SSDLite_(plate)_and_LPRNet_(ocr)_2_EdgeTPU_ANPR_ALPR.ipynb

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  "metadata": {
    "colab": {
      "name": "SSDLite (plate) and LPRNet (ocr) 2 EdgeTPU - ANPR ALPR.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPIB8JSIo2gKRE8AD3a0pNn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardodeazambuja/ALPR-EdgeTPU-RPI0/blob/main/SSDLite_(plate)_and_LPRNet_(ocr)_2_EdgeTPU_ANPR_ALPR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdeqGMBKOQYT"
      },
      "source": [
        "# Automatic License Plate Recognition model that runs on the Raspberry Pi Zero with an EdgeTPU (Coral USB)\n",
        "\n",
        "Original models from: https://github.com/GreenWaves-Technologies/licence_plate_recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3z74rs4E_niU"
      },
      "source": [
        "The EdgeTPU compiler had a problem with the LPRNet:\n",
        "\n",
        "```!wget https://github.com/ricardodeazambuja/licence_plate_recognition/raw/master/model/lprnet.tflite```\n",
        "\n",
        "By recreating this model using Keras, I noticed the problem occurred when a CONV2D was connected to a dense layer. After splitting the model into two, the compiler didn't have the memory swelling problem.\n",
        "\n",
        "Splitting the model into two worked and also using the ```-a``` option together with ```-i```:\n",
        "```!edgetpu_compiler -i \"Conv_14/Relu,Squeeze\":\"TFLite_Detection_PostProcess\" -s -a lprnet.tflite ssdlite_ocr.tflite```\n",
        "\n",
        "However, the best solution so far was to convert the first dense layer into a conv layer (below)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2yxfLEeIDyJx"
      },
      "source": [
        "### Download the TFLite models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgESKfoxeT1q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9be83fd-614f-469a-8d40-c3dd20c93617"
      },
      "source": [
        "!wget https://github.com/ricardodeazambuja/licence_plate_recognition/raw/master/model/ssdlite_ocr.tflite -O ssdlite_ocr.tflite\n",
        "!wget https://github.com/ricardodeazambuja/licence_plate_recognition/raw/master/model/lprnet.tflite -O lprnet.tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-31 18:18:44--  https://github.com/ricardodeazambuja/licence_plate_recognition/raw/master/model/ssdlite_ocr.tflite\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/ricardodeazambuja/licence_plate_recognition/master/model/ssdlite_ocr.tflite [following]\n",
            "--2021-05-31 18:18:44--  https://raw.githubusercontent.com/ricardodeazambuja/licence_plate_recognition/master/model/ssdlite_ocr.tflite\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3150480 (3.0M) [application/octet-stream]\n",
            "Saving to: ‘ssdlite_ocr.tflite’\n",
            "\n",
            "ssdlite_ocr.tflite  100%[===================>]   3.00M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2021-05-31 18:18:45 (24.5 MB/s) - ‘ssdlite_ocr.tflite’ saved [3150480/3150480]\n",
            "\n",
            "--2021-05-31 18:18:45--  https://github.com/ricardodeazambuja/licence_plate_recognition/raw/master/model/lprnet.tflite\n",
            "Resolving github.com (github.com)... 140.82.113.4\n",
            "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/ricardodeazambuja/licence_plate_recognition/master/model/lprnet.tflite [following]\n",
            "--2021-05-31 18:18:45--  https://raw.githubusercontent.com/ricardodeazambuja/licence_plate_recognition/master/model/lprnet.tflite\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1471656 (1.4M) [application/octet-stream]\n",
            "Saving to: ‘lprnet.tflite’\n",
            "\n",
            "lprnet.tflite       100%[===================>]   1.40M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2021-05-31 18:18:46 (18.8 MB/s) - ‘lprnet.tflite’ saved [1471656/1471656]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dFirGOQZk-p"
      },
      "source": [
        "## Install EdgeTPU Compiler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1sbe_f_hXRP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb446b6a-9480-47b5-a9fe-6c5d0c320ec9"
      },
      "source": [
        "# Install the edgetpu compiler.\n",
        "!curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -\n",
        "!echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list\n",
        "!sudo apt-get update\n",
        "!sudo apt-get install edgetpu-compiler"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  2537  100  2537    0     0  97576      0 --:--:-- --:--:-- --:--:-- 97576\n",
            "OK\n",
            "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\n",
            "Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Get:2 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease [6,722 B]\n",
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            "Ign:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
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            "Get:11 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release.gpg [836 B]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
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            "Hit:15 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
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            "Get:17 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,413 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:19 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:21 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:7 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 Packages [2,174 B]\n",
            "Ign:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages\n",
            "Get:22 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Packages [798 kB]\n",
            "Get:23 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,770 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,584 kB]\n",
            "Get:25 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,184 kB]\n",
            "Get:26 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [452 kB]\n",
            "Get:27 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [906 kB]\n",
            "Get:28 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [41.5 kB]\n",
            "Fetched 13.1 MB in 3s (4,326 kB/s)\n",
            "Reading package lists... Done\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  edgetpu-compiler\n",
            "0 upgraded, 1 newly installed, 0 to remove and 87 not upgraded.\n",
            "Need to get 7,865 kB of archives.\n",
            "After this operation, 31.2 MB of additional disk space will be used.\n",
            "Get:1 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 edgetpu-compiler amd64 15.0 [7,865 kB]\n",
            "Fetched 7,865 kB in 0s (16.1 MB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package edgetpu-compiler.\n",
            "(Reading database ... 160706 files and directories currently installed.)\n",
            "Preparing to unpack .../edgetpu-compiler_15.0_amd64.deb ...\n",
            "Unpacking edgetpu-compiler (15.0) ...\n",
            "Setting up edgetpu-compiler (15.0) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mchTiceyk02p"
      },
      "source": [
        "## Flatbuffer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HLaEbL1n8Gm"
      },
      "source": [
        "Install / compile flatc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHLmT9k1Efu0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2a8aac2-21b5-4034-fc77-23860850ecb3"
      },
      "source": [
        "# %%bash\n",
        "# rm -rf /content/flatbuffers\n",
        "# git clone https://github.com/ricardodeazambuja/flatbuffers.git\n",
        "# cd flatbuffers\n",
        "# mkdir build \n",
        "# cd build \n",
        "# cmake -DFLATBUFFERS_STATIC_FLATC=ON -DFLATBUFFERS_BUILD_FLATLIB=OFF .. \n",
        "# make -j4 \n",
        "# sudo make install \n",
        "# flatc --version\n",
        "\n",
        "# !sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test \n",
        "# !sudo apt-get update\n",
        "# !sudo apt-get install --only-upgrade libstdc++6\n",
        "# !sudo add-apt-repository -y --remove ppa:ubuntu-toolchain-r/test \n",
        "# !sudo apt-get update\n",
        "# !strings /usr/lib/x86_64-linux-gnu/libstdc++.so.6 | grep GLIBCXX\n",
        "\n",
        "# I compiled it as static file (no dependencies!)\n",
        "!wget https://github.com/ricardodeazambuja/flatbuffers/releases/download/v2.0.1a/flatc.zip\n",
        "!unzip flatc.zip\n",
        "!rm flatc.zip\n",
        "!chmod +x flatc\n",
        "!sudo mv flatc /usr/local/bin/\n",
        "!flatc --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-31 18:20:06--  https://github.com/ricardodeazambuja/flatbuffers/releases/download/v2.0.1a/flatc.zip\n",
            "Resolving github.com (github.com)... 140.82.112.4\n",
            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/370410221/3147c280-c082-11eb-97c3-bb160a4cacc6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210531%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210531T182006Z&X-Amz-Expires=300&X-Amz-Signature=473f2f57d292ecd309877628872af6cd418e81f5e709f503162649a700d0d005&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=370410221&response-content-disposition=attachment%3B%20filename%3Dflatc.zip&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-05-31 18:20:06--  https://github-releases.githubusercontent.com/370410221/3147c280-c082-11eb-97c3-bb160a4cacc6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210531%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210531T182006Z&X-Amz-Expires=300&X-Amz-Signature=473f2f57d292ecd309877628872af6cd418e81f5e709f503162649a700d0d005&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=370410221&response-content-disposition=attachment%3B%20filename%3Dflatc.zip&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
            "Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2269345 (2.2M) [application/octet-stream]\n",
            "Saving to: ‘flatc.zip’\n",
            "\n",
            "flatc.zip           100%[===================>]   2.16M  13.6MB/s    in 0.2s    \n",
            "\n",
            "2021-05-31 18:20:06 (13.6 MB/s) - ‘flatc.zip’ saved [2269345/2269345]\n",
            "\n",
            "Archive:  flatc.zip\n",
            "  inflating: flatc                   \n",
            "flatc version 2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3ctmHl7iwDs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b9a0110-8a41-4f1b-93c5-9229b899845e"
      },
      "source": [
        "# Download the schema for tflite\n",
        "!wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/schema/schema.fbs -O schema.fbs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-31 18:20:07--  https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/schema/schema.fbs\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 30137 (29K) [text/plain]\n",
            "Saving to: ‘schema.fbs’\n",
            "\n",
            "schema.fbs          100%[===================>]  29.43K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2021-05-31 18:20:07 (11.9 MB/s) - ‘schema.fbs’ saved [30137/30137]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_Pbfhhzi2li",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "011a163c-59da-4b93-a6b3-1f00d8ee2c04"
      },
      "source": [
        "# Convert tflite to json\n",
        "%env schema=schema.fbs\n",
        "# If schema version is smaller than v3, it needs to have --raw-binary\n",
        "%env raw=\n",
        "!flatc -t $raw --strict-json --defaults-json $schema -- lprnet.tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "env: schema=schema.fbs\n",
            "env: raw=\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_iUAJKzB9q1"
      },
      "source": [
        "## Convert first dense layer of LPRNet.tflite into convolutional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ma5lFQx5QDKO"
      },
      "source": [
        "Replace the dense1 with conv2d. The same weights can be used!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOTpqJ6iPWvM"
      },
      "source": [
        "from copy import deepcopy\n",
        "import json\n",
        "import numpy as np\n",
        "\n",
        "with open(\"lprnet.json\") as f:\n",
        "  model_json2 = json.load(f)\n",
        "\n",
        "with open(\"lprnet.json\") as f:\n",
        "  model_json = json.load(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfzluIEcPdNY"
      },
      "source": [
        "subgraphs = model_json2['subgraphs'][0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u89REdOAPloj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49dbd87a-e0c0-46f1-abdd-f5c3ebfcdfd2"
      },
      "source": [
        "subgraphs['operators'][19]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'builtin_options': {'asymmetric_quantize_inputs': False,\n",
              "  'fused_activation_function': 'NONE',\n",
              "  'keep_num_dims': False,\n",
              "  'weights_format': 'DEFAULT'},\n",
              " 'builtin_options_type': 'FullyConnectedOptions',\n",
              " 'custom_options_format': 'FLEXBUFFERS',\n",
              " 'inputs': [57, 62, 60],\n",
              " 'mutating_variable_inputs': [],\n",
              " 'opcode_index': 2,\n",
              " 'outputs': [59]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6HIOKX0RLUU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "354e8442-432c-45f2-b0ce-1ecd327e12c1"
      },
      "source": [
        "model_json['subgraphs'][0]['operators'][17]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'builtin_options': {'dilation_h_factor': 1,\n",
              "  'dilation_w_factor': 1,\n",
              "  'fused_activation_function': 'RELU',\n",
              "  'padding': 'SAME',\n",
              "  'stride_h': 1,\n",
              "  'stride_w': 1},\n",
              " 'builtin_options_type': 'Conv2DOptions',\n",
              " 'custom_options_format': 'FLEXBUFFERS',\n",
              " 'inputs': [16, 20, 18],\n",
              " 'mutating_variable_inputs': [],\n",
              " 'opcode_index': 1,\n",
              " 'outputs': [19]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-ylmlzuQGF3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f75e739f-3fac-4bbc-bbb5-fa6b12313e03"
      },
      "source": [
        "model_json['subgraphs'][0]['operators'][18]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'builtin_options': {'asymmetric_quantize_inputs': False,\n",
              "  'fused_activation_function': 'RELU',\n",
              "  'keep_num_dims': False,\n",
              "  'weights_format': 'DEFAULT'},\n",
              " 'builtin_options_type': 'FullyConnectedOptions',\n",
              " 'custom_options_format': 'FLEXBUFFERS',\n",
              " 'inputs': [19, 58, 56],\n",
              " 'mutating_variable_inputs': [],\n",
              " 'opcode_index': 2,\n",
              " 'outputs': [57]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0FvWlsrQ0Vx"
      },
      "source": [
        "model_json['subgraphs'][0]['operators'][18] = deepcopy(subgraphs['operators'][17]) # reuse the other conv layer\n",
        "model_json['subgraphs'][0]['operators'][18]['builtin_options']['stride_w'] = 88\n",
        "model_json['subgraphs'][0]['operators'][18]['inputs'] = [19,58,56]\n",
        "model_json['subgraphs'][0]['operators'][18]['outputs'] = [57]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9w9SaWL1RRIY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "864d7e3d-a86e-4899-c47b-5987c401d394"
      },
      "source": [
        "model_json['subgraphs'][0]['operators'][18]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'builtin_options': {'dilation_h_factor': 1,\n",
              "  'dilation_w_factor': 1,\n",
              "  'fused_activation_function': 'RELU',\n",
              "  'padding': 'SAME',\n",
              "  'stride_h': 1,\n",
              "  'stride_w': 88},\n",
              " 'builtin_options_type': 'Conv2DOptions',\n",
              " 'custom_options_format': 'FLEXBUFFERS',\n",
              " 'inputs': [19, 58, 56],\n",
              " 'mutating_variable_inputs': [],\n",
              " 'opcode_index': 1,\n",
              " 'outputs': [57]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9qxW76bTYPg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a30e652a-1eff-4524-e044-607f3e7b3cae"
      },
      "source": [
        "model_json['subgraphs'][0]['tensors'][58]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'buffer': 6,\n",
              " 'is_variable': False,\n",
              " 'name': 'fully_connected/weights_quant/FakeQuantWithMinMaxVars/transpose',\n",
              " 'quantization': {'details_type': 'NONE',\n",
              "  'max': [0.049147401005],\n",
              "  'min': [-0.049147401005],\n",
              "  'quantized_dimension': 0,\n",
              "  'scale': [0.000386987405],\n",
              "  'zero_point': [128]},\n",
              " 'shape': [64, 6248],\n",
              " 'type': 'UINT8'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8-PYgT6Rcit"
      },
      "source": [
        "model_json['subgraphs'][0]['tensors'][58]['name'] = 'Conv_15/Conv2D/ReadVariableOp'\n",
        "model_json['subgraphs'][0]['tensors'][58]['shape'] = [64,1,88,71]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FowSK_OESzk-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "964245f9-7092-48f9-fa70-8fa126df1626"
      },
      "source": [
        "model_json['subgraphs'][0]['tensors'][58] # only the weights need to change shape\n",
        "                                          # while the biases don't change"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'buffer': 6,\n",
              " 'is_variable': False,\n",
              " 'name': 'Conv_15/Conv2D/ReadVariableOp',\n",
              " 'quantization': {'details_type': 'NONE',\n",
              "  'max': [0.049147401005],\n",
              "  'min': [-0.049147401005],\n",
              "  'quantized_dimension': 0,\n",
              "  'scale': [0.000386987405],\n",
              "  'zero_point': [128]},\n",
              " 'shape': [64, 1, 88, 71],\n",
              " 'type': 'UINT8'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Bu_fwSWSWh3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8540ca5b-9656-493c-cc65-7af11b2f4f00"
      },
      "source": [
        "# Save the modified version to json\n",
        "with open('model_dense2conv_mod.json', 'w') as f:\n",
        "    json.dump(model_json, f)\n",
        "\n",
        "# Convert from json to tflite (flatbuffer compiler)\n",
        "!flatc -b --strict-json --defaults-json schema.fbs model_dense2conv_mod.json\n",
        "\n",
        "# Compile for the edgetpu both models together to share the cache\n",
        "!edgetpu_compiler -s model_dense2conv_mod.tflite ssdlite_ocr.tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Edge TPU Compiler version 15.0.340273435\n",
            "\n",
            "Models compiled successfully in 1565 ms.\n",
            "\n",
            "Input model: model_dense2conv_mod.tflite\n",
            "Input size: 1.40MiB\n",
            "Output model: model_dense2conv_mod_edgetpu.tflite\n",
            "Output size: 1.79MiB\n",
            "On-chip memory used for caching model parameters: 1.69MiB\n",
            "On-chip memory remaining for caching model parameters: 246.00KiB\n",
            "Off-chip memory used for streaming uncached model parameters: 0.00B\n",
            "Number of Edge TPU subgraphs: 1\n",
            "Total number of operations: 25\n",
            "Operation log: model_dense2conv_mod_edgetpu.log\n",
            "\n",
            "Operator                       Count      Status\n",
            "\n",
            "RESHAPE                        2          Mapped to Edge TPU\n",
            "CONCATENATION                  1          Mapped to Edge TPU\n",
            "FULLY_CONNECTED                1          Mapped to Edge TPU\n",
            "MAX_POOL_2D                    3          Mapped to Edge TPU\n",
            "CONV_2D                        17         Mapped to Edge TPU\n",
            "LOGISTIC                       1          Mapped to Edge TPU\n",
            "\n",
            "Input model: ssdlite_ocr.tflite\n",
            "Input size: 3.00MiB\n",
            "Output model: ssdlite_ocr_edgetpu.tflite\n",
            "Output size: 4.48MiB\n",
            "On-chip memory used for caching model parameters: 246.00KiB\n",
            "On-chip memory remaining for caching model parameters: 0.00B\n",
            "Off-chip memory used for streaming uncached model parameters: 3.98MiB\n",
            "Number of Edge TPU subgraphs: 1\n",
            "Total number of operations: 111\n",
            "Operation log: ssdlite_ocr_edgetpu.log\n",
            "\n",
            "Model successfully compiled but not all operations are supported by the Edge TPU. A percentage of the model will instead run on the CPU, which is slower. If possible, consider updating your model to use only operations supported by the Edge TPU. For details, visit g.co/coral/model-reqs.\n",
            "Number of operations that will run on Edge TPU: 110\n",
            "Number of operations that will run on CPU: 1\n",
            "\n",
            "Operator                       Count      Status\n",
            "\n",
            "CONV_2D                        55         Mapped to Edge TPU\n",
            "DEPTHWISE_CONV_2D              29         Mapped to Edge TPU\n",
            "CONCATENATION                  2          Mapped to Edge TPU\n",
            "RESHAPE                        13         Mapped to Edge TPU\n",
            "ADD                            10         Mapped to Edge TPU\n",
            "CUSTOM                         1          Operation is working on an unsupported data type\n",
            "LOGISTIC                       1          Mapped to Edge TPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SguIdBcWSz-8"
      },
      "source": [
        "### Testing the convertion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjYzow2ODHKh"
      },
      "source": [
        "# Now we install tflite_runtime package to evaluate the model (Python 3.7 => cp37).\n",
        "!pip3 install -q https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-cp37-cp37m-linux_x86_64.whl  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C642piq3E5hI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6cdb724-87ee-4285-8f52-d1e02ec32fb3"
      },
      "source": [
        "#\n",
        "# Uncomment one of the lines bellow to download a test image:\n",
        "#\n",
        "\n",
        "!wget https://stage-drupal.car.co.uk/s3fs-public/styles/original_size/public/2019-09/why-are-number-plates-yellow-and-white.jpg -O car_with_plate.jpg\n",
        "# !wget https://i.pinimg.com/originals/b3/c9/5e/b3c95e698e61c2d7ac9b076f61194fcc.jpg -O car_with_plate.jpg\n",
        "# !wget https://www.menswearstyle.co.uk/content/blogs/2c23c162-3858-4d36-a38f-632b6a09dc40_blog_ln_.jpg -O car_with_plate.jpg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-31 18:33:09--  https://stage-drupal.car.co.uk/s3fs-public/styles/original_size/public/2019-09/why-are-number-plates-yellow-and-white.jpg\n",
            "Resolving stage-drupal.car.co.uk (stage-drupal.car.co.uk)... 65.8.247.55, 65.8.247.70, 65.8.247.35, ...\n",
            "Connecting to stage-drupal.car.co.uk (stage-drupal.car.co.uk)|65.8.247.55|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 167246 (163K) [image/jpeg]\n",
            "Saving to: ‘car_with_plate.jpg’\n",
            "\n",
            "car_with_plate.jpg  100%[===================>] 163.33K  --.-KB/s    in 0.05s   \n",
            "\n",
            "2021-05-31 18:33:10 (3.47 MB/s) - ‘car_with_plate.jpg’ saved [167246/167246]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S59xVOCE6tg"
      },
      "source": [
        "from PIL import Image\n",
        "from PIL import ImageDraw\n",
        "from IPython.display import display\n",
        "\n",
        "\n",
        "def draw_bboxes(image, boxes, classes, scores, threshold=0.5, labels={1:'mask', 2: 'no_mask', 3:'poor_mask'}):\n",
        "  colors = ((128, 255, 102), (102, 255, 255), (232, 123, 212))\n",
        "  colors = dict(zip(labels.keys(),colors))\n",
        "  image_width, image_height = image.size\n",
        "  draw = ImageDraw.Draw(image)\n",
        "  # Visualization of the results of a detection.\n",
        "  for i in range(len(boxes)):\n",
        "    if scores[i] > threshold:\n",
        "      ymin = int(max(1, (boxes[i][0] * image_height)))\n",
        "      xmin = int(max(1, (boxes[i][1] * image_width)))\n",
        "      ymax = int(min(image_height, (boxes[i][2] * image_height)))\n",
        "      xmax = int(min(image_width, (boxes[i][3] * image_width)))\n",
        "      draw.rectangle((xmin, ymin, xmax, ymax), width=3, outline=colors[int(classes[i])])\n",
        "      text = f'{labels[int(classes[i])]} {scores[i]*100:1.2f}%'\n",
        "      draw.text((xmin+3, ymin-10), text, fill=colors[int(classes[i])], width=2)\n",
        "  return image, (xmin, ymin, xmax, ymax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BHo85F-NrmK"
      },
      "source": [
        "from tflite_runtime.interpreter import Interpreter\n",
        "# import tensorflow as tf\n",
        "# Interpreter = tf.lite.Interpreter\n",
        "\n",
        "class inference_for_single_image_tflite:\n",
        "  def __init__(self, path_to_model):\n",
        "    self.interpreter = Interpreter(path_to_model)\n",
        "    self.interpreter.allocate_tensors()\n",
        "    self.interpreter.invoke() # warmup\n",
        "\n",
        "    self.input_details = self.interpreter.get_input_details()\n",
        "    self.output_details = self.interpreter.get_output_details()\n",
        "    self.width = self.input_details[0]['shape'][2]\n",
        "    self.height = self.input_details[0]['shape'][1]\n",
        "\n",
        "  def __call__(self, input_tensor):\n",
        "    self.interpreter.set_tensor(self.input_details[0]['index'], input_tensor)\n",
        "    self.interpreter.invoke()\n",
        "\n",
        "    return [self.interpreter.get_tensor(self.output_details[out_i]['index'])[0] for out_i in range(len(self.output_details))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNFvnT0lDSHU"
      },
      "source": [
        "PATH_TO_TFLITE = 'ssdlite_ocr.tflite'\n",
        "run_inference_for_single_image_tflite_ssd = inference_for_single_image_tflite(PATH_TO_TFLITE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "HgeAj8mIFRjU",
        "outputId": "0cf611b3-1e5f-45c4-f33a-2647348bae8d"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "image = Image.open(\"car_with_plate.jpg\")\n",
        "image = image.convert('RGB')\n",
        "image_width, image_height = image.size\n",
        "\n",
        "# tflite will not have the image resizing in the model\n",
        "input_width = run_inference_for_single_image_tflite_ssd.width\n",
        "input_height = run_inference_for_single_image_tflite_ssd.height\n",
        "\n",
        "if image_width!=input_width or image_height!=input_height:\n",
        "  resized_image = image.resize((input_width, input_height))\n",
        "  np_image = np.asarray(resized_image)\n",
        "else:\n",
        "  np_image = np.asarray(image)\n",
        "\n",
        "image_np_expanded = np.expand_dims(np_image, axis=0)\n",
        "\n",
        "# Actual detection.\n",
        "boxes, classes, scores, _ = run_inference_for_single_image_tflite_ssd(image_np_expanded)\n",
        "\n",
        "classes = [i+1 for i in classes] # class indices here start at 0\n",
        "\n",
        "img, box = draw_bboxes(image.copy(), boxes, classes, scores, threshold=0.5, labels={1:'plate'})\n",
        "plate = image.crop(box)\n",
        "plate"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=249x57 at 0x7F20FD5AEFD0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiLxYDu3GJXn"
      },
      "source": [
        "# This LPRNet was trained on Chinese license plates:\n",
        "char2value = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, '<Anhui>': 10, '<Beijing>': 11, '<Chongqing>': 12, '<Fujian>': 13, '<Gansu>': 14, '<Guangdong>': 15, '<Guangxi>': 16, '<Guizhou>': 17, '<Hainan>': 18, '<Hebei>': 19, '<Heilongjiang>': 20, '<Henan>': 21, '<HongKong>': 22, '<Hubei>': 23, '<Hunan>': 24, '<InnerMongolia>': 25, '<Jiangsu>': 26, '<Jiangxi>': 27, '<Jilin>': 28, '<Liaoning>': 29, '<Macau>': 30, '<Ningxia>': 31, '<Qinghai>': 32, '<Shaanxi>': 33, '<Shandong>': 34, '<Shanghai>': 35, '<Shanxi>': 36, '<Sichuan>': 37, '<Tianjin>': 38, '<Tibet>': 39, '<Xinjiang>': 40, '<Yunnan>': 41, '<Zhejiang>': 42, '<police>': 43, 'A': 44, 'B': 45, 'C': 46, 'D': 47, 'E': 48, 'F': 49, 'G': 50, 'H': 51, 'I': 52, 'J': 53, 'K': 54, 'L': 55, 'M': 56, 'N': 57, 'O': 58, 'P': 59, 'Q': 60, 'R': 61, 'S': 62, 'T': 63, 'U': 64, 'V': 65, 'W': 66, 'X': 67, 'Y': 68, 'Z': 69, '_': 70}\n",
        "\n",
        "# Generates a dictionary to revert:\n",
        "value2char = {v:k for k,v in char2value.items()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWf7ly00HMrZ"
      },
      "source": [
        "PATH_TO_TFLITE = 'model_dense2conv_mod.tflite'\n",
        "run_inference_lprnet_conv = inference_for_single_image_tflite(PATH_TO_TFLITE)\n",
        "\n",
        "PATH_TO_TFLITE = 'lprnet.tflite'\n",
        "run_inference_lprnet_dense = inference_for_single_image_tflite(PATH_TO_TFLITE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOAR7cEfHYEV"
      },
      "source": [
        "image_width, image_height = plate.size\n",
        "\n",
        "# tflite will not have the image resizing in the model\n",
        "input_width = run_inference_lprnet_dense.width\n",
        "input_height = run_inference_lprnet_dense.height\n",
        "\n",
        "if image_width!=input_width or image_height!=input_height:\n",
        "  resized_image = plate.resize((input_width, input_height))\n",
        "  np_image = np.asarray(resized_image)\n",
        "else:\n",
        "  np_image = np.asarray(plate)\n",
        "\n",
        "image_np_expanded = np.expand_dims(np_image, axis=0)\n",
        "\n",
        "lprnet_dense_output = run_inference_lprnet_dense(image_np_expanded)[0]\n",
        "lprnet_conv_output = run_inference_lprnet_conv(image_np_expanded)[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HN-otKiJNnv",
        "outputId": "30f04e90-aa4d-4636-e9f2-1867d8f29692"
      },
      "source": [
        "lprnet_conv_output.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88, 71)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvY8Eo4rTuoZ",
        "outputId": "cb4ffa78-a342-4a86-aef0-7fc69a4d2646"
      },
      "source": [
        "lprnet_conv_output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[102, 108, 108, ..., 107, 105, 113],\n",
              "       [ 99, 110, 107, ..., 109, 108, 146],\n",
              "       [ 99, 111, 110, ..., 112, 109, 151],\n",
              "       ...,\n",
              "       [109, 127, 116, ..., 115, 113, 157],\n",
              "       [106, 129, 115, ..., 117, 111, 165],\n",
              "       [106, 132, 117, ..., 123, 115, 140]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t69A7RuqJiZn",
        "outputId": "8cf22a32-7349-4e42-eab1-07ffd5abffb7"
      },
      "source": [
        "abs(lprnet_dense_output-lprnet_conv_output).sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "dliBe7PwJCaN",
        "outputId": "b550836e-4b60-46a9-eb72-5218fc177b36"
      },
      "source": [
        "plate_characters = \" \"\n",
        "output_characters = [\"_\"]\n",
        "for c in lprnet_conv_output.argmax(axis=1)[7:]:\n",
        "  output_characters.append(str(value2char[c]))\n",
        "  # Ignores if repeated or \"_\"\n",
        "  if (output_characters[-1] == output_characters[-2]) or (output_characters[-1] == \"_\"):\n",
        "    continue\n",
        "  plate_characters += str(output_characters[-1])\n",
        "\n",
        "plate_characters"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' SNGGXNZ'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    }
  ]
}