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wyzely-detect
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Merge pull request #5 from slashtechno/object-detection 

Add object detection and switch to Deepface
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slashtechno 2023-10-14 22:38:56 -05:00 committed by GitHub
parent 6438e91492 cb6c705e9d
commit 0daa8ddd3b
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22 changed files with 4521 additions and 322 deletions
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42
.Dockerfile.old Normal file
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FROM python:3.10-bullseye
# Install Dlib (for face_recognition)
RUN apt-get -y update && apt-get install -y --fix-missing \
build-essential \
cmake \
gfortran \
git \
wget \
curl \
graphicsmagick \
libgraphicsmagick1-dev \
libatlas-base-dev \
libavcodec-dev \
libavformat-dev \
libgtk2.0-dev \
libjpeg-dev \
liblapack-dev \
libswscale-dev \
pkg-config \
python3-dev \
python3-numpy \
software-properties-common \
zip
RUN apt-get clean
RUN rm -rf /tmp/* /var/tmp/*
ENV CFLAGS=-static
# Install dos2unix
# RUN apt-get install -y dos2unix
# Upgrade pip
RUN pip3 install --upgrade pip
# Copy directory to container
WORKDIR /app
COPY . ./
# Run dos2unix on all files in /app
# RUN dos2unix /app/*
# Install from requirements.txt
RUN pip3 install -r requirements.txt
# Install wait-for-it so this can easily be used with docker-compose
# Example: command: ["./wait-for-it.sh", "bridge:8554", "--", "python", "main.py"]
RUN wget https://raw.githubusercontent.com/vishnubob/wait-for-it/master/wait-for-it.sh && chmod +x wait-for-it.sh && mv wait-for-it.sh /bin
CMD ["python3", "main.py"]

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.config/
Dockerfile
.venv
docker-compose.yml

2
.github/workflows/docker.yml vendored
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@ -25,7 +25,7 @@ jobs:
uses: actions/checkout@v3
- name: Log in to the Container registry
uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a
uses: docker/login-action@v3.0.0
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}

6
.gitignore vendored
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.env
config/
using_yolov8.ipynb
yolov8n.pt
.venv/
__pycache__/
faces/*
!faces/.gitkeep

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.python-version Normal file
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3.10.5

15
.vscode/launch.json vendored Normal file
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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python: Module",
"type": "python",
"request": "launch",
"module": "set_detect_notify",
"justMyCode": true
}
]
}

50
Dockerfile
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FROM python:3.10-bullseye
FROM python:3.10.5-buster
RUN apt update && apt install libgl1 -y
RUN pip install poetry
# Install Dlib (for face_recognition)
RUN apt-get -y update && apt-get install -y --fix-missing \
build-essential \
cmake \
gfortran \
git \
wget \
curl \
graphicsmagick \
libgraphicsmagick1-dev \
libatlas-base-dev \
libavcodec-dev \
libavformat-dev \
libgtk2.0-dev \
libjpeg-dev \
liblapack-dev \
libswscale-dev \
pkg-config \
python3-dev \
python3-numpy \
software-properties-common \
zip
RUN apt-get clean
RUN rm -rf /tmp/* /var/tmp/*
ENV CFLAGS=-static
# Install dos2unix
# RUN apt-get install -y dos2unix
# Upgrade pip
RUN pip3 install --upgrade pip
# Copy directory to container
WORKDIR /app
COPY . ./
# Run dos2unix on all files in /app
# RUN dos2unix /app/*
# Install from requirements.txt
RUN pip3 install -r requirements.txt
# Install wait-for-it so this can easily be used with docker-compose
# Example: command: ["./wait-for-it.sh", "bridge:8554", "--", "python", "main.py"]
RUN wget https://raw.githubusercontent.com/vishnubob/wait-for-it/master/wait-for-it.sh && chmod +x wait-for-it.sh && mv wait-for-it.sh /bin
CMD ["python3", "main.py"]
COPY . .
RUN poetry install
ENTRYPOINT ["poetry", "run", "python", "-m", "set_detect_notify"]

65
README.md
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# Wyze Face Recognition
Recognize faces in Wyze Cam footage and send notifications to your phone (or other devices)
# Set, Detect, Notify
Recognize faces/objects in (Wyze Cam) footage and send notifications to your phone (or other devices)
## Pre-requisites
* Docker
* Docker Compose
* A Wyze Cam
### Features
- Recognize objects
- Recognize faces
- Send notifications to your phone (or other devices) using [ntfy](https://ntfy.sh/)
- Optionally, run headless with Docker
- Either use a webcam or an RTSP feed
- Use [mrlt8/docker-wyze-bridge](https://github.com/mrlt8/docker-wyze-bridge) to get RTSP feeds from Wyze Cams
## What's not needed
* A Wyze Cam subscription
## How to use
1. Clone this repo
` git clone https://github.com/slackner/wyze-face-recognition.git`
2. Add images to the `config` directory
3. Copy `config/config.json.example` to `config/config.json` and edit the faces array to match the images you added, and the face names
4. Either set the `WYZE_EMAIL` and `WYZE_PASSWORD` environment variables, or edit `docker-compose.yml` and add your Wyze credentials
5. Run `docker-compose up -d`
## Prerequisites
### Poetry/Python
- Camera, either a webcam or a Wyze Cam
- All RTSP feeds _should_ work, however.
- Python
- Poetry
### Docker
- A Wyze Cam
- Any other RTSP feed _should_ work, as mentioned above
- Python
- Poetry
## What's not required
- A Wyze subscription
## Usage
### Installation
1. Clone this repo with `git clone https://github.com/slashtechno/wyze-face-recognition.git`
2. `cd` into the cloned repository
3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
#### Docker
1. Modify to `docker-compose.yml` to achieve desired configuration
2. Run in the background with `docker compose up -d
#### Poetry
1. `poetry install`
2. `poetry run -- set-detect-notify`
### Configuration
The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.
- For face recognition, put images of faces in subdirectories `./faces` (this can be changed with `--faces-directory`)
- Keep in mind, on the first run, face rec
- By default, notifications are sent for all objects. This can be changed with one or more occurrences of `--detect-object` to specify which objects to detect
- Currently, all classes in the [COCO](https://cocodataset.org/) dataset can be detected
- To specify where notifications are sent, specify a [ntfy](https://ntfy.sh/) URL with `--ntfy-url`
- To configure the program when using Docker, edit `docker-compose.yml` and/or set environment variables.
- **For further information, use `--help`**
### How to uninstall
1. Run `docker-compose down` in the `wyze-face-recognition` directory
- If you used Docker, run `docker-compose down --rmi all` in the cloned repository
- If you used Poetry, just delete the virtual environment and then the cloned repository

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config/example-config.json
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{
"URL": "rtsp://bridge:8554/cv",
"RUN_SCALE": "0.5",
"VIEW_SCALE": "0.5",
"faces": {
"person1": {
"image": "config/person1.jpg",
"last_seen": ""
},
"person2": {
"image": "config/person2.jpg",
"last_seen": ""
}
},
"display": false,
"ntfy_url": "http://ntfy:80/cam"
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from deepface import DeepFace\n",
"import cv2\n",
"from pathlib import Path\n",
"import uuid\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take pictures"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Take a picture using opencv with <uuid>.jpg\n",
"# Then delete it after\n",
"cap = cv2.VideoCapture(0)\n",
"ret, frame = cap.read()\n",
"cap.release()\n",
"# uuid_str = str(uuid.uuid4())\n",
"# uuid_path = Path(uuid_str + \".jpg\")\n",
"# cv2.imwrite(str(uuid_path), frame)\n",
"# dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
"# Don't throw an error if no face is detected (enforce_detection=False)\n",
"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False)\n",
"# Get the identity of the person\n",
"for i, pd_dataframe in enumerate(dfs):\n",
" # Sort the dataframe by confidence\n",
" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
" print(f'On dataframe {i}')\n",
" print(pd_dataframe)\n",
" # Get the most likely identity\n",
" # print(f'Most likely identity: {pd_dataframe.iloc[0][\"identity\"]}')\n",
" # We could use Path to get the parent directory of the image to use as the identity\n",
" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
" # Get the most likely identity's confidence\n",
" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
"\n",
"# uuid_path.unlink()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stream"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DeepFace.stream(db_path=\"faces\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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docker-compose.yml
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@ -6,19 +6,21 @@ services:
container_name: bridge-wyze
restart: unless-stopped
image: mrlt8/wyze-bridge:latest
ports:
- 1935:1935 # RTMP
- 8554:8554 # RTSP
- 8888:8888 # HLS
- 5000:5000 # WEB-UI
# I think we can remove the ports, since we're using the network
# Just an unnecesary security risk
# ports:
# - 1935:1935 # RTMP
# - 8554:8554 # RTSP (this is really the only one we need)
# - 8888:8888 # HLS
# - 5000:5000 # WEB-UI
environment:
- WYZE_EMAIL=${WYZE_EMAIL} # Replace with wyze email
- WYZE_PASSWORD=${WYZE_PASSWORD} # Replace with wyze password
networks:
all:
aliases:
- bridge
- wyze-bridge
# aliases:
# - bridge
# - wyze-bridge
ntfy:
image: binwiederhier/ntfy
container_name: ntfy-wyze
@ -37,28 +39,33 @@ services:
facial_recognition:
container_name: face-recognition-wyze
restart: unless-stopped
image: ghcr.io/slashtechno/wyze_face_recognition:latest
# image: ghcr.io/slashtechno/wyze_face_recognition:latest
build:
context: .
dockerfile: Dockerfile
volumes:
# ./config is mounted as /app/config
- ./config:/app/config
- ./faces:/app/faces
networks:
all:
environment:
- RUN_BY_COMPOSE=true
- URL=rtsp://bridge:8554/cv
- NO_DISPLAY=true
- NTFY_URL=http://ntfy:80/set-detect-notify
depends_on:
- bridge
# Use curl to check if the rtsp stream is up, then run the face recognition
command: >
/bin/sh -c "
while true; do
curl -s http://bridge:8888/cv/0.m3u8 > /dev/null
if [ $? -eq 0 ]; then
echo 'Stream is up, running face recognition'
python3 /app/main.py
else
echo 'Stream is down, waiting 5 seconds'
sleep 5
fi
done
"
# command: >
# /bin/sh -c "
# while true; do
# curl -s http://bridge:8888/cv/0.m3u8 > /dev/null
# if [ $? -eq 0 ]; then
# echo 'Stream is up, running face recognition'
# python3 /app/main.py
# else
# echo 'Stream is down, waiting 5 seconds'
# sleep 5
# fi
# done
# "
tty: true

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main.py
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import datetime
import face_recognition
import cv2
import numpy as np
from dotenv import load_dotenv
import os
import json
import pathlib
import requests
import time
load_dotenv()
URL = os.getenv("URL")
RUN_SCALE = os.getenv("RUN_SCALE")
VIEW_SCALE = os.getenv("VIEW_SCALE")
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
# RUN_SCALE = 0.25
# VIEW_SCALE = 0.75
DISPLAY = False
RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE")
NTFY_URL = os.getenv("NTFY_URL")
def find_face_from_name(name):
for face in config["faces"]:
if config["faces"][face]["name"] == name:
return face
return None
def write_config():
with open(config_path, "w") as config_file:
json.dump(config, config_file, indent=4)
print("Hello, world!")
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
known_face_encodings = []
known_face_names = []
process_this_frame = True
# Load the config file, if it does not exist or is blank, create it
config = {
# If RUN_BY_COMPOSE is true, set url to rtsp://wyze-bridge:8554/wyze_cam_name, otherwise set it to "rtsp://localhost:8554/wyze_cam_name"
"URL": "rtsp://localhost:8554/wyze_cam_name"
if not RUN_BY_COMPOSE
else "rtsp://bridge:8554/wyze_cam_name",
"run_scale": "0.25",
"view_scale": "0.75",
"faces": {
"example1": {"image": "config/example1.jpg", "last_seen": ""},
"example2": {"image": "config/example2.jpg", "last_seen": ""},
},
"ntfy_url": "https://ntfy.sh/example",
"display": True,
}
config_path = pathlib.Path("config/config.json")
if config_path.exists():
with open(config_path, "r") as config_file:
config = json.load(config_file)
else:
with open(config_path, "w") as config_file:
json.dump(config, config_file, indent=4)
print("Config file created, please edit it and restart the program")
print("For relative paths, use the format config/example.jpg")
exit()
if URL:
config["URL"] = URL
else:
URL = config["URL"]
if RUN_SCALE:
config["RUN_SCALE"] = RUN_SCALE
else:
RUN_SCALE = float(config["RUN_SCALE"])
if VIEW_SCALE:
config["VIEW_SCALE"] = VIEW_SCALE
else:
VIEW_SCALE = float(config["VIEW_SCALE"])
if DISPLAY:
config["DISPLAY"] = DISPLAY
else:
DISPLAY = config["display"]
if NTFY_URL:
config["ntfy_url"] = NTFY_URL
else:
NTFY_URL = config["ntfy_url"]
print(f"Current config: {config}")
for face in config["faces"]:
# Load a sample picture and learn how to recognize it.
image = face_recognition.load_image_file(config["faces"][face]["image"])
face_encoding = face_recognition.face_encodings(image)[0]
known_face_encodings.append(face_encoding)
# Append the key to the list of known face names
known_face_names.append(face)
video_capture = cv2.VideoCapture(URL)
# Eliminate lag by setting the buffer size to 1
# This makes it so that the video capture will only grab the most recent frame
# However, this means that the video may be choppy
video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# Print the resolution of the video
print(
f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}"
)
print("Beginning video capture...")
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Only process every other frame of video to save time
# Resize frame of video to a smaller size for faster face recognition processing
run_frame = cv2.resize(frame, (0, 0), fx=RUN_SCALE, fy=RUN_SCALE)
view_frame = cv2.resize(frame, (0, 0), fx=VIEW_SCALE, fy=VIEW_SCALE)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_run_frame = run_frame[:, :, ::-1]
# Find all the faces and face encodings in the current frame of video
# model cnn is gpu accelerated, but hog is cpu only
face_locations = face_recognition.face_locations(
rgb_run_frame, model="hog"
) # This crashes the program without output on my laptop when it's running without Docker compose
face_encodings = face_recognition.face_encodings(rgb_run_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(
known_face_encodings, face_encoding
)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
last_seen = config["faces"][name]["last_seen"]
# If it's never been seen, set the last seen time to x+5 seconds ago so it will be seen
# Kind of a hacky way to do it, but it works... hopefully
if last_seen == "":
print(f"{name} has been seen for the first time")
config["faces"][name]["last_seen"] = (
datetime.datetime.now() - datetime.timedelta(seconds=15)
).strftime(DATETIME_FORMAT)
write_config()
# Check if the face has been seen in the last 5 seconds
if datetime.datetime.now() - datetime.datetime.strptime(
last_seen, DATETIME_FORMAT
) > datetime.timedelta(seconds=10):
print(f"{name} has been seen")
# Send a notification
print(f"Sending notification to{NTFY_URL}")
requests.post(
NTFY_URL,
data=f'"{name}" has been seen',
headers={
"Title": "Face Detected",
"Priority": "default",
"Tags": "neutral_face",
},
)
# Update the last seen time
config["faces"][name]["last_seen"] = datetime.datetime.now().strftime(
DATETIME_FORMAT
)
# print("Writing config...")
write_config()
face_names.append(name)
# Display the results
# Iterate over each face found in the frame to draw a box around it
# Zip is used to iterate over two lists at the same time
for (top, right, bottom, left), name in zip(face_locations, face_names):
# print(f"Face found at {top}, {right}, {bottom}, {left} with name {name}")
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top = int(top * (VIEW_SCALE / RUN_SCALE))
right = int(right * (VIEW_SCALE / RUN_SCALE))
bottom = int(bottom * (VIEW_SCALE / RUN_SCALE))
left = int(left * (VIEW_SCALE / RUN_SCALE))
# Draw a box around the face
cv2.rectangle(view_frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(
view_frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED
)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(
view_frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1
)
# Display the resulting image if DISPLAY is set to true
if config["display"]:
cv2.imshow("Scaled View", view_frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Release handle to the webcam
print("Releasing video capture")
video_capture.release()
cv2.destroyAllWindows()

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[tool.poetry]
name = "set_detect_notify"
version = "0.1.0"
description = "Detect all the things"
authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
license = "MIT"
readme = "README.md"
packages = [{include = "set_detect_notify"}]
[tool.poetry.dependencies]
# python = "^3.10"
python = ">=3.10, <3.12"
python-dotenv = "^1.0.0"
httpx = "^0.25.0"
opencv-python = "^4.8.1.78"
ultralytics = "^8.0.190"
hjson = "^3.1.0"
numpy = "^1.23.2"
# https://github.com/python-poetry/poetry/issues/6409
torch = ">=2.0.0, !=2.0.1, !=2.1.0"
tensorflow-io-gcs-filesystem = "0.31.0"
deepface = "^0.0.79"
[tool.poetry.group.dev.dependencies]
black = "^23.9.1"
ruff = "^0.0.291"
ipykernel = "^6.25.2"
nbconvert = "^7.9.2"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.ruff]
# More than the default (88) of `black` to make comments less of a headache
# Where possible, `black` will attempt to format to 88 characters
# However, setting ruff to 135 will allow for longer lines that can't be auto-formatted
line-length = 135
[tool.poetry.scripts]
set-detect-notify = "set_detect_notify.__main__:main"

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requirements.txt
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# certifi @ file:///croot/certifi_1665076670883/work/certifi
click==8.1.3
dlib==19.24.0
face-recognition==1.3.0
face-recognition-models==0.3.0
numpy==1.23.5
opencv-python==4.6.0.66
Pillow==10.0.1
python-dotenv==0.21.0
urllib3==1.26.13
requests==2.31.0

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set_detect_notify/__main__.py Normal file
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# import face_recognition
import cv2
import dotenv
from pathlib import Path
import os
# import hjson as json
import torch
from ultralytics import YOLO
import argparse
from .utils import notify
from .utils import utils
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def main():
global objects_and_peoples
global args
# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
if Path(".env").is_file():
dotenv.load_dotenv()
print("Loaded .env file")
else:
print("No .env file found")
argparser = argparse.ArgumentParser(
prog="Detect It",
description="Detect it all!",
epilog=":)",
)
# required='RUN_SCALE' not in os.environ,
argparser.add_argument(
"--run-scale",
# Set it to the env RUN_SCALE if it isn't blank, otherwise set it to 0.25
default=os.environ["RUN_SCALE"]
if "RUN_SCALE" in os.environ and os.environ["RUN_SCALE"] != ""
# else 0.25,
else 1,
type=float,
help="The scale to run the detection at, default is 0.25",
)
argparser.add_argument(
"--view-scale",
# Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75
default=os.environ["VIEW_SCALE"]
if "VIEW_SCALE" in os.environ and os.environ["VIEW_SCALE"] != ""
# else 0.75,
else 1,
type=float,
help="The scale to view the detection at, default is 0.75",
)
argparser.add_argument(
"--no-display",
default=os.environ["NO_DISPLAY"]
if "NO_DISPLAY" in os.environ and os.environ["NO_DISPLAY"] != ""
else False,
action="store_true",
help="Don't display the video feed",
)
argparser.add_argument(
"--confidence-threshold",
default=os.environ["CONFIDENCE_THRESHOLD"]
if "CONFIDENCE_THRESHOLD" in os.environ
and os.environ["CONFIDENCE_THRESHOLD"] != ""
else 0.6,
type=float,
help="The confidence threshold to use",
)
argparser.add_argument(
"--faces-directory",
default=os.environ["FACES_DIRECTORY"]
if "FACES_DIRECTORY" in os.environ and os.environ["FACES_DIRECTORY"] != ""
else "faces",
type=str,
help="The directory to store the faces. Should contain 1 subdirectory of images per person",
)
argparser.add_argument(
"--detect-object",
nargs="*",
default=[],
type=str,
help="The object(s) to detect. Must be something the model is trained to detect",
)
stream_source = argparser.add_mutually_exclusive_group()
stream_source.add_argument(
"--url",
default=os.environ["URL"]
if "URL" in os.environ and os.environ["URL"] != ""
else None, # noqa: E501
type=str,
help="The URL of the stream to use",
)
stream_source.add_argument(
"--capture-device",
default=os.environ["CAPTURE_DEVICE"]
if "CAPTURE_DEVICE" in os.environ and os.environ["CAPTURE_DEVICE"] != ""
else 0, # noqa: E501
type=int,
help="The capture device to use. Can also be a url.",
)
# Defaults for the stuff here and down are already set in notify.py.
# Setting them here just means that argparse will display the default values as defualt
# TODO: Perhaps just remove the default parameter and just add to the help message that the default is set is x
notifcation_services = argparser.add_argument_group("Notification Services")
notifcation_services.add_argument(
"--ntfy-url",
default=os.environ["NTFY_URL"]
if "NTFY_URL" in os.environ and os.environ["NTFY_URL"] != ""
else "https://ntfy.sh/set-detect-notify",
type=str,
help="The URL to send notifications to",
)
timers = argparser.add_argument_group("Timers")
timers.add_argument(
"--detection-duration",
default=os.environ["DETECTION_DURATION"]
if "DETECTION_DURATION" in os.environ and os.environ["DETECTION_DURATION"] != ""
else 2,
type=int,
help="The duration (in seconds) that an object must be detected for before sending a notification",
)
timers.add_argument(
"--detection-window",
default=os.environ["DETECTION_WINDOW"]
if "DETECTION_WINDOW" in os.environ and os.environ["DETECTION_WINDOW"] != ""
else 15,
type=int,
help="The time (seconds) before the detection duration resets",
)
timers.add_argument(
"--notification-window",
default=os.environ["NOTIFICATION_WINDOW"]
if "NOTIFICATION_WINDOW" in os.environ
and os.environ["NOTIFICATION_WINDOW"] != ""
else 30,
type=int,
help="The time (seconds) before another notification can be sent",
)
args = argparser.parse_args()
# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu
# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
# Currently, I have been unable to set up Poetry to use GPU for Torch
for i in range(torch.cuda.device_count()):
print(torch.cuda.get_device_properties(i).name)
if torch.cuda.is_available():
torch.cuda.set_device(0)
print("Set CUDA device")
else:
print("No CUDA device available, using CPU")
model = YOLO("yolov8n.pt")
# Depending on if the user wants to use a stream or a capture device,
# Set the video capture to the appropriate source
if args.url:
video_capture = cv2.VideoCapture(args.url)
else:
video_capture = cv2.VideoCapture(args.capture_device)
# Eliminate lag by setting the buffer size to 1
# This makes it so that the video capture will only grab the most recent frame
# However, this means that the video may be choppy
video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
# Print the resolution of the video
print(
f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}" # noqa: E501
)
print("Beginning video capture...")
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Only process every other frame of video to save time
# Resize frame of video to a smaller size for faster recognition processing
run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame, verbose=False)
for i, r in enumerate(results):
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# The following is stuff for people
# This is still in the for loop as each result, no matter if anything is detected, will be present.
# Thus, there will always be one result (r)
if face_details := utils.recognize_face(
path_to_directory=Path(args.faces_directory), run_frame=run_frame
):
plot_boxes.append(face_details)
objects_and_peoples = notify.thing_detected(
thing_name=face_details["label"],
objects_and_peoples=objects_and_peoples,
detection_type="peoples",
detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# The following is stuff for objects
# Setup dictionary of object names
if (
objects_and_peoples["objects"] == {}
or objects_and_peoples["objects"] is None
):
for name in r.names.values():
objects_and_peoples["objects"][name] = {
"last_detection_time": None,
"detection_duration": None,
# "first_detection_time": None,
"last_notification_time": None,
}
# Also, make sure that the objects to detect are in the list of objects_and_peoples
# If it isn't, print a warning
for obj in args.detect_object:
if obj not in objects_and_peoples:
print(
f"Warning: {obj} is not in the list of objects the model can detect!"
)
for box in r.boxes:
# Get the name of the object
class_id = r.names[box.cls[0].item()]
# Get the coordinates of the object
cords = box.xyxy[0].tolist()
cords = [round(x) for x in cords]
# Get the confidence
conf = round(box.conf[0].item(), 2)
# Print it out, adding a spacer between each object
# print("Object type:", class_id)
# print("Coordinates:", cords)
# print("Probability:", conf)
# print("---")
# Now do stuff (if conf > 0.5)
if conf < args.confidence_threshold or (
class_id not in args.detect_object and args.detect_object != []
):
# If the confidence is too low
# or if the object is not in the list of objects to detect and the list of objects to detect is not empty
# then skip this iteration
continue
# Add the object to the list of objects to plot
plot_boxes.append(
{
"label": class_id,
"x1": cords[0],
"y1": cords[1],
"x2": cords[2],
"y2": cords[3],
}
)
objects_and_peoples = notify.thing_detected(
thing_name=class_id,
objects_and_peoples=objects_and_peoples,
detection_type="objects",
detection_window=args.detection_window,
detection_duration=args.detection_duration,
notification_window=args.notification_window,
ntfy_url=args.ntfy_url,
)
# To debug plotting, use r.plot() to cross reference the bounding boxes drawn by the plot_label() and r.plot()
frame_to_show = utils.plot_label(
boxes=plot_boxes,
full_frame=frame,
# full_frame=r.plot(),
run_scale=args.run_scale,
view_scale=args.view_scale,
)
# Display the resulting frame
# cv2.imshow("", r)
if not args.no_display:
cv2.imshow(f"Video{i}", frame_to_show)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Release handle to the webcam
print("Releasing video capture")
video_capture.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()

0
set_detect_notify/utils/__init__.py Normal file
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import httpx
import time
"""
Structure of objects_and_peoples
Really, the only reason peoples is a separate dictionary is to prevent duplicates, though it just makes the code more complicated.
{
"objects": {
"object_name": {
"last_detection_time": float,
"detection_duration": float,
"last_notification_time": float,
},
},
"peoples": {
"person_name": {
"last_detection_time": float,
"detection_duration": float,
"last_notification_time": float,
},
},
}
"""
# objects_and_peoples = {}
def thing_detected(
thing_name: str,
objects_and_peoples: dict,
detection_type: str = "objects",
detection_window: int = 15,
detection_duration: int = 2,
notification_window: int = 15,
ntfy_url: str = "https://ntfy.sh/set-detect-notify",
) -> dict:
"""
A function to make sure 2 seconds of detection is detected in 15 seconds, 15 seconds apart.
Takes a dict that will be retured with the updated detection times. MAKE SURE TO SAVE THE RETURNED DICTIONARY
"""
# "Alias" the objects and peoples dictionaries so it's easier to work with
respective_type = objects_and_peoples[detection_type]
# (re)start cycle
try:
if (
# If the object has not been detected before
respective_type[thing_name]["last_detection_time"] is None
# If the last detection was more than 15 seconds ago
or time.time() - respective_type[thing_name]["last_detection_time"]
> detection_window
):
# Set the last detection time to now
respective_type[thing_name]["last_detection_time"] = time.time()
print(f"First detection of {thing_name} in this detection window")
# This line is important. It resets the detection duration when the object hasn't been detected for a while
# If detection duration is None, don't print anything.
# Otherwise, print that the detection duration is being reset due to inactivity
if respective_type[thing_name]["detection_duration"] is not None:
print(
f"Resetting detection duration for {thing_name} since it hasn't been detected for {detection_window} seconds" # noqa: E501
)
respective_type[thing_name]["detection_duration"] = 0
else:
# Check if the last NOTIFICATION was less than 15 seconds ago
# If it was, then don't do anything
if (
time.time() - respective_type[thing_name]["last_detection_time"]
<= notification_window
):
pass
# If it was more than 15 seconds ago, reset the detection duration
# This effectively resets the notification timer
else:
print("Notification timer has expired - resetting")
respective_type[thing_name]["detection_duration"] = 0
respective_type[thing_name]["detection_duration"] += (
time.time() - respective_type[thing_name]["last_detection_time"]
)
# print("Updating detection duration")
respective_type[thing_name]["last_detection_time"] = time.time()
except KeyError:
# If the object has not been detected before
respective_type[thing_name] = {
"last_detection_time": time.time(),
"detection_duration": 0,
"last_notification_time": None,
}
print(f"First detection of {thing_name} ever")
# (re)send notification
# Check if detection has been ongoing for 2 seconds or more in the past 15 seconds
if (
respective_type[thing_name]["detection_duration"] >= detection_duration
and time.time() - respective_type[thing_name]["last_detection_time"]
<= detection_window
):
# If the last notification was more than 15 seconds ago, then send a notification
if (
respective_type[thing_name]["last_notification_time"] is None
or time.time() - respective_type[thing_name]["last_notification_time"]
> notification_window
):
respective_type[thing_name]["last_notification_time"] = time.time()
print(f"Detected {thing_name} for {detection_duration} seconds")
headers = construct_ntfy_headers(
title=f"{thing_name} detected",
tag="rotating_light",
priority="default",
)
send_notification(
data=f"{thing_name} detected for {detection_duration} seconds",
headers=headers,
url=ntfy_url,
)
# Reset the detection duration
print("Just sent a notification - resetting detection duration")
respective_type[thing_name]["detection_duration"] = 0
# Take the aliased objects_and_peoples and update the respective dictionary
objects_and_peoples[detection_type] = respective_type
return objects_and_peoples
def construct_ntfy_headers(
title: str = "Object/Person Detected",
tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis
priority="default", # https://docs.ntfy.sh/publish/#message-priority
) -> dict:
return {"Title": title, "Priority": priority, "Tags": tag}
def send_notification(data: str, headers: dict, url: str):
if url is None or data is None:
raise ValueError("url and data cannot be None")
httpx.post(url, data=data.encode("utf-8"), headers=headers)

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import cv2
import numpy as np
from pathlib import Path
from deepface import DeepFace
first_face_try = True
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
boxes: list = None,
# opencv image
full_frame: np.ndarray = None,
# run_scale is the scale of the image that was used to run the model
# So the coordinates will be scaled up to the view frame size
run_scale: float = None,
# view_scale is the scale of the image, in relation to the full frame
# So the coordinates will be scaled appropriately when coming from run_frame
view_scale: float = None,
font: int = cv2.FONT_HERSHEY_SIMPLEX,
):
# x1 and y1 are the top left corner of the box
# x2 and y2 are the bottom right corner of the box
# Example scaling: full_frame: 1 run_frame: 0.5 view_frame: 0.25
view_frame = cv2.resize(full_frame, (0, 0), fx=view_scale, fy=view_scale)
for thing in boxes:
cv2.rectangle(
# Image
view_frame,
# Top left corner
(
int((thing["x1"] / run_scale) * view_scale),
int((thing["y1"] / run_scale) * view_scale),
),
# Bottom right corner
(
int((thing["x2"] / run_scale) * view_scale),
int((thing["y2"] / run_scale) * view_scale),
),
# Color
(0, 255, 0),
# Thickness
2,
)
cv2.putText(
# Image
view_frame,
# Text
thing["label"],
# Origin
(
int((thing["x1"] / run_scale) * view_scale),
int((thing["y1"] / run_scale) * view_scale) - 10,
),
# Font
font,
# Font Scale
1,
# Color
(0, 255, 0),
# Thickness
1,
)
return view_frame
def recognize_face(
path_to_directory: Path = Path("faces"),
# opencv image
run_frame: np.ndarray = None,
) -> np.ndarray:
"""
Accepts a path to a directory of images of faces to be used as a refference
In addition, accepts an opencv image to be used as the frame to be searched
Returns a single dictonary as currently only 1 face can be detected in each frame
dict contains the following keys: label, x1, y1, x2, y2
The directory should be structured as follows:
faces/
name/
image1.jpg
image2.jpg
image3.jpg
name2/
image1.jpg
image2.jpg
image3.jpg
(not neccessarily jpgs, but you get the idea)
Point is, `name` is the name of the person in the images in the directory `name`
That name will be used as the label for the face in the frame
"""
global first_face_try
# If it's the first time the function is being run, remove representations_vgg_face.pkl, if it exists
if first_face_try:
try:
Path("representations_vgg_face.pkl").unlink()
print("Removing representations_vgg_face.pkl")
except FileNotFoundError:
pass
first_face_try = False
# face_dataframes is a vanilla list of dataframes
try:
face_dataframes = DeepFace.find(
run_frame,
db_path=str(path_to_directory),
enforce_detection=True,
silent=True,
)
except ValueError as e:
if (
str(e)
== "Face could not be detected. Please confirm that the picture is a face photo or consider to set enforce_detection param to False."
):
return None
# Iteate over the dataframes
for df in face_dataframes:
# The last row is the highest confidence
# So we can just grab the path from there
# iloc = Integer LOCation
path_to_image = Path(df.iloc[-1]["identity"])
# Get the name of the parent directory
label = path_to_image.parent.name
# Return the coordinates of the box in xyxy format, rather than xywh
# This is because YOLO uses xyxy, and that's how plot_label expects
# Also, xyxy is just the top left and bottom right corners of the box
coordinates = {
"x1": df.iloc[-1]["source_x"],
"y1": df.iloc[-1]["source_y"],
"x2": df.iloc[-1]["source_x"] + df.iloc[-1]["source_w"],
"y2": df.iloc[-1]["source_y"] + df.iloc[-1]["source_h"],
}
# After some brief testing, it seems positve matches are > 0.3
# I have not seen any false positives, so there is no threashold yet
distance = df.iloc[-1]["VGG-Face_cosine"]
# if 0.5 < distance < 0.7:
# label = "Unknown"
to_return = dict(label=label, **coordinates)
print(
f"Confindence: {distance}, filname: {path_to_image.name}, to_return: {to_return}"
)
return to_return
"""
Example dataframe, for reference
identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \_('_')_/)
"""