Initial commit
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person.jpg
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config.json
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MIT License
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Copyright (c) 2022 slashtechno
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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import datetime
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import face_recognition
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import cv2
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import numpy as np
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from dotenv import load_dotenv
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import os
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import json
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import pathlib
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load_dotenv()
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URL = os.getenv("URL")
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RUN_SCALE = os.getenv("RUN_SCALE")
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VIEW_SCALE = os.getenv("VIEW_SCALE")
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DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
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# RUN_SCALE = 0.25
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# VIEW_SCALE = 0.75
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def find_face_from_name(name):
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for face in config["faces"]:
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if config["faces"][face]["name"] == name:
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return face
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return None
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def write_config():
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with open(config_path, "w") as config_file:
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json.dump(config, config_file, indent=4)
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# Initialize some variables
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face_locations = []
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face_encodings = []
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face_names = []
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known_face_encodings = []
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known_face_names = []
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process_this_frame = True
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# Load the config file, if it does not exist or is blank, create it
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config = {
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"URL": "rtsp://localhost:8554/example",
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"RUN_SCALE": "0.25",
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"VIEW_SCALE": "0.75",
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"faces": {
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"example1": {"image": "example1.jpg", "last_seen": ""},
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"example2": {"image": "example2.jpg", "last_seen": ""},
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},
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}
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config_path = pathlib.Path("config.json")
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if config_path.exists():
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with open(config_path, "r") as config_file:
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config = json.load(config_file)
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else:
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with open(config_path, "w") as config_file:
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json.dump(config, config_file, indent=4)
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print("Config file created, please edit it and restart the program")
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exit()
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if URL:
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config["URL"] = URL
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else:
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URL = config["URL"]
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if RUN_SCALE:
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config["RUN_SCALE"] = RUN_SCALE
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else:
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RUN_SCALE = float(config["RUN_SCALE"])
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if VIEW_SCALE:
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config["VIEW_SCALE"] = VIEW_SCALE
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else:
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VIEW_SCALE = float(config["VIEW_SCALE"])
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for face in config["faces"]:
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# Load a sample picture and learn how to recognize it.
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image = face_recognition.load_image_file(config["faces"][face]["image"])
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face_encoding = face_recognition.face_encodings(image)[0]
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known_face_encodings.append(face_encoding)
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# Append the key to the list of known face names
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known_face_names.append(face)
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video_capture = cv2.VideoCapture(URL)
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# Eliminate lag by setting the buffer size to 1
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# This makes it so that the video capture will only grab the most recent frame
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# However, this means that the video may be choppy
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video_capture.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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while True:
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# Grab a single frame of video
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ret, frame = video_capture.read()
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# Only process every other frame of video to save time
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# if process_this_frame:
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if True:
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# Resize frame of video to a smaller size for faster face recognition processing
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run_frame = cv2.resize(frame, (0, 0), fx=RUN_SCALE, fy=RUN_SCALE)
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view_frame = cv2.resize(frame, (0, 0), fx=VIEW_SCALE, fy=VIEW_SCALE)
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# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
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rgb_run_frame = run_frame[:, :, ::-1]
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# Find all the faces and face encodings in the current frame of video
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# model cnn is gpu accelerated, but hog is cpu only
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face_locations = face_recognition.face_locations(rgb_run_frame, model="hog")
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face_encodings = face_recognition.face_encodings(rgb_run_frame, face_locations)
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face_names = []
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for face_encoding in face_encodings:
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# See if the face is a match for the known face(s)
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matches = face_recognition.compare_faces(
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known_face_encodings, face_encoding
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)
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name = "Unknown"
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# Or instead, use the known face with the smallest distance to the new face
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face_distances = face_recognition.face_distance(
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known_face_encodings, face_encoding
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)
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best_match_index = np.argmin(face_distances)
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if matches[best_match_index]:
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# print("For debugging, I found a face!!!! :D this should not be included in the final product lol :P")
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name = known_face_names[best_match_index]
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last_seen = config["faces"][name]["last_seen"]
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# If it's never been seen, set the last seen time to six seconds ago so it will be seen
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# Kind of a hacky way to do it, but it works... hopefully
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if last_seen == "":
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print(f"{name} has been seen")
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config["faces"][name]["last_seen"] = (
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datetime.datetime.now() - datetime.timedelta(seconds=6)
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).strftime(DATETIME_FORMAT)
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write_config()
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# Check if the face has been seen in the last 5 seconds
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if datetime.datetime.now() - datetime.datetime.strptime(
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last_seen, DATETIME_FORMAT
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) > datetime.timedelta(seconds=5):
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print(f"{name} has been seen")
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# Update the last seen time
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config["faces"][name]["last_seen"] = datetime.datetime.now().strftime(
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DATETIME_FORMAT
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)
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write_config()
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face_names.append(name)
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process_this_frame = not process_this_frame
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# Display the results
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# Iterate over each face found in the frame to draw a box around it
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# Zip is used to iterate over two lists at the same time
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for (top, right, bottom, left), name in zip(face_locations, face_names):
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# Scale back up face locations since the frame we detected in was scaled to 1/4 size
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top = int(top * (VIEW_SCALE / RUN_SCALE))
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right = int(right * (VIEW_SCALE / RUN_SCALE))
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bottom = int(bottom * (VIEW_SCALE / RUN_SCALE))
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left = int(left * (VIEW_SCALE / RUN_SCALE))
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# Draw a box around the face
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cv2.rectangle(view_frame, (left, top), (right, bottom), (0, 0, 255), 2)
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# Draw a label with a name below the face
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cv2.rectangle(
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view_frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED
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)
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font = cv2.FONT_HERSHEY_DUPLEX
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cv2.putText(
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view_frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1
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)
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# Display the resulting image
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cv2.imshow("Scaled View", view_frame)
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# Hit 'q' on the keyboard to quit!
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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# Release handle to the webcam
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video_capture.release()
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cv2.destroyAllWindows()
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