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wyzely-detect
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Moved processing to `utils/utils.py` 

Crashes when another face is introduced
This commit is contained in:
slashtechno 2023-12-22 15:22:01 -06:00
parent e2e4554031
commit bec1d5b979
Signed by: slashtechno
GPG Key ID: 8EC1D9D9286C2B17
3 changed files with 181 additions and 117 deletions
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128
wyzely_detect/__main__.py
View File

@ -7,17 +7,12 @@ import cv2
import torch
from ultralytics import YOLO
from .utils import notify, utils
from .utils import utils
from .utils.cli_args import argparser
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
args = None
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def main():
global objects_and_peoples
@ -70,121 +65,32 @@ def main():
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# 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)
frame_to_show = utils.process_footage(
frame = frame,
run_scale = args.run_scale,
view_scale = args.view_scale,
path_to_faces = Path(args.faces_directory)
path_to_faces_exists = path_to_faces.is_dir()
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)
# Only run if path_to_faces exists
# May be better to check every iteration, but this also works
if path_to_faces_exists:
if face_details := utils.recognize_face(
path_to_directory=path_to_faces,
run_frame=run_frame,
min_confidence=args.face_confidence_threshold,
faces_directory=Path(args.faces_directory),
face_confidence_threshold=args.face_confidence_threshold,
no_remove_representations=args.no_remove_representations,
):
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,
model=model,
detect_object=args.detect_object,
object_confidence_threshold=args.object_confidence_threshold,
)
# 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.object_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)
# TODO: When multi-camera support is added, this needs to be changed to allow all feeds
if not args.no_display:
cv2.imshow(f"Video{i}", frame_to_show)
# When a face isn't recognized: "cv2.error: OpenCV(4.8.1) D:\a\opencv-python\opencv-python\opencv\modules\highgui\src\window.cpp:971: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'cv::imshow'"
# Seems to be because frame_to_show is null
cv2.imshow("Video", frame_to_show)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord("q"):

1
wyzely_detect/utils/cli_args.py
View File

@ -92,6 +92,7 @@ def set_argparse():
help="The URL to send notifications to",
)
# Various timers
timers = argparser.add_argument_group("Timers")
timers.add_argument(
"--detection-duration",

159
wyzely_detect/utils/utils.py
View File

@ -2,9 +2,165 @@ import cv2
import numpy as np
from pathlib import Path
from deepface import DeepFace
from . import notify
first_face_try = True
# TODO: When multi-camera support is added, this will need to be changed so that each camera has its own dict
objects_and_peoples = {
"objects": {},
"peoples": {},
}
def process_footage(
# Frame
frame: np.ndarray = None,
# scale
run_scale: float = None,
view_scale: float = None,
# Face stuff
faces_directory: str = None,
face_confidence_threshold: float = None,
no_remove_representations: bool = False,
# Timer stuff
detection_window: int = None,
detection_duration: int = None,
notification_window: int = None,
ntfy_url: str = None,
# Object stuff
# YOLO object
model = None,
detect_object: list = None,
object_confidence_threshold = None
) -> np.ndarray:
"""
Takes in a frame and processes it
"""
global objects_and_peoples
# Resize frame of video to a smaller size for faster recognition processing
run_frame = cv2.resize(frame, (0, 0), fx=run_scale, fy=run_scale)
# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
results = model(run_frame, verbose=False)
path_to_faces = Path(faces_directory)
path_to_faces_exists = path_to_faces.is_dir()
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)
# Only run if path_to_faces exists
# May be better to check every iteration, but this also works
if path_to_faces_exists:
if face_details := recognize_face(
path_to_directory=path_to_faces,
run_frame=run_frame,
# Perhaps make these names match?
min_confidence=face_confidence_threshold,
no_remove_representations=no_remove_representations,
):
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=detection_window,
detection_duration=detection_duration,
notification_window=notification_window,
ntfy_url=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 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 < object_confidence_threshold or (
class_id not in detect_object and 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=detection_window,
detection_duration=detection_duration,
notification_window=notification_window,
ntfy_url=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 = plot_label(
boxes=plot_boxes,
full_frame=frame,
# full_frame=r.plot(),
run_scale=run_scale,
view_scale=view_scale,
)
# Unsure if this should also return the objects_and_peoples dict
return frame_to_show
def plot_label(
# list of dicts with each dict containing a label, x1, y1, x2, y2
@ -18,7 +174,7 @@ def plot_label(
# So the coordinates will be scaled appropriately when coming from run_frame
view_scale: float = None,
font: int = cv2.FONT_HERSHEY_SIMPLEX,
):
) -> np.ndarray:
# 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
@ -176,6 +332,7 @@ def recognize_face(
f"Cosine similarity: {cosine_similarity}, filname: {path_to_image.name}, to_return: {to_return}"
)
return to_return
return None
"""
Example dataframe, for reference