Signlangtranslator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QUANWN3rpfC9"
   },
   "source": [
    "# 0. Setup Paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "146BB11JpfDA"
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "42hJEdo_pfDB"
   },
   "outputs": [],
   "source": [
    "CUSTOM_MODEL_NAME = 'my_ssd_mobnet' \n",
    "PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'\n",
    "PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'\n",
    "TF_RECORD_SCRIPT_NAME = 'generate_tfrecord.py'\n",
    "LABEL_MAP_NAME = 'label_map.pbtxt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "hbPhYVy_pfDB"
   },
   "outputs": [],
   "source": [
    "paths = {\n",
    "    'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'),\n",
    "    'SCRIPTS_PATH': os.path.join('Tensorflow','scripts'),\n",
    "    'APIMODEL_PATH': os.path.join('Tensorflow','models'),\n",
    "    'ANNOTATION_PATH': os.path.join('Tensorflow', 'workspace','annotations'),\n",
    "    'IMAGE_PATH': os.path.join('Tensorflow', 'workspace','images'),\n",
    "    'MODEL_PATH': os.path.join('Tensorflow', 'workspace','models'),\n",
    "    'PRETRAINED_MODEL_PATH': os.path.join('Tensorflow', 'workspace','pre-trained-models'),\n",
    "    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME), \n",
    "    'OUTPUT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'export'), \n",
    "    'TFJS_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfjsexport'), \n",
    "    'TFLITE_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfliteexport'), \n",
    "    'PROTOC_PATH':os.path.join('Tensorflow','protoc')\n",
    " }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "LwhWZMI0pfDC"
   },
   "outputs": [],
   "source": [
    "files = {\n",
    "    'PIPELINE_CONFIG':os.path.join('Tensorflow', 'workspace','models', CUSTOM_MODEL_NAME, 'pipeline.config'),\n",
    "    'TF_RECORD_SCRIPT': os.path.join(paths['SCRIPTS_PATH'], TF_RECORD_SCRIPT_NAME), \n",
    "    'LABELMAP': os.path.join(paths['ANNOTATION_PATH'], LABEL_MAP_NAME)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "HR-TfDGrpfDC"
   },
   "outputs": [],
   "source": [
    "for path in paths.values():\n",
    "    if not os.path.exists(path):\n",
    "        if os.name == 'posix':\n",
    "            !mkdir -p {path}\n",
    "        if os.name == 'nt':\n",
    "            !mkdir {path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OLU-rs_ipfDE"
   },
   "source": [
    "# 1. Download TF Models Pretrained Models from Tensorflow Model Zoo and Install TFOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.tensorflow.org/install/source_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "K-Cmz2edpfDE",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in c:\\users\\utaku\\desktop\\projects\\asl\\aslsys\\lib\\site-packages (3.2)\n"
     ]
    }
   ],
   "source": [
    "if os.name=='nt':\n",
    "    !pip install wget\n",
    "    import wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iA1DIq5OpfDE"
   },
   "outputs": [],
   "source": [
    "#CLONINg models from tensorflow model zoo garden\n",
    "if not os.path.exists(os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection')):\n",
    "    !git clone https://github.com/tensorflow/models {paths['APIMODEL_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rJjMHbnDs3Tv"
   },
   "outputs": [],
   "source": [
    "# Install Tensorflow Object Detection \n",
    "if os.name=='posix':  \n",
    "    !apt-get install protobuf-compiler\n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install . \n",
    "    \n",
    "if os.name=='nt':\n",
    "    url=\"https://github.com/protocolbuffers/protobuf/releases/download/v3.15.6/protoc-3.15.6-win64.zip\"\n",
    "    wget.download(url)\n",
    "    !move protoc-3.15.6-win64.zip {paths['PROTOC_PATH']}\n",
    "    !cd {paths['PROTOC_PATH']} && tar -xf protoc-3.15.6-win64.zip\n",
    "    os.environ['PATH'] += os.pathsep + os.path.abspath(os.path.join(paths['PROTOC_PATH'], 'bin'))   \n",
    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && copy object_detection\\\\packages\\\\tf2\\\\setup.py setup.py && python setup.py build && python setup.py install\n",
    "    !cd Tensorflow/models/research/slim && pip install -e . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "VERIFICATION_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'builders', 'model_builder_tf2_test.py')\n",
    "# Verify Installation\n",
    "!python {VERIFICATION_SCRIPT}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow==2.7.0 tensorflow-gpu==2.7.0 --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install Pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyyaml\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip uninstall protobuf matplotlib -y\n",
    "!pip install protobuf matplotlib==3.3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import object_detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                      Version   Editable project location\n",
      "---------------------------- --------- -------------------------------------------------------------------\n",
      "absl-py                      1.0.0\n",
      "asttokens                    2.0.5\n",
      "astunparse                   1.6.3\n",
      "backcall                     0.2.0\n",
      "black                        21.12b0\n",
      "cachetools                   5.0.0\n",
      "certifi                      2021.10.8\n",
      "charset-normalizer           2.0.10\n",
      "click                        8.0.3\n",
      "colorama                     0.4.4\n",
      "cycler                       0.11.0\n",
      "debugpy                      1.5.1\n",
      "decorator                    5.1.1\n",
      "entrypoints                  0.3\n",
      "executing                    0.8.2\n",
      "flatbuffers                  2.0\n",
      "gast                         0.4.0\n",
      "google-auth                  2.5.0\n",
      "google-auth-oauthlib         0.4.6\n",
      "google-pasta                 0.2.0\n",
      "grpcio                       1.43.0\n",
      "h5py                         3.6.0\n",
      "idna                         3.3\n",
      "importlib-metadata           4.10.1\n",
      "ipykernel                    6.7.0\n",
      "ipython                      8.0.1\n",
      "jedi                         0.18.1\n",
      "jupyter-client               7.1.2\n",
      "jupyter-core                 4.9.1\n",
      "keras                        2.7.0\n",
      "Keras-Preprocessing          1.1.2\n",
      "kiwisolver                   1.3.2\n",
      "libclang                     13.0.0\n",
      "lvis                         0.5.3\n",
      "Markdown                     3.3.6\n",
      "matplotlib                   3.3.1\n",
      "matplotlib-inline            0.1.3\n",
      "mypy-extensions              0.4.3\n",
      "nest-asyncio                 1.5.4\n",
      "numpy                        1.22.1\n",
      "oauthlib                     3.1.1\n",
      "object-detection             0.1\n",
      "opt-einsum                   3.3.0\n",
      "pandas                       1.4.0\n",
      "parso                        0.8.3\n",
      "pathspec                     0.9.0\n",
      "pickleshare                  0.7.5\n",
      "Pillow                       9.0.0\n",
      "pip                          21.3.1\n",
      "platformdirs                 2.4.1\n",
      "prompt-toolkit               3.0.26\n",
      "protobuf                     3.19.4\n",
      "pure-eval                    0.2.2\n",
      "pyasn1                       0.4.8\n",
      "pyasn1-modules               0.2.8\n",
      "Pygments                     2.11.2\n",
      "pyparsing                    3.0.7\n",
      "python-dateutil              2.8.2\n",
      "pywin32                      303\n",
      "PyYAML                       6.0\n",
      "pyzmq                        22.3.0\n",
      "requests                     2.27.1\n",
      "requests-oauthlib            1.3.0\n",
      "rsa                          4.8\n",
      "scipy                        1.8.0rc3\n",
      "setuptools                   56.0.0\n",
      "six                          1.16.0\n",
      "slim                         0.1       c:\\users\\utaku\\desktop\\projects\\asl\\tensorflow\\models\\research\\slim\n",
      "stack-data                   0.1.4\n",
      "tensorboard                  2.8.0\n",
      "tensorboard-data-server      0.6.1\n",
      "tensorboard-plugin-wit       1.8.1\n",
      "tensorflow                   2.7.0\n",
      "tensorflow-estimator         2.7.0\n",
      "tensorflow-gpu               2.7.0\n",
      "tensorflow-io                0.23.1\n",
      "tensorflow-io-gcs-filesystem 0.23.1\n",
      "termcolor                    1.1.0\n",
      "tf-models-official           2.7.0\n",
      "tf-slim                      1.1.0\n",
      "tomli                        1.2.3\n",
      "tornado                      6.1\n",
      "traitlets                    5.1.1\n",
      "typing_extensions            4.0.1\n",
      "urllib3                      1.26.8\n",
      "wcwidth                      0.2.5\n",
      "Werkzeug                     2.0.2\n",
      "wget                         3.2\n",
      "wheel                        0.37.1\n",
      "wrapt                        1.13.3\n",
      "zipp                         3.7.0\n"
     ]
    }
   ],
   "source": [
    "!pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csofht2npfDE",
    "outputId": "ff5471b2-bed2-43f2-959c-327a706527b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) moved.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
      "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.index\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !wget {PRETRAINED_MODEL_URL}\n",
    "    !mv {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}\n",
    "if os.name == 'nt':\n",
    "    wget.download(PRETRAINED_MODEL_URL)\n",
    "    !move {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M5KJTnkfpfDC"
   },
   "source": [
    "# 2. Create Label Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "p1BVDWo7pfDC"
   },
   "outputs": [],
   "source": [
    "labels = [{'name':'hello', 'id':1}, {'name':'yes', 'id':2}, {'name':'no', 'id':3}, {'name':'thanks', 'id':4}, {'name':'iloveyou', 'id':5}, {'name':'please', 'id':6}]\n",
    "\n",
    "with open(files['LABELMAP'], 'w') as f:\n",
    "    for label in labels:\n",
    "        f.write('item { \\n')\n",
    "        f.write('\\tname:\\'{}\\'\\n'.format(label['name']))\n",
    "        f.write('\\tid:{}\\n'.format(label['id']))\n",
    "        f.write('}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C88zyVELpfDC"
   },
   "source": [
    "# 3. Create TF records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kvf5WccwrFGq",
    "outputId": "49902aeb-0bd7-4298-e1a0-5b4a64eb2064"
   },
   "outputs": [],
   "source": [
    "# OPTIONAL IF RUNNING ON COLAB\n",
    "ARCHIVE_FILES = os.path.join(paths['IMAGE_PATH'], 'archive.tar.gz')\n",
    "if os.path.exists(ARCHIVE_FILES):\n",
    "  !tar -zxvf {ARCHIVE_FILES}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWpb_BVUpfDD",
    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Tensorflow\\scripts'...\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UPFToGZqpfDD",
    "outputId": "0ebb456f-aadc-4a1f-96e6-fbfec1923e1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\train.record\n",
      "Successfully created the TFRecord file: Tensorflow\\workspace\\annotations\\test.record\n"
     ]
    }
   ],
   "source": [
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'train')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'train.record')} \n",
    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'test')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'test.record')} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pytz\n",
      "  Using cached pytz-2021.3-py2.py3-none-any.whl (503 kB)\n",
      "Installing collected packages: pytz\n",
      "Successfully installed pytz-2021.3\n"
     ]
    }
   ],
   "source": [
    "!pip install pytz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qT4QU7pLpfDE"
   },
   "source": [
    "# 4. Copy Model Config to Training Folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "cOjuTFbwpfDF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1 file(s) copied.\n"
     ]
    }
   ],
   "source": [
    "if os.name =='posix':\n",
    "    !cp {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}\n",
    "if os.name == 'nt':\n",
    "    !copy {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ga8gpNslpfDF"
   },
   "source": [
    "# 5. Update Config For Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Z9hRrO_ppfDF"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from object_detection.utils import config_util\n",
    "from object_detection.protos import pipeline_pb2\n",
    "from google.protobuf import text_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "c2A0mn4ipfDF"
   },
   "outputs": [],
   "source": [
    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uQA13-afpfDF",
    "outputId": "907496a4-a39d-4b13-8c2c-e5978ecb1f10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': ssd {\n",
       "   num_classes: 90\n",
       "   image_resizer {\n",
       "     fixed_shape_resizer {\n",
       "       height: 320\n",
       "       width: 320\n",
       "     }\n",
       "   }\n",
       "   feature_extractor {\n",
       "     type: \"ssd_mobilenet_v2_fpn_keras\"\n",
       "     depth_multiplier: 1.0\n",
       "     min_depth: 16\n",
       "     conv_hyperparams {\n",
       "       regularizer {\n",
       "         l2_regularizer {\n",
       "           weight: 3.9999998989515007e-05\n",
       "         }\n",
       "       }\n",
       "       initializer {\n",
       "         random_normal_initializer {\n",
       "           mean: 0.0\n",
       "           stddev: 0.009999999776482582\n",
       "         }\n",
       "       }\n",
       "       activation: RELU_6\n",
       "       batch_norm {\n",
       "         decay: 0.996999979019165\n",
       "         scale: true\n",
       "         epsilon: 0.0010000000474974513\n",
       "       }\n",
       "     }\n",
       "     use_depthwise: true\n",
       "     override_base_feature_extractor_hyperparams: true\n",
       "     fpn {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       additional_layer_depth: 128\n",
       "     }\n",
       "   }\n",
       "   box_coder {\n",
       "     faster_rcnn_box_coder {\n",
       "       y_scale: 10.0\n",
       "       x_scale: 10.0\n",
       "       height_scale: 5.0\n",
       "       width_scale: 5.0\n",
       "     }\n",
       "   }\n",
       "   matcher {\n",
       "     argmax_matcher {\n",
       "       matched_threshold: 0.5\n",
       "       unmatched_threshold: 0.5\n",
       "       ignore_thresholds: false\n",
       "       negatives_lower_than_unmatched: true\n",
       "       force_match_for_each_row: true\n",
       "       use_matmul_gather: true\n",
       "     }\n",
       "   }\n",
       "   similarity_calculator {\n",
       "     iou_similarity {\n",
       "     }\n",
       "   }\n",
       "   box_predictor {\n",
       "     weight_shared_convolutional_box_predictor {\n",
       "       conv_hyperparams {\n",
       "         regularizer {\n",
       "           l2_regularizer {\n",
       "             weight: 3.9999998989515007e-05\n",
       "           }\n",
       "         }\n",
       "         initializer {\n",
       "           random_normal_initializer {\n",
       "             mean: 0.0\n",
       "             stddev: 0.009999999776482582\n",
       "           }\n",
       "         }\n",
       "         activation: RELU_6\n",
       "         batch_norm {\n",
       "           decay: 0.996999979019165\n",
       "           scale: true\n",
       "           epsilon: 0.0010000000474974513\n",
       "         }\n",
       "       }\n",
       "       depth: 128\n",
       "       num_layers_before_predictor: 4\n",
       "       kernel_size: 3\n",
       "       class_prediction_bias_init: -4.599999904632568\n",
       "       share_prediction_tower: true\n",
       "       use_depthwise: true\n",
       "     }\n",
       "   }\n",
       "   anchor_generator {\n",
       "     multiscale_anchor_generator {\n",
       "       min_level: 3\n",
       "       max_level: 7\n",
       "       anchor_scale: 4.0\n",
       "       aspect_ratios: 1.0\n",
       "       aspect_ratios: 2.0\n",
       "       aspect_ratios: 0.5\n",
       "       scales_per_octave: 2\n",
       "     }\n",
       "   }\n",
       "   post_processing {\n",
       "     batch_non_max_suppression {\n",
       "       score_threshold: 9.99999993922529e-09\n",
       "       iou_threshold: 0.6000000238418579\n",
       "       max_detections_per_class: 100\n",
       "       max_total_detections: 100\n",
       "       use_static_shapes: false\n",
       "     }\n",
       "     score_converter: SIGMOID\n",
       "   }\n",
       "   normalize_loss_by_num_matches: true\n",
       "   loss {\n",
       "     localization_loss {\n",
       "       weighted_smooth_l1 {\n",
       "       }\n",
       "     }\n",
       "     classification_loss {\n",
       "       weighted_sigmoid_focal {\n",
       "         gamma: 2.0\n",
       "         alpha: 0.25\n",
       "       }\n",
       "     }\n",
       "     classification_weight: 1.0\n",
       "     localization_weight: 1.0\n",
       "   }\n",
       "   encode_background_as_zeros: true\n",
       "   normalize_loc_loss_by_codesize: true\n",
       "   inplace_batchnorm_update: true\n",
       "   freeze_batchnorm: false\n",
       " },\n",
       " 'train_config': batch_size: 128\n",
       " data_augmentation_options {\n",
       "   random_horizontal_flip {\n",
       "   }\n",
       " }\n",
       " data_augmentation_options {\n",
       "   random_crop_image {\n",
       "     min_object_covered: 0.0\n",
       "     min_aspect_ratio: 0.75\n",
       "     max_aspect_ratio: 3.0\n",
       "     min_area: 0.75\n",
       "     max_area: 1.0\n",
       "     overlap_thresh: 0.0\n",
       "   }\n",
       " }\n",
       " sync_replicas: true\n",
       " optimizer {\n",
       "   momentum_optimizer {\n",
       "     learning_rate {\n",
       "       cosine_decay_learning_rate {\n",
       "         learning_rate_base: 0.07999999821186066\n",
       "         total_steps: 50000\n",
       "         warmup_learning_rate: 0.026666000485420227\n",
       "         warmup_steps: 1000\n",
       "       }\n",
       "     }\n",
       "     momentum_optimizer_value: 0.8999999761581421\n",
       "   }\n",
       "   use_moving_average: false\n",
       " }\n",
       " fine_tune_checkpoint: \"PATH_TO_BE_CONFIGURED\"\n",
       " num_steps: 50000\n",
       " startup_delay_steps: 0.0\n",
       " replicas_to_aggregate: 8\n",
       " max_number_of_boxes: 100\n",
       " unpad_groundtruth_tensors: false\n",
       " fine_tune_checkpoint_type: \"classification\"\n",
       " fine_tune_checkpoint_version: V2,\n",
       " 'train_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " },\n",
       " 'eval_config': metrics_set: \"coco_detection_metrics\"\n",
       " use_moving_averages: false,\n",
       " 'eval_input_configs': [label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }\n",
       " ],\n",
       " 'eval_input_config': label_map_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " shuffle: false\n",
       " num_epochs: 1\n",
       " tf_record_input_reader {\n",
       "   input_path: \"PATH_TO_BE_CONFIGURED\"\n",
       " }}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "9vK5lotDpfDF"
   },
   "outputs": [],
   "source": [
    "pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"r\") as f:                                                                                                                                                                                                                     \n",
    "    proto_str = f.read()                                                                                                                                                                                                                                          \n",
    "    text_format.Merge(proto_str, pipeline_config)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "rP43Ph0JpfDG"
   },
   "outputs": [],
   "source": [
    "pipeline_config.model.ssd.num_classes = len(labels)\n",
    "pipeline_config.train_config.batch_size = 4\n",
    "pipeline_config.train_config.fine_tune_checkpoint = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'checkpoint', 'ckpt-0')\n",
    "pipeline_config.train_config.fine_tune_checkpoint_type = \"detection\"\n",
    "pipeline_config.train_input_reader.label_map_path= files['LABELMAP']\n",
    "pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'train.record')]\n",
    "pipeline_config.eval_input_reader[0].label_map_path = files['LABELMAP']\n",
    "pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'test.record')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "oJvfgwWqpfDG"
   },
   "outputs": [],
   "source": [
    "config_text = text_format.MessageToString(pipeline_config)                                                                                                                                                                                                        \n",
    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"wb\") as f:                                                                                                                                                                                                                     \n",
    "    f.write(config_text)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zr3ON7xMpfDG"
   },
   "source": [
    "# 6. Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "B-Y2UQmQpfDG"
   },
   "outputs": [],
   "source": [
    "TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "jMP2XDfQpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --num_train_steps=10000\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A4OXXi-ApfDH",
    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\model_main_tf2.py --model_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --num_train_steps=10000\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i3ZsJR-qpfDH",
    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_YRZu7npfDH"
   },
   "source": [
    "# 7. Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "80L7-fdPpfDH"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --model_dir={} --pipeline_config_path={} --checkpoint_dir={}\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lYsgEPx9pfDH",
    "outputId": "8632d48b-91d2-45d9-bcb8-c1b172bf6eed"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lqTV2jGBpfDH"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orvRk02UpfDI"
   },
   "source": [
    "# 8. Load Train Model From Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "8TYk4_oIpfDI"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from object_detection.utils import label_map_util\n",
    "from object_detection.utils import visualization_utils as viz_utils\n",
    "from object_detection.builders import model_builder\n",
    "from object_detection.utils import config_util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "tDnQg-cYpfDI"
   },
   "outputs": [],
   "source": [
    "# Load pipeline config and build a detection model\n",
    "configs = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])\n",
    "detection_model = model_builder.build(model_config=configs['model'], is_training=False)\n",
    "\n",
    "# Restore checkpoint\n",
    "ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\n",
    "ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-11')).expect_partial()\n",
    "\n",
    "@tf.function\n",
    "def detect_fn(image):\n",
    "    image, shapes = detection_model.preprocess(image)\n",
    "    prediction_dict = detection_model.predict(image, shapes)\n",
    "    detections = detection_model.postprocess(prediction_dict, shapes)\n",
    "    return detections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0EmsmbBZpfDI"
   },
   "source": [
    "# 9. Detect from an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Y_MKiuZ4pfDI"
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "cBDbIhNapfDI"
   },
   "outputs": [],
   "source": [
    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Lx3crOhOzITB"
   },
   "outputs": [],
   "source": [
    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', 'hello.a386cc76-810c-11ec-8938-d14f6f1810bb.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "Tpzn1SMry1yK",
    "outputId": "c392a2c5-10fe-4fc4-9998-a1d4c7db2bd3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(IMAGE_PATH)\n",
    "image_np = np.array(img)\n",
    "\n",
    "input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "detections = detect_fn(input_tensor)\n",
    "\n",
    "num_detections = int(detections.pop('num_detections'))\n",
    "detections = {key: value[0, :num_detections].numpy()\n",
    "              for key, value in detections.items()}\n",
    "detections['num_detections'] = num_detections\n",
    "\n",
    "# detection_classes should be ints.\n",
    "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "label_id_offset = 1\n",
    "image_np_with_detections = image_np.copy()\n",
    "\n",
    "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "            image_np_with_detections,\n",
    "            detections['detection_boxes'],\n",
    "            detections['detection_classes']+label_id_offset,\n",
    "            detections['detection_scores'],\n",
    "            category_index,\n",
    "            use_normalized_coordinates=True,\n",
    "            max_boxes_to_draw=5,\n",
    "            min_score_thresh=.8,\n",
    "            agnostic_mode=False)\n",
    "\n",
    "plt.imshow(cv2.cvtColor(image_np_with_detections, cv2.COLOR_BGR2RGB))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IsNAaYAo0WVL"
   },
   "source": [
    "# 10. Real Time Detections from your Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip uninstall opencv-python-headless -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "o_grs6OGpfDJ"
   },
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "while cap.isOpened(): \n",
    "    ret, frame = cap.read()\n",
    "    image_np = np.array(frame)\n",
    "    \n",
    "    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
    "    detections = detect_fn(input_tensor)\n",
    "    \n",
    "    num_detections = int(detections.pop('num_detections'))\n",
    "    detections = {key: value[0, :num_detections].numpy()\n",
    "                  for key, value in detections.items()}\n",
    "    detections['num_detections'] = num_detections\n",
    "\n",
    "    # detection_classes should be ints.\n",
    "    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "    label_id_offset = 1\n",
    "    image_np_with_detections = image_np.copy()\n",
    "\n",
    "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "                image_np_with_detections,\n",
    "                detections['detection_boxes'],\n",
    "                detections['detection_classes']+label_id_offset,\n",
    "                detections['detection_scores'],\n",
    "                category_index,\n",
    "                use_normalized_coordinates=True,\n",
    "                max_boxes_to_draw=5,\n",
    "                min_score_thresh=.5,\n",
    "                agnostic_mode=False)\n",
    "\n",
    "    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))\n",
    "    \n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzlM4jt0pfDJ"
   },
   "source": [
    "# 10. Freezing the Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "n4olHB2npfDJ"
   },
   "outputs": [],
   "source": [
    "FREEZE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'exporter_main_v2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "0AjO93QDpfDJ"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --input_type=image_tensor --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(FREEZE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['OUTPUT_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F6Lsp3tCpfDJ",
    "outputId": "c3828529-bf06-4df5-d7f3-145890ec3edd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python Tensorflow\\models\\research\\object_detection\\exporter_main_v2.py  --input_type=image_tensor --pipeline_config_path=Tensorflow\\workspace\\models\\my_ssd_mobnet\\pipeline.config --trained_checkpoint_dir=Tensorflow\\workspace\\models\\my_ssd_mobnet --output_directory=Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wTPmdqaXpfDK"
   },
   "source": [
    "# 11. Conversion to TFJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gZ6UzY_fpfDK",
    "outputId": "0c84722e-1c2b-4002-d857-80827ade828a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflowjs"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 21.3.1; however, version 22.0 is available.\n",
      "You should consider upgrading via the 'c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  Downloading tensorflowjs-3.13.0-py3-none-any.whl (77 kB)\n",
      "Requirement already satisfied: six<2,>=1.12.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflowjs) (1.15.0)\n",
      "Collecting tensorflow-hub<0.13,>=0.7.0\n",
      "  Using cached tensorflow_hub-0.12.0-py2.py3-none-any.whl (108 kB)\n",
      "Requirement already satisfied: tensorflow<3,>=2.1.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflowjs) (2.5.0)\n",
      "Requirement already satisfied: keras-nightly~=2.5.0.dev in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.5.0.dev2021032900)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.17.3)\n",
      "Requirement already satisfied: google-pasta~=0.2 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.2.0)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.12.1)\n",
      "Requirement already satisfied: h5py~=3.1.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.1.0)\n",
      "Requirement already satisfied: wheel~=0.35 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.36.2)\n",
      "Requirement already satisfied: gast==0.4.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.4.0)\n",
      "Requirement already satisfied: tensorboard~=2.5 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.5.0)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.6.3)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0rc0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (2.5.0)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.7.4.3)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.1.2)\n",
      "Requirement already satisfied: numpy~=1.19.2 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.19.5)\n",
      "Requirement already satisfied: grpcio~=1.34.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (1.34.1)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (3.3.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorflow<3,>=2.1.0->tensorflowjs) (0.13.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (0.6.1)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (2.26.0)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (2.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.5)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (1.34.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (56.0.0)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (1.8.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (3.3.4)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (4.2.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (1.3.0)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (1.26.6)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (2021.5.30)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\utaku\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow<3,>=2.1.0->tensorflowjs) (3.1.1)\n",
      "Installing collected packages: tensorflow-hub, tensorflowjs\n",
      "Successfully installed tensorflow-hub-0.12.0 tensorflowjs-3.13.0\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "0oxbVynHpfDK"
   },
   "outputs": [],
   "source": [
    "command = \"tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_boxes,detection_classes,detection_features,detection_multiclass_scores,detection_scores,num_detections,raw_detection_boxes,raw_detection_scores' --output_format=tfjs_graph_model --signature_name=serving_default {} {}\".format(os.path.join(paths['OUTPUT_PATH'], 'saved_model'), paths['TFJS_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DB2AGNmJpfDK",
    "outputId": "fbc9f747-f511-47e8-df8f-5ea65cef0374"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_boxes,detection_classes,detection_features,detection_multiclass_scores,detection_scores,num_detections,raw_detection_boxes,raw_detection_scores' --output_format=tfjs_graph_model --signature_name=serving_default Tensorflow\\workspace\\models\\my_ssd_mobnet\\export\\saved_model Tensorflow\\workspace\\models\\my_ssd_mobnet\\tfjsexport\n"
     ]
    }
   ],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K7rfT4-hpfDK",
    "outputId": "532707fd-6feb-4bc6-84a3-325b5d16303c"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o8_hm-itpfDK"
   },
   "outputs": [],
   "source": [
    "# Test Code: https://github.com/nicknochnack/RealTimeSignLanguageDetectionwithTFJS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtUw73FHpfDK"
   },
   "source": [
    "# 12. Conversion to TFLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XviMtewLpfDK"
   },
   "outputs": [],
   "source": [
    "TFLITE_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'export_tflite_graph_tf2.py ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "us86cjC4pfDL"
   },
   "outputs": [],
   "source": [
    "command = \"python {} --pipeline_config_path={} --trained_checkpoint_dir={} --output_directory={}\".format(TFLITE_SCRIPT ,files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'], paths['TFLITE_PATH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n1r5YO3rpfDL",
    "outputId": "5fcdf7a4-eee2-4365-f1ca-1751968379ea"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-xWpHN8pfDL",
    "outputId": "7f6bacd8-d077-43b5-c131-5b081fba24a4"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iJfYMbN6pfDL"
   },
   "outputs": [],
   "source": [
    "FROZEN_TFLITE_PATH = os.path.join(paths['TFLITE_PATH'], 'saved_model')\n",
    "TFLITE_MODEL = os.path.join(paths['TFLITE_PATH'], 'saved_model', 'detect.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"tflite_convert \\\n",
    "--saved_model_dir={} \\\n",
    "--output_file={} \\\n",
    "--input_shapes=1,300,300,3 \\\n",
    "--input_arrays=normalized_input_image_tensor \\\n",
    "--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \\\n",
    "--inference_type=FLOAT \\\n",
    "--allow_custom_ops\".format(FROZEN_TFLITE_PATH, TFLITE_MODEL, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E8GwUeoFpfDL",
    "outputId": "fac43ea4-cc85-471b-a362-e994b06fd583"
   },
   "outputs": [],
   "source": [
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nbd7gqHMpfDL",
    "outputId": "7c8fe6d5-2415-4641-8548-39d425c202f7"
   },
   "outputs": [],
   "source": [
    "!{command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5NQqZRdA21Uc"
   },
   "source": [
    "# 13. Zip and Export Models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tTVTGCQp2ZJJ"
   },
   "outputs": [],
   "source": [
    "!tar -czf models.tar.gz {paths['CHECKPOINT_PATH']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "whShhB0x3PYJ",
    "outputId": "b773201d-35c9-46a8-b893-4a76bd4d5d97"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "3. Training and Detection.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "aslsys",
   "language": "python",
   "name": "aslsys"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}