object_detection_face_detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "object_detection_face_detector.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lvisdd/object_detection_tutorial/blob/master/object_detection_face_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cor8fWw3gF0W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# restart (or reset) your virtual machine\n",
        "#!kill -9 -1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vMoF2OaFTxi",
        "colab_type": "text"
      },
      "source": [
        "# [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIucMKsaFXBY",
        "colab_type": "code",
        "outputId": "b82c9280-e7c4-484e-daa7-9655c53634ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "!git clone https://github.com/tensorflow/models.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 18, done.\u001b[K\n",
            "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
            "remote: Compressing objects: 100% (17/17), done.\u001b[K\n",
            "remote: Total 30176 (delta 7), reused 11 (delta 1), pack-reused 30158\u001b[K\n",
            "Receiving objects: 100% (30176/30176), 510.33 MiB | 15.16 MiB/s, done.\n",
            "Resolving deltas: 100% (18883/18883), done.\n",
            "Checking out files: 100% (3061/3061), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zesR5I_qFbJg",
        "colab_type": "text"
      },
      "source": [
        "# COCO API installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D44-QM5cFaqX",
        "colab_type": "code",
        "outputId": "e91b308d-e594-4b42-9234-9ea2c570a121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!git clone https://github.com/cocodataset/cocoapi.git\n",
        "%cd cocoapi/PythonAPI\n",
        "!make\n",
        "!cp -r pycocotools /content/models/research/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cocoapi'...\n",
            "remote: Enumerating objects: 959, done.\u001b[K\n",
            "remote: Total 959 (delta 0), reused 0 (delta 0), pack-reused 959\u001b[K\n",
            "Receiving objects: 100% (959/959), 11.69 MiB | 6.35 MiB/s, done.\n",
            "Resolving deltas: 100% (571/571), done.\n",
            "/content/cocoapi/PythonAPI\n",
            "python setup.py build_ext --inplace\n",
            "running build_ext\n",
            "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n",
            "/usr/local/lib/python3.6/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/cocoapi/PythonAPI/pycocotools/_mask.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "building 'pycocotools._mask' extension\n",
            "creating build\n",
            "creating build/common\n",
            "creating build/temp.linux-x86_64-3.6\n",
            "creating build/temp.linux-x86_64-3.6/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I../common -I/usr/include/python3.6m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.6/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n",
            "       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n",
            "                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n",
            "       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n",
            "                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n",
            "   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n",
            "   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n",
            "                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n",
            "     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n",
            "                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "       if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n",
            "                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I../common -I/usr/include/python3.6m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.6/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "creating build/lib.linux-x86_64-3.6\n",
            "creating build/lib.linux-x86_64-3.6/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/../common/maskApi.o build/temp.linux-x86_64-3.6/pycocotools/_mask.o -o build/lib.linux-x86_64-3.6/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so\n",
            "copying build/lib.linux-x86_64-3.6/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so -> pycocotools\n",
            "rm -rf build\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNtknm_fFhcP",
        "colab_type": "text"
      },
      "source": [
        "# Protobuf Compilation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PwXOE1wFk9I",
        "colab_type": "code",
        "outputId": "4cf5e956-794b-4361-ad21-807257724664",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/models/research/\n",
        "!protoc object_detection/protos/*.proto --python_out=."
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNPcWjYMFq_n",
        "colab_type": "text"
      },
      "source": [
        "# Add Libraries to PYTHONPATH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gc8H-ScuFpv5",
        "colab_type": "code",
        "outputId": "47ffdd9b-7240-4952-b790-7c24fb1fed91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "%cd /content/models/research/\n",
        "%env PYTHONPATH=/env/python:/content/models/research:/content/models/research/slim:/content/models/research/object_detection\n",
        "%env"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n",
            "env: PYTHONPATH=/env/python:/content/models/research:/content/models/research/slim:/content/models/research/object_detection\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CLICOLOR': '1',\n",
              " 'CLOUDSDK_CONFIG': '/content/.config',\n",
              " 'COLAB_GPU': '1',\n",
              " 'CUDA_PKG_VERSION': '10-0=10.0.130-1',\n",
              " 'CUDA_VERSION': '10.0.130',\n",
              " 'CUDNN_VERSION': '7.6.2.24',\n",
              " 'DATALAB_SETTINGS_OVERRIDES': '{\"kernelManagerProxyPort\":6000,\"kernelManagerProxyHost\":\"172.28.0.3\",\"jupyterArgs\":[\"--ip=\\\\\"172.28.0.2\\\\\"\"]}',\n",
              " 'DEBIAN_FRONTEND': 'noninteractive',\n",
              " 'ENV': '/root/.bashrc',\n",
              " 'GCS_READ_CACHE_BLOCK_SIZE_MB': '16',\n",
              " 'GIT_PAGER': 'cat',\n",
              " 'GLIBCPP_FORCE_NEW': '1',\n",
              " 'GLIBCXX_FORCE_NEW': '1',\n",
              " 'HOME': '/root',\n",
              " 'HOSTNAME': 'af555329000e',\n",
              " 'JPY_PARENT_PID': '16',\n",
              " 'LANG': 'en_US.UTF-8',\n",
              " 'LAST_FORCED_REBUILD': '20190507',\n",
              " 'LD_LIBRARY_PATH': '/usr/lib64-nvidia',\n",
              " 'LD_PRELOAD': '/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4',\n",
              " 'LIBRARY_PATH': '/usr/local/cuda/lib64/stubs',\n",
              " 'MPLBACKEND': 'module://ipykernel.pylab.backend_inline',\n",
              " 'NCCL_VERSION': '2.4.7',\n",
              " 'NO_GCE_CHECK': 'True',\n",
              " 'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',\n",
              " 'NVIDIA_REQUIRE_CUDA': 'cuda>=10.0 brand=tesla,driver>=384,driver<385 brand=tesla,driver>=410,driver<411',\n",
              " 'NVIDIA_VISIBLE_DEVICES': 'all',\n",
              " 'OLDPWD': '/',\n",
              " 'PAGER': 'cat',\n",
              " 'PATH': '/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/tools/node/bin:/tools/google-cloud-sdk/bin:/opt/bin',\n",
              " 'PWD': '/',\n",
              " 'PYTHONPATH': '/env/python:/content/models/research:/content/models/research/slim:/content/models/research/object_detection',\n",
              " 'PYTHONWARNINGS': 'ignore:::pip._internal.cli.base_command',\n",
              " 'SHELL': '/bin/bash',\n",
              " 'SHLVL': '1',\n",
              " 'TERM': 'xterm-color',\n",
              " 'TF_FORCE_GPU_ALLOW_GROWTH': 'true',\n",
              " '_': '/tools/node/bin/node',\n",
              " '__EGL_VENDOR_LIBRARY_DIRS': '/usr/lib64-nvidia:/usr/share/glvnd/egl_vendor.d/'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lFy4jC7FwYp",
        "colab_type": "text"
      },
      "source": [
        "# Testing the Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoZSvZzpFxFo",
        "colab_type": "code",
        "outputId": "7ffe4a9c-9a37-4396-818f-83f12fda96f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "!python object_detection/builders/model_builder_test.py"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0827 16:47:24.121168 140720291608448 lazy_loader.py:50] \n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "W0827 16:47:24.430399 140720291608448 deprecation_wrapper.py:119] From /content/models/research/slim/nets/inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "W0827 16:47:24.483575 140720291608448 deprecation_wrapper.py:119] From /content/models/research/slim/nets/mobilenet/mobilenet.py:397: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "Running tests under Python 3.6.8: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[       OK ] ModelBuilderTest.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[       OK ] ModelBuilderTest.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[       OK ] ModelBuilderTest.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[       OK ] ModelBuilderTest.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[       OK ] ModelBuilderTest.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[       OK ] ModelBuilderTest.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTest.test_session\n",
            "[  SKIPPED ] ModelBuilderTest.test_session\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[       OK ] ModelBuilderTest.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "[       OK ] ModelBuilderTest.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 16 tests in 0.152s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6eeEiH4HRoH",
        "colab_type": "code",
        "outputId": "4fa16285-a00d-44ff-de6b-9bc6ca283cf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/models/research/object_detection"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research/object_detection\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7D-paEA1zx6",
        "colab_type": "text"
      },
      "source": [
        "## [Tensorflow Face Detector](https://github.com/yeephycho/tensorflow-face-detection)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkzL8IUZO34M",
        "colab_type": "code",
        "outputId": "6cf20a01-a447-425f-dc15-f71011b06bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fda6xWGx2DEQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "cd9a43b5-9742-4089-db73-b558eacf4d69"
      },
      "source": [
        "!git clone https://github.com/yeephycho/tensorflow-face-detection.git"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'tensorflow-face-detection'...\n",
            "remote: Enumerating objects: 118, done.\u001b[K\n",
            "remote: Total 118 (delta 0), reused 0 (delta 0), pack-reused 118\n",
            "Receiving objects: 100% (118/118), 20.31 MiB | 7.90 MiB/s, done.\n",
            "Resolving deltas: 100% (59/59), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aqph6cQ92PBx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8728bb94-2565-45d4-a5a5-d917bd9a9faf"
      },
      "source": [
        "%cd tensorflow-face-detection"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tensorflow-face-detection\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbGh03AT2DMH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c4b1294e-1c51-4b1b-abd0-e3912f46dee9"
      },
      "source": [
        "!wget https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-08-27 16:47:32--  https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.23.128, 2404:6800:4008:c02::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.23.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 61306 (60K) [image/jpeg]\n",
            "Saving to: ‘grace_hopper.jpg’\n",
            "\n",
            "\rgrace_hopper.jpg      0%[                    ]       0  --.-KB/s               \rgrace_hopper.jpg    100%[===================>]  59.87K  --.-KB/s    in 0s      \n",
            "\n",
            "2019-08-27 16:47:32 (124 MB/s) - ‘grace_hopper.jpg’ saved [61306/61306]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZjLqE6U85PR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filename = 'grace_hopper.jpg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyqq0LgZ4fZJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!python inference_usbCam_face.py grace_hopper.jpg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OfdPTik7wDU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5f9d579c-cea8-4263-e7fc-f1ddf21b004f"
      },
      "source": [
        "import sys\n",
        "import time\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "\n",
        "from utils import label_map_util\n",
        "from utils import visualization_utils_color as vis_util\n",
        "\n",
        "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
        "PATH_TO_CKPT = './model/frozen_inference_graph_face.pb'\n",
        "\n",
        "# List of the strings that is used to add correct label for each box.\n",
        "PATH_TO_LABELS = './protos/face_label_map.pbtxt'\n",
        "\n",
        "NUM_CLASSES = 2\n",
        "\n",
        "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
        "categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n",
        "category_index = label_map_util.create_category_index(categories)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0827 16:47:35.285600 140077768431488 deprecation_wrapper.py:119] From /content/tensorflow-face-detection/utils/label_map_util.py:116: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B76J__do72lD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class TensoflowFaceDector(object):\n",
        "    def __init__(self, PATH_TO_CKPT):\n",
        "        \"\"\"Tensorflow detector\n",
        "        \"\"\"\n",
        "\n",
        "        self.detection_graph = tf.Graph()\n",
        "        with self.detection_graph.as_default():\n",
        "            od_graph_def = tf.GraphDef()\n",
        "            with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
        "                serialized_graph = fid.read()\n",
        "                od_graph_def.ParseFromString(serialized_graph)\n",
        "                tf.import_graph_def(od_graph_def, name='')\n",
        "\n",
        "\n",
        "        with self.detection_graph.as_default():\n",
        "            config = tf.ConfigProto()\n",
        "            config.gpu_options.allow_growth = True\n",
        "            self.sess = tf.Session(graph=self.detection_graph, config=config)\n",
        "            self.windowNotSet = True\n",
        "\n",
        "\n",
        "    def run(self, image):\n",
        "        \"\"\"image: bgr image\n",
        "        return (boxes, scores, classes, num_detections)\n",
        "        \"\"\"\n",
        "\n",
        "        image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "        # the array based representation of the image will be used later in order to prepare the\n",
        "        # result image with boxes and labels on it.\n",
        "        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
        "        image_np_expanded = np.expand_dims(image_np, axis=0)\n",
        "        image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')\n",
        "        # Each box represents a part of the image where a particular object was detected.\n",
        "        boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')\n",
        "        # Each score represent how level of confidence for each of the objects.\n",
        "        # Score is shown on the result image, together with the class label.\n",
        "        scores = self.detection_graph.get_tensor_by_name('detection_scores:0')\n",
        "        classes = self.detection_graph.get_tensor_by_name('detection_classes:0')\n",
        "        num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')\n",
        "        # Actual detection.\n",
        "        start_time = time.time()\n",
        "        (boxes, scores, classes, num_detections) = self.sess.run(\n",
        "            [boxes, scores, classes, num_detections],\n",
        "            feed_dict={image_tensor: image_np_expanded})\n",
        "        elapsed_time = time.time() - start_time\n",
        "        print('inference time cost: {}'.format(elapsed_time))\n",
        "\n",
        "        return (boxes, scores, classes, num_detections)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYrCRG_P8QTa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This is needed to display the images.\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wrCrX4W9hGC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "9a593def-5bf0-4131-b863-0c80fbb506e7"
      },
      "source": [
        "tDetector = TensoflowFaceDector(PATH_TO_CKPT)\n",
        "\n",
        "original = cv2.imread(filename)\n",
        "image = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "(boxes, scores, classes, num_detections) = tDetector.run(image)\n",
        "\n",
        "vis_util.visualize_boxes_and_labels_on_image_array(\n",
        "    image,\n",
        "    np.squeeze(boxes),\n",
        "    np.squeeze(classes).astype(np.int32),\n",
        "    np.squeeze(scores),\n",
        "    category_index,\n",
        "    use_normalized_coordinates=True,\n",
        "    line_thickness=4)\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.imshow(image)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "inference time cost: 2.3050696849823\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f65f04c9978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n3ZvXve71bL32GqIw5NfXXc9lP/gBn/34BdzzhM0oxzDoL/Ct71/FY844g698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GF94w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Rms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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}