detection pattern used for all monitors.
# choose any set of classes from here https://github.com/pjreddie/darknet/blob/master/data/coco.names
# for everything, make it .*
#detect_pattern=(person|dog|cat|bird|car|motorbike|bus|truck)
detect_pattern=.*
# global settings for
# bestmatch, alarm, snapshot OR a specific frame ID
frame_id=bestmatch
# Typically best match means it will first try alarm
# and then snapshot. If you want it the reverse way,
# make the order 's,a'. Don't get imaginative here -
# 's,a' is the only thing it understands. Everything else
# means alarm then snapshot.
#bestmatch_order = 's,a'
# this is the to resize the image before analysis is done
resize=1200
# set to yes, if you want to remove images after analysis
# setting to yes is recommended to avoid filling up space
# keep to no while debugging/inspecting masks
# Note this does NOT delete debug images later
delete_after_analyze=yes
# If yes, will write an image called <filename>-bbox.jpg as well
# which contains the bounding boxes. This has NO relation to
# write_image_to_zm
# Typically, if you enable delete_after_analyze you may
# also want to set write_debug_image to no.
write_debug_image=no
# if yes, will write an image with bounding boxes
# this needs to be yes to be able to write a bounding box
# image to ZoneMinder that is visible from its console
write_image_to_zm=yes
# Adds percentage to detections
# hog/face shows 100% always
show_percent=yes
# color to be used to draw the polygons you specified
poly_color=(0,0,255)
# If yes, will import zones automatically from monitors
#import_zm_zones=no
# This section gives you an option to get brief animations
# of the event, delivered as part of the push notification to mobile devices
# Animations are created only if an object is detected
[animation]
# Seems like GIF/MP4 animations only
# work in IOS. Too bad.
# NOTE: Animation ONLY works with ZM 1.35 master as of Mar 16, 2020
# You also require zmNinja 1.3.91 or above
# If you are not running that version, animation will not work
# Animation frames will be created, but they won't be pushed to your device
# If yes, object detection will attempt to create
# a short GIF file around the object detection frame
# that can be sent via push notifications for instant playback
# Note this required additional software support. Default:no
create_animation=no
# Format of animation burst
# valid options are "mp4", "gif", "mp4,gif"
# Note that gifs will be of a shorter duration
# as they take up much more disk space than mp4
# Note that if you use mp4, the thumbnail that shows
# with push notifications may look transparent. My guess
# is this is related to how the video is being formed
# in ZM as it is a partial video when we process it
animation_types='mp4'
# default width of animation image. Be cautious when you increase this
# most mobile platforms give a very brief amount of time (in seconds)
# to download the image.
# Given your ZM instance will be serving the image, it will anyway be slow
# Making the total animation size bigger resulted in the notification not
# getting an image at all (timed out)
animation_width=640
# animation_retry_sleep refers to how long to wait before trying to grab
# frame information if it failed. animation_max_tries defines how many times it
# will try and retrieve frames before it gives up
animation_retry_sleep=15
animation_max_tries=3
## Monitor specific settings
#
# - Format: [monitor-<mid>]
#
# Parameters:
# polygon areas where object detection will be done.
# You can name them anything except the keywords defined in the optional
# params below. You can put as many polygons as you want per [monitor-<mid>]
# (see examples).
#
# detect_pattern: overrides the detection patterns used for this monitor.
#
# Examples:
[monitor-8]
# my driveway
match_past_detections=no
wait=5
detect_pattern=(person|car|motorbike|bus|truck|boat)
#alpr_pattern=^(.*x11)
#delete_after_analyze=no
#detect_pattern=.*
#import_zm_zones=yes
my_driveway_perimeter=306,356 1003,341 1074,683 154,715
# use license plate recognition for my driveway
# see alpr section later for more data needed
resize=no
models=yolo,alpr
# tiny switches to tiny yolo weights, instead of full Yolo. Much faster, but less accurate
#yolo_type=tiny
[monitor-10]
# my front lawn
# here we want anything except potted plant
# exclusion in regular expressions is not
# as straightforward as you may think, so
# follow this pattern
# detect_pattern = ^(?!object1|object2|objectN)
# the characters in front implement what is
# called a negative look ahead
detect_pattern=^(?!potted plant|pottedplant|bench|broccoli)
#detect_pattern=.*
# local model overrides global
models=yolo
# setting import_zm_zones to yes will import ZM defined zones
#import_zm_zones=yes
[monitor-5]
# my basement
detect_pattern=(person)
#detect_pattern=.*
#poly_color=(255,0,0)
#detect_pattern=^(?!chair|bed)
param=219,304 1113,278 1066,863 177,852
models=yolo,face
[monitor-6]
# deck
detect_pattern=^(?!chair|table|bench|bird|bicycle|frisbee)
#detect_pattern=^(?!chair|table|bench|bird)
models=yolo
#yolo_type=tiny
boundary=100,100 2988,10 2988,2220 10,2220
[monitor-2]
#doorbell
detect_pattern=(person)
#detect_pattern=.*
# try face, if it works, don't do yolo
detection_mode=first
models=face,yolo
frame_id=bestmatch
# try diff. sizes. In my case, 600 was enough
#resize=600
# My doorbell camera needs more accurate face detection
# cnn did a much better job than HOG, but its _much_ slower
face_model=cnn
face_train_model=cnn
face_recog_dist_threshold=0.6
match_past_detections=no
#if you hard code a frame, you need to make sure it is created
#before we acess it. wait (sec) helps
#frame_id=32
#wait=3
#[monitor-4]
# detect_pattern=(cat|dog)
# kitchen_door=313,221 392,210 418,592 367,659
# No 'detect_pattern', global value would be used.
# [monitor-7]
# entrance_door=313,221 392,210 418,592 367,659
# Machine learning options that are not specific to a model
[ml]
# Starting version 4.2 of OpenCV, the DNN models support CUDA
# If you have compiled OpenCV 4.2 with CUDA support correctly
# set this to yes. Note that if you have just installed a package
# chances are it is not properly set up with CUDA. It is much better
# you compile OpenCV from source (and uninstall any opencv packages you
# installed via pip or apt-get)
# Read https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/ on how to do it right.
# Play special attention to putting in the right CUDA_ARCH_BIN value that
# matches your GPU or you'll face "invalid device errors in make_policy"
# while trying to actually run it (compile will work fine)
use_opencv_dnn_cuda=yes
# You can now run the machine learning code on a different server
# This frees up your ZM server for other things
# To do this, you need to setup https://github.com/pliablepixels/mlapi
# on your desired server and confiure it with a user. See its instructions
# once set up, you can choose to do object/face recognition via that
# external serer
# URL that will be used
#ml_gateway=http://192.168.1.21:5000/api/v1
# If you enable ml_gateway, and it is down
# you can set ml_fallback_local to yes
# if you want to instantiate local object detection
# on gateway failure. Default is no
#ml_fallback_local=yes
# API/password for remote gateway
ml_user=!ML_USER
ml_password=!ML_PASSWORD
# config files for yolo
[yolo]
yolo_type=full
#yolo_type=tiny
#yolo_min_confidence=0.5
yolo_min_confidence=0.3
# For Yolo full
config={{base_data_path}}/models/yolov3/yolov3.cfg
weights={{base_data_path}}/models/yolov3/yolov3.weights
labels={{base_data_path}}/models/yolov3/yolov3_classes.txt
# FOR CSPN. Note that model name is yolo
#config={{base_data_path}}/models/cspn/csresnext50-panet-spp-original-optimal.cfg
#weights={{base_data_path}}/models/cspn/csresnext50-panet-spp-original-optimal_final.weights
#labels={{base_data_path}}/models/cspn/coco.names
# For tiny Yolo
tiny_config={{base_data_path}}/models/tinyyolo/yolov3-tiny.cfg
tiny_weights={{base_data_path}}/models/tinyyolo/yolov3-tiny.weights
tiny_labels={{base_data_path}}/models/tinyyolo/yolov3-tiny.txt
# config params for HOG
[hog]
stride=(4,4)
padding=(8,8)
scale=1.05
mean_shift=-1
[face]
# this directly will be where you store known images on a per directory basis
known_images_path={{base_data_path}}/known_faces
# if yes, then unknown faces will be stored and you can analyze them later
# and move to known_faces and retrain
save_unknown_faces=yes
# How many pixels to extend beyond the face for a better perspective
save_unknown_faces_leeway_pixels=50
# this directly is where zm_detect will store faces it could not identify
# (if save_unknown_faces is yes). You can then inspect this folder later,
# and copy unknown faces to the right places in known_faces and retrain
unknown_images_path={{base_data_path}}/unknown_faces
# read https://github.com/ageitgey/face_recognition/wiki/Face-Recognition-Accuracy-Problems
# read https://github.com/ageitgey/face_recognition#automatically-find-all-the-faces-in-an-image
# and play around
# quick overview:
# num_jitters is how many times to distort images
# upsample_times is how many times to upsample input images (for small faces, for example)
# model can be hog or cnn. cnn may be more accurate, but I haven't found it to be
face_num_jitters=1
face_model=hog
face_upsample_times=1
# This is maximum distance of the face under test to the closest matched
# face cluster. The larger this distance, larger the chances of misclassification.
#
face_recog_dist_threshold=0.6
# When we are first training the face recognition model with known faces,
# by default we use hog because we assume you will supply well lit, front facing faces
# However, if you are planning to train with profile photos or hard to see faces, you
# may want to change this to cnn. Note that this increases training time, but training only
# happens once, unless you retrain again by removing the training model
face_train_model=hog
#if a face doesn't match known names, we will detect it as 'unknown face'
# you can change that to something that suits your personality better ;-)
#unknown_face_name=invader
[alpr]
# keep this to yes. no mode is not supported today
alpr_use_after_detection_only=yes
# plate_recognizer, open_alpr, open_alpr_cmdline
alpr_service=plate_recognizer
# Many of the ALPR providers offer both a cloud version
# and local SDK version. Sometimes local SDK format differs from
# the cloud instance. Set this to local or cloud. Default cloud
alpr_api_type=cloud
# If you want to host a local SDK https://app.platerecognizer.com/sdk/
#alpr_url=https://localhost:8080/alpr
# Plate recog replace with your api key
alpr_key=!PLATEREC_ALPR_KEY
# if yes, then it will log usage statistics of the ALPR service
platerec_stats=no
# If you want to specify regions. See http://docs.platerecognizer.com/#regions-supported
#platerec_regions=['us','cn','kr']
# minimal confidence for actually detecting a plate
platerec_min_dscore=0.1
# minimal confidence for the translated text
platerec_min_score=0.2
# ----| If you are using openALPR web service |-----
#alpr_service=open_alpr
#alpr_key=!OPENALPR_ALPR_KEY
# For an explanation of params, see http://doc.openalpr.com/api/?api=cloudapi
#openalpr_recognize_vehicle=1
#openalpr_country=us
#openalpr_state=ca
# openalpr returns percents, but we convert to between 0 and 1
#openalpr_min_confidence=0.3
# ----| If you are using openALPR command line |-----
# Before you do any of this, make sure you have openALPR
# compiled and working properly as per http://doc.openalpr.com/compiling.html
# the alpr binary needs to be operational and capable of detecting plates
# Note this is not really very accurate unless you
# have a camera directly with a good view of the palates
# the cloud based API service is far more accurate
#openalpr_cmdline_binary=alpr
# Do an alpr -help to see options, plug them in here
# like say '-j -p ca -c US' etc.
# keep the -j because its JSON
# Note that alpr_pattern is honored
# For the rest, just stuff them in the cmd line options
#openalpr_cmdline_params=-j
#openalpr_cmdline_min_confidence=0.3
zmeventnotification.ini:
Code: Select all
# Configuration file for zmeventnotification.pl
[general]
secrets = /etc/zm/secrets.ini
base_data_path=/var/lib/zmeventnotification
# The ES now supports a means for a special kind of
# websocket connection which can dynamically control ES
# behaviour
# Default is no
use_escontrol_interface=no
# this is where all escontrol admin overrides
# will be stored.
escontrol_interface_file=/var/lib/zmeventnotification/misc/escontrol_interface.dat
# the password for accepting control interfaces
escontrol_interface_password=!ESCONTROL_INTERFACE_PASSWORD
# If you see the ES getting 'stuck' after several hours
# see https://rt.cpan.org/Public/Bug/Display.html?id=131058
# You can use restart_interval to have it automatically restart
# every X seconds. (Default is 7200 = 2 hours) Set to 0 to disable this.
# restart_interval = 432000
restart_interval = 0
# list of monitors which ES will ignore
# Note that there is an attribute later that does
# not process hooks for specific monitors. This one is different
# It can be used to completely skip ES processing for the
# monitors defined
# skip_monitors = 2,3,4
[network]
# Port for Websockets connection (default: 9000).
port = 9000
# Address for Websockets server (default: [::]).
# If you are facing connection issues or SSL issues, put in your IP here
# If you want to listen to multiple interfaces try 0.0.0.0
#address = 1.2.3.4
[auth]
# Check username/password against ZoneMinder database (default: yes).
enable = yes
# Authentication timeout, in seconds (default: 20).
timeout = 20
[push]
# This is to enable sending push notifications via any 3rd party service.
# Typically, if you enable this, you might want to turn off fcm
# Note that zmNinja will only receive notifications via FCM, but other 3rd
# party services have their own apps to get notifications
use_api_push = no
# This is the script that will send the notification
# Some sample scripts are provided, write your own
# Each script gets:
# arg1 - event ID
# arg2 - Monitor ID
# arg3 - Monitor Name
# arg4 - alarm cause
# arg5 - Type of event (event_start or event_end)
# arg6 (optional) - image path
api_push_script=/var/lib/zmeventnotification/bin/pushapi_pushover.py
[fcm]
# Use FCM for messaging (default: yes).
enable = yes
# Custom FCM API key. Uncomment if you are using
# your own API key (most people will not need to uncomment)
# api_key =
# Auth token store location (default: /var/lib/zmeventnotification/push/tokens.txt).
token_file = {{base_data_path}}/push/tokens.txt
# Date format to use when sending notification
# over push (FCM)
# See https://metacpan.org/pod/POSIX::strftime::GNU
# For example, a 24 hr format would be
#date_format = %H:%M, %d-%b
date_format = %I:%M %p, %d-%b
# Use MQTT for messaging (default: no)
[mqtt]
enable = no
# MQTT server (default: 127.0.0.1)
server = 127.0.0.1
# Authenticate to MQTT server as user
# username = !MQTT_USERNAME
# Password
# password = !MQTT_PASSWORD
# Set retain flag on MQTT messages (default: no)
retain = no
[ssl]
# Enable SSL (default: yes)
enable = yes
cert = !ES_CERT_FILE
key = !ES_KEY_FILE
#cert = /etc/apache2/ssl/zoneminder.crt
#key = /etc/apache2/ssl/zoneminder.key
# Location to SSL cert (no default).
# cert = /etc/apache2/ssl/yourportal/zoneminder.crt
# Location to SSL key (no default).
# key = /etc/apache2/ssl/yourportal/zoneminder.key
[customize]
# Display messages to console (default: no).
console_logs = yes
# debug level for ES messages. Default 2. Note that this is
# not controllable by ZM LOG_DEBUG_LEVEL as in Perl, ZM doesn't
# support debug levels
es_debug_level = 2
# Interval, in seconds, after which we will check for new events (default: 5).
event_check_interval = 5
# Interval, in seconds, to reload known monitors (default: 300).
monitor_reload_interval = 300
# Read monitor alarm cause (Requires ZoneMinder >= 1.31.2, default: no)
# Enabling this to 1 for lower versions of ZM will result in a crash
read_alarm_cause = yes
# Tag event IDs with the alarm (default: no).
tag_alarm_event_id = yes
# Use custom notification sound (default: no).
use_custom_notification_sound = no
# include picture in alarm (default: no).
include_picture = yes
# send event end notifications (default: no)
# Note that if you are using hooks for end notifications, they may change
# the final decision. This needs to be yes if you want end notifications with
# or without hooks
send_event_end_notification = yes
# URL to access the event image
# This URL can be anything you want
# What I've put here is a way to extract an image with the highest score given an eventID (even one that is recording)
# This requires the latest version of index.php which was merged on Oct 9, 2018 and may only work in ZM 1.32+
# https://github.com/ZoneMinder/zoneminder/blob/master/web/index.php
# If you use this URL as I've specified below, keep the EVENTID phrase intact.
# The notification server will replace it with the correct eid of the alarm
# BESTMATCH should be used only if you are using bestmatch for FID in detect_wrapper.sh
# objdetect is ONLY available in ZM 1.33+
# objdetect_mp4 and objdetect_gif is ONLY available
# in ZM 1.35+
picture_url = !ZMES_PICTURE_URL
picture_portal_username=!ZM_USER
picture_portal_password=!ZM_PASSWORD
# This is a master on/off setting for hooks. If it is set to no
# hooks will not be used no matter what is set in the [hook] section
# This makes it easy for folks not using hooks to just turn this off
# default:no
use_hooks = yes
[hook]
# NOTE: This entire section is only valid if use_hooks is yes above
# Shell script name here to be called every time an alarm is detected
# the script will get passed $1=alarmEventID, $2=alarmMonitorId
# $3 monitor Name, $4 alarm cause
# script needs to return 0 to send alarm (default: none)
#
# This script is called when an event first starts. If the script returns "0"
# (success), then a notification is sent to channels specified in
# event_start_notify_on_hook_success. If the script returns "1" (fail)
# then a notification is sent to channels specified in
# event_start_notify_on_hook_fail
event_start_hook = '{{base_data_path}}/bin/zm_event_start.sh'
#This script is called after event_start_hook completes. You can do
# your housekeeping work here
#event_start_hook_notify_userscript = '{{base_data_path}}/contrib/example.py'
# This script is called when an event ends. If the script returns "0"
# (success), then a notification is sent to channels specified in
# event_end_notify_on_hook_success. If the script returns "1" (fail)
# then a notification is sent to channels specified in
# event_end_notify_on_hook_fail
event_end_hook = '{{base_data_path}}/bin/zm_event_end.sh'
#This script is called after event_start_hook completes. You can do
# your housekeeping work here
#event_start_hook_notify_userscript = '{{base_data_path}}/contrib/example.py'
# Possible channels = web,fcm,mqtt,api
# all is short for web,fcm,mqtt,api
# use none for no notifications, or comment out the attribute
# When an event starts and hook returns 0, send notification to all. Default: none
event_start_notify_on_hook_success = all
# When an event starts and hook returns 1, send notification only to desktop. Default: none
event_start_notify_on_hook_fail = none
# When an event ends and hook returns 0, send notification to fcm,web,api. Default: none
event_end_notify_on_hook_success = fcm,web,api
# When an event ends and hook returns 1, don't send notifications. Default: none
event_end_notify_on_hook_fail = none
#event_end_notify_on_hook_fail = web
# Since event_end and event_start are two different hooks, it is entirely possible
# that you can get an end notification but not a start notification. This can happen
# if your start script returns 1 but the end script returns 0, for example. To avoid
# this, set this to yes (default:yes)
event_end_notify_if_start_success = yes
# If yes, the text returned by the script
# overwrites the alarm header
# useful if your script is detecting people, for example
# and you want that to be shown in your notification (default:yes)
use_hook_description = yes
# If yes will will append an [a] for alarmed frame match
# [s] for snapshot match or [x] if not using bestmatch
# really only a debugging feature but useful to know
# where object detection is working or failing
keep_frame_match_type = yes
# list of monitors for which hooks will not run
# hook_skip_monitors = 2
# if enabled, will pass the right folder for the hook script
# to store the detected image, so it shows up in ZM console view too
# Requires ZM >=1.33. Don't enable this if you are running an older version
# Note: you also need to set write_image_to_zm=yes in objectconfig.ini
# default: no
hook_pass_image_path = yes
