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wyzely-detect
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Manually plot boxes, rather than using `r.plot()`

This commit is contained in:
slashtechno 2023-10-13 17:44:38 -05:00
parent ccf560ab2a
commit 6dfb06989f
Signed by: slashtechno
GPG Key ID: 8EC1D9D9286C2B17
7 changed files with 2482 additions and 2416 deletions
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2
.python-version
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@ -1 +1 @@
3.10.5 3.10.5

28
.vscode/launch.json vendored
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@ -1,15 +1,15 @@
{ {
// Use IntelliSense to learn about possible attributes. // Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes. // Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0", "version": "0.2.0",
"configurations": [ "configurations": [
{ {
"name": "Python: Module", "name": "Python: Module",
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"module": "src", "module": "src",
"justMyCode": true "justMyCode": true
} }
] ]
} }

4588
poetry.lock generated
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File diff suppressed because it is too large Load Diff

118
pyproject.toml
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@ -1,59 +1,59 @@
[tool.poetry] [tool.poetry]
name = "set-detect-notify" name = "set-detect-notify"
version = "0.1.0" version = "0.1.0"
description = "Detect all the things" description = "Detect all the things"
authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"] authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
license = "MIT" license = "MIT"
readme = "README.md" readme = "README.md"
packages = [{include = "set-detect-notify"}] packages = [{include = "set-detect-notify"}]
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = "^3.10" python = "^3.10"
python-dotenv = "^1.0.0" python-dotenv = "^1.0.0"
httpx = "^0.25.0" httpx = "^0.25.0"
opencv-python = "^4.8.1.78" opencv-python = "^4.8.1.78"
ultralytics = "^8.0.190" ultralytics = "^8.0.190"
hjson = "^3.1.0" hjson = "^3.1.0"
numpy = "^1.23.2" numpy = "^1.23.2"
# torch = [ # torch = [
# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, # { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, # { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ] # ]
# torchaudio = [ # torchaudio = [
# { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, # { version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, # { version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ] # ]
# torchvision = [ # torchvision = [
# { version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" }, # { version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
# { version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" }, # { version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
# ] # ]
torch = "^2.1.0" torch = "^2.1.0"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
black = "^23.9.1" black = "^23.9.1"
ruff = "^0.0.291" ruff = "^0.0.291"
ipykernel = "^6.25.2" ipykernel = "^6.25.2"
# [[tool.poetry.source]] # [[tool.poetry.source]]
# name = "torch_cpu" # name = "torch_cpu"
# url = "https://download.pytorch.org/whl/cpu" # url = "https://download.pytorch.org/whl/cpu"
# priority = "supplemental" # priority = "supplemental"
# #
# [[tool.poetry.source]] # [[tool.poetry.source]]
# name = "torch_cu118" # name = "torch_cu118"
# url = "https://download.pytorch.org/whl/cu118" # url = "https://download.pytorch.org/whl/cu118"
# priority = "supplemental" # priority = "supplemental"
# #
# [tool.poetry.extras] # [tool.poetry.extras]
# cuda = [] # cuda = []
# #
# [[tool.poetry.source]] # [[tool.poetry.source]]
# name = "PyPI" # name = "PyPI"
# priority = "primary" # priority = "primary"
[build-system] [build-system]
requires = ["poetry-core"] requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api" build-backend = "poetry.core.masonry.api"

52
set-detect-notify/__main__.py
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@ -13,6 +13,7 @@ from ultralytics import YOLO
import argparse import argparse
from .utils import notify from .utils import notify
from .utils import utils
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S" DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None args = None
@ -48,13 +49,13 @@ def main():
type=float, type=float,
help="The scale to run the detection at, default is 0.25", help="The scale to run the detection at, default is 0.25",
) )
# argparser.add_argument( argparser.add_argument(
# '--view-scale', '--view-scale',
# # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75 # 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, # noqa: E501 default=os.environ['VIEW_SCALE'] if 'VIEW_SCALE' in os.environ and os.environ['VIEW_SCALE'] != '' else 0.75, # noqa: E501
# type=float, type=float,
# help="The scale to view the detection at, default is 0.75", help="The scale to view the detection at, default is 0.75",
# ) )
argparser.add_argument( argparser.add_argument(
"--confidence-threshold", "--confidence-threshold",
@ -160,10 +161,12 @@ def main():
# Only process every other frame of video to save time # Only process every other frame of video to save time
# Resize frame of video to a smaller size for faster recognition processing # 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) 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) # view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame, verbose=False) results = model(run_frame, verbose=False)
for r in results: for i, r in enumerate(results):
# list of dicts with each dict containing a label, x1, y1, x2, y2
plot_boxes = []
# Setup dictionary of object names # Setup dictionary of object names
if not object_names: if not object_names:
for name in r.names.values(): for name in r.names.values():
@ -174,6 +177,7 @@ def main():
"last_notification_time": None, "last_notification_time": None,
} }
for box in r.boxes: for box in r.boxes:
# Get the name of the object # Get the name of the object
class_id = r.names[box.cls[0].item()] class_id = r.names[box.cls[0].item()]
# Get the coordinates of the object # Get the coordinates of the object
@ -192,6 +196,18 @@ def main():
# If the confidence is less than 0.5, then SKIP!!!! # If the confidence is less than 0.5, then SKIP!!!!
continue 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],
}
)
# End goal: Send a notification when an object has been detected for 2 seconds in the past 15 seconds. # 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 # However, don't send a notification if the last notification was less than 15 seconds ago
@ -265,13 +281,17 @@ def main():
# Reset the detection duration # Reset the detection duration
print("Just sent a notification - resetting detection duration") print("Just sent a notification - resetting detection duration")
object_names[class_id]["detection_duration"] = 0 object_names[class_id]["detection_duration"] = 0
im_array = r.plot() frame_to_show = utils.plot_label(
# Scale back up the coordinates of the locations of detected objects. boxes=plot_boxes,
# im_array = np.multiply(im_array, 1/args.run_scale) full_frame=frame,
# print(type(im_array)) # full_frame=r.plot(),
# print(im_array) run_scale=args.run_scale,
# exit() view_scale=args.view_scale,
cv2.imshow("View", im_array) )
# Display the resulting frame
# cv2.imshow("", r)
cv2.imshow(f"Video{i}", frame_to_show)
# Hit 'q' on the keyboard to quit! # Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord("q"): if cv2.waitKey(1) & 0xFF == ord("q"):

64
set-detect-notify/utils/notify.py
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@ -1,32 +1,32 @@
import datetime import datetime
import httpx import httpx
def construct_ntfy_headers( def construct_ntfy_headers(
title: str = "Object/Person Detected", title: str = "Object/Person Detected",
tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis tag="rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis
priority="default", # https://docs.ntfy.sh/publish/#message-priority priority="default", # https://docs.ntfy.sh/publish/#message-priority
) -> dict: ) -> dict:
return {"Title": title, "Priority": priority, "Tags": tag} return {"Title": title, "Priority": priority, "Tags": tag}
def send_notification(data: str, headers: dict, url: str): def send_notification(data: str, headers: dict, url: str):
if url is None or data is None: if url is None or data is None:
raise ValueError("url and data cannot be None") raise ValueError("url and data cannot be None")
httpx.post(url, data=data.encode("utf-8"), headers=headers) httpx.post(url, data=data.encode("utf-8"), headers=headers)
def check_last_seen(last_seen: datetime.datetime, seconds: int = 15): def check_last_seen(last_seen: datetime.datetime, seconds: int = 15):
""" """
Check if a time is older than a given number of seconds Check if a time is older than a given number of seconds
If it is, return True If it is, return True
If last_seen is empty/null, return True If last_seen is empty/null, return True
""" """
if ( if (
datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds) datetime.datetime.now() - last_seen > datetime.timedelta(seconds=seconds)
or last_seen == "" or last_seen == ""
or last_seen is None or last_seen is None
): ):
return True return True
else: else:
return False return False

46
set-detect-notify/utils/utils.py Normal file
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@ -0,0 +1,46 @@
import cv2
import numpy as np
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,
):
view_frame = cv2.resize(full_frame, (0, 0), fx=view_scale, fy=view_scale)
for thing in boxes:
cv2.rectangle(
# Image
view_frame,
# Start point
(int(thing["x1"] * (run_scale/view_scale)), int(thing["y1"] * (run_scale/view_scale))),
# End point
(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))),
# Font
font,
# Font Scale
1,
# Color
(0, 255, 0),
# Thickness
1
)
return view_frame