Changed repo URL and added repo to pyproject.toml
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@ -28,7 +28,7 @@ Recognize faces/objects in a video stream (from a webcam or a security camera) a
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## Usage
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## Usage
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### Installation
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### Installation
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1. Clone this repo with `git clone https://github.com/slashtechno/wyze-face-recognition.git` <!-- This URL should be changed once the repo is renamed -->
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1. Clone this repo with `git clone https://github.com/slashtechno/wyzely-detect`
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2. `cd` into the cloned repository
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2. `cd` into the cloned repository
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3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
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3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker
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@ -3,6 +3,8 @@ name = "wyzely-detect"
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version = "0.1.0"
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version = "0.1.0"
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description = "Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices"
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description = "Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices"
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authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
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authors = ["slashtechno <77907286+slashtechno@users.noreply.github.com>"]
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repository = "https://github.com/slashtechno/wyzely-detect"
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keywords = ["object-detection", "face-detection", "wyze", "security", "yolov8", "unified-push"]
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license = "MIT"
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license = "MIT"
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readme = "README.md"
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readme = "README.md"
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packages = [{include = "wyzely_detect"}]
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packages = [{include = "wyzely_detect"}]
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@ -160,7 +160,7 @@ def main():
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args = argparser.parse_args()
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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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# Check if a CUDA GPU is available. If it is, set it via torch. If not, set it to cpu
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# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
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# https://github.com/ultralytics/ultralytics/issues/3084#issuecomment-1732433168
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# Currently, I have been unable to set up Poetry to use GPU for Torch
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# Currently, I have been unable to set up Poetry to use GPU for Torch
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for i in range(torch.cuda.device_count()):
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for i in range(torch.cuda.device_count()):
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