Machine Learning Hooks - Object Detection & Face Detection

Forum for questions and support relating to the 1.34.x releases only.
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mjalafoo
Posts: 8
Joined: Tue Apr 06, 2021 2:50 pm

Machine Learning Hooks - Object Detection & Face Detection

Post by mjalafoo » Wed Apr 07, 2021 10:26 am

Hi There,

I am facing a challenge with getting the OBJECT DETECTION and FACE DETECTION to function. I followed the guidelines documented and rebuilt the system multiple times without luck. I have the following versions installed:
- ZoneMinder v1.34.23
- Event Server v6.1.19
- Python 3.8.5
- OpenCV v4.5.2-dev
- Perl v5.30.0

When I run the following command sudo -u www-data /var/lib/zmeventnotification/bin/zm_train_faces.py :

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INF [zm_train_faces] [Setting up signal handler for logs]
INF [zm_train_faces] [Switching global logger to ZMLog]
Traceback (most recent call last):
  File "/var/lib/zmeventnotification/bin/zm_train_faces.py", line 12, in <module>
    import pyzm.ml.face_train as train
ModuleNotFoundError: No module named 'pyzm.ml.face_train'
Below is a sample log when the following command sudo -u www-data /usr/bin/zmeventnotification.pl --debug is run and an event is triggered:

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DBG-2:2021-04-07,12:14:55 PARENT: ----------> Tick START (active forks:0, total forks:0, running for:1 min)<--------------
DBG-2:2021-04-07,12:14:55 PARENT: After tick: TOTAL: 0,  ES_CONTROL: 0, FCM+WEB: 0, FCM: 0, WEB: 0, MQTT:0, invalid WEB: 0, PENDING: 0
DBG-2:2021-04-07,12:14:55 PARENT: There are 0 active child forks...
INF:2021-04-07,12:14:55 PARENT: New event 10529 reported for Monitor:8 (Name:Master Bedroom) Motion All[last processed eid:]
04/07/2021 12:14:55.394888 zmeventnotification[17955].INF [main:1010] [PARENT: New event 10529 reported for Monitor:8 (Name:Master Bedroom) Motion All[last processed eid:]]
DBG-2:2021-04-07,12:14:55 PARENT: checkEvents() new events found=1
DBG-2:2021-04-07,12:14:55 PARENT: There are 1 new Events to process
DBG-2:2021-04-07,12:14:55 PARENT: ---------->Tick END (active forks:1, total forks:1)<--------------
DBG-1:2021-04-07,12:14:55 PARENT: Forked process:17964 to handle alarm eid:10529
DBG-2:2021-04-07,12:14:55 |----> FORK:Master Bedroom (8), eid:10529 Adding event path:/var/cache/zoneminder/events/8/2021-04-07/10529 to hook for image storage
DBG-1:2021-04-07,12:14:55 |----> FORK:Master Bedroom (8), eid:10529 Invoking hook on event start:'/var/lib/zmeventnotification/bin/zm_event_start.sh' 10529 8 "Master Bedroom" "Motion All" "/var/cache/zoneminder/events/8/2021-04-07/10529"
DBG-2:2021-04-07,12:15:00 PARENT: ----------> Tick START (active forks:1, total forks:1, running for:1 min)<--------------
DBG-2:2021-04-07,12:15:00 PARENT: After tick: TOTAL: 0,  ES_CONTROL: 0, FCM+WEB: 0, FCM: 0, WEB: 0, MQTT:0, invalid WEB: 0, PENDING: 0
DBG-2:2021-04-07,12:15:00 PARENT: There are 1 active child forks...
DBG-2:2021-04-07,12:15:00 PARENT: We've already worked on Monitor:8, Event:10529, not doing anything more
DBG-2:2021-04-07,12:15:00 PARENT: checkEvents() new events found=0
DBG-2:2021-04-07,12:15:00 PARENT: There are 0 new Events to process
DBG-2:2021-04-07,12:15:00 PARENT: ---------->Tick END (active forks:1, total forks:1)<--------------
DBG-2:2021-04-07,12:15:04 |----> FORK:Master Bedroom (8), eid:10529 parse of hook: and []
DBG-1:2021-04-07,12:15:04 |----> FORK:Master Bedroom (8), eid:10529 hook start returned with text: json:[] exit:1
DBG-2:2021-04-07,12:15:05 PARENT: ----------> Tick START (active forks:1, total forks:1, running for:1 min)<--------------
DBG-2:2021-04-07,12:15:05 PARENT: After tick: TOTAL: 0,  ES_CONTROL: 0, FCM+WEB: 0, FCM: 0, WEB: 0, MQTT:0, invalid WEB: 0, PENDING: 0
DBG-2:2021-04-07,12:15:05 PARENT: There are 1 active child forks...
DBG-2:2021-04-07,12:15:05 PARENT: We've already worked on Monitor:8, Event:10529, not doing anything more
DBG-2:2021-04-07,12:15:05 PARENT: checkEvents() new events found=0
DBG-2:2021-04-07,12:15:05 PARENT: There are 0 new Events to process
DBG-2:2021-04-07,12:15:05 PARENT: ---------->Tick END (active forks:1, total forks:1)<--------------
DBG-2:2021-04-07,12:15:06 |----> FORK:Master Bedroom (8), eid:10529 rules: Checking rules for alarm caused by eid:10529, monitor:8, at: Wed Apr  7 12:15:06 2021 with cause:Motion All
DBG-1:2021-04-07,12:15:06 |----> FORK:Master Bedroom (8), eid:10529 rules: No rules found for Monitor, allowing:8
DBG-1:2021-04-07,12:15:06 |----> FORK:Master Bedroom (8), eid:10529 Matching alarm to connection rules...
DBG-3:2021-04-07,12:15:08 |----> FORK:Master Bedroom (8), eid:10529 For 8 (Master Bedroom), SHM says: state=0, eid=10529
INF:2021-04-07,12:15:08 |----> FORK:Master Bedroom (8), eid:10529 Event 10529 for Monitor 8 has finished
04/07/2021 12:15:08.077698 zmeventnotification[17964].INF [main:1010] [|----> FORK:Master Bedroom (8), eid:10529 Event 10529 for Monitor 8 has finished]
DBG-3:2021-04-07,12:15:08 |----> FORK:Master Bedroom (8), eid:10529 Event end object is: state=>pending with cause=>Motion: All
INF:2021-04-07,12:15:10 |----> FORK:Master Bedroom (8), eid:10529 end hooks/use hooks not being used, going to directly send out a notification if checks pass
04/07/2021 12:15:10.114345 zmeventnotification[17964].INF [main:1010] [|----> FORK:Master Bedroom (8), eid:10529 end hooks/use hooks not being used, going to directly send out a notification if checks pass]

The ZMDETECT_m8.log is created but empty when this code sudo -u www-data /var/lib/zmeventnotification/bin/zm_event_start.sh 10529 8 is run.


All the following dependencies are installed:

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sudo apt install pip3 -y
sudo apt install make cmake -y 
sudo perl -MCPAN -e "install Crypt::MySQL"
sudo perl -MCPAN -e "install Config::IniFiles"
sudo perl -MCPAN -e "install Crypt::Eksblowfish::Bcrypt"
sudo perl -MCPAN -e "install Time::Piece"
sudo apt-get install libyaml-perl
sudo perl -MCPAN -e "install Net::WebSocket::Server"
sudo apt install libjson-perl -y
sudo apt install libssl-dev -y
perl -MCPAN -e "install LWP::Protocol::https"
perl -MCPAN -e "install Net::MQTT::Simple"
sudo apt install libopenblas-dev liblapack-dev libblas-dev  -y
sudo -H pip3 install face_recognition
sudo -H pip3 install numpy
The following perl modules are installed:

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   Config::IniFiles
   Crypt::MySQL
   Digest::SHA1
   ExtUtils::Config
   ExtUtils::Helpers
   ExtUtils::InstallPaths
   LWP::Protocol::https
   Module::Build
   Module::Build::Tiny
   Mozilla::CA
   Net::MQTT::Simple
   Net::WebSocket::Server
   Perl
   Protocol::WebSocket
   Test::RequiresInternet
   Time::Piece
----------------------------------CONFIG FILES------------------------------------------------------------
ZMEVENTNOTIFICATION.INI

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# 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 = 7200

# 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

# 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

# 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
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

# You can create your own custom attributes in the [custom] section

[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=1
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
detection_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=snapshot

# 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=yes

# 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]

# 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
yolo4_object_processor=cpu

# 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
yolo3_object_processor=cpu

# 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
tinyyolo_object_processor=cpu


[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
detection_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 
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': 'object,face,alpr',
            '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']

		},
		'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}}',
				'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'], # use in combination with TPU face det above
				'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
			}]
		}
	}



SECRETS.INI

Code: Select all

# your secrets file
[secrets]

# fid can have the following values:
# a particular <frameid>, alarm or snapshot
# starting ZM 1.35, you can also specify
# objdetect_mp4, objdetect_gif or objdetect_image
# this needs create_animation enabled in objectconfig.ini and associated flags
# If you keep it to objdetect, if you created a GIF file in objectconfig, then
# a GIF file will be used else an image. If you opted for MP4 in objectconfig,
# you need to change this to objdetect_mp4

# Note that on Android, mp4/gif does not work. iOS only.
ZMES_PICTURE_URL=https://XXXXX/zm/index.php?view=image&eid=EVENTID&fid=objdetect&width=600

#ZMES_PICTURE_URL=https://portal/zm/index.php?view=image&eid=EVENTID&fid=snapshot&width=600
ZM_USER=XXXXX
ZM_PASSWORD=XXXXX
ES_ADMIN_INTERFACE_PASSWORD=your_admin_interface_password

ZM_PORTAL=https://XXXXX/zm
ZM_API_PORTAL=https://XXXXX/zm/api
ES_CERT_FILE=/etc/letsencrypt/live/XXXXX/fullchain.pem
ES_KEY_FILE=/etc/letsencrypt/live/XXXXX/privkey.pem
ML_USER=your_mlapi_user
ML_PASSWORD=your_mlapi_password
PLATEREC_ALPR_KEY=your_plate_recognizer_api_key
OPENALPR_ALPR_KEY=your_openalpr_api_key

ESCONTROL_INTERFACE_PASSWORD=yourescontrolpassword

MQTT_USERNAME=your_mqtt_username
MQTT_PASSWORD=your_mqtt_password

PUSHOVER_APP_TOKEN=your_pushover_app_token
PUSHOVER_USER_KEY=your_pushover_user_key

mjalafoo
Posts: 8
Joined: Tue Apr 06, 2021 2:50 pm

Re: Machine Learning Hooks - Object Detection & Face Detection

Post by mjalafoo » Wed Apr 07, 2021 10:32 am

In case anyone asks about PYZM below is the status when sudo -H pip3 install pyzm is executed:

Code: Select all

Requirement already satisfied: pyzm in /usr/local/lib/python3.8/dist-packages (0.3.45)
Requirement already satisfied: numpy>=1.13.3 in /usr/lib/python3/dist-packages (from pyzm) (1.17.4)
Requirement already satisfied: requests>=2.18.4 in /usr/lib/python3/dist-packages (from pyzm) (2.22.0)
Requirement already satisfied: dateparser>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from pyzm) (1.0.0)
Requirement already satisfied: imutils>=0.5.3 in /usr/local/lib/python3.8/dist-packages (from pyzm) (0.5.4)
Requirement already satisfied: SQLAlchemy<1.4.0,>=1.3.20 in /usr/local/lib/python3.8/dist-packages (from pyzm) (1.3.24)
Requirement already satisfied: Pillow in /usr/lib/python3/dist-packages (from pyzm) (7.0.0)
Requirement already satisfied: portalocker>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from pyzm) (2.3.0)
Requirement already satisfied: progressbar2>=3.53.1 in /usr/local/lib/python3.8/dist-packages (from pyzm) (3.53.1)
Requirement already satisfied: Shapely>=1.7.0 in /usr/local/lib/python3.8/dist-packages (from pyzm) (1.7.1)
Requirement already satisfied: psutil>=5.7.3 in /usr/local/lib/python3.8/dist-packages (from pyzm) (5.8.0)
Requirement already satisfied: websocket-client>=0.57.0 in /usr/local/lib/python3.8/dist-packages (from pyzm) (0.58.0)
Requirement already satisfied: mysql-connector-python>=8.0.16 in /usr/local/lib/python3.8/dist-packages (from pyzm) (8.0.23)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from dateparser>=1.0.0->pyzm) (2.8.1)
Requirement already satisfied: regex!=2019.02.19 in /usr/local/lib/python3.8/dist-packages (from dateparser>=1.0.0->pyzm) (2021.4.4)
Requirement already satisfied: tzlocal in /usr/local/lib/python3.8/dist-packages (from dateparser>=1.0.0->pyzm) (2.1)
Requirement already satisfied: pytz in /usr/lib/python3/dist-packages (from dateparser>=1.0.0->pyzm) (2019.3)
Requirement already satisfied: python-utils>=2.3.0 in /usr/local/lib/python3.8/dist-packages (from progressbar2>=3.53.1->pyzm) (2.5.6)
Requirement already satisfied: six in /usr/lib/python3/dist-packages (from progressbar2>=3.53.1->pyzm) (1.14.0)
Requirement already satisfied: protobuf>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from mysql-connector-python>=8.0.16->pyzm) (3.15.7)

Maximo1970
Posts: 56
Joined: Sun May 28, 2017 4:29 pm

Re: Machine Learning Hooks - Object Detection & Face Detection

Post by Maximo1970 » Thu Apr 08, 2021 9:19 am

ModuleNotFoundError: No module named 'pyzm.ml.face_train'


This would suggest either the module is missing or is not in the default location. Have you checked the location of the file pyzm.ml.face_train

mjalafoo
Posts: 8
Joined: Tue Apr 06, 2021 2:50 pm

Re: Machine Learning Hooks - Object Detection & Face Detection

Post by mjalafoo » Thu Apr 08, 2021 8:21 pm

This seems to be a bug in the zm_train_face.py already captured by the developer.
That's a bug. Please update to master of zmeventnotification
(commit fix: https://github.com/pliablepixels/zmeven ... 6d09d1d070)
However, now after applying the bug fix (I haven't updated to master, I just applied the fix in the zm_train_face.py), the problem goes away, but the process ends with KILLED as shown below:

Code: Select all

INF [zm_train_faces] [Setting up signal handler for logs]
INF [zm_train_faces] [Switching global logger to ZMLog]
INF [zm_train_faces] [Ignoring monitor specific settings, as you did not provide a monitor id]
Killed


Any idea what am I doing wrong?
Is it something with the structure of the KNOWN_FACES folder?
Please note I do not hace yet (faces.dat) generated inside KNOWN_FACES forlder.

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