v2e_tutorial.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "v2e_tutorial.ipynb",
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PKEBUPsEBei"
      },
      "source": [
        "# v2e Tutorial\n",
        "\n",
        "This colab script is to demonstrate the use of v2e.\n",
        "\n",
        "For more technical details, please find in our paper:\n",
        "**v2e: From Video Frames to Realistic DVS Events** (https://arxiv.org/abs/2006.07722)\n",
        "\n",
        "Our YouTube Tutorial: https://www.youtube.com/watch?v=THJqRC_q2kY&t\n",
        "\n",
        "Colab provides free GPU resources, you can also use this script as a tool for converting videos to events."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Orb8FSchSLdb"
      },
      "source": [
        "![v2e-brief-steps.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UDwjPx0c_nv"
      },
      "source": [
        "## Check Colab Runtime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fo7kCiuvTHaG"
      },
      "source": [
        "**Make sure you are using GPU runtime. Otherwise, go to Runtime->Change runtime type to change.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTMpdLLbTEBL"
      },
      "source": [
        "!nvidia-smi  # check if the GPU is working properly\n",
        "%rm -rf ./sample_data  # remove the sample data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C96U3vqyEqiA"
      },
      "source": [
        "## Setup v2e"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYc08bdiFA3h"
      },
      "source": [
        "# Installing dependencies\n",
        "%pip install numba\n",
        "%pip install engineering-notation\n",
        "%pip install opencv-contrib-python\n",
        "%pip install argcomplete\n",
        "\n",
        "# other dependencies that's already preinstalled in the environment\n",
        "# you need to install these additional dependencies in your local environment\n",
        "\n",
        "# pip install torch torchvision -U  # Follow the instructions here: https://pytorch.org/get-started/locally/\n",
        "# pip install numpy\n",
        "# pip install matplotlib\n",
        "# pip install h5py\n",
        "# pip install tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ki40vG_D9iv"
      },
      "source": [
        "# Clone and install\n",
        "!git clone https://github.com/SensorsINI/v2e\n",
        "%cd /content/v2e\n",
        "#!python setup.py develop\n",
        "%pip install .\n",
        "!mkdir input\n",
        "%cd /content/\n",
        "%pwd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGNugMcsGtLx"
      },
      "source": [
        "# download super-slomo checkpoint\n",
        "# actual checkpoint is here: https://drive.google.com/file/d/17QSN207h05S_b2ndXjLrqPbBTnYIl0Vb/view?usp=sharing\n",
        "# in local environment, download and put it in the folder /path/to/v2e/input\n",
        "\n",
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=17QSN207h05S_b2ndXjLrqPbBTnYIl0Vb' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=17QSN207h05S_b2ndXjLrqPbBTnYIl0Vb\" -O /content/v2e/input/SuperSloMo39.ckpt && rm -rf /tmp/cookies.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrbohlAlGH3V"
      },
      "source": [
        "## Upload a Video"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkEz9MRmT4gY"
      },
      "source": [
        "### For the purpose of this tutorial, let's use a sample video\n",
        "\n",
        "You can later remove this section for your own use"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZj-yekATto5"
      },
      "source": [
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1kHW820MQ9SoYX8HVeMJO0uugnxp2JptO' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1kHW820MQ9SoYX8HVeMJO0uugnxp2JptO\" -O v2e_tutorial_video.avi && rm -rf /tmp/cookies.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-IYpIg1TzWl"
      },
      "source": [
        "---\n",
        "### For your own video, upload it here\n",
        "\n",
        "Click file explorer on the left, there are three buttons on the top: Upload, Refresh and Mount Drive.\n",
        "\n",
        "Click the upload button and upload.\n",
        "\n",
        "You can upload a video file or a zip file that contains a folder of images.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LfnoSa6IXSZF"
      },
      "source": [
        "### If you have data on your Google Drive (Recommend)\n",
        "\n",
        "Click file explorer on the left, there are three buttons on the top: Upload, Refresh and Mount Drive.\n",
        "\n",
        "Click \"Mount Drive\", Colab will ask for your permission to mount.\n",
        "Once you agreed, the drive will be mounted at `/content/drive`\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Z-tLaJGl74P"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkDNQvjOZ0ap"
      },
      "source": [
        "### Select the Video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sad8e8R4Xaab"
      },
      "source": [
        "#@title In the file explorer, right click the video file, and then click \"Copy Path\". Then, paste the path here: { run: \"auto\", vertical-output: true }\n",
        "video_path = \"/content/v2e_tutorial_video.avi\" #@param {type:\"string\"}\n",
        "\n",
        "import os\n",
        "if video_path != \"\" and os.path.isfile(video_path):\n",
        "    display(\"The chosen video file: {}\".format(video_path))\n",
        "else:\n",
        "    print(\"The file path is empty or invalid, choose a file\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH0s04kmbkpp"
      },
      "source": [
        "# play the sample video, you can ignore it, support both AVI and MP4\n",
        "# you can ignore this if you don't need\n",
        "if \".avi\" in video_path:\n",
        "    !ffmpeg -y -i $video_path /tmp/output.mp4 -loglevel quiet\n",
        "else:\n",
        "    !cp $video_path /tmp/output.mp4\n",
        "\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "mp4 = open(\"/tmp/output.mp4\",'rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"<video controls src={} width=50%/>\".format(data_url))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnuyEojJf1d5"
      },
      "source": [
        "# If the video is a zip file, decompress it and remove the file\n",
        "# otherwise, you can ignore this\n",
        "\n",
        "if \".zip\" in video_path:\n",
        "    # decompress the file\n",
        "    !unzip $video_path\n",
        "    %rm $video_path\n",
        "\n",
        "# select the video path again here\n",
        "#@title In the file explorer, right click the video file, and then click \"Copy Path\". Then, paste the path here: { run: \"auto\", vertical-output: true }\n",
        "video_path = \"\" #@param {type:\"string\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpuzzqhAHQkR"
      },
      "source": [
        "## Convert the Video to Events!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtWMzbVpHh8U"
      },
      "source": [
        "#@title **Configure V2E Options** { run: \"auto\", vertical-output: true, display-mode: \"both\" }\n",
        "#@markdown The full documentation is here: https://github.com/SensorsINI/v2e#render-emulated-dvs-events-from-conventional-video\n",
        "\n",
        "#@markdown This GUI supports only common use configs, for finer control, please check the v2e documentation and edit the code below.\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown ## Output Options\n",
        "\n",
        "output_folder = \"/content/v2e-output\" #@param {type:\"string\"}\n",
        "#@markdown - folder to store outputs. (default: v2e-output)\n",
        "overwrite = True #@param {type:\"boolean\"}\n",
        "#@markdown - overwrites files in existing folder (checks existence of non-empty output_folder). (default: True)\n",
        "unique_output_folder = False #@param {type:\"boolean\"}\n",
        "#@markdown - makes unique output folder based on output_folder, e.g. output1 (if non-empty output_folder already exists) (default: False)\n",
        "out_filename = \"events.h5\" #@param {type:\"string\"}\n",
        "#@markdown - Output DVS events as hdf5 event database.\n",
        "davis_output = True #@param {type:\"boolean\"}\n",
        "#@markdown - If also save frames in HDF5. (default: False)\n",
        "\n",
        "#@markdown ### Output DVS Video Options\n",
        "skip_video_output = False #@param {type:\"boolean\"}\n",
        "#@markdown - Skip producing video outputs, including the original video, SloMo video, and DVS video (default: False)\n",
        "dvs_exposure = \"duration .033\" #@param {type:\"string\"}\n",
        "#@markdown - Mode to finish DVS frame event integration: duration time: Use fixed accumulation time in seconds, e.g. --dvs_exposure duration .005; count n: Count n events per frame, -dvs_exposure count 5000; area_event N M: frame ends when any area of M x M pixels fills with N events, -dvs_exposure area_count 500 64 (default: duration 0.01)\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ## Input Options\n",
        "input_frame_rate = 30 #@param {type:\"number\"}\n",
        "#@markdown - Manually define the video frame rate when the video is presented as a list of image files. When the input video is a video file, this option will be ignored.\n",
        "input_slowmotion_factor =  1#@param {type:\"number\"}\n",
        "#@markdown - Sets the known slow-motion factor of the input video, i.e. how much the video is slowed down, i.e., the ratio of shooting frame rate to playback frame rate. input_slowmotion_factor<1 for sped-up video and input_slowmotion_factor>1 for slowmotion video.If an input video is shot at 120fps yet is presented as a 30fps video (has specified playback frame rate of 30Hz, according to file's FPS setting), then set --input_slowdown_factor=4.It means that each input frame represents (1/30)/4 s=(1/120)s.If input is video with intended frame intervals of 1ms that is in AVI file with default 30 FPS playback spec, then use ((1/30)s)*(1000Hz)=33.33333. (default: 1.0)\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ## DVS Time Resolution Options\n",
        "disable_slomo = True #@param {type:\"boolean\"}\n",
        "#@markdown - Disables slomo interpolation; the output DVS events will have exactly the timestamp resolution of the source video (which is perhaps modified by --input_slowmotion_factor). (default: False)\n",
        "timestamp_resolution = 0.001 #@param {type:\"number\"}\n",
        "#@markdown - (Ignored by --disable_slomo.) Desired DVS timestamp resolution in seconds; determines slow motion upsampling factor; the video will be upsampled from source fps to achieve the at least this timestamp resolution.I.e. slowdown_factor = (1/fps)/timestamp_resolution; using a high resolution e.g. of 1ms will result in slow rendering since it will force high upsampling ratio. Can be combind with --auto_timestamp_resolution to limit upsampling to a maximum limit value. (default: None)\n",
        "auto_timestamp_resolution = True #@param {type:\"boolean\"}\n",
        "#@markdown - (Ignored by --disable_slomo.) If True (default), upsampling_factor is automatically determined to limit maximum movement between frames to 1 pixel. If False, --timestamp_resolution sets the upsampling factor for input video. Can be combined with --timestamp_resolution to ensure DVS events have at most some resolution. (default: False)\n",
        "\n",
        "# This is the SloMo model path\n",
        "slomo_model = \"/content/v2e/input/SuperSloMo39.ckpt\"\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "#@markdown ## DVS Model Options\n",
        "condition = \"Noisy\" #@param [\"Custom\", \"Clean\", \"Noisy\"]\n",
        "#@markdown - Custom: Use following slidebar to adjust your DVS model.\n",
        "#@markdown - Clean: a preset DVS model, generate clean events, without non-idealities.\n",
        "#@markdown - Noisy: a preset DVS model, generate noisy events.\n",
        "\n",
        "thres = 0.2 #@param {type:\"slider\", min:0.05, max:1, step:0.01}\n",
        "#@markdown - threshold in log_e intensity change to trigger a positive/negative event. (default: 0.2) \n",
        "sigma = 0.03 #@param {type:\"slider\", min:0.01, max:0.25, step:0.001}\n",
        "#@markdown - 1-std deviation threshold variation in log_e intensity change. (default: 0.03)\n",
        "cutoff_hz = 200 #@param {type:\"slider\", min:0, max:300, step:1}\n",
        "#@markdown - photoreceptor first-order IIR lowpass filter cutoff-off 3dB frequency in Hz - see https://ieeexplore.ieee.org/document/4444573 (default: 300)\n",
        "leak_rate_hz = 5.18 #@param {type:\"slider\", min:0, max:100, step:0.01}\n",
        "#@markdown - leak event rate per pixel in Hz - see https://ieeexplore.ieee.org/abstract/document/7962235 (default: 0.01)\n",
        "shot_noise_rate_hz = 2.716 #@param {type:\"slider\", min:0, max:100, step:0.001}\n",
        "#@markdown - Temporal noise rate of ON+OFF events in darkest parts of scene; reduced in brightest parts. (default: 0.001)\n",
        "\n",
        "if condition == \"Clean\":\n",
        "    thres = 0.2\n",
        "    sigma = 0.02\n",
        "    cutoff_hz = 0\n",
        "    leak_rate_hz = 0\n",
        "    shot_noise_rate_hz = 0\n",
        "elif condition == \"Noisy\":\n",
        "    thres = 0.2\n",
        "    sigma_thres = 0.05\n",
        "    cutoff_hz = 30\n",
        "    leak_rate_hz = 0.1\n",
        "    shot_noise_rate_hz = 5\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "v2e_command = [\"v2e\"]\n",
        "\n",
        "# set the input folder\n",
        "# the video_path can be a video file or a folder of images\n",
        "v2e_command += [\"-i\", video_path]\n",
        "\n",
        "# set the output folder\n",
        "v2e_command += [\"-o\", output_folder]\n",
        "\n",
        "# if the output will rewrite the previous output\n",
        "if overwrite:\n",
        "    v2e_command.append(\"--overwrite\")\n",
        "\n",
        "# if there the output folder is unique\n",
        "v2e_command += [\"--unique_output_folder\", \"{}\".format(unique_output_folder).lower()]\n",
        "\n",
        "# set output configs, for the sake of this tutorial, let's just output HDF5 record\n",
        "if davis_output:\n",
        "    v2e_command += [\"--davis_output\"]\n",
        "v2e_command += [\"--dvs_h5\", out_filename]\n",
        "v2e_command += [\"--dvs_aedat2\", \"None\"]\n",
        "v2e_command += [\"--dvs_text\", \"None\"]\n",
        "\n",
        "# in Colab, let's say no preview\n",
        "v2e_command += [\"--no_preview\"]\n",
        "\n",
        "# if skip video output\n",
        "if skip_video_output:\n",
        "    v2e_command += [\"--skip_video_output\"]\n",
        "else:\n",
        "    # set DVS video rendering params\n",
        "    v2e_command += [\"--dvs_exposure\", dvs_exposure]\n",
        "\n",
        "# set slomo related options\n",
        "v2e_command += [\"--input_frame_rate\", \"{}\".format(input_frame_rate)]\n",
        "v2e_command += [\"--input_slowmotion_factor\", \"{}\".format(input_slowmotion_factor)]\n",
        "\n",
        "# set slomo data\n",
        "if disable_slomo:\n",
        "    v2e_command += [\"--disable_slomo\"]\n",
        "    v2e_command += [\"--auto_timestamp_resolution\", \"false\"]\n",
        "else:\n",
        "    v2e_command += [\"--slomo_model\", slomo_model]\n",
        "    if auto_timestamp_resolution:\n",
        "        v2e_command += [\"--auto_timestamp_resolution\", \"{}\".format(auto_timestamp_resolution).lower()]\n",
        "    else:\n",
        "        v2e_command += [\"--timestamp_resolution\", \"{}\".format(timestamp_resolution)]\n",
        "\n",
        "# threshold\n",
        "v2e_command += [\"--pos_thres\", \"{}\".format(thres)]\n",
        "v2e_command += [\"--neg_thres\", \"{}\".format(thres)]\n",
        "\n",
        "# sigma\n",
        "v2e_command += [\"--sigma_thres\", \"{}\".format(sigma)]\n",
        "\n",
        "# DVS non-idealities\n",
        "v2e_command += [\"--cutoff_hz\", \"{}\".format(cutoff_hz)]\n",
        "v2e_command += [\"--leak_rate_hz\", \"{}\".format(leak_rate_hz)]\n",
        "v2e_command += [\"--shot_noise_rate_hz\", \"{}\".format(shot_noise_rate_hz)]\n",
        "\n",
        "# Final v2e command\n",
        "\n",
        "final_v2e_command = \" \".join(v2e_command)\n",
        "\n",
        "print(\"The Final v2e command:\")\n",
        "display(final_v2e_command)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xWKgO8AHiPt"
      },
      "source": [
        "# Run command!\n",
        "!$final_v2e_command"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GV4-HyZhKgAf"
      },
      "source": [
        "## Quick Visualization and Output Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdQSUcWbKk0P"
      },
      "source": [
        "# Output file path\n",
        "import os\n",
        "import h5py\n",
        "out_filepath = os.path.join(output_folder, out_filename)\n",
        "out_dataset = h5py.File(out_filepath, \"r\")\n",
        "\n",
        "file_stats = os.stat(out_filepath)\n",
        "\n",
        "# Summarize file\n",
        "\n",
        "events = out_dataset[\"events\"]\n",
        "\n",
        "num_events = events.shape[0]\n",
        "duration = (events[-1, 0]-events[0, 0])/1e6  # in seconds\n",
        "event_rate = num_events/duration/1000\n",
        "\n",
        "display(\"File Size (MB): {:.2f}\".format(file_stats.st_size / (1024*1024)))\n",
        "display(\"Number of Events: {}\".format(events.shape[0]))\n",
        "display(\"Recording Duration: {:.2f}s\".format(duration))\n",
        "display(\"Event Rate: {:.2f} KEVs/s\".format(event_rate))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRq7TprOhbXB"
      },
      "source": [
        "# SloMo Video\n",
        "slomo_vid_path = os.path.join(output_folder, \"video_slomo.avi\")\n",
        "!ffmpeg -y -i $slomo_vid_path /tmp/output.mp4 -loglevel quiet\n",
        "\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "mp4 = open(\"/tmp/output.mp4\",'rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"<video controls src={} width=50%/>\".format(data_url))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28paqWVLhe0p"
      },
      "source": [
        "# DVS Video\n",
        "dvs_vid_path = os.path.join(output_folder, \"dvs-video.avi\")\n",
        "!ffmpeg -y -i $dvs_vid_path /tmp/output.mp4 -loglevel quiet\n",
        "\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "mp4 = open(\"/tmp/output.mp4\",'rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"<video controls src={} width=50%/>\".format(data_url))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByQWZ3KGKYxk"
      },
      "source": [
        "## Download the Output File\n",
        "\n",
        "Use the file explorer on the left to download the data.\n",
        "\n",
        "Because Colab notebook itself support a slow download. I hence recommend that you can save your output in a Google Drive folder."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nPVVg9qGUXXg"
      },
      "source": [
        "## Beyond Single File Conversion\n",
        "\n",
        "This tutorial setup is for a single file conversion.\n",
        "For multiple files, the ideal way is to use `v2e.py` script as subprocesses like we showed here.\n",
        "\n",
        "For a simple demonstration, please checkout the following example for converting\n",
        "the Caltech-101 dataset.\n",
        "\n",
        "Link: https://gist.github.com/duguyue100/f60bdc1eb0e5b51586ca3594d9d72cb7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-UbfKoIFT8vE"
      },
      "source": [
        "# Use v2e Event Emulator in 3rd-party program\n",
        "\n",
        "You might want to use the EventEmulator in a third party application.\n",
        "\n",
        "A typical scenario will be that a simulator output frames and the the emulator will convert the frames into events.\n",
        "\n",
        "In this section, we provide an example of using the emulator to convert a video into events.\n",
        "\n",
        "**Side Note**: The performance bottleneck of the emulator is at writing events into the disk. There are two ways to look at this problem:\n",
        "\n",
        "1. When collecting training data, it's probably fine writing all the data into disk.\n",
        "\n",
        "2. Use library such as `zeromq` to build a multi-process application so that one process runs the emulator and another application writes the events into the disk."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCaLGVMsUf3g"
      },
      "source": [
        "# define a emulator\n",
        "from v2ecore.emulator import EventEmulator\n",
        "\n",
        "emulator = EventEmulator(\n",
        "    pos_thres=0.2,\n",
        "    neg_thres=0.2,\n",
        "    sigma_thres=0.03,\n",
        "    cutoff_hz=200,\n",
        "    leak_rate_hz=1,\n",
        "    shot_noise_rate_hz=10,\n",
        "    device=\"cuda\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqZrxs58Wofv"
      },
      "source": [
        "import torch\n",
        "# **IMPORTANT** make torch static, likely get faster emulation\n",
        "# might also cause memory issue\n",
        "torch.set_grad_enabled(False)\n",
        "# read a video from opencv\n",
        "import cv2\n",
        "\n",
        "cap = cv2.VideoCapture(video_path)\n",
        "\n",
        "# num of frames\n",
        "fps = cap.get(cv2.CAP_PROP_FPS)\n",
        "print(\"FPS: {}\".format(fps))\n",
        "num_of_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "print(\"Num of frames: {}\".format(num_of_frames))\n",
        "\n",
        "duration = num_of_frames/fps\n",
        "delta_t = 1/fps\n",
        "current_time = 0.\n",
        "\n",
        "print(\"Clip Duration: {}s\".format(duration))\n",
        "print(\"Delta Frame Tiem: {}s\".format(delta_t))\n",
        "print(\"=\"*50)\n",
        "\n",
        "new_events = None\n",
        "\n",
        "idx = 0\n",
        "# Only Emulate the first 10 frame\n",
        "while(cap.isOpened()):\n",
        "    # Capture frame-by-frame\n",
        "    ret, frame = cap.read()\n",
        "    if ret is True and idx < 10:\n",
        "        # convert it to Luma frame\n",
        "        luma_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
        "        print(\"=\"*50)\n",
        "        print(\"Current Frame {} Time {}s\".format(idx, current_time))\n",
        "        print(\"-\"*50)\n",
        "\n",
        "        # emulate events\n",
        "        # **IMPORTANT** The unit of timestamp here is in second, a floating number\n",
        "        new_events = emulator.generate_events(luma_frame, current_time)\n",
        "\n",
        "        ### The output events is in a numpy array that has a data type of np.int32\n",
        "        ### THe shape is (N, 4), each row is one event in the format of (t, x, y, p)\n",
        "        ### The unit of timestamp here is in microseconds\n",
        "\n",
        "        # update time\n",
        "        current_time += delta_t\n",
        "\n",
        "        # print event stats\n",
        "        if new_events is not None:\n",
        "            num_events = new_events.shape[0]\n",
        "            start_t = new_events[0, 0]\n",
        "            end_t = new_events[-1, 0]\n",
        "            event_time = (new_events[-1, 0]-new_events[0, 0])\n",
        "            event_rate_kevs = (num_events/delta_t)/1e3\n",
        "\n",
        "            print(\"Number of Events: {}\\n\"\n",
        "                  \"Duration: {}s\\n\"\n",
        "                  \"Start T: {:.5f}s\\n\"\n",
        "                  \"End T: {:.5f}s\\n\"\n",
        "                  \"Event Rate: {:.2f}KEV/s\".format(\n",
        "                      num_events, event_time, start_t, end_t,\n",
        "                      event_rate_kevs))\n",
        "        idx += 1\n",
        "        print(\"=\"*50)\n",
        "    else:\n",
        "        break\n",
        "\n",
        "cap.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFwW4FY4u8cX"
      },
      "source": [
        "## Conversion for the first frame is slow\n",
        "\n",
        "When emulator processes the first frame, the emulator also need to initialize itself by generating many variables and random numbers.\n",
        "Therefore, it's much much slower to process the first frame.\n",
        "\n",
        "I suggest that this can be dealt with as a separate case in your code when needed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxTEhJCeuU2S"
      },
      "source": [
        "# Reset a emulator\n",
        "emulator.reset()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8Uf60nmpNoM"
      },
      "source": [
        "## What a ROS module could look like?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWYMcWwWpMpB"
      },
      "source": [
        "# NOT RUNNABLE, just for demonstration\n",
        "\n",
        "# define a emulator\n",
        "from v2ecore.emulator import EventEmulator\n",
        "import rospy\n",
        "\n",
        "class EventGenerator:\n",
        "    def __init__(self):\n",
        "        self.emulator = EventEmulator(\n",
        "            pos_thres=0.2,\n",
        "            neg_thres=0.2,\n",
        "            sigma_thres=0.03,\n",
        "            cutoff_hz=200,\n",
        "            leak_rate_hz=1,\n",
        "            shot_noise_rate_hz=10,\n",
        "            device=\"cuda\")\n",
        "        \n",
        "        self.events = None\n",
        "        \n",
        "        # init a node\n",
        "        rospy.init_node(\"event_generator\", anonymous=True)\n",
        "\n",
        "        # define a event publisher\n",
        "        self.event_publisher = rospy.Publisher(\n",
        "            '/dvs_events', events_message, queue_size=10)\n",
        "\n",
        "        # subscribe to an RGB camera\n",
        "        self.camera = rospy.Subscriber(\n",
        "            '/rgb_topic', Image, self.callback, queue_size=10,\n",
        "            buff_size=262144)\n",
        "        \n",
        "        rospy.Timer(rospy.Duration(0.01), self.send_events)\n",
        "\n",
        "    def callback(self, camera_info):\n",
        "        # fetch data\n",
        "        self.image = get_image()\n",
        "        self.t = get_time()\n",
        "\n",
        "        # generate events\n",
        "        self.events = self.emulator.generate_events(self.image, self.t)\n",
        "\n",
        "    def send_events(self):\n",
        "        if self.events is not None:\n",
        "            # someone need to write this message\n",
        "            msg = EventMSG()\n",
        "\n",
        "            msg.events = self.events \n",
        "            self.event_publisher.publish(msg)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    event_generator = EventGenerator()\n",
        "    rospy.spin()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aygRkwQxucvc"
      },
      "source": [
        "## Final note, Color Events\n",
        "\n",
        "1. A luma frame cannot represent all colors in a RGB frame.\n",
        "2. If you want to get color events, one for each R, G, and B channels, use three emulators for different channels.\n",
        "3. Your emulation speed might be slowed down at least 3X."
      ]
    }
  ]
}