- Try deleting everything and installing it again
- Check your settings with my settings
Use these images
deepquestai_deepstack
robmarkcole_deepstack-ui
1 Like
I have made amendments to the integration. Now we not only determine the name, but also checks the confidence. Without confidence, it often determines incorrectly. For example, the camera recognized the face of user 1 and the confidence of the recognized face is not indicated, then deepstack can report that it is user 2, and this is already an error. Therefore, I added the condition if the username and confidence are above 70, then this is true, if if the username and confidence are below 70, then this is false.
faces.append(
{"name": name, "confidence": confidence, "bounding_box": box, "prediction": pred}
)
if name in ['Divan', 'divan'] and confidence > 70:
name = 'Диван'
elif name in ['Oleg', 'oleg'] and confidence > 70:
name = 'Олег'
elif name in ['Michael', 'michael'] and confidence > 70:
name = 'Майкл'
elif name in ['Toni', 'toni'] and confidence > 70:
name = 'Тони'
elif name in ['Julianna', 'julianna'] and confidence > 70:
name = 'Джулианна'
else:
name = 'unknown'
names_list.append(name)
faces[0]['bounding_box']['names'] = ', '.join(names_list)
return faces
An example of if the confidence is below 70. The system will not report the user name and will show unknown
Ниже перевод текст на русском языке
Развернуть
Я внес в интеграцию поправки. Теперь не только определяем имя, но и сверяет уверенность. Без уверенности определяет часто ошибочно. Например камера распознала лицо пользователя 1 и уверенность распознанного лица не указывается, то deepstack может сообщить, что это пользователь 2, а это уже ошибка. Поэтому я добавил условие если имя пользователя и уверенность выше 70, то это верно, если если имя пользователя и уверенность ниже 70, то это ложно.
Вставьте этот код в файлик image_processing.py
faces.append(
{"name": name, "confidence": confidence, "bounding_box": box, "prediction": pred}
)
if name in ['Divan', 'divan'] and confidence > 70:
name = 'Диван'
elif name in ['Oleg', 'oleg'] and confidence > 70:
name = 'Олег'
elif name in ['Michael', 'michael'] and confidence > 70:
name = 'Майкл'
elif name in ['Toni', 'toni'] and confidence > 70:
name = 'Тони'
elif name in ['Julianna', 'julianna'] and confidence > 70:
name = 'Джулианна'
else:
name = 'unknown'
names_list.append(name)
faces[0]['bounding_box']['names'] = ', '.join(names_list)
return faces
Пример того, если уверенность ниже 70. Система не сообщит имя пользователя и покажет неизвестно
Пример того, если уверенность выше 70. Система сообщит правильное имя пользователя
Hello, I had the same problem, I found the solution on Deepstack forum here.
It’s because the DETECTION endpoint is not activated. Run deepstack container with environmental variable “-e VISION-DETECTION=True”.
Yeah, this. Tried to train Deepstack with all kinds of images - high res, cam shots, selfies - and it’s just a mess. Thinks the gf is me and I am her like 80% of the time. Gets confused by people wearing glasses. It’s practically unusable.
Is there some kind of best practice on what sort of images should be uploaded for training? I’ve read that people have had better luck with Compareface, may have to give it a go…
I need your help. Thanks to one person who gave me an amazing option and who encouraged me to redo everything. This option is good because I will not need to go into the code of the deep stack_force integration itself, but I can do it using the Home Assistant.
As a result, I did this. I have created a sensor that outputs the names of recognized faces and I can also change the face recognition parameters via input_number. deepstack_confidence_face
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Names of identified persons'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
If desired, you can create an automation that will send pictures in telegram. In fact, you can implement many more automation options. For example, a husband and wife in the house. The husband came to the kitchen, the camera recognized the husband and turned on the coffee maker for him, and the wife came and turned on the kettle for her, or turned on the TV in the husband’s room, and the laptop or climate control for the wife. If the camera sees only the husband, then the climate will adjust to the husband, and if the wife saw the camera, then to the wife, and if they are together, then the climate is by agreement for two, etc. There are a lot of options.
alias: 'Process Data from Facial Recognition'
description: ''
trigger:
- platform: state
entity_id: image_processing.detect_face_eufy_camera
condition: []
action:
- service: telegram_bot.send_photo
data:
file: /config/www/deepstack/snapshots/detect_face_eufy_camera_latest.jpg
caption: >
{% if is_state('image_processing.detect_face_eufy_camera', 'unknown') %}
{% else %}
*Someone's in the hallway:* {% set detect_face =
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face =
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','confidence')| map(attribute='confidence') |
join(', ') %} {% set set_confidence =
states('input_number.deepstack_confidence_face')%} {% if detect_face and
confidence_face >= set_confidence %} {{
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %} unknown {% endif %}{% endif %}
target: 11111111
disable_notification: false
- service: telegram_bot.send_photo
mode: single
I’m trying to implement the translation of names into Russian in the Home Assistant and so that it outputs a list. So that it was instead of Igor, Oleg, Masha = Igor, Oleg, Masha and changed depending on which person was identified. I was able to configure only for single names, i.e. only one name is translated
Let me clarify, with the edits in the integration in the python file, this works, but it can’t be made to work in Home Assistant
faces.append(
{"name": name, "confidence": confidence, "bounding_box": box, "prediction": pred}
)
if name in ['Divan', 'divan'] and confidence > 70:
name = 'Диван'
elif name in ['Oleg', 'oleg'] and confidence > 70:
name = 'Олег'
elif name in ['Michael', 'michael'] and confidence > 70:
name = 'Майкл'
elif name in ['Toni', 'toni'] and confidence > 70:
name = 'Тони'
elif name in ['Julianna', 'julianna'] and confidence > 70:
name = 'Джулианна'
else:
name = 'unknown'
names_list.append(name)
faces[0]['bounding_box']['names'] = ', '.join(names_list)
return faces
Here is my version, which works only if one name is recognized, and it does not work with several names
{% set names = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | list | join(', ') %}
{% set total_faces = state_attr('image_processing.detect_face_eufy_camera','total_faces') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% if names in ["Igor" , "igor"] and confidence_face > '60' %}
{% set names_list = "Игорь" %}
{% elif names in ['Oleg', 'oleg'] and confidence_face > '60' %}
{% set names_list = "Олег" %}
{% elif names in ['Masha','masha'] and confidence_face > '60' %}
{% set names_list = "Маша" %}
{% elif names in ['Marina','marina'] and confidence_face > '60' %}
{% set names_list = "Марина" %}
{% elif names in ['unknown'] %}
Неизвестное лицо
{% endif %}
{{ names_list }}
Текст на русском (Text in Russian)
Нуждаюсь в вашей помощи. Благодаря одному человеку, который подкинул мне потрясающий вариант и который побудил меня переделать все. Этот вариант хорош тем, что мне не нужно будет лезть в код самой интеграции deepstack_face, а можно сделать средствами Home Assistant.
В итоге сделал так. Создал сенсор, который выводит имена опознанных лиц и также я могу менять уверенность распознавания лица через input_number.deepstack_confidence_face
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Names of identified persons'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
При желании можно создать автоматизацию, которая будет слать снимки в телеграм. На самом деле можно реализовать гораздо больше вариантов автоматизации. Например муж и жена в доме. Муж пришел на кухню, камера распознала мужа и включила ему кофеварку, а пришла жена и включила ей чайник или в комнате мужу включила телевизор, а жене ноутбук или настройка климата. Если камера видит только мужа, то климат будет подстраиваться под мужа, а если камера увидела жена, то под жену, а если они вдвоем, то климат по договоренности для двоих и т.д. Вариантов куча.
alias: 'Process Data from Facial Recognition'
description: ''
trigger:
- platform: state
entity_id: image_processing.detect_face_eufy_camera
condition: []
action:
- service: telegram_bot.send_photo
data:
file: /config/www/deepstack/snapshots/detect_face_eufy_camera_latest.jpg
caption: >
{% if is_state('image_processing.detect_face_eufy_camera', 'unknown') %}
{% else %}
*Someone's in the hallway:* {% set detect_face =
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face =
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','confidence')| map(attribute='confidence') |
join(', ') %} {% set set_confidence =
states('input_number.deepstack_confidence_face')%} {% if detect_face and
confidence_face >= set_confidence %} {{
state_attr('image_processing.detect_face_eufy_camera','faces') |
selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %} unknown {% endif %}{% endif %}
target: 11111111
disable_notification: false
- service: telegram_bot.send_photo
mode: single
Я пытаюсь в Home Assistant реализовать перевод имен на русский и чтобы это выводило списком. Чтобы было вместо Igor, Oleg, Masha = Игорь, Олег, Маша и менялось в зависимости от того какое лицо было опознано. Я смог настроить только для одиночных имен, т.е переводится только одно имя
Уточню, с правками в интеграции в файлике python это работает, но в Home Assistant не получается заставить работать
faces.append(
{"name": name, "confidence": confidence, "bounding_box": box, "prediction": pred}
)
if name in ['Divan', 'divan'] and confidence > 70:
name = 'Диван'
elif name in ['Oleg', 'oleg'] and confidence > 70:
name = 'Олег'
elif name in ['Michael', 'michael'] and confidence > 70:
name = 'Майкл'
elif name in ['Toni', 'toni'] and confidence > 70:
name = 'Тони'
elif name in ['Julianna', 'julianna'] and confidence > 70:
name = 'Джулианна'
else:
name = 'unknown'
names_list.append(name)
faces[0]['bounding_box']['names'] = ', '.join(names_list)
return faces
Вот мой вариант, который работает только если опознано одно имя, а с несколькими именами это не работает
{% set names = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | list | join(', ') %}
{% set total_faces = state_attr('image_processing.detect_face_eufy_camera','total_faces') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% if names in ["Igor" , "igor"] and confidence_face > '60' %}
{% set names_list = "Игорь" %}
{% elif names in ['Oleg', 'oleg'] and confidence_face > '60' %}
{% set names_list = "Олег" %}
{% elif names in ['Masha','masha'] and confidence_face > '60' %}
{% set names_list = "Маша" %}
{% elif names in ['Marina','marina'] and confidence_face > '60' %}
{% set names_list = "Марина" %}
{% elif names in ['unknown'] %}
Неизвестное лицо
{% endif %}
{{ names_list }}
1 Like
The issue is resolved. He was lying right on the surface. A wonderful deepstack client from tomer/deepstack-trainer. Russian Russian names I was able to specify there and the recognized names in Russian began to be displayed in the sensor
I have improved the sensor that displays names and attributes
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Names of identified persons'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
attribute_templates:
faces: "{{ state_attr('image_processing.detect_face_eufy_camera','faces') }}"
total_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_faces') }}"
total_matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_matched_faces') }}"
matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','matched_faces') }}"
last_detection: "{{ state_attr('image_processing.detect_face_eufy_camera','last_detection') }}"
friendly_name: "{{ state_attr('image_processing.detect_face_eufy_camera','friendly_name') }}"
device_class: "{{ state_attr('image_processing.detect_face_eufy_camera','device_class') }}"
Script for sending photos in a telegram
service: notify.notify
data:
message: ''
data:
photo:
file: /config/www/deepstack/snapshots/detect_face_eufy_camera_latest.jpg
caption: 'Someone's in the hallway 📷: *{{ states.sensor.persons_names.state }}*'
Текст на русском (Text in Russian)
Вопрос решен. Он лежал прямо на поверхности. Замечательный клиент deepstack от t0mer/deepstack-trainer. Я смог там указать имена на русском языке и в сенсоре стали отображаться опознанные имена на русском
Доработал сенсор который выводит имена и атрибуты
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Имена идентифицированных лиц'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
attribute_templates:
faces: "{{ state_attr('image_processing.detect_face_eufy_camera','faces') }}"
total_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_faces') }}"
total_matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_matched_faces') }}"
matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','matched_faces') }}"
last_detection: "{{ state_attr('image_processing.detect_face_eufy_camera','last_detection') }}"
friendly_name: "{{ state_attr('image_processing.detect_face_eufy_camera','friendly_name') }}"
device_class: "{{ state_attr('image_processing.detect_face_eufy_camera','device_class') }}"
Скрипт для отправки фото в телеграм
service: notify.notify
data:
message: ''
data:
photo:
file: /config/www/deepstack/snapshots/detect_face_eufy_camera_latest.jpg
caption: 'Кто-то в коридоре 📷: *{{ states.sensor.persons_names.state }}*'
3 Likes
wow, @DivanX10 , you made such a contribution to this topic, I greatly appreciate it!
My problem is that it was such a long time ago when I tried to pull this together, and since I’m far from a coder, simply an enthusiast, I’d really appreaciate if you could create a short write up from the very beginning, how to properly pull this thing together. Only if you don’t mind. Nevertheless, I’ll try to redo it from scratch based on your input, but if you can make my journey just slightly easier, that’d be fantastic. No rush, no pressure, only if you feel like, I think others would also benefit from it. But kudos for all your previous contribution, it’s already a big help to noobs like me 
@DivanX10 , if you allow me, I have one more question quickly about the trainer, more specifically about training images. I tried before with image training with many methods, and I’ve came to the realisation that the key to successfully teaching a person’s images is to upload MANY pictures in one batch, otherwise only the LAST image uploaded (sent to deepstack) will be the latest and ONLY reference image.
Did you notice that is it the same with this trainer, as well, or you can slowly upload images one by one over a period of time as it accumulates more images of a given person?
thx in advance!
I use packages.
Reading the article Convenient configuration (configuration) Home Assistant.
This is written in configuration.yaml as follows
homeassistant:
packages: !include_dir_named includes/packages # We place any configurations in the folder packages
How to make a sensor with attributes?
We read about creating a sensor with attributes here
You can create a sensor and control not only the content of the state, but also what is contained in the attributes. Here is an example:
input_text:
t_state:
t_attribute:
sensor:
- platform: template
sensors:
my:
value_template: "{{ states('input_text.t_state') }}"
attribute_templates:
a: "{{ states('input_text.t_attribute') }}"
In the packages folder, we create a file with any name, I called face_identify_deepstack_sensor.yaml
In the face_identify_deepstack_sensor.yaml file, we insert the code, where image_processing.detect_face_eufy_camera is the name of my camera (the deepstack config is below) and I also use the auxiliary element number input_number. deepstack_confidence_face. It allows you to adjust the confidence of facial recognition through lovelace. Instead of image_processing. detect_face_beauty_camera, you need to specify the name of your camera
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Names of identified persons'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
attribute_templates:
faces: "{{ state_attr('image_processing.detect_face_eufy_camera','faces') }}"
total_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_faces') }}"
total_matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_matched_faces') }}"
matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','matched_faces') }}"
last_detection: "{{ state_attr('image_processing.detect_face_eufy_camera','last_detection') }}"
friendly_name: "{{ state_attr('image_processing.detect_face_eufy_camera','friendly_name') }}"
device_class: "{{ state_attr('image_processing.detect_face_eufy_camera','device_class') }}"
Creating an auxiliary element number input_number.deepstack_confidence_face. If you do it through the GUI
If you do it via yaml. To the input_number.yaml file
deepstack_confidence_face:
name: "Deepstack: Face Recognition Confidence"
min: 40
max: 100
step: 1
mode: slider
icon: mdi:face-recognition
These are the settings we use to work with deepstack
image_processing:
- platform: deepstack_face
ip_address: 192.168.1.47
port: 5100
timeout: 10000
detect_only: False
save_file_folder: /config/www/deepstack/snapshots/
save_timestamped_file: False
save_faces: False
save_faces_folder: /config/www/deepstack/faces/
show_boxes: False
source:
# Connecting the camera Eufy Indoor Cam 2K Pan & Tilt
- entity_id: camera.eufy_camera_hall
name: detect_face_eufy_camera #( this is the name of my camera. As we call it here, it will be called, so I have it called image_processing.detect_face_ugon_camera)
Explanation of options
detect_only: Configuring detect_only = True results in faster processing than recognition mode, but any trained faces will not be listed in the matched_faces attribute
save_timestamped_file: Whether to save a photo with the last date of the snapshot to the folder-true\false
save_faces: Whether to save faces to a folder - true\false
show_boxes: Enable/disable the selection of the face with a red frame - true\false
Deepstack Training Recommendation
For deepstack face recognition to work correctly, I use photos from different angles, this will allow me to recognize a person’s faces more correctly. If you upload photos that look straight, then there will often be erroneous recognition when viewing from an angle. It is also necessary to use confidence, the higher the confidence, the more correct it will be to identify the person. The best option for face recognition is a confidence of at least 70. So I created
Текст на русском (Text in Russian)
Я использую packages.
Читаем статью Удобная настройка (конфигурация) Home Assistant.
Прописывается это в configuration.yaml следующим образом
homeassistant:
packages: !include_dir_named includes/packages # Размещаем любые конфигурации в папке packages
Как сделать сенсор c атрибутами?
Про создание сенсора с атрибутами читаем блог Ивана Бессарабова
Создание template сенсора с атрибутами
Можно создать сенсор и управлять не только содержимым стейта, но и тем что содержится в атрибутах. Вот пример:
input_text:
t_state:
t_attribute:
sensor:
- platform: template
sensors:
my:
value_template: "{{ states('input_text.t_state') }}"
attribute_templates:
a: "{{ states('input_text.t_attribute') }}"
В папке packages создаем файлик с любым именем, я назвал face_identify_deepstack_sensor.yaml
В файлик face_identify_deepstack_sensor.yaml вставляем код, где image_processing.detect_face_eufy_camera это имя моей камеры (конфиг deepstack ниже) и еще я использую вспомогательный элемент число input_number.deepstack_confidence_face. Оно позволяет регулировать уверенность распознавания лица через lovelace. Вам вместо image_processing.detect_face_eufy_camera нужно указывать имя своей камеры
sensor:
- platform: template
sensors:
persons_names:
friendly_name: 'Имена идентифицированных лиц'
icon_template: mdi:face-recognition
value_template: >
{% set detect_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% set set_confidence = states('input_number.deepstack_confidence_face')%}
{% if detect_face and confidence_face >= set_confidence %}
{{ state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') }}
{% else %}
unknown
{% endif %}
attribute_templates:
faces: "{{ state_attr('image_processing.detect_face_eufy_camera','faces') }}"
total_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_faces') }}"
total_matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','total_matched_faces') }}"
matched_faces: "{{ state_attr('image_processing.detect_face_eufy_camera','matched_faces') }}"
last_detection: "{{ state_attr('image_processing.detect_face_eufy_camera','last_detection') }}"
friendly_name: "{{ state_attr('image_processing.detect_face_eufy_camera','friendly_name') }}"
device_class: "{{ state_attr('image_processing.detect_face_eufy_camera','device_class') }}"
Создаем вспомогательный элемент число input_number.deepstack_confidence_face. Если делать через GUI
Если делать через через yaml. В файлик input_number.yaml
deepstack_confidence_face:
name: "Deepstack: Уверенность распознавания лица"
min: 40
max: 100
step: 1
mode: slider
icon: mdi:face-recognition
Вот эти настройки мы используем для работы deepstack
image_processing:
- platform: deepstack_face
ip_address: 192.168.1.47
port: 5100
timeout: 10000
detect_only: False
save_file_folder: /config/www/deepstack/snapshots/
save_timestamped_file: False
save_faces: False
save_faces_folder: /config/www/deepstack/faces/
show_boxes: False
source:
# Подключаем камеру Eufy Indoor Cam 2K Pan & Tilt
- entity_id: camera.eufy_camera_hall
name: detect_face_eufy_camera #( это имя нашей камеры, как мы тут именуем, так и будет называться, поэтому у меня оно именуется image_processing.detect_face_eufy_camera)
Пояснение по опциям
detect_only: Настройка detect_only = True приводит к более быстрой обработке, чем режим распознавания, но любые обученные лица не будут перечислены в атрибуте matched_faces
save_timestamped_file: Сохранять ли в папку фото с последней датой снимка - да\нет
save_faces: Сохранять ли лица в папку - да\нет
show_boxes: Включить\выключить выделение лица красной рамочкой - да\нет
Рекомендация по обучению deepstack
Для правильной работы распознавания лиц deepstack я использую фото с разными углами, это позволит более правильно распознавать лица человека. Если загружать фото которые смотрят прямо, то при угловом обзоре часто будет ошибочное распознавание. Также обязательно нужно использовать уверенность, чем выше уверенность, тем правильнее будет опознавать лицо. Самый оптимальный вариант для распознавания лица, это уверенность не ниже 70. Поэтому я создал
4 Likes
Good day all.
I’m trying to get deepstack object and face and UI running with home assistant.
When installing it on ubuntu in docker it works fine and it detects and recognises faces.
As soon as I close the SSH connection or restart the host, deepstack object and face containers stop running and I can’t start them anymore.Deepstack UI keeps working.
Error message in pertained when trying to start:
“Failure - starting container with non-empty request body was deprecated since API v1.22 and removed in v1.24.
Any ideas what the reason could be?
wow, just wow.
Thank you in the name of the community!!
Source: docker - клиент новее сервера (версия API клиента: 1.24, версия API сервера: 1.21) - Question-It.com
Docker runs on a client / server model, each release of the Docker Engine has a specific version of the API.
The combination of the release version and the Docker API version looks like this:
https://docs.docker.com/engine/api/v1.26/#section/Versioning
According to the table above, Docker API v1.24 is used in Docker Engine 1.12.x, and Docker API v1.21 is used in Docker Engine 1.9. x.The server requires an API version equal to or later than the client.
You have the following three options.
- Upgrade the server part to Docker Engine 1.12. x or higher.
- Downgrade the client part to Engine 1.9. x or lower.
- Downgrade the API version used at runtime by exporting ’ DOCKER_API_VERSION=1.21` to a client-side environment variable.
Текст на русском (Text in Russian )
Источник: docker - клиент новее сервера (версия API клиента: 1.24, версия API сервера: 1.21) - Question-It.com
Docker работает на модели клиент / сервер, каждый выпуск Docker Engine имеет определенную версию API.
Комбинация версии выпуска и версии API Docker выглядит следующим образом:
Docker Engine API v1.26 Reference
Согласно приведенной выше таблице, Docker API v1.24 используется в Docker Engine 1.12.x, а Docker API v1.21 используется в Docker Engine 1.9.x. Серверу требуется версия API, равная или более поздняя, чем у клиента.
У вас есть следующие три варианта.
- Обновите серверную часть до Docker Engine 1.12.x или выше.
- Понизьте клиентскую часть до Engine 1.9.x или ниже.
- Понизьте версию API, используемую во время выполнения, экспортировав
DOCKER_API_VERSION=1.21в переменную среды на стороне клиента.
@DivanX10 , do you happen to know the answer to this question? Or what is your experience with the trainer you recommended, which btw looks very promising!
I tried before with image training with many methods, and I’ve came to the realisation that the key to successfully teaching a person’s images is to upload MANY pictures in one batch, otherwise only the LAST image uploaded (sent to deepstack) will be the latest and ONLY reference image. Did you notice that is it the same with this trainer as well, or you can slowly upload images one by one over a period of time as it accumulates more images of a given person?
I uploaded several photos at once and in parts at different times. I didn’t notice any difference in the recognition operation. It also does not depend on the client here. It depends more on the angle of the photos and the clarity of the image from the camera. If the camera takes a clear picture, without blurring and without squares, then the recognition confidence is higher than 70%, if the picture is not very good, then recognition is 50-65% and can often be wrong. It also correctly recognizes if you look at the camera without moving and it works even if you look at the camera from an angle. For integration, I use Agent DVR, where pictures are taken in real time, aka WEBRTC, and the pictures are not quite perfect, but good, which gives + for correct recognition. I also use filters to exclude false identification, without filters, it is often mistakenly identified.
What filters do I use?
- I set the confidence at 70%
- In automation, I use the condition, if the entrance door is closed, then ignore it, if it is identified as unknown, if the entrance door is open and people come in, then I allow you to send unknown
Текст на русском (Text in Russian)
Я загружал и сразу несколько фоток и частями в разное время. Разницу в работе распознавания я не заметил. От клиента тут тоже не зависит. Тут больше зависит от ракурса фоток и четкости снимка от камеры. Если камера делает четкий снимок, без размытии и без квадратиков, то уверенность распознавания выше 70%, если снимок не очень получился, то распознание составляет 50-65% и часто может ошибаться. Также правильно распознает, если смотреть на камеру не подвижно и это работает, даже если смотреть на камеру под углом. Для интеграции я использую Agent DVR, там снимки делаются в реальном времени, он же WEBRTC и снимки получаются не совсем идеальными, но хорошими, что дает + для правильного распознавания. Также я использую фильтры для исключения ложных опознавании, без фильтров часто опознается ошибочно.
Какие фильтры я использую?
- Выставляю уверенность в 70%
- В автоматизации использую условие, если входная дверь закрыта, то игнорить, если опознано как unknown, если входная дверь открыта и заходят люди, то разрешаю присылать unknown
2 Likes
Thanks for this info, I’ll try to solve it!
It’s a good starting point for me 
Dear @DivanX10
really would not like to waste your time, but if you can, plase help me. I think I get where you’re going with this adjustable confidence treshold thingy, but unfortunately I have a slightly simpler method, and since I’m terrible with value templating, I can hardly extract the needed information from your help. If you can assist me a little bit on how to properly syntax my value template for my use case, I would praise your name.
My approach in a nutshell:
- I run Blue Iris NVR with deepstack integrated
- based on AI confirmed person detection I trigger a virtual “person is moving before the gate” sensor in HA
- when this virtual sensor gets triggered, I run an automation which calls the deepstack integration in HA to run a face recognition, save a snapshot of the result, and send a notification to my ios companion app
- the automation is condition splitted, based on the deepstack integration’s sensor states. And currently I desperately miss the filtering possibility on confidence level, this is what I need to solve!
My goal with the notifications:
- if it is a known, recognized person, above a certain confidence level, then include the name in the notifiiciations, later I’d like to trigger other automations based on the person (for instance open garden gate to someone I trust), don’t know yet how to solve it yet
- if it is an unknown person, then send a critical alert to my phone (so it passes through possible muted state of my phone) saying unknown person is standing before the gate.
- I just put a default branch as well if all conditions fail somehow, but the AI confirmed alert from blue iris still triggers my “motion sensor”, then it just simply sends a camera stream to my phone so I can check what the heck is going on.
Here’s my automation in it’s current form, and I desperately need to take into account the confidence level of the recognition, because many times faces are recognized as MY face, and the deepstack sensor name state attribute GETS my name in it, just with a very low confidence level. So even if a known person’s name is received, but BELOW the needed confidence (let’s say 70), then the UNKNOWN PERSON branch should be running ot the automation. I hope I explained it clearly
alias: Face recognition notification test
description: ''
trigger:
- platform: state
entity_id: binary_sensor.street_person_motiondetection
to: 'on'
condition: []
action:
- service: image_processing.scan
data: {}
target:
entity_id: image_processing.face_counter
- delay: '00:00:01'
- event: image_processing.detect_face
event_data:
entity_id: image_processing.face_counter
- delay: '00:00:01'
- choose:
- conditions:
- condition: template
value_template: >-
{{ 'Zsolt' in
state_attr('image_processing.face_counter','faces').0.name }}
sequence:
- service: notify.mobile_app_zsolt_iphone_12_pro
data:
message: 'Confidence of recognition: ' # here I would need to extraxt confidence level from sensor state with a value template
title: Zsolt is at the front gate!
data:
attachment:
url: >-
https://XXXXXXXXXXXXXX/local/deepstackrecog/face_counter_latest.jpg
content-type: jpeg
hide-thumbnail: false
- conditions:
- condition: template
value_template: >-
{{ 'unknown' in
state_attr('image_processing.face_counter','faces').0.name }}
sequence:
- service: notify.mobile_app_zsolt_iphone_12_pro
data:
title: Unknown person at the gate!
message: DepStack AI couldn't recognize the person standing in front of the gate.
data:
attachment:
url: https://XXXXXXXXXXXXXX/local/deepstackrecog/face_counter_latest.jpg
content-type: jpeg
hide-thumbnail: false
default:
- service: notify.mobile_app_zsolt_iphone_12_pro
data:
message: Someone's at the front gate!
title: There's movement in front of the gate
data:
push:
category: camera
entity_id: camera.blueiris_street
mode: single
oh, and btw, the instructional links you provided prooved to be a big rabbit hole to me, now I’m hunting after a good energy meter to be able to measure my house’s consumption against the power output of my solar panels 
Try to do this if you need to select names from identified persons and you can also set confidence for each person. You can even output it to the sensor
You can check this code in the template itself, and then it is not difficult to implement your wishes, for example, to create a sensor of recognized names or use it as a condition for automation
{% set names = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% if names in 'Brad' and confidence_face > '60' %} That's Right, this is Nonsense
{% elif names in 'Brad' and confidence_face < '59' %} A mistake, this is not Nonsense
{% elif names in 'Joly' and confidence_face > '60' %} That's right, this is Jolie
{% elif names in 'Joly' and confidence_face < '59' %} A mistake, it's not Jolie
{% elif names and 'Joly' and 'Brad' and confidence_face > '50' %} That's right, it's Brad and Jolie
{% elif names and 'Joly' and 'Brad' and confidence_face < '40' %} A mistake, it's not Jolie or Brad
{% endif %}
Текст на русском (Text in Russian)
Попробуйте сделать так, если вам надо выделять имена из опознанных лиц и также можете задать уверенность для каждого лица. Можете даже вывести это в сенсор
Это код можете проверить в самом шаблоне, а дальше уже не сложно реализовать свои хотелки, к примеру создать сенсор опознанных имен или использовать в качестве условии для автоматизации
{% set names = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','name')| map(attribute='name') | join(', ') %}
{% set confidence_face = state_attr('image_processing.detect_face_eufy_camera','faces') | selectattr('faces','!=','confidence')| map(attribute='confidence') | join(', ') %}
{% if names in 'Brad' and confidence_face > '60' %} Верно, это Брэд
{% elif names in 'Brad' and confidence_face < '59' %} Ошибка, это не Брэд
{% elif names in 'Joly' and confidence_face > '60' %} Верно, это Джоли
{% elif names in 'Joly' and confidence_face < '59' %} Ошибка, это не Джоли
{% elif names and 'Joly' and 'Brad' and confidence_face > '50' %} Верно, это Бред и Джоли
{% elif names and 'Joly' and 'Brad' and confidence_face < '40' %} Ошибка, это не Джоли и не Брэд
{% endif %}
1 Like
How do I configure Agent DVR to work with Deepstack?
How can I display a list of identified persons without brackets?
-
Go to the general settings of Agent DVR
-
Choosing intelligence
-
We specify the IP and port of the deepstack of our server and save it
-
Go to the camera settings
-
Click edit camera
-
Choosing actions
-
Adding the actions we need
-
Creating an action for face recognition. We will transmit to HA via MQTT. You can create any topic. It is important to correctly specify the topics when creating an MQTT sensor.
-
Creating an action for face recognition. When the movement stops, the status will be turned off. If this is not done, the name of the identified person will remain. You don’t have to use this action if you don’t want the name to disappear when the movement stops
-
Creating a motion detection sensor. This will be useful for us in order to launch the Deepstack Face integration for face recognition in HA
-
Creating a sensor for stopping motion detection.
-
Open the"Face Recognition" section
-
In the face recognition settings, select detect, specify confidence and enable the face recognition function
-
Open the "Tracking device"section
-
We specify to fix only objects. If we specify a simple mode, then everything will be recorded, even turning on and off lights and animals. Objects capture only what we specified in the objects filters, it can only be people or people and a dog or a car. Recognizes only the specified object from the filter.
-
Open the "Objects"section
-
In this “Objects” section, we specify which objects the camera should respond to.
-
Then we need to apply the saved parameters. You can also restart the Agent DVR
-
Reloading the Agent DVR
We create sensors for Agent DVR and output them to Lovelace
# Sensors via MQTT
sensor:
- platform: mqtt
name: "agentdvr eufy camera hall motion"
state_topic: "Agent/cameras/eufy camera hall/motion"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall face detected"
state_topic: "Agent/cameras/eufy camera hall/face"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall motion detected"
state_topic: "Agent/cameras/eufy camera hall/motion detected"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall object detected"
state_topic: "Agent/cameras/eufy camera hall/object detected"
icon: mdi:webcam
This is how the panel and the sensor itself look in the developer panel
How can I add identified persons to the list without brackets?
A visual example in the template. Without brackets and with brackets
If we use this code, a list without brackets will be displayed
{% for k, v in state_attr("image_processing.detect_face_eufy_camera", "matched_faces").items() -%}
{{ k }}: {{ v }}
{% endfor %}
This code will display a list with brackets
{{ state_attr("image_processing.detect_face_eufy_camera", "matched_faces") }}
Текст на русском (Text in Russian)
Как настроить Agent DVR для работы с Deepstack?
Как вывести список опознанных лиц без скобок?
-
Заходим в общие настройки Agent DVR
-
Выбираем интеллект
-
Указываем IP и порт deepstack своего сервера и сохраняемся
-
Заходим в настройки камеры
-
Нажимаем редактировать камеру
-
Выбираем действия
-
Добавляем нужные нам действия
-
Создаем действие для распознавание лица. Передавать в ХА будем через MQTT. Топик можно создать любой. Важно при создании MQTT сенсора верно указать топики.
-
Создаем действие для распознавания лица. При прекращении движения, статус будет выключен. Если этого не сделать, то имя опознанного лица останется. Можете не использовать это действие, если не хотите, чтобы имя исчезало при прекращении движения
-
Создаем сенсор обнаружения движения. Это нам пригодится для того, чтобы запускать в ХА интеграцию Deepstack Face для распознавания лица
-
Создаем сенсор прекращения обнаружения движения.
-
Открываем раздел “Распознавание лиц”
-
В настройках распознавание лиц выбираем детектировать, указываем уверенность и включаем функцию распознавание лица
-
Открываем раздел “Следящее устройство”
-
Указываем фиксировать только объекты. Если укажем простой режим, то фиксировать будет все, даже включение и выключение света и животных. Объекты фиксируют только то, что мы указали в фильтры объекты, это может быть только люди или люди и собака или машина. Распознает только указанный объект из фильтра.
-
Открываем раздел “Объекты”
-
В этом разделе “Объекты” мы указываем на какие объекты камера должна реагировать.
-
Потом нам надо применить сохраненные параметры. Можно еще и перезапустить Agent DVR
-
Перезагружаем Agent DVR
Создаем сенсоры для Agent DVR и выводим их в Lovelace
# Сенсоры через MQTT
sensor:
- platform: mqtt
name: "agentdvr eufy camera hall motion"
state_topic: "Agent/cameras/eufy camera hall/motion"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall face detected"
state_topic: "Agent/cameras/eufy camera hall/face"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall motion detected"
state_topic: "Agent/cameras/eufy camera hall/motion detected"
icon: mdi:webcam
- platform: mqtt
name: "agentdvr eufy camera hall object detected"
state_topic: "Agent/cameras/eufy camera hall/object detected"
icon: mdi:webcam
Вот так выглядит панель и сам сенсор в панели разработчика
Как вывести опознанные лица в список без скобок?
Наглядный пример в шаблоне. Без скобок и со скобками
Если будем использовать этот код, то отобразится список без скобок
{% for k, v in state_attr("image_processing.detect_face_eufy_camera", "matched_faces").items() -%}
{{ k }}: {{ v }}
{% endfor %}
Этот код отобразится список со скобками
{{ state_attr("image_processing.detect_face_eufy_camera", "matched_faces") }}
I am in a hurry to tell you the good news. Maybe it will help you. A few days earlier, several people encountered a similar problem. Only object recognition worked for them, but there was no face recognition and an error occurred
Deepstack Exception: Timeout connecting to Deepstack, the current timeout is 30 seconds, try increasing this value
The problem turned out to be that some people put the image of docker deep quest ai/deepstack:cpu-2021.06.1 on hardware with an Intel Celeron J3455 processor. They write about the problem itself here
In order for Deepstack to work on a machine with an Intel Celeron J3455 processor, you need to deploy the docker deepquestai/deepstack:cpu-x5-beta image, you can try the cpu-x6-beta image, but you haven’t tried it with it
image docker deepquestai/deepstack:cpu-x5-beta download here
image docker deepquestai/deepstack:cpu-x6-beta download here
Текст на русском (Text in Russian)
Спешу сообщить хорошую новость. Может оно вам поможет. Несколькими днями ранее несколько людей столкнулось с подобной проблемой. У них работало только распознавание объектов, а распознавание лиц нет и возникала ошибка:
Deepstack Exception: Timeout connecting to Deepstack, the current timeout is 30 seconds, try increasing this value
Проблема оказалась в том, что некоторые ставят образ docker deepquestai/deepstack:cpu-2021.06.1 на железо с процессором Intel Celeron J3455. Про саму проблему пишут тут
Для того, чтобы Deepstack заработал на машине с процессором Intel Celeron J3455, надо развернуть образ docker deepquestai/deepstack:cpu-x5-beta, можно попробовать образ cpu-x6-beta, но с ним не пробовали
образ docker deepquestai/deepstack:cpu-x5-beta скачать здесь
образ docker deepquestai/deepstack:cpu-x6-beta скачать здесь
1 Like
@DivanX10 , and what about Unknown? where there’s absolutely no face is recognized, and it gives back unknown? How do I extract that?
Many many thanks