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wyzely-detect
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Manage timers in notify.py

This commit is contained in:
slashtechno 2023-10-14 15:40:36 -05:00
parent 3c6919d2c6
commit 3bf1966bfd
Signed by: slashtechno
GPG Key ID: 8EC1D9D9286C2B17
6 changed files with 220 additions and 106 deletions
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2
.vscode/launch.json vendored
View File

@ -8,7 +8,7 @@
"name": "Python: Module",
"type": "python",
"request": "launch",
"module": "src",
"module": "set-detect-notify",
"justMyCode": true
}
]

16
deepface-test.ipynb
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@ -31,11 +31,12 @@
"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",
"\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",
@ -44,12 +45,13 @@
" 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: {pd_dataframe.iloc[0][\"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()"
"# uuid_path.unlink()"
]
},
{

4
pyproject.toml
View File

@ -38,4 +38,6 @@ build-backend = "poetry.core.masonry.api"
[tool.ruff]
# More than the default (88) of `black` to make comments less of a headache
line-length = 120
# 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

113
set-detect-notify/__main__.py
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@ -1,10 +1,8 @@
# import face_recognition
import cv2
import numpy as np
import dotenv
from pathlib import Path
import os
import time
# import hjson as json
import torch
@ -18,11 +16,14 @@ from .utils import utils
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None
object_names = {}
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def main():
global object_names
global objects_and_peoples
global args
# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
@ -77,6 +78,15 @@ def main():
help="The object(s) to detect. Must be something the model is trained to detect",
)
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",
)
stream_source = argparser.add_mutually_exclusive_group()
stream_source.add_argument(
"--url",
@ -95,6 +105,10 @@ def main():
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",
@ -178,18 +192,18 @@ def main():
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# Setup dictionary of object names
if not object_names:
if objects_and_peoples["objects"] == {} or objects_and_peoples["objects"] is None:
for name in r.names.values():
object_names[name] = {
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 object_names
# 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 object_names:
if obj not in objects_and_peoples:
print(
f"Warning: {obj} is not in the list of objects the model can detect!"
)
@ -228,79 +242,18 @@ def main():
}
)
# End goal: Send a notification when an object has been detected for 2 seconds in the past 15 seconds.
# However, don't send a notification if the last notification was less than 15 seconds ago
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,
)
# (re)start cycle
if (
# If the object has not been detected before
object_names[class_id]["last_detection_time"] is None
# If the last detection was more than 15 seconds ago
or time.time() - object_names[class_id]["last_detection_time"]
> args.detection_window
):
# Set the last detection time to now
object_names[class_id]["last_detection_time"] = time.time()
print(f"First detection of {class_id} 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 object_names[class_id]["detection_duration"] is not None:
print(
f"Resetting detection duration for {class_id} since it hasn't been detected for {args.detection_window} seconds" # noqa: E501
)
object_names[class_id]["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() - object_names[class_id]["last_detection_time"]
<= args.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")
object_names[class_id]["detection_duration"] = 0
object_names[class_id]["detection_duration"] += (
time.time() - object_names[class_id]["last_detection_time"]
)
# print("Updating detection duration")
object_names[class_id]["last_detection_time"] = time.time()
# (re)send notification
# Check if detection has been ongoing for 2 seconds or more in the past 15 seconds
if (
object_names[class_id]["detection_duration"]
>= args.detection_duration
and time.time() - object_names[class_id]["last_detection_time"]
<= args.detection_window
):
# If the last notification was more than 15 seconds ago, then send a notification
if (
object_names[class_id]["last_notification_time"] is None
or time.time()
- object_names[class_id]["last_notification_time"]
> args.notification_window
):
object_names[class_id]["last_notification_time"] = time.time()
print(
f"Detected {class_id} for {args.detection_duration} seconds"
)
headers = notify.construct_ntfy_headers(
title=f"{class_id} detected",
tag="rotating_light",
priority="default",
)
notify.send_notification(
data=f"{class_id} detected for {args.detection_duration} seconds",
headers=headers,
url=args.ntfy_url,
)
# Reset the detection duration
print("Just sent a notification - resetting detection duration")
object_names[class_id]["detection_duration"] = 0
# TODO: On 10-14-2023, while testing, it seemed the bounding box was too low. Troubleshoot if it's a plotting problem.
# To do so, use r.plot() to cross reference the bounding box drawn by the plot_label function and r.plot()
frame_to_show = utils.plot_label(
boxes=plot_boxes,
full_frame=frame,

134
set-detect-notify/utils/notify.py
View File

@ -1,5 +1,122 @@
import datetime
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.
TODO: Make a function to check if a person is in the objects dictionary and vice versa
{
"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
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()
# (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(
@ -15,18 +132,3 @@ def send_notification(data: str, headers: dict, url: str):
raise ValueError("url and data cannot be None")
httpx.post(url, data=data.encode("utf-8"), headers=headers)
def check_last_seen(last_seen: datetime.datetime, seconds: int = 15):
"""
Check if a time is older than a given number of seconds
If it is, return True
If last_seen is empty/null, return True
"""
if (
datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds)
or last_seen == ""
or last_seen is None
):
return True
else:
return False

57
set-detect-notify/utils/utils.py
View File

@ -1,6 +1,7 @@
import cv2
import numpy as np
from pathlib import Path
from deepface import DeepFace
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
@ -55,3 +56,57 @@ def plot_label(
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 list of dictionaries, containing 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
'''
# face_dataframes is a vanilla list of dataframes
face_dataframes = DeepFace.find(run_frame, db_path=str(path_to_directory))
# 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"],
}
return [dict(label=label, **coordinates)]
'''
Example dataframe, for reference
identity (path to image) | source_x | source_y | source_w | source_h | VGG-Face_cosine (pretty much the confidence \_('_')_/)
'''