Simple object detection
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GPG Key ID:
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.env
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config/
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config/
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using_yolov8.ipynb
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3.10.5
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BIN
environment.yml
BIN
environment.yml
Binary file not shown.
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main.py
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main.py
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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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import requests
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import time
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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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DISPLAY = False
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RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE")
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NTFY_URL = os.getenv("NTFY_URL")
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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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print("Hello, world!")
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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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# If RUN_BY_COMPOSE is true, set url to rtsp://wyze-bridge:8554/wyze_cam_name, otherwise set it to "rtsp://localhost:8554/wyze_cam_name"
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"URL": "rtsp://localhost:8554/wyze_cam_name"
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if not RUN_BY_COMPOSE
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else "rtsp://bridge:8554/wyze_cam_name",
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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": "config/example1.jpg", "last_seen": ""},
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"example2": {"image": "config/example2.jpg", "last_seen": ""},
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},
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"ntfy_url": "https://ntfy.sh/example",
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"display": True,
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}
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config_path = pathlib.Path("config/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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print("For relative paths, use the format config/example.jpg")
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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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if DISPLAY:
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config["DISPLAY"] = DISPLAY
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else:
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DISPLAY = config["display"]
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if NTFY_URL:
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config["ntfy_url"] = NTFY_URL
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else:
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NTFY_URL = config["ntfy_url"]
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print(f"Current config: {config}")
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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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# Print the resolution of the video
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print(
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f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}"
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)
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print("Beginning video capture...")
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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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# 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(
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rgb_run_frame, model="hog"
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) # This crashes the program without output on my laptop when it's running without Docker compose
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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(known_face_encodings, face_encoding)
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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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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 x+5 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 for the first time")
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config["faces"][name]["last_seen"] = (
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datetime.datetime.now() - datetime.timedelta(seconds=15)
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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=10):
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print(f"{name} has been seen")
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# Send a notification
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print(f"Sending notification to{NTFY_URL}")
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requests.post(
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NTFY_URL,
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data=f'"{name}" has been seen',
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headers={
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"Title": "Face Detected",
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"Priority": "default",
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"Tags": "neutral_face",
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},
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)
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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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# print("Writing config...")
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write_config()
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face_names.append(name)
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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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# print(f"Face found at {top}, {right}, {bottom}, {left} with name {name}")
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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 if DISPLAY is set to true
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if config["display"]:
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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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print("Releasing video capture")
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video_capture.release()
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cv2.destroyAllWindows()
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File diff suppressed because it is too large
Load Diff
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[tool.poetry]
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name = "detect-it"
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version = "0.1.0"
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description = "Detect all the things"
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authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
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license = "MIT"
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readme = "README.md"
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packages = [{include = "detect_it"}]
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[tool.poetry.dependencies]
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python = "^3.10"
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python-dotenv = "^1.0.0"
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httpx = "^0.25.0"
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opencv-python = "^4.8.1.78"
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ultralytics = "^8.0.190"
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hjson = "^3.1.0"
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numpy = "^1.23.2"
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torch = [
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{ version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
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{ version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
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]
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torchaudio = [
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{ version = "^2.0.0+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
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{ version = "^2.0.0+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
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]
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torchvision = [
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{ version = "^0.15+cu118", source = "torch_cu118", markers = "extra=='cuda'" },
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{ version = "^0.15+cpu", source = "torch_cpu", markers = "extra!='cuda'" },
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]
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[tool.poetry.group.dev.dependencies]
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black = "^23.9.1"
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ruff = "^0.0.291"
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ipykernel = "^6.25.2"
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[[tool.poetry.source]]
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name = "torch_cpu"
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url = "https://download.pytorch.org/whl/cpu"
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priority = "supplemental"
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[[tool.poetry.source]]
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name = "torch_cu118"
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url = "https://download.pytorch.org/whl/cu118"
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priority = "supplemental"
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[tool.poetry.extras]
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cuda = []
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[[tool.poetry.source]]
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name = "PyPI"
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priority = "primary"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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# certifi @ file:///croot/certifi_1665076670883/work/certifi
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click==8.1.3
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dlib==19.24.0
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face-recognition==1.3.0
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face-recognition-models==0.3.0
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numpy==1.23.5
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opencv-python==4.6.0.66
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Pillow==9.3.0
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python-dotenv==0.21.0
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urllib3==1.26.13
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requests==2.31.0
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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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import dotenv
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from pathlib import Path
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import os
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import time
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# import hjson as json
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import torch
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from ultralytics import YOLO
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import argparse
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from .utils import notify, config_utils
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DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
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args = None
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def main():
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global args
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# RUN_BY_COMPOSE = os.getenv("RUN_BY_COMPOSE") # Replace this with code to check for gpu
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if Path(".env").is_file():
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dotenv.load_dotenv()
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print("Loaded .env file")
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else:
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print("No .env file found")
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argparser = argparse.ArgumentParser(
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prog="Detect It",
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description="Detect it all!",
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epilog=":)",
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)
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# required='RUN_SCALE' not in os.environ,
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argparser.add_argument(
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'--run-scale',
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# Set it to the env RUN_SCALE if it isn't blank, otherwise set it to 0.25
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default=os.environ['RUN_SCALE'] if 'RUN_SCALE' in os.environ and os.environ['RUN_SCALE'] != '' else 0.25, # noqa: E501
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type=float,
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help="The scale to run the detection at, default is 0.25",
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)
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# argparser.add_argument(
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# '--view-scale',
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# # Set it to the env VIEW_SCALE if it isn't blank, otherwise set it to 0.75
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# default=os.environ['VIEW_SCALE'] if 'VIEW_SCALE' in os.environ and os.environ['VIEW_SCALE'] != '' else 0.75, # noqa: E501
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# type=float,
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# help="The scale to view the detection at, default is 0.75",
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# )
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stream_source = argparser.add_mutually_exclusive_group()
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# stream_source.add_argument(
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# '--url',
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# default=os.environ['URL'] if 'URL' in os.environ and os.environ['URL'] != '' else None, # noqa: E501
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# type=str,
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# help="The URL of the stream to use",
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# )
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stream_source.add_argument(
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'--capture-device',
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default=os.environ['CAPTURE_DEVICE'] if 'CAPTURE_DEVICE' in os.environ and os.environ['CAPTURE_DEVICE'] != '' else 0, # noqa: E501
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type=int,
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help="The capture device to use. Can also be a url."
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)
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notifcation_services = argparser.add_argument_group("Notification Services")
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notifcation_services.add_argument(
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'--ntfy-url',
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default=os.environ['NTFY_URL'] if 'NTFY_URL' in os.environ and os.environ['NTFY_URL'] != '' else None, # noqa: E501
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type=str,
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help="The URL to send notifications to",
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)
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args = argparser.parse_args()
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# Check if a CUDA GPU is available. If it is, set it via torch. Ff not, set it to cpu
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# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
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device = "0" if torch.cuda.is_available() else "cpu"
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if device == "0":
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torch.cuda.set_device(0)
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print("Set CUDA device")
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else:
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print("No CUDA device available, using CPU")
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model = YOLO("yolov8n.pt")
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video_capture = cv2.VideoCapture(args.capture_device)
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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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# Print the resolution of the video
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print(
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f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}" # noqa: E501
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)
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print("Beginning video capture...")
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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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# Resize frame of video to a smaller size for faster recognition processing
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run_frame = cv2.resize(frame, (0, 0), fx=args.run_scale, fy=args.run_scale)
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# view_frame = cv2.resize(frame, (0, 0), fx=args.view_scale, fy=args.view_scale)
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results = model(run_frame)
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for r in results:
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im_array = r.plot()
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# Scale back up the coordinates of the locations of detected objects.
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# im_array = np.multiply(im_array, 1/args.run_scale)
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# print(type(im_array))
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# print(im_array)
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# exit()
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cv2.imshow("View", im_array)
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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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print("Releasing video capture")
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video_capture.release()
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cv2.destroyAllWindows()
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main()
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@ -0,0 +1,4 @@
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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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|
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@ -0,0 +1,38 @@
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import datetime
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import httpx
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def construct_ntfy_headers(
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title: str = "Object/Person Detected",
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tag = "rotating_light", # https://docs.ntfy.sh/publish/#tags-emojis
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priority = "default", # https://docs.ntfy.sh/publish/#message-priority
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) -> (dict):
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return {
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'Title': title,
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'Priority': priority,
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'Tags': tag
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}
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def send_notification(
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data: str,
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headers: dict,
|
||||
url: str
|
||||
):
|
||||
if url is None or data is None:
|
||||
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
|
||||
Loading…
Reference in New Issue