notebooks/async-api/async-api.ipynb

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   "source": [
    "# Asynchronous Inference with OpenVINO™\n",
    "This notebook demonstrates how to use the [Async API](https://docs.openvino.ai/2024/openvino-workflow/running-inference/optimize-inference/general-optimizations.html) for asynchronous execution with OpenVINO.\n",
    "\n",
    "OpenVINO Runtime supports inference in either synchronous or asynchronous mode. The key advantage of the Async API is that when a device is busy with inference, the application can perform other tasks in parallel (for example, populating inputs or scheduling other requests) rather than wait for the current inference to complete first.\n",
    "\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Imports](#Imports)\n",
    "- [Prepare model and data processing](#Prepare-model-and-data-processing)\n",
    "    - [Download test model](#Download-test-model)\n",
    "    - [Load the model](#Load-the-model)\n",
    "    - [Create functions for data processing](#Create-functions-for-data-processing)\n",
    "    - [Get the test video](#Get-the-test-video)\n",
    "- [How to improve the throughput of video processing](#How-to-improve-the-throughput-of-video-processing)\n",
    "    - [Sync Mode (default)](#Sync-Mode-(default))\n",
    "    - [Test performance in Sync Mode](#Test-performance-in-Sync-Mode)\n",
    "    - [Async Mode](#Async-Mode)\n",
    "    - [Test the performance in Async Mode](#Test-the-performance-in-Async-Mode)\n",
    "    - [Compare the performance](#Compare-the-performance)\n",
    "- [`AsyncInferQueue`](#AsyncInferQueue)\n",
    "    - [Setting Callback](#Setting-Callback)\n",
    "    - [Test the performance with `AsyncInferQueue`](#Test-the-performance-with-AsyncInferQueue)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "%pip install -q \"openvino>=2023.1.0\"\n",
    "%pip install -q opencv-python\n",
    "if platform.system() != \"windows\":\n",
    "    %pip install -q \"matplotlib>=3.4\"\n",
    "else:\n",
    "    %pip install -q \"matplotlib>=3.4,<3.7\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import time\n",
    "import numpy as np\n",
    "import openvino as ov\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fetch the notebook utils script from the openvino_notebooks repo\n",
    "import requests\n",
    "\n",
    "r = requests.get(\n",
    "    url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\",\n",
    ")\n",
    "open(\"notebook_utils.py\", \"w\").write(r.text)\n",
    "\n",
    "import notebook_utils as utils"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare model and data processing\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "### Download test model\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "We use a pre-trained model from OpenVINO's [Open Model Zoo](https://docs.openvino.ai/2024/documentation/legacy-features/model-zoo.html) to start the test. In this case, the model will be executed to detect the person in each frame of the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################|| Downloading person-detection-0202 ||################\n",
      "\n",
      "========== Retrieving model/intel/person-detection-0202/FP16/person-detection-0202.xml from the cache\n",
      "\n",
      "========== Retrieving model/intel/person-detection-0202/FP16/person-detection-0202.bin from the cache\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# directory where model will be downloaded\n",
    "base_model_dir = \"model\"\n",
    "\n",
    "# model name as named in Open Model Zoo\n",
    "model_name = \"person-detection-0202\"\n",
    "precision = \"FP16\"\n",
    "model_path = f\"model/intel/{model_name}/{precision}/{model_name}.xml\"\n",
    "download_command = f\"omz_downloader \" f\"--name {model_name} \" f\"--precision {precision} \" f\"--output_dir {base_model_dir} \" f\"--cache_dir {base_model_dir}\"\n",
    "! $download_command"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select inference device\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9c9f01fc0014058909e1d61e7bdd56d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "core = ov.Core()\n",
    "device = widgets.Dropdown(\n",
    "    options=core.available_devices + [\"AUTO\"],\n",
    "    value=\"CPU\",\n",
    "    description=\"Device:\",\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the model\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize OpenVINO runtime\n",
    "core = ov.Core()\n",
    "\n",
    "# read the network and corresponding weights from file\n",
    "model = core.read_model(model=model_path)\n",
    "\n",
    "# compile the model for the CPU (you can choose manually CPU, GPU etc.)\n",
    "# or let the engine choose the best available device (AUTO)\n",
    "compiled_model = core.compile_model(model=model, device_name=device.value)\n",
    "\n",
    "# get input node\n",
    "input_layer_ir = model.input(0)\n",
    "N, C, H, W = input_layer_ir.shape\n",
    "shape = (H, W)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create functions for data processing\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(image):\n",
    "    \"\"\"\n",
    "    Define the preprocess function for input data\n",
    "\n",
    "    :param: image: the orignal input frame\n",
    "    :returns:\n",
    "            resized_image: the image processed\n",
    "    \"\"\"\n",
    "    resized_image = cv2.resize(image, shape)\n",
    "    resized_image = cv2.cvtColor(np.array(resized_image), cv2.COLOR_BGR2RGB)\n",
    "    resized_image = resized_image.transpose((2, 0, 1))\n",
    "    resized_image = np.expand_dims(resized_image, axis=0).astype(np.float32)\n",
    "    return resized_image\n",
    "\n",
    "\n",
    "def postprocess(result, image, fps):\n",
    "    \"\"\"\n",
    "    Define the postprocess function for output data\n",
    "\n",
    "    :param: result: the inference results\n",
    "            image: the orignal input frame\n",
    "            fps: average throughput calculated for each frame\n",
    "    :returns:\n",
    "            image: the image with bounding box and fps message\n",
    "    \"\"\"\n",
    "    detections = result.reshape(-1, 7)\n",
    "    for i, detection in enumerate(detections):\n",
    "        _, image_id, confidence, xmin, ymin, xmax, ymax = detection\n",
    "        if confidence > 0.5:\n",
    "            xmin = int(max((xmin * image.shape[1]), 10))\n",
    "            ymin = int(max((ymin * image.shape[0]), 10))\n",
    "            xmax = int(min((xmax * image.shape[1]), image.shape[1] - 10))\n",
    "            ymax = int(min((ymax * image.shape[0]), image.shape[0] - 10))\n",
    "            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)\n",
    "            cv2.putText(\n",
    "                image,\n",
    "                str(round(fps, 2)) + \" fps\",\n",
    "                (5, 20),\n",
    "                cv2.FONT_HERSHEY_SIMPLEX,\n",
    "                0.7,\n",
    "                (0, 255, 0),\n",
    "                3,\n",
    "            )\n",
    "    return image"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the test video\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_path = \"https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/CEO%20Pat%20Gelsinger%20on%20Leading%20Intel.mp4\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to improve the throughput of video processing\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Below, we compare the performance of the synchronous and async-based approaches:\n",
    "\n",
    "### Sync Mode (default)\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Let us see how video processing works with the default approach.<br />\n",
    "Using the synchronous approach, the frame is captured with OpenCV and then immediately processed:\n",
    "\n",
    "![drawing](https://user-images.githubusercontent.com/91237924/168452573-d354ea5b-7966-44e5-813d-f9053be4338a.png)\n",
    "\n",
    "```\n",
    "while(true) {\n",
    "// capture frame\n",
    "// populate CURRENT InferRequest\n",
    "// Infer CURRENT InferRequest\n",
    "//this call is synchronous\n",
    "// display CURRENT result\n",
    "}\n",
    "```\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sync_api(source, flip, fps, use_popup, skip_first_frames):\n",
    "    \"\"\"\n",
    "    Define the main function for video processing in sync mode\n",
    "\n",
    "    :param: source: the video path or the ID of your webcam\n",
    "    :returns:\n",
    "            sync_fps: the inference throughput in sync mode\n",
    "    \"\"\"\n",
    "    frame_number = 0\n",
    "    infer_request = compiled_model.create_infer_request()\n",
    "    player = None\n",
    "    try:\n",
    "        # Create a video player\n",
    "        player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)\n",
    "        # Start capturing\n",
    "        start_time = time.time()\n",
    "        player.start()\n",
    "        if use_popup:\n",
    "            title = \"Press ESC to Exit\"\n",
    "            cv2.namedWindow(title, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)\n",
    "        while True:\n",
    "            frame = player.next()\n",
    "            if frame is None:\n",
    "                print(\"Source ended\")\n",
    "                break\n",
    "            resized_frame = preprocess(frame)\n",
    "            infer_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))\n",
    "            # Start the inference request in synchronous mode\n",
    "            infer_request.infer()\n",
    "            res = infer_request.get_output_tensor(0).data\n",
    "            stop_time = time.time()\n",
    "            total_time = stop_time - start_time\n",
    "            frame_number = frame_number + 1\n",
    "            sync_fps = frame_number / total_time\n",
    "            frame = postprocess(res, frame, sync_fps)\n",
    "            # Display the results\n",
    "            if use_popup:\n",
    "                cv2.imshow(title, frame)\n",
    "                key = cv2.waitKey(1)\n",
    "                # escape = 27\n",
    "                if key == 27:\n",
    "                    break\n",
    "            else:\n",
    "                # Encode numpy array to jpg\n",
    "                _, encoded_img = cv2.imencode(\".jpg\", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])\n",
    "                # Create IPython image\n",
    "                i = display.Image(data=encoded_img)\n",
    "                # Display the image in this notebook\n",
    "                display.clear_output(wait=True)\n",
    "                display.display(i)\n",
    "    # ctrl-c\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Interrupted\")\n",
    "    # Any different error\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "    finally:\n",
    "        if use_popup:\n",
    "            cv2.destroyAllWindows()\n",
    "        if player is not None:\n",
    "            # stop capturing\n",
    "            player.stop()\n",
    "        return sync_fps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test performance in Sync Mode\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source ended\n",
      "average throuput in sync mode: 55.59 fps\n"
     ]
    }
   ],
   "source": [
    "sync_fps = sync_api(source=video_path, flip=False, fps=30, use_popup=False, skip_first_frames=800)\n",
    "print(f\"average throuput in sync mode: {sync_fps:.2f} fps\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Async Mode\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Let us see how the OpenVINO Async API can improve the overall frame rate of an application. The key advantage of the Async approach is as follows: while a device is busy with the inference, the application can do other things in parallel (for example, populating inputs or scheduling other requests) rather than wait for the current inference to complete first.\n",
    "\n",
    "![drawing](https://user-images.githubusercontent.com/91237924/168452572-c2ff1c59-d470-4b85-b1f6-b6e1dac9540e.png)\n",
    "\n",
    "In the example below, inference is applied to the results of the video decoding. So it is possible to keep multiple infer requests, and while the current request is processed, the input frame for the next is being captured. This essentially hides the latency of capturing, so that the overall frame rate is rather determined only by the slowest part of the pipeline (decoding vs inference) and not by the sum of the stages.\n",
    "\n",
    "```\n",
    "while(true) {\n",
    "// capture frame\n",
    "// populate NEXT InferRequest\n",
    "// start NEXT InferRequest\n",
    "// this call is async and returns immediately\n",
    "// wait for the CURRENT InferRequest\n",
    "// display CURRENT result\n",
    "// swap CURRENT and NEXT InferRequests\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def async_api(source, flip, fps, use_popup, skip_first_frames):\n",
    "    \"\"\"\n",
    "    Define the main function for video processing in async mode\n",
    "\n",
    "    :param: source: the video path or the ID of your webcam\n",
    "    :returns:\n",
    "            async_fps: the inference throughput in async mode\n",
    "    \"\"\"\n",
    "    frame_number = 0\n",
    "    # Create 2 infer requests\n",
    "    curr_request = compiled_model.create_infer_request()\n",
    "    next_request = compiled_model.create_infer_request()\n",
    "    player = None\n",
    "    async_fps = 0\n",
    "    try:\n",
    "        # Create a video player\n",
    "        player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)\n",
    "        # Start capturing\n",
    "        start_time = time.time()\n",
    "        player.start()\n",
    "        if use_popup:\n",
    "            title = \"Press ESC to Exit\"\n",
    "            cv2.namedWindow(title, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)\n",
    "        # Capture CURRENT frame\n",
    "        frame = player.next()\n",
    "        resized_frame = preprocess(frame)\n",
    "        curr_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))\n",
    "        # Start the CURRENT inference request\n",
    "        curr_request.start_async()\n",
    "        while True:\n",
    "            # Capture NEXT frame\n",
    "            next_frame = player.next()\n",
    "            if next_frame is None:\n",
    "                print(\"Source ended\")\n",
    "                break\n",
    "            resized_frame = preprocess(next_frame)\n",
    "            next_request.set_tensor(input_layer_ir, ov.Tensor(resized_frame))\n",
    "            # Start the NEXT inference request\n",
    "            next_request.start_async()\n",
    "            # Waiting for CURRENT inference result\n",
    "            curr_request.wait()\n",
    "            res = curr_request.get_output_tensor(0).data\n",
    "            stop_time = time.time()\n",
    "            total_time = stop_time - start_time\n",
    "            frame_number = frame_number + 1\n",
    "            async_fps = frame_number / total_time\n",
    "            frame = postprocess(res, frame, async_fps)\n",
    "            # Display the results\n",
    "            if use_popup:\n",
    "                cv2.imshow(title, frame)\n",
    "                key = cv2.waitKey(1)\n",
    "                # escape = 27\n",
    "                if key == 27:\n",
    "                    break\n",
    "            else:\n",
    "                # Encode numpy array to jpg\n",
    "                _, encoded_img = cv2.imencode(\".jpg\", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])\n",
    "                # Create IPython image\n",
    "                i = display.Image(data=encoded_img)\n",
    "                # Display the image in this notebook\n",
    "                display.clear_output(wait=True)\n",
    "                display.display(i)\n",
    "            # Swap CURRENT and NEXT frames\n",
    "            frame = next_frame\n",
    "            # Swap CURRENT and NEXT infer requests\n",
    "            curr_request, next_request = next_request, curr_request\n",
    "    # ctrl-c\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Interrupted\")\n",
    "    # Any different error\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "    finally:\n",
    "        if use_popup:\n",
    "            cv2.destroyAllWindows()\n",
    "        if player is not None:\n",
    "            # stop capturing\n",
    "            player.stop()\n",
    "        return async_fps"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the performance in Async Mode\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source ended\n",
      "average throuput in async mode: 75.17 fps\n"
     ]
    }
   ],
   "source": [
    "async_fps = async_api(source=video_path, flip=False, fps=30, use_popup=False, skip_first_frames=800)\n",
    "print(f\"average throuput in async mode: {async_fps:.2f} fps\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare the performance\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 0.4\n",
    "fontsize = 14\n",
    "\n",
    "plt.rc(\"font\", size=fontsize)\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
    "\n",
    "rects1 = ax.bar([0], sync_fps, width, color=\"#557f2d\")\n",
    "rects2 = ax.bar([width], async_fps, width)\n",
    "ax.set_ylabel(\"frames per second\")\n",
    "ax.set_xticks([0, width])\n",
    "ax.set_xticklabels([\"Sync mode\", \"Async mode\"])\n",
    "ax.set_xlabel(\"Higher is better\")\n",
    "\n",
    "fig.suptitle(\"Sync mode VS Async mode\")\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `AsyncInferQueue`\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Asynchronous mode pipelines can be supported with the [`AsyncInferQueue`](https://docs.openvino.ai/2024/openvino-workflow/running-inference/integrate-openvino-with-your-application/python-api-exclusives.html#asyncinferqueue) wrapper class. This class automatically spawns the pool of `InferRequest` objects (also called “jobs”) and provides synchronization mechanisms to control the flow of the pipeline. It is a simpler way to manage the infer request queue in Asynchronous mode."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Callback\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "When `callback` is set, any job that ends inference calls upon the Python function. The `callback` function must have two arguments: one is the request that calls the `callback`, which provides the `InferRequest` API; the other is called “user data”, which provides the possibility of passing runtime values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def callback(infer_request, info) -> None:\n",
    "    \"\"\"\n",
    "    Define the callback function for postprocessing\n",
    "\n",
    "    :param: infer_request: the infer_request object\n",
    "            info: a tuple includes original frame and starts time\n",
    "    :returns:\n",
    "            None\n",
    "    \"\"\"\n",
    "    global frame_number\n",
    "    global total_time\n",
    "    global inferqueue_fps\n",
    "    stop_time = time.time()\n",
    "    frame, start_time = info\n",
    "    total_time = stop_time - start_time\n",
    "    frame_number = frame_number + 1\n",
    "    inferqueue_fps = frame_number / total_time\n",
    "\n",
    "    res = infer_request.get_output_tensor(0).data[0]\n",
    "    frame = postprocess(res, frame, inferqueue_fps)\n",
    "    # Encode numpy array to jpg\n",
    "    _, encoded_img = cv2.imencode(\".jpg\", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])\n",
    "    # Create IPython image\n",
    "    i = display.Image(data=encoded_img)\n",
    "    # Display the image in this notebook\n",
    "    display.clear_output(wait=True)\n",
    "    display.display(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inferqueue(source, flip, fps, skip_first_frames) -> None:\n",
    "    \"\"\"\n",
    "    Define the main function for video processing with async infer queue\n",
    "\n",
    "    :param: source: the video path or the ID of your webcam\n",
    "    :retuns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    # Create infer requests queue\n",
    "    infer_queue = ov.AsyncInferQueue(compiled_model, 2)\n",
    "    infer_queue.set_callback(callback)\n",
    "    player = None\n",
    "    try:\n",
    "        # Create a video player\n",
    "        player = utils.VideoPlayer(source, flip=flip, fps=fps, skip_first_frames=skip_first_frames)\n",
    "        # Start capturing\n",
    "        start_time = time.time()\n",
    "        player.start()\n",
    "        while True:\n",
    "            # Capture frame\n",
    "            frame = player.next()\n",
    "            if frame is None:\n",
    "                print(\"Source ended\")\n",
    "                break\n",
    "            resized_frame = preprocess(frame)\n",
    "            # Start the inference request with async infer queue\n",
    "            infer_queue.start_async({input_layer_ir.any_name: resized_frame}, (frame, start_time))\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Interrupted\")\n",
    "    # Any different error\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "    finally:\n",
    "        infer_queue.wait_all()\n",
    "        player.stop()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the performance with `AsyncInferQueue`\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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     "text": [
      "average throughput in async mode with async infer queue: 103.81 fps\n"
     ]
    }
   ],
   "source": [
    "frame_number = 0\n",
    "total_time = 0\n",
    "inferqueue(source=video_path, flip=False, fps=30, skip_first_frames=800)\n",
    "print(f\"average throughput in async mode with async infer queue: {inferqueue_fps:.2f} fps\")"
   ]
  }
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