video_processing_new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a91ef8dc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ignoring empty camera frame.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import mediapipe as mp\n",
    "import matplotlib.pyplot as plt\n",
    "mp_drawing = mp.solutions.drawing_utils\n",
    "mp_drawing_styles = mp.solutions.drawing_styles\n",
    "mp_pose = mp.solutions.pose\n",
    "\n",
    "# For webcam input:\n",
    "#cap = cv2.VideoCapture(0)\n",
    "cap = cv2.VideoCapture(r\"C:\\Users\\Hp\\Documents\\Lausanne\\EPFL\\Courses\\MA3\\sensi\\Project\\Data\\videos\\video_camil_cut.mp4\")\n",
    "ys_l_shoulder = []\n",
    "ys_r_shoulder = []\n",
    "with mp_pose.Pose(\n",
    "    min_detection_confidence=0.5,\n",
    "    min_tracking_confidence=0.5) as pose:\n",
    "  while cap.isOpened():\n",
    "    success, image = cap.read()\n",
    "    if not success:\n",
    "      print(\"Ignoring empty camera frame.\")\n",
    "      # If loading a video, use 'break' instead of 'continue'.\n",
    "      break\n",
    "\n",
    "    # To improve performance, optionally mark the image as not writeable to\n",
    "    # pass by reference.\n",
    "    image.flags.writeable = False\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    results = pose.process(image)\n",
    "\n",
    "    # Draw the pose annotation on the image.\n",
    "    image.flags.writeable = True\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "    if results.pose_landmarks!=None :\n",
    "        #print(results.pose_landmarkss)\n",
    "        landmarks = [(data_point.x, data_point.y, data_point.z) for data_point in results.pose_landmarks.landmark]\n",
    "        # y is important for distance\n",
    "        ys_l_shoulder.append(landmarks[12][1])\n",
    "        ys_r_shoulder.append(landmarks[11][1])\n",
    "\n",
    "#mp_drawing.draw_landmarks(\n",
    "#        image,\n",
    "#        results.pose_landmarks,\n",
    "#        mp_pose.POSE_CONNECTIONS,\n",
    "#        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())\n",
    "    # Flip the image horizontally for a selfie-view display.\n",
    "    cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))\n",
    "    if cv2.waitKey(5) & 0xFF == 27:\n",
    "      break\n",
    "\n",
    "\n",
    "plt.plot(ys_l_shoulder)\n",
    "plt.plot(ys_r_shoulder)\n",
    "plt.show()\n",
    "cap.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e15a644c",
   "metadata": {
    "scrolled": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.optimize\n",
    "\n",
    "def func(x,y):\n",
    "    h_sh = 1.5 # tbd\n",
    "    fov = 77/180 * np.pi\n",
    "    alpha = 45/180*np.pi-fov/2 # tbd\n",
    "    gamma = np.arctan(x/h_sh)\n",
    "    delta = np.pi/2 - fov/2 - alpha - gamma\n",
    "    ai = h_sh + x*np.tan(np.pi/2-fov/2-alpha)\n",
    "    ah = ai * np.sin(fov/2+alpha)\n",
    "    lh = ah * np.tan(fov/2)\n",
    "    lm = 2*lh\n",
    "    ap = h_sh/np.cos(gamma)\n",
    "    ph = ap*np.sin(delta)\n",
    "    funz = 1/2-ph/lm-y\n",
    "    \n",
    "    return funz\n",
    "\n",
    "inc = scipy.optimize.fsolve(func, 0, args=(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "34eeca2a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1b9c751c0>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.linspace(0.3,0.7,100)\n",
    "z = []\n",
    "for i in y:\n",
    "    \n",
    "    z.append(scipy.optimize.fsolve(func,0,args=(i)))\n",
    "    \n",
    "    \n",
    "plt.plot(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ac1247",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2111bae4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# For static images:\n",
    "IMAGE_FILES = []\n",
    "BG_COLOR = (192, 192, 192) # gray\n",
    "with mp_pose.Pose(\n",
    "    static_image_mode=True,\n",
    "    model_complexity=2,\n",
    "    enable_segmentation=True,\n",
    "    min_detection_confidence=0.5) as pose:\n",
    "  for idx, file in enumerate(IMAGE_FILES):\n",
    "    image = cv2.imread(file)\n",
    "    image_height, image_width, _ = image.shape\n",
    "    # Convert the BGR image to RGB before processing.\n",
    "    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "    if not results.pose_landmarks:\n",
    "      continue\n",
    "    print(\n",
    "        f'Nose coordinates: ('\n",
    "        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '\n",
    "        f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'\n",
    "    )\n",
    "\n",
    "    annotated_image = image.copy()\n",
    "    # Draw segmentation on the image.\n",
    "    # To improve segmentation around boundaries, consider applying a joint\n",
    "    # bilateral filter to \"results.segmentation_mask\" with \"image\".\n",
    "    condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1\n",
    "    bg_image = np.zeros(image.shape, dtype=np.uint8)\n",
    "    bg_image[:] = BG_COLOR\n",
    "    annotated_image = np.where(condition, annotated_image, bg_image)\n",
    "    # Draw pose landmarks on the image.\n",
    "    mp_drawing.draw_landmarks(\n",
    "        annotated_image,\n",
    "        results.pose_landmarks,\n",
    "        mp_pose.POSE_CONNECTIONS,\n",
    "        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())\n",
    "    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)\n",
    "    # Plot pose world landmarks.\n",
    "    mp_drawing.plot_landmarks(\n",
    "        results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}