Hm, just tried it again, using objectconfig.ini, but also not successful. See output below and also my objectconfig.ini + zmeventnotification.ini. Any guidance and help is warmly welcome. Thanks a lot.
Code: Select all
# 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
# REMEMBER: Most of these are just variables. The place where all
# of this comes together is the ml_sequence and stream_sequence
# structures.
[general]
# Please don't change this. It is used by the config upgrade script
version=1.2
# You can now limit the # of detection process
# per target processor. If not specified, default is 1
# Other detection processes will wait to acquire lock
cpu_max_processes=3
tpu_max_processes=1
gpu_max_processes=1
# Time to wait in seconds per processor to be free, before
# erroring out. Default is 120 (2 mins)
cpu_max_lock_wait=100
tpu_max_lock_wait=100
gpu_max_lock_wait=100
#pyzm_overrides={'conf_path':'/etc/zm','log_level_debug':0}
pyzm_overrides={'log_level_debug':5}
# 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
# 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=no
# 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.
# Note: You can specify label/object specific max_diff_areas as well. If present, they override this value
# example:
# person_past_det_max_diff_area=5%
# car_past_det_max_diff_area=5000px
past_det_max_diff_area=5%
# this is the maximum size a detected object can have. You can specify it in px or % just like past_det_max_diff_area
# This is pretty useful to eliminate bogus detection. In my case, depending on shadows and other lighting conditions,
# I sometimes see "car" or "person" detected that covers most of my driveway view. That is practically impossible
# and therefore I set mine to 70% because I know any valid detected objected cannot be larger than that area
max_detection_size=90%
# sequence of models to run for detection
my_model_sequence=object,face,alpr
# 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=user
#basic_password=password
# base data path for various files the ES+OD needs
# we support in config variable substitution as well
base_data_path=/var/lib/zmeventnotification
# global settings for
# bestmatch, alarm, snapshot OR a specific frame ID
frame_id=bestmatch
# this is the to resize the image before analysis is done
resize=800
# 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=(255,255,255)
poly_thickness=2
#import_zm_zones=yes
only_triggered_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
#
# NOTE: This will DELAY the time taken to send you push notifications
# It will try to first creat the animation, which may take upto a minute
# depending on how soon it gets access to frames. See notes below
[animation]
# 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
animation_types='mp4,gif'
# 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
# When an event is detected, ZM it writes frames a little late
# On top of that, it looks like with caching enabled, the API layer doesn't
# get access to DB records for much longer (around 30 seconds), at least on my
# system. 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=4
# if animation_types is gif then when can generate a fast preview gif
# every second frame is skipped and the frame rate doubled
# to give quick preview, Default (no)
fast_gif=no
[remote]
# 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://192.168.1.183:5000/api/v1
#ml_gateway=http://10.6.1.13:5000/api/v1
#ml_gateway=http://192.168.1.21:5000/api/v1
#ml_gateway=http://10.9.0.2:5000/api/v1
#ml_fallback_local=yes
# API/password for remote gateway
ml_user=!ML_USER
ml_password=!ML_PASSWORD
# config for object
[object]
# Updated note: Don't use use_sequence=no. No longer supported reliably
# If you are using legacy format (use_sequence=no) then these parameters will
# be used during ML inferencing
object_detection_pattern=(person|car|motorbike|bus|truck|boat)
object_min_confidence=0.3
object_framework=coral_edgetpu
object_processor=tpu
object_weights={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names
# If you are using the new ml_sequence format (use_sequence=yes) then
# you can fiddle with these parameters and look at ml_sequence later
# Note that these can be named anything. You can add custom variables, ad-infinitum
# Google Coral
# The mobiledet model came out in Nov 2020 and is supposed to be faster and more accurate but YMMV
tpu_object_weights_mobiledet={{base_data_path}}/models/coral_edgetpu/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite
tpu_object_weights_mobilenet={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
tpu_object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names
tpu_object_framework=coral_edgetpu
tpu_object_processor=tpu
tpu_min_confidence=0.6
# Yolo v4 on GPU (falls back to CPU if no GPU)
yolo4_object_weights={{base_data_path}}/models/yolov4/yolov4.weights
yolo4_object_labels={{base_data_path}}/models/yolov4/coco.names
yolo4_object_config={{base_data_path}}/models/yolov4/yolov4.cfg
yolo4_object_framework=opencv
yolo4_object_processor=gpu
# Yolo v3 on GPU (falls back to CPU if no GPU)
yolo3_object_weights={{base_data_path}}/models/yolov3/yolov3.weights
yolo3_object_labels={{base_data_path}}/models/yolov3/coco.names
yolo3_object_config={{base_data_path}}/models/yolov3/yolov3.cfg
yolo3_object_framework=opencv
yolo3_object_processor=gpu
# Tiny Yolo V4 on GPU (falls back to CPU if no GPU)
tinyyolo_object_config={{base_data_path}}/models/tinyyolov4/yolov4-tiny.cfg
tinyyolo_object_weights={{base_data_path}}/models/tinyyolov4/yolov4-tiny.weights
tinyyolo_object_labels={{base_data_path}}/models/tinyyolov4/coco.names
tinyyolo_object_framework=opencv
tinyyolo_object_processor=gpu
[face]
face_detection_pattern=.*
known_images_path={{base_data_path}}/known_faces
unknown_images_path={{base_data_path}}/unknown_faces
save_unknown_faces=yes
save_unknown_faces_leeway_pixels=100
face_detection_framework=dlib
# 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=cnn
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=cnn
#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]
alpr_detection_pattern=.*
alpr_use_after_detection_only=yes
# 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 are using plate recognizer | ------
alpr_service=plate_recognizer
#alpr_service=open_alpr_cmdline
# If you want to host a local SDK https://app.platerecognizer.com/sdk/
#alpr_url=http://192.168.1.21:8080/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=yes
# 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 |-----
#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 |-----
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 -d
openalpr_cmdline_min_confidence=0.3
## Monitor specific settings
# Examples:
# Let's assume your monitor ID is 999
[monitor-999]
# my driveway
match_past_detections=no
wait=5
object_detection_pattern=(person)
# Advanced example - here we want anything except potted plant
# exclusion in regular expressions is not
# as straightforward as you may think, so
# follow this pattern
# object_detection_pattern = ^(?!object1|object2|objectN)
# the characters in front implement what is
# called a negative look ahead
# object_detection_pattern=^(?!potted plant|pottedplant|bench|broccoli)
#alpr_detection_pattern=^(.*x11)
#delete_after_analyze=no
#detection_pattern=.*
#import_zm_zones=yes
# 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).
my_driveway=306,356 1003,341 1074,683 154,715
# You are now allowed to specify detection pattern per zone
# the format is <polygonname>_zone_detection_pattern=<regexp>
# So if your polygon is called my_driveway, its associated
# detection pattern will be my_driveway_zone_detection_pattern
# If none is specified, the value in object_detection_pattern
# will be used
# This also applies to ZM zones. Let's assume you have
# import_zm_zones=yes and let's suppose you have a zone in ZM
# called Front_Door. In that case, all you need to do is put in a
# front_door_zone_detection_pattern=(person|car) here
#
# NOTE: ZM Zones are converted to lowercase, and spaces are replaced
# with underscores@3
my_driveway_zone_detection_pattern=(person)
some_other_area=0,0 200,300 700,900
# use license plate recognition for my driveway
# see alpr section later for more data needed
resize=no
my_model_sequence=object,alpr
[ml]
# When enabled, you can specify complex ML inferencing logic in ml_sequence
# Anything specified in ml_sequence will override any other ml attributes
# Also, when enabled, stream_sequence will override any other frame related
# attributes
# Updated note: Don't set use_sequence=no. No longer supported reliably
use_sequence = yes
# if enabled, will not grab exclusive locks before running inferencing
# locking seems to cause issues on some unique file systems
disable_locks= no
# Chain of frames
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence.detect_stream
# Very important: Make sure final ending brace is indented
stream_sequence = {
'frame_strategy': 'most_models',
'frame_set': 'snapshot,alarm',
'contig_frames_before_error': 5,
'max_attempts': 3,
'sleep_between_attempts': 4,
'resize':800
}
# Chain of ML models to use
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence
# Very important: Make sure final ending brace is indented
ml_sequence= {
'general': {
'model_sequence': '{{my_model_sequence}}',
'disable_locks': '{{disable_locks}}',
'match_past_detections': '{{match_past_detections}}',
'past_det_max_diff_area': '5%',
'car_past_det_max_diff_area': '10%',
#'ignore_past_detection_labels': ['dog', 'cat'],
# when matching past detections, names in a group are treated the same
'aliases': [['car','bus','truck','boat'], ['broccoli', 'pottedplant']]
},
'object': {
'general':{
'pattern':'{{object_detection_pattern}}',
'same_model_sequence_strategy': 'first' # also 'most', 'most_unique's
},
'sequence': [{
#First run on TPU with higher confidence
'name': 'TPU object detection',
'enabled': 'no',
'object_weights':'{{tpu_object_weights_mobiledet}}',
'object_labels': '{{tpu_object_labels}}',
'object_min_confidence': {{tpu_min_confidence}},
'object_framework':'{{tpu_object_framework}}',
'tpu_max_processes': {{tpu_max_processes}},
'tpu_max_lock_wait': {{tpu_max_lock_wait}},
'max_detection_size':'{{max_detection_size}}'
},
{
# YoloV4 on GPU if TPU fails (because sequence strategy is 'first')
'name': 'YoloV4 GPU/CPU',
'enabled': 'yes', # don't really need to say this explictly
'object_config':'{{yolo4_object_config}}',
'object_weights':'{{yolo4_object_weights}}',
'object_labels': '{{yolo4_object_labels}}',
'object_min_confidence': {{object_min_confidence}},
'object_framework':'{{yolo4_object_framework}}',
'object_processor': '{{yolo4_object_processor}}',
'gpu_max_processes': {{gpu_max_processes}},
'gpu_max_lock_wait': {{gpu_max_lock_wait}},
'cpu_max_processes': {{cpu_max_processes}},
'cpu_max_lock_wait': {{cpu_max_lock_wait}},
'max_detection_size':'{{max_detection_size}}'
}]
},
'face': {
'general':{
'pattern': '{{face_detection_pattern}}',
#'pre_existing_labels': ['person'], # when put in general section, it will check if a previous detection type (like object) found this label
'same_model_sequence_strategy': 'union' # combines all outputs of this sequence
},
'sequence': [
{
'name': 'TPU face detection',
'enabled': 'no',
'face_detection_framework': 'tpu',
'face_weights':'/var/lib/zmeventnotification/models/coral_edgetpu/ssd_mobilenet_v2_face_quant_postprocess_edgetpu.tflite',
'face_min_confidence': 0.3,
},
{
'name': 'DLIB based face recognition',
'enabled': 'yes',
#'pre_existing_labels': ['face'], # If you use TPU detection first, we can run this ONLY if TPU detects a face first
'save_unknown_faces':'{{save_unknown_faces}}',
'save_unknown_faces_leeway_pixels':{{save_unknown_faces_leeway_pixels}},
'face_detection_framework': '{{face_detection_framework}}',
'known_images_path': '{{known_images_path}}',
'unknown_images_path': '{{unknown_images_path}}',
'face_model': '{{face_model}}',
'face_train_model': '{{face_train_model}}',
'face_recog_dist_threshold': '{{face_recog_dist_threshold}}',
'face_num_jitters': '{{face_num_jitters}}',
'face_upsample_times':'{{face_upsample_times}}',
'gpu_max_processes': {{gpu_max_processes}},
'gpu_max_lock_wait': {{gpu_max_lock_wait}},
'cpu_max_processes': {{cpu_max_processes}},
'cpu_max_lock_wait': {{cpu_max_lock_wait}},
'max_size':800
}]
},
'alpr': {
'general':{
'same_model_sequence_strategy': 'first',
'pre_existing_labels':['car', 'motorbike', 'bus', 'truck', 'boat'],
'pattern': '{{alpr_detection_pattern}}'
},
'sequence': [{
'name': 'Platerecognizer cloud',
'enabled': 'yes',
'alpr_api_type': '{{alpr_api_type}}',
'alpr_service': '{{alpr_service}}',
'alpr_key': '{{alpr_key}}',
'platrec_stats': '{{platerec_stats}}',
'platerec_min_dscore': {{platerec_min_dscore}},
'platerec_min_score': {{platerec_min_score}},
'max_size':1600,
#'platerec_payload': {
#'regions':['us'],
#'camera_id':12,
#},
#'platerec_config': {
# 'region':'strict',
# 'mode': 'fast'
#}
}]
}
}
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
# Use the new FCM V1 protocol (recommended)
use_fcmv1 = yes
# if yes, will replace notifications with the latest one
# default: no
replace_push_messages = no
# 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
# Set priority for android push. Default is high.
# You can set it to high or normal.
# There is weird foo going on here. If you set it to high,
# and don't interact with push, users report after a while they
# get delayed by Google. I haven't quite figured out what is the precise
# value to put here to make sure it always reaches you. Also make sure
# you read the zmES faq on delayed push
fcm_android_priority = high
# If you see messages not being delivered in doze mode for android
# Even AFTER you disable battery optimization for the app, try making this 0
# otherwise leave it unspecified. The value here is in seconds
# it specifies how long the message will be valid before it is discarded
# Some reports say if you set this to 0, android will try and deliver it immediately
# while others say it won't. YMMV.
# fcm_android_ttl = 0
# Make this yes if you are having push notification issues and want me to
# take a look at what is going on on my server side. Default is no
# WARNING: If you enable this, I'll get to see your push notification details
# including your full token and possibly your ZM user and password that you provide
# as part of the picture URL. I try and automatically replace it, but no guarantees if
# the regex fails in certain conditions. SO KEEP THIS OFF unless you've chatted with me
# and you know we have exchanged notes. You should turn this off once debugging is over
fcm_log_raw_message=no
# If you want me to help you debug what is going wrong in FCM, it helps
# if you add a unique ID here so that I know what to look for
# it can be anything. Default is NONE
#fcm_log_message_id=I_LOVE_LAMBSHANKS
# When push messages are sent, your ES sends a message to my cloud function
# that sends the message to Google's FCM servers. Thisis the key that authorizes
# your ES to send messages to my cloud function. By default, it will contain a key
# that authorizes with my server. If you switch to your own server, change this value
# You will also need to switch the server URL to your own server, use fcm_v1_url for that
#fcm_v1_key = myspecialkey
# fcm_v1_url = https://my_custom_url/function_name
# Use MQTT for messaging (default: no)
[mqtt]
enable = no
# Allow you to set a custom MQTT topic name
# default: zoneminder
#topic = my topic name
# 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
# MQTT over TLS
# Location to MQTT broker CA certificate. Uncomment this line will enable MQTT over TLS.
# tls_ca = /config/certs/ca.pem
# To enable 2-ways TLS, add client certificate and private key
# Location to client certificate and private key
# tls_cert = /config/es-pub.pem
# tls_key = /config/es-key.pem
# To allow insecure TLS (disable peer verifier), (default: no)
# tls_insecure = yes
[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]
# Link to json file that has rules which can be customized
# es_rules=/etc/zm/es_rules.json
# Display messages to console (default: no).
# Note that you can keep this to no and just
# use --debug when running from CLI too
console_logs = no
# debug level for ES messages. Default 4. Note that this is
# not controllable by ZM LOG_DEBUG_LEVEL as in Perl, ZM doesn't
# support debug levels
es_debug_level = 4
# 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 start notifications (default: yes)
# If no, starting notifications will not be sent out
send_event_start_notification = 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
# When a hook is invoked, the ES forks a child. If you are in a situation
# where your motion sensititivy in ZM is not set properly, you may land up
# triggering hundreds of child processes of zm_detect that may potentially
# crash your system. Note that there are global locks around the ML code which
# are controlled by xxx_max_processes in the objectconfig/mlapiconfig.files
# which will avoid parallel running of models. But this is if you are facing issues
# by the simple fact that too many zm_detect processes are forked (which will apply
# whether you use mlapi or not). While I do feel the core issue is that you need
# to fix your ZM sensitivity, this parameter helps control.
# NOTE: When you put in value for this, any hooks that attempt to kick off
# beyond this limit will simply be ignored. There is no queueing.
# A value of 0 (default) means there are no limits
max_parallel_hooks=0
# 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_end_hook completes. You can do
# your housekeeping work here
#event_end_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
