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.gitignore
vendored
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.gitignore
vendored
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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.idea/
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11
main.py
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11
main.py
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from tracker import Tracker
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tracker = Tracker(
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mqtt_address="10.1.1.100",
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mqtt_client_id="cvzone_tracker_01",
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show_img=False)
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while True:
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tracker.loop()
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tracker.release()
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3
requirements.txt
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3
requirements.txt
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cvzone==1.5.6
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mediapipe==0.8.10
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paho-mqtt==1.6.1
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71
tracker.py
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71
tracker.py
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import cv2
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import paho.mqtt.client as mqtt
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from cvzone.FaceDetectionModule import FaceDetector
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class Tracker(object):
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def __init__(self, mqtt_address="", mqtt_port=1883, mqtt_client_id="", show_img=False):
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self.show_img = show_img
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self.min_face_score = 0.5
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self.cap = cv2.VideoCapture(0)
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self.face_detector = FaceDetector()
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self.img = None
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self.face_found = False
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self.mqtt_address = mqtt_address
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self.mqtt_port = mqtt_port
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self.mqtt_client_id = mqtt_client_id
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self.is_mqtt_connected = False
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self.mqtt_client = mqtt.Client(mqtt_client_id)
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self.mqtt_connect()
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def mqtt_connect(self):
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self.mqtt_client.will_set("home/" + self.mqtt_client_id + "/status", "disconnected", 0, False)
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self.mqtt_client.on_connect = self.mqtt_on_connect
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self.mqtt_client.connect_async(self.mqtt_address, self.mqtt_port, 60)
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self.mqtt_client.loop_start()
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def mqtt_on_connect(self, client, userdata, flags, rc):
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print("Connected with result code " + str(rc))
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self.is_mqtt_connected = True
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self.mqtt_client.publish("home/" + self.mqtt_client_id + "/status", "connected")
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def mqtt_publish(self, topic, payload):
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self.mqtt_client.publish(topic, payload)
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def release(self):
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self.cap.release()
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cv2.destroyAllWindows()
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def read_img(self):
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success, img = self.cap.read()
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self.img = img
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def detect_face(self):
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img, face_bboxs = self.face_detector.findFaces(self.img, draw=self.show_img)
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if face_bboxs:
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if self.show_img:
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center = face_bboxs[0]["center"]
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cv2.circle(self.img, center, 5, (255, 0, 255), cv2.FILLED)
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score = face_bboxs[0]["score"][0]
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return score >= self.min_face_score
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return False
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def loop(self):
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self.read_img()
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# Look for faces
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face_detected = self.detect_face()
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if face_detected:
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if not self.face_found:
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self.mqtt_publish("home/" + self.mqtt_client_id + "/face_detected", 1)
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self.face_found = True
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else:
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if self.face_found:
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self.mqtt_publish("home/" + self.mqtt_client_id + "/face_detected", 0)
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self.face_found = False
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if self.show_img:
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cv2.imshow("Image", self.img)
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cv2.waitKey(1)
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