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wyzely-detect
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slashtechno 2022-12-17 15:54:47 -06:00
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person.jpg
config.json

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MIT License
Copyright (c) 2022 slashtechno
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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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
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
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)
# 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 = {
"URL": "rtsp://localhost:8554/example",
"RUN_SCALE": "0.25",
"VIEW_SCALE": "0.75",
"faces": {
"example1": {"image": "example1.jpg", "last_seen": ""},
"example2": {"image": "example2.jpg", "last_seen": ""},
},
}
config_path = pathlib.Path("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")
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"])
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)
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Only process every other frame of video to save time
# if process_this_frame:
if True:
# 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")
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]:
# print("For debugging, I found a face!!!! :D this should not be included in the final product lol :P")
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 six 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")
config["faces"][name]["last_seen"] = (
datetime.datetime.now() - datetime.timedelta(seconds=6)
).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=5):
print(f"{name} has been seen")
# Update the last seen time
config["faces"][name]["last_seen"] = datetime.datetime.now().strftime(
DATETIME_FORMAT
)
write_config()
face_names.append(name)
process_this_frame = not process_this_frame
# 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):
# 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
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
video_capture.release()
cv2.destroyAllWindows()