150 lines
4.1 KiB
Plaintext
150 lines
4.1 KiB
Plaintext
{
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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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"# Don't throw an error if no face is detected (enforce_detection=False)\n",
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"dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\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=['model_name=\"ArcFace\", detector_backend=\"opencv\")'], 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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" # print(f'Most likely identity: {pd_dataframe.iloc[0][\"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: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
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" # Get the most likely identity's confidence\n",
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" print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_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\", model_name=\"ArcFace\", detector_backend=\"opencv\")"
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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 normal frame by frame"
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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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},
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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\n",
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"\n",
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"def main():\n",
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" cap = cv2.VideoCapture(0)\n",
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" while True:\n",
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" ret, frame = cap.read()\n",
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" dfs = DeepFace.find(frame, db_path = \"faces\", enforce_detection=False, silent=False, model_name=\"ArcFace\", detector_backend=\"opencv\")\n",
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" for i, pd_dataframe in enumerate(dfs):\n",
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" print(f'On dataframe {i}')\n",
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" print(pd_dataframe)\n",
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" print(f'Most likely identity: {Path(pd_dataframe.iloc[0][\"identity\"]).parent.name}')\n",
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" print(f'Confidence: {pd_dataframe.iloc[0][\"ArcFace_cosine\"]}')\n",
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" cv2.imshow(\"frame\", frame)\n",
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" if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n",
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" break\n",
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" cap.release()\n",
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" cv2.destroyAllWindows()\n",
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" \n",
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"\n",
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"\n",
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"main()"
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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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"Other functions\n"
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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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