Create Deepface Jupyter Notebook
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config/
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using_yolov8.ipynb
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yolov8n.pt
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__pycache__/
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.venv/
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__pycache__/
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faces/*
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!faces/.gitkeep
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from deepface import DeepFace\n",
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"import cv2\n",
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"from pathlib import Path\n",
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"import uuid\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Take pictures"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Take a picture using opencv with <uuid>.jpg\n",
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"# Then delete it after\n",
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"cap = cv2.VideoCapture(0)\n",
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"ret, frame = cap.read()\n",
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"cap.release()\n",
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"uuid_str = str(uuid.uuid4())\n",
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"uuid_path = Path(uuid_str + \".jpg\")\n",
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"cv2.imwrite(str(uuid_path), frame)\n",
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"dfs = DeepFace.find(img_path=str(uuid_path), db_path = \"faces\")\n",
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"\n",
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"# Get the identity of the person\n",
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"for i, pd_dataframe in enumerate(dfs):\n",
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" # Sort the dataframe by confidence\n",
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" # inplace=True means that the dataframe is modified so we don't need to assign it to a new variable\n",
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" pd_dataframe.sort_values(by=['VGG-Face_cosine'], inplace=True, ascending=False)\n",
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" print(f'On dataframe {i}')\n",
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" print(pd_dataframe)\n",
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" # Get the most likely identity\n",
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" # We could use Path to get the parent directory of the image to use as the identity\n",
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" print(f'Most likely identity: {pd_dataframe.iloc[0][\"identity\"]}')\n",
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" # Get the most likely identity's confidence\n",
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" print(f'Confidence: {pd_dataframe.iloc[0][\"VGG-Face_cosine\"]}')\n",
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"\n",
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"uuid_path.unlink()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Stream"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"DeepFace.stream(db_path=\"faces\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -8,51 +8,29 @@ readme = "README.md"
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packages = [{include = "set-detect-notify"}]
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[tool.poetry.dependencies]
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python = "^3.10"
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# python = "^3.10"
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python = ">=3.10, <3.12"
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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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# https://github.com/python-poetry/poetry/issues/6409
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torch = "^2.1.0"
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tensorflow-io-gcs-filesystem = "0.31.0"
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deepface = "^0.0.79"
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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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nbconvert = "^7.9.2"
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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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#
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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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#
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# [tool.poetry.extras]
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# cuda = []
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#
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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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