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@ -1 +1 @@
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3.11.5
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3.10.5
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@ -49,7 +49,6 @@ This assumes you have Python 3.10 or 3.11 installed
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#### Poetry
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#### Poetry
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1. `poetry install`
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1. `poetry install`
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a. For GPU support, use `poetry install -E cuda --with gpu`
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2. `poetry run -- wyzely-detect`
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2. `poetry run -- wyzely-detect`
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### Configuration
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### Configuration
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The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.
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The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.
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@ -21,12 +21,11 @@ ultralytics = "^8.0.190"
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hjson = "^3.1.0"
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hjson = "^3.1.0"
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numpy = "^1.23.2"
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numpy = "^1.23.2"
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# https://github.com/python-poetry/poetry/issues/6409#issuecomment-1911735833
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# https://github.com/python-poetry/poetry/issues/6409
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# To install with GPU, use poetry install -E cuda --with gpu
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torch = ">=2.0.0, !=2.0.1, !=2.1.0"
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torch = {version = "2.1.*", source = "pytorch-cpu", markers = "extra!='cuda'" }
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# https://stackoverflow.com/a/76477590/18270659
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# https://stackoverflow.com/a/76477590/18270659
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# https://discfuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
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# https://discuss.tensorflow.org/t/tensorflow-io-gcs-filesystem-with-windows/18849/4
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# Might be able to remove this version constraint later
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# Might be able to remove this version constraint later
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# Working versions:
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# Working versions:
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# Python version 3.10.12 and 3.10.5 both work
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# Python version 3.10.12 and 3.10.5 both work
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@ -34,33 +33,10 @@ torch = {version = "2.1.*", source = "pytorch-cpu", markers = "extra!='cuda'" }
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# cuDNN version - 8.8.1
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# cuDNN version - 8.8.1
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# Installed from Nvidia website - nvidia-cuda-toolkit is not installed, but default PopOS drivers are installed
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# Installed from Nvidia website - nvidia-cuda-toolkit is not installed, but default PopOS drivers are installed
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tensorflow-io-gcs-filesystem = "0.31.0"
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tensorflow-io-gcs-filesystem = "0.31.0"
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tensorflow = {version = "^2.14.0", markers = "extra!='cuda'"}
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tensorflow = {version = "^2.14.0", extras = ["and-cuda"]}
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deepface = "^0.0.79"
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deepface = "^0.0.79"
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prettytable = "^3.9.0"
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[tool.poetry.group.gpu]
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optional = true
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[tool.poetry.group.gpu.dependencies]
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torch = {version = "2.1.*", source = "pytorch-cu121", markers = "extra=='cuda'"}
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tensorflow = {version = "^2.14.0", extras = ["and-cuda"], markers = "extra=='cuda'"}
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[tool.poetry.extras]
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# Might be better to rename this to nocpu since it's more accurate
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cuda = []
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[[tool.poetry.source]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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priority = "explicit"
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[[tool.poetry.source]]
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name = "pytorch-cu121"
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url = "https://download.pytorch.org/whl/cu121"
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priority = "explicit"
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[tool.poetry.group.dev.dependencies]
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[tool.poetry.group.dev.dependencies]
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black = "^23.9.1"
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black = "^23.9.1"
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@ -1,9 +1,8 @@
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# import face_recognition
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# import face_recognition
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from pathlib import Path
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from pathlib import Path
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import cv2
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import cv2
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import os
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from prettytable import PrettyTable
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from PrettyTable import PrettyTable
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# import hjson as json
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# import hjson as json
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import torch
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import torch
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def main():
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def main():
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global objects_and_peoples
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global objects_and_peoples
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global args
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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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args = argparser.parse_args()
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args = argparser.parse_args()
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# Set the video capture to the appropriate source
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# Set the video capture to the appropriate source
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if not args.rtsp_url and not args.capture_device:
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if not args.rtsp_url and not args.capture_device:
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print("No stream or capture device set, defaulting to capture device 0")
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print("No stream or capture device set, defaulting to capture device 0")
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video_sources = {
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args.capture_device = [0]
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"devices": [cv2.VideoCapture(0)]
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}
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else:
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else:
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video_sources = {
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video_sources = {
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"streams": [cv2.VideoCapture(url) for url in args.rtsp_url],
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"streams": [cv2.VideoCapture(url) for url in args.rtsp_url],
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# This makes it so that the video capture will only grab the most recent frame
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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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# However, this means that the video may be choppy
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# Only do this for streams
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# Only do this for streams
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try:
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for stream in video_sources["streams"]:
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for stream in video_sources["streams"]:
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stream.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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stream.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# If there are no streams, this will throw a KeyError
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except KeyError:
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pass
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# Print out the resolution of the video sources. Ideally, change this so the device ID/url is also printed
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# Print the resolution of the video
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pretty_table = PrettyTable(field_names=["Source Type", "Resolution"])
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print(
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for source_type, sources in video_sources.items():
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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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for source in sources:
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pretty_table.add_row(
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[source_type, f"{source.get(cv2.CAP_PROP_FRAME_WIDTH)}x{source.get(cv2.CAP_PROP_FRAME_HEIGHT)}"]
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)
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)
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print(pretty_table)
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print
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print
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print("Beginning video capture...")
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print("Beginning video capture...")
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while True:
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while True:
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@ -1,13 +1,8 @@
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import cv2
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import cv2
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import os
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import numpy as np
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import numpy as np
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from pathlib import Path
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from pathlib import Path
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# https://stackoverflow.com/a/42121886/18270659
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from deepface import DeepFace
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os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
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from . import notify
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from deepface import DeepFace # noqa: E402
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from . import notify # noqa: E402
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first_face_try = True
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first_face_try = True
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