Footstep_counter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install and Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-28 23:57:38.387581: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import mediapipe as mp\n",
    "import numpy as np\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_pose = mp.solutions.pose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining Joints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.imgur.com/3j8BPdc.png\" style=\"height:300px\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_angle(a,b,c):\n",
    "    a = np.array(a) # First\n",
    "    b = np.array(b) # Mid\n",
    "    c = np.array(c) # End\n",
    "    \n",
    "    radians = np.arctan2(c[1]-b[1], c[0]-b[0]) - np.arctan2(a[1]-b[1], a[0]-b[0])\n",
    "    angle = np.abs(radians*180.0/np.pi)\n",
    "    \n",
    "    if angle >180.0:\n",
    "        angle = 360-angle\n",
    "        \n",
    "    return angle "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Footstep Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize constants\n",
    "STAGE_DOWN = 'DOWN'\n",
    "STAGE_UP = 'UP'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# intialize counter variables\n",
    "counter = 5\n",
    "stage = None\n",
    "r_stage = STAGE_DOWN\n",
    "l_stage = STAGE_DOWN\n",
    "\n",
    "## Setup mediapipe instance\n",
    "with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:\n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor image to RGB\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False\n",
    "      \n",
    "        # Make detection\n",
    "        results = pose.process(image)\n",
    "    \n",
    "        # Recolor back to BGR\n",
    "        image.flags.writeable = True\n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # Extract landmarks\n",
    "        try:\n",
    "            landmarks = results.pose_landmarks.landmark\n",
    "            \n",
    "            # Get coordinates\n",
    "            rhip = [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y]\n",
    "            rknee = [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y]\n",
    "            rankle = [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]\n",
    "\n",
    "            lhip = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]\n",
    "            lknee = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]\n",
    "            lankle = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]\n",
    "            \n",
    "            # Calculate angle\n",
    "            r_angle = calculate_angle(rhip, rknee, rankle)\n",
    "            l_angle = calculate_angle(lhip, lknee, lankle)\n",
    "            \n",
    "            # Visualize angle\n",
    "            cv2.putText(image, str(r_angle), \n",
    "                           tuple(np.multiply(rknee, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )\n",
    "            cv2.putText(image, str(l_angle), \n",
    "                           tuple(np.multiply(lknee, [640, 480]).astype(int)), \n",
    "                           cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA\n",
    "                                )           \n",
    "            \n",
    "            # counter logic\n",
    "            if stage == None:\n",
    "                if r_angle < 160 and r_stage == STAGE_DOWN:\n",
    "                    r_stage = STAGE_UP\n",
    "                    stage = STAGE_UP\n",
    "                if l_angle < 160 and l_stage == STAGE_DOWN:\n",
    "                    l_stage = STAGE_UP\n",
    "                    stage = STAGE_UP\n",
    "            elif stage == STAGE_UP:\n",
    "                if r_angle >= 170 and r_stage == STAGE_UP:\n",
    "                    r_stage = STAGE_DOWN\n",
    "                    stage = None\n",
    "                    counter +=1\n",
    "                if l_angle >= 170 and l_stage == STAGE_UP:\n",
    "                    l_stage = STAGE_DOWN\n",
    "                    stage = None\n",
    "                    counter +=1\n",
    "                                       \n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "        # Render footstep counter\n",
    "        # Setup status box\n",
    "        cv2.rectangle(image, (0,0), (225,73), (245,117,16), -1)\n",
    "        \n",
    "        # Rep data\n",
    "        cv2.putText(image, 'REPS', (15,12), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv2.LINE_AA)\n",
    "        cv2.putText(image, str(counter), \n",
    "                    (10,60), \n",
    "                    cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 2, cv2.LINE_AA)       \n",
    "        \n",
    "        # Render detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,\n",
    "                                mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=2), \n",
    "                                mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2) \n",
    "                                 )               \n",
    "        \n",
    "        cv2.imshow('Mediapipe Feed', image)\n",
    "\n",
    "        # exit the program by pressing \"q\" on the keyboard\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "        \n",
    "        # reset counter and stages by pressing \"r\" on the keyboard\n",
    "        if cv2.waitKey(10) & 0xFF == ord('r'):\n",
    "            counter = 0\n",
    "            l_stage = STAGE_DOWN\n",
    "            r_stage = STAGE_DOWN\n",
    "            stage = None\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}