{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4268bbde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "import pyopenpose as op\n",
    "import ipywidgets\n",
    "from ipywebrtc import CameraStream\n",
    "import cv2 \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b530e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: tqdm in ./.local/lib/python3.8/site-packages (4.64.1)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47bbe21b",
   "metadata": {},
   "outputs": [],
   "source": [
    " from tqdm.notebook import tqdm\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8185becc",
   "metadata": {},
   "source": [
    "# Jupyter notebook for OpenPose experiments\n",
    "\n",
    "Read any image or video from the `/data` directory and render the output here "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb916658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Nov  2 15:54:10 2022       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 470.141.03   Driver Version: 470.141.03   CUDA Version: 11.4     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:08:00.0 Off |                  N/A |\r\n",
      "|  0%   41C    P5    61W / 350W |     21MiB / 24265MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5222af69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CameraStream.facing_user(audio=False)\n",
    "# requires SSL website... :-("
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "266062c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = dict()\n",
    "params[\"model_folder\"] = \"/openpose/models/\"\n",
    "# params[\"face\"] = True\n",
    "params[\"hand\"] = True\n",
    "# params[\"heatmaps_add_parts\"] = True\n",
    "# params[\"heatmaps_add_bkg\"] = True\n",
    "# params[\"heatmaps_add_PAFs\"] = True\n",
    "# params[\"heatmaps_scale\"] = 3\n",
    "# params[\"upsampling_ratio\"] = 1\n",
    "# params[\"body\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f034fee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting OpenPose Python Wrapper...\n",
      "Auto-detecting all available GPUs... Detected 1 GPU(s), using 1 of them starting at GPU 0.\n"
     ]
    }
   ],
   "source": [
    "# Starting OpenPose\n",
    "opWrapper = op.WrapperPython()\n",
    "opWrapper.configure(params)\n",
    "opWrapper.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37f999b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# upload = ipywidgets.FileUpload()\n",
    "# display(upload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de658948",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b4208da",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Process Image\n",
    "# datum = op.Datum()\n",
    "# imageToProcess = cv2.imread(args[0].image_path)\n",
    "# datum.cvInputData = imageToProcess\n",
    "# opWrapper.emplaceAndPop(op.VectorDatum([datum]))\n",
    "\n",
    "# # Display Image\n",
    "# print(\"Body keypoints: \\n\" + str(datum.poseKeypoints))\n",
    "# print(\"Face keypoints: \\n\" + str(datum.faceKeypoints))\n",
    "# print(\"Left hand keypoints: \\n\" + str(datum.handKeypoints[0]))\n",
    "# print(\"Right hand keypoints: \\n\" + str(datum.handKeypoints[1]))\n",
    "# cv2.imwrite(\"/data/result_body.jpg\",datum.cvOutputData)\n",
    "\n",
    "# print(dir(datum))\n",
    "# #    cv2.imshow(\"OpenPose 1.7.0 - Tutorial Python API\", datum.cvOutputData)\n",
    "#    cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c994aa0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "402ad8b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "275242c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frames per second :  24.0 FPS\n",
      "Frame count :  500.0\n"
     ]
    }
   ],
   "source": [
    "vid_capture = cv2.VideoCapture('/data/0001-0500.mp4')\n",
    "\n",
    "# Create a video capture object, in this case we are reading the video from a file\n",
    "if (vid_capture.isOpened() == False):\n",
    "  print(\"Error opening the video file\")\n",
    "# Read fps and frame count\n",
    "else:\n",
    "  # Get frame rate information\n",
    "  # You can replace 5 with CAP_PROP_FPS as well, they are enumerations\n",
    "  fps = vid_capture.get(5)\n",
    "  print('Frames per second : ', fps,'FPS')\n",
    " \n",
    "  # Get frame count\n",
    "  # You can replace 7 with CAP_PROP_FRAME_COUNT as well, they are enumerations\n",
    "  frame_count = vid_capture.get(7)\n",
    "  print('Frame count : ', frame_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b0149d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_size = (int(vid_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vid_capture.get(cv2.CAP_PROP_FRAME_HEIGHT\n",
    ")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a536a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1920, 1080)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66003b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = cv2.VideoWriter(\n",
    "    '/data/output.mp4',\n",
    "    # see http://mp4ra.org/#/codecs for codecs\n",
    "#     cv2.VideoWriter_fourcc('m','p','4','v'),\n",
    "#      cv2.VideoWriter_fourcc(*'mp4v'),\n",
    "#     cv2.VideoWriter_fourcc('a','v','1','C'),\n",
    "    cv2.VideoWriter_fourcc(*'vp09'),\n",
    "#     cv2.VideoWriter_fourcc(*'avc1'),\n",
    "    fps,\n",
    "    frame_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6ed91535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "633b01b6f8474017ab2ba3d454b3adea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500.0 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream ended\n"
     ]
    }
   ],
   "source": [
    "# See also:\n",
    "# with tqdm.wrapattr(file_obj, \"read\", total=file_obj.size) as fobj:\n",
    "# ...     while True:\n",
    "# ...         chunk = fobj.read(chunk_size)\n",
    "# ...         if not chunk:\n",
    "# ...             break\n",
    "t = tqdm(total=frame_count) # Initialise\n",
    "while(vid_capture.isOpened()):\n",
    "    t.update(1)\n",
    "# with tqdm.wrapattr(vid_capture, \"read\", total=frame_count) as vid_obj:\n",
    "#     # vid_capture.read() methods returns a tuple, first element is a bool \n",
    "    # and the second is frame\n",
    "#     while True:\n",
    "    ret, frame = vid_capture.read()\n",
    "    if ret == True:\n",
    "        datum = op.Datum()\n",
    "        datum.cvInputData = frame\n",
    "        opWrapper.emplaceAndPop(op.VectorDatum([datum]))\n",
    "\n",
    "        # Display Image\n",
    "        # print(\"Body keypoints: \\n\" + str(datum.poseKeypoints))\n",
    "        # print(\"Face keypoints: \\n\" + str(datum.faceKeypoints))\n",
    "        # print(\"Left hand keypoints: \\n\" + str(datum.handKeypoints[0]))\n",
    "        # print(\"Right hand keypoints: \\n\" + str(datum.handKeypoints[1]))\n",
    "        #cv2.imwrite(f\"/data/out/result_body_scale{i:03d}.jpg\",datum.cvOutputData)\n",
    "        output.write(datum.cvOutputData)\n",
    "    else:\n",
    "        print(\"Stream ended\")\n",
    "        break\n",
    "t.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27580409",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Release the objects\n",
    "vid_capture.release()\n",
    "output.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f3ad121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b632f75a46f4306bcadddf38ac4887e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Video(value=b'\\x00\\x00\\x00\\x1cftypisom\\x00\\x00\\x02\\x00isomiso2mp41\\x00\\x00\\x00\\x08free\\x00U\\x0c\\xe0...')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display(HTML(\"\"\"<video width=\"100\" height=\"100\" controls><source src=\"/data/output.mp4\" type=\"video/mp4\"></video>\"\"\"))\n",
    "ipywidgets.Video.from_file('/data/outputs.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d97230b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import Video\n",
    "# Video('/data/outputs.mp4', embed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b462645",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}