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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# Load a model\n",
    "model = YOLO('yolov8n-pose.pt')  # pretrained YOLOv8n model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PoseEstimator():\n",
    "    def __init__(self):\n",
    "        self.model = YOLO('yolov8n-pose.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
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      "video 1/1 (frame 3/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 117.0ms\n",
      "video 1/1 (frame 4/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 111.9ms\n",
      "video 1/1 (frame 5/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 119.1ms\n",
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      "video 1/1 (frame 399/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 133.4ms\n",
      "video 1/1 (frame 400/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 186.8ms\n",
      "video 1/1 (frame 401/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 174.7ms\n",
      "video 1/1 (frame 402/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 166.3ms\n",
      "video 1/1 (frame 403/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 151.0ms\n",
      "video 1/1 (frame 404/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 146.7ms\n",
      "video 1/1 (frame 405/465) /Users/theorousseaux/Documents/Hackathon/FitnessEquation/Modules/PoseEstimation/../../data/pose/squat.mp4: 640x576 1 person, 154.7ms\n",
      "Speed: 6.5ms preprocess, 145.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 576)\n",
      "Results saved to \u001b[1m/Users/theorousseaux/Documents/Hackathon/FitnessEquation/runs/pose/predict\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "estimator = PoseEstimator()\n",
    "results = estimator.model('../../data/pose/squat.mp4', save=True)"
   ]
  }
 ],
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