Added support for `ntfy.sh`
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environment.yml
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environment.yml
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78
main.py
78
main.py
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@ -19,6 +19,7 @@ DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S"
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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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@ -46,14 +47,17 @@ 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" if not RUN_BY_COMPOSE else "rtsp://bridge: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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"display": True
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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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@ -83,32 +87,12 @@ 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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# Try this 5 times, 5 seconds apart. If the stream is not available, exit
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# for i in range(5):
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# # Check if HLS stream is available using the requests library
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# # If it is not, print an error and exit
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# url = URL.replace("rtsp", "http").replace(":8554", ":8888")
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# print(f"Checking if HLS stream is available at {url}...")
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# try:
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# # Replace rtsp with http and the port with 8888
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# r = requests.get(url)
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# if r.status_code != 200:
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# print("HLS stream not available, please check your URL")
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# exit()
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# except requests.exceptions.RequestException as e:
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# print("HLS stream not available, please check your URL")
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# if i == 4:
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# exit()
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# else:
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# print(f"Retrying in 5 seconds ({i+1}/5)")
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# time.sleep(5)
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# continue
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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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@ -124,70 +108,73 @@ video_capture = cv2.VideoCapture(URL)
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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(f"Video resolution: {video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)}x{video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)}")
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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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# 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_locations = face_recognition.face_locations(rgb_run_frame, model="hog") # 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(
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known_face_encodings, face_encoding
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)
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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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# 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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# 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")
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config["faces"][name]["last_seen"] = (
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datetime.datetime.now() - datetime.timedelta(seconds=6)
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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=5):
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) > datetime.timedelta(seconds=10):
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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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# 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": "urgent",
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"Tags": "neutral_face",
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},
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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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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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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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@ -215,5 +202,6 @@ while True:
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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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