docs/tutorials/image_ops.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "image_ops.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.3"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GWEKvPCCxJke"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "l-m8KQ-nxK5l",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O8FuVCLYxi_l"
      },
      "source": [
        "# TensorFlow Addons Image: Operations\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/addons/tutorials/image_ops\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/addons/blob/master/docs/tutorials/image_ops.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/addons/blob/master/docs/tutorials/image_ops.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "      <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/image_ops.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2a5ksOt-xsOl"
      },
      "source": [
        "## Overview\n",
        "This notebook will demonstrate how to use the some image operations in TensorFlow Addons.\n",
        "\n",
        "Here is the list of image operations we'll be covering in this example:\n",
        "\n",
        "- `tfa.image.mean_filter2d`\n",
        "\n",
        "- `tfa.image.rotate`\n",
        "\n",
        "- `tfa.image.transform`\n",
        "\n",
        "- `tfa.image.random_hsv_in_yiq`\n",
        "\n",
        "- `tfa.image.adjust_hsv_in_yiq`\n",
        "\n",
        "- `tfa.image.dense_image_warp`\n",
        "\n",
        "- `tfa.image.euclidean_dist_transform`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DMbjxr4PyMPF"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5hVIKCrhWh4a",
        "outputId": "365ae823-365c-4141-9d84-e4ccfb0c85c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "try:\n",
        "  %tensorflow_version 2.x\n",
        "except:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2ZdFry6yAp-c",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow_addons as tfa\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q6Z2rsP8yp2v"
      },
      "source": [
        "# Prepare and Inspect Images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9gbgJP10z9WO"
      },
      "source": [
        "## Download the images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IgUsVhBQ6dSg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "20ecbebf-6ed9-4e59-f958-c94b2921ba74"
      },
      "source": [
        "img_path = tf.keras.utils.get_file('tensorflow.png','https://tensorflow.org/images/tf_logo.png')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://tensorflow.org/images/tf_logo.png\n",
            "40960/39781 [==============================] - 0s 1us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uheQOL-y0Fj3"
      },
      "source": [
        "## Inspect the images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MFGirRRZ0Y9k"
      },
      "source": [
        "### TensorFlow Icon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NRlvNQdm1YI8",
        "outputId": "1922ef25-a9f8-4fb4-8f60-65402a6fd969",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "img_raw = tf.io.read_file(img_path)\n",
        "img = tf.io.decode_image(img_raw)\n",
        "img = tf.image.convert_image_dtype(img, tf.float32)\n",
        "img = tf.image.resize(img, [500,500])\n",
        "\n",
        "plt.title(\"TensorFlow Logo with shape {}\".format(img.shape))\n",
        "_ = plt.imshow(img)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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a1LUhwYlkOAnBL/aBy96fM0dzWgQRQhdvArd/rSnbt/7nRDLERe3Csju/B6dU4MnfIoRC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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "clXQrFVa2nN7"
      },
      "source": [
        "### Make a black and white version"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tbaIkUCS2eNv",
        "outputId": "7e2796dd-26d9-4856-fa6e-7b5a084583c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "bw_img = 1.0 - tf.image.rgb_to_grayscale(img)\n",
        "\n",
        "plt.title(\"Mask image with shape {}\".format(bw_img.shape))\n",
        "_ = plt.imshow(bw_img[...,0], cmap='gray')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4InMgdFHHHXccK1asYPz48ZUupVUigquvvpqnn36awYMH++KmCnEgdEFTp05l0qRJQOXG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\nNgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UwqfpOm--vV2"
      },
      "source": [
        "# Play with tfa.image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jIa5HnomPds3"
      },
      "source": [
        "## Mean filtering\n",
        "Mean filtering is a filtering technique, which is often used to remove noise from an image or signal. The idea is to run through the image pixel by pixel and replacing it with the average values of neighboring pixels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SutWnbRoHl6i",
        "outputId": "52333d9d-788d-4c46-df94-f24456abb67f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "mean = tfa.image.mean_filter2d(img, filter_shape=11)\n",
        "_ = plt.imshow(mean)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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93h+XgG8ek43NVIA/cM7dDzwAfNn/DXvR5iLwoHPuXwMfAh4SkQeArwNPOOfu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\n5aoQDDBya7UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Mp6cU7I0-r2h"
      },
      "source": [
        "## Rotate\n",
        "This operation rotates the given image by the angle (in radians) input by the user.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9kxUES9sM8Jl",
        "outputId": "1340b774-5fbd-4c94-ae26-a35fa3c50546",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "rotate = tfa.image.rotate(img, tf.constant(np.pi/8))\n",
        "_ = plt.imshow(rotate)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8b2b7w/4OYF3YXgc8CRCOvxDOrxTXGccZj6EiIOntwE4zu6/MG0vaLGmbpG0l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\nLgKO03BcBByn4bgIOE7DcRFwnIbz/wEU6YHlJ93xtwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WjMdSDKlBcPh"
      },
      "source": [
        "## Transform\n",
        "This operation transforms the given image on the basis of the transform vector given by the user. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HTh1Qpps8Rg5",
        "outputId": "00dd3893-ffbe-4bdd-de3c-9f037651b36b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "transform = tfa.image.transform(img, [1.0, 1.0, -250, 0.0, 1.0, 0.0, 0.0, 0.0])\n",
        "_ = plt.imshow(transform)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V4f9ROAVmAZKBMIhe/hbsjH/KDNPncb7m3obrtCAItPQAumGP341967VJ9Ro8BGikAaG6\nPYCJXlnIBZA+Nl8AsUR04Wy4aHN+Ur2oECBd2bgWBMkpgFlmmVn8GogWgO0pLA1YRQF0o/bThqdV\nAJ3GAExgFmFE2MFvgYsMXbgndHcHF8DAIUCDcc0EzCmApKqZ5LU85f7wumW3Y64eqGsasBu1Dgeq\n0PbCGaAHYOn3pItne6fsulFwCGAwngVBoC8BpDGL4Rc74Wutc9qaTcgRAnDSjys5EShNp3Cgtj0B\nF0CCYXDgK+HiPcML4LzixwDGtiDIgAIIT0S87zL49Ssauz0FAFR+JmBeajkmMGkCMIvRluvyfRiI\n8P+X4NPPoL32LaYRdUkDNuu1uRdkKAEkdc4A9iunw/fWhsIkPFCHMYC5rEw9Q4A0tesJTJ4AACz/\nTMDFK+GS2eRdf4QCyEzLtVOFBUGGFECzbRaDgR2zERCxNd7ps8cAqpoGHIRajQlUoa2Fc+MlcN3Z\nyXbbPWCNsg1b4YB/BcQUvi59jdOA8fpZooLfx8xm0c5bset+HTDUYQygjgKofYpwWgSQfC1l+OjO\nxjhchEnftHAB1DENGO5GW7h/CNUNk5DNEpbtHRjDQBH20mPQy3+b+Cdhhnyos2UMoOQlwYqkFuHA\nNAjALAjg5UdjG2PYaOHmSNJ6Qw34ZVHjNGAM8MLP0e0XEDem/Q4pAAivdDJ9EP7Nt9H+hxKH6KOZ\nBiQRgOpx6+Si8uFAFdpXOA0BWJivfsHj6EWLx1d/WWnAbvXlTQNqTlaxgdY+CvssRpZR1xCYzSaN\nvTJCUZTU+Y5/hKXHFFfJmKllinDSBGBmcOPF2HVnwzn/jH1+Fm2M0b5jFECZacBhFwRpFIXHRjL4\n0hKI4+TDQUnupPvz5sEMNb4k8I0XkHwlWIwdWF8BdKOyEpg4AWDYzRvhmN/HPjeLHXBw0qXstXZf\nkZScBsysDwZeE9AUnvf/Hk/SrUqfOAThbyJF8NpzYemb4H1xvs9b1JBKhgNVaFPRGJbcQ80LacxX\nVN40YLsE2j+ZCD3TgHp0c+tzDPtpwPa2yFI9ivB5iKM+A0dswDQ3f3LY7/NrXIeFj8eURG0+RViF\n9kwcbQKoUxowPQaQ1ZaYxg0PdvrjsPeioW/+SaUWYwIugBFQl5mAA34vQBR2LQK++RqI5yb+OPmo\njARcACOgyDRglxBgnN8LkPVIi5ID9osd8LcnJIuleGcgN5WYLHTkkUeW3YTJ4/yahgAhoO8UAsyr\nv7EmYCNd+YsdRB4O9EVlegJOgbSlARtUeiZgsw1tj+3newGW/CrR79wz/0Sn6+CmS2DSqPFMwE4h\nQCcBiDkBmEH0O/dm1zHl9MpuVCIccAqi4BAg90zAYRcE6RECzH98mwAArfMxpSzypDe9JzAplDkT\nkOFnAuYVQHsIYIDOcAFkkXd+g0tgEqhLGrBZb39pwAZZ3wzkAsimnwlOLoG6U+cFQfpMA7YLABdA\nJv3OcPQxgTozxjRg1nLcY08D4iFALwaZ4uw9gbpSxzTggCEAeAiQh0E/4+ASqCNTmgYEPATowDAf\ncvJwoG6UlQbsVt+40oAugEyG/ZSj9wTqxAQtCOJpwGIo4mPOLoG6MGELgrSUp/A0YH6KWufAJVAH\nhk0DtpSF332mATPr8zRgaRS50ImPCVSdMS4IUnQaMF74YvTCz0FCmKcBC6LolY68J1Bl6jITMCMN\naGfG6L33I9k8AbTjacD8jGKpM5dAVanzgiDrw3cm7PNS0MLGV6nkCwFisPfvzq5jyhnVWoceDlSR\nOi8IkvpuQEWC338O/vJg7NlH5ndM2kMAgf5zRjudkS526j2BqlHHmYANMr8c1OCUe5pf7NMsbU8D\nCvR7HgJkMerVjntKQNI+km6X9CNJ90g6P5QfKuk2SVslfUPSwlC+d9jfGo4fMtL/wSRR55mAHb4d\n2AD22g/99u3J14c1bnjaxgBcAJmMY7nzPD2B54Bjzez1wBuA1ZKOBi4FLjOzw4AngPDF7qwFngjl\nl4XznF5khAADCSC84xpJCNBTAFGX+iLL7gE00oAwLwSY3zwlPy99E3rvtpZxgUYPwNOA2Yzr+w56\nSsASng67e4UfA44Fvh3KrwROCtsnhn3C8eM0Kd/eMCrqvCBIFwG01GGz8Esvg2jhnKQ8BOjIOG+Z\nXGMCkmYkbQZ2AjcADwK7zWxPOGUbsDxsLwceAQjHnwSWZDznOkmbJG3atWvXcP+LOlOXNGCz3t4h\nQObDNJPUsSb5WxsugE6M+z0zlwTMbNbM3gCsAI4CXjVsxWZ2uZmtMrNVS5cuHfbp6kmdFwTpQwBz\nxGiv/dER56D3PzXA4yefMjrNfaUIzWy3pJuBY4BFkhaEd/sVwPZw2nZgJbBN0gLgJcBjBbZ5Mqjz\ngiADCSBULOBNF3kaMIOyouY82YGlkhaF7X2BE4AtwM3AyeG0NcDVYfuasE84fpP51wu1Usc04FA9\nAKcXZQ6b5ekJHARcKWmG5JL9ppldK+le4CpJFwI/BK4I518B/KWkrcDjwCkjaHd9mcA0oDMcZY+b\n95SAmd0JHJFR/hDJ+EB7+b8A7y6kdZNGnRcEcQGMhLIFAD5teHyM4NOAY1sQxAUwEqogAPBpw+Oh\nzguCuABGQlUEAC6B0VPnBUFcACOhSgIADwdGy1SmAZ1uVE0A4D2B0VFUGjCNpwFrTRUFAC6B0VDz\nBUGc4qmqAMDDgeKZkAVBnOKosgDAJVAso0gDlrogiDMsVRcAeDhQHCNKA3oIUF/qIADwnkAxlDUT\nMDXeMK8+nwlYKnURALgEhqfMmYD4TMAqUicBgIcDwzElC4I4+ambAMAlMDhjSgNWZ0EQpxd1FAB4\nODAYPhPQaaOuAgDvCfSPLwjitFFnAYBLoD98QRCnjboLADwcyI8vCOK0MQkCAJdAPnxBEKeNSREA\neDjQG18QxGljkgQALoHuVOB7AXxBkGoxaQIADwc6Yo0sQJTc03VIAzYWdrcz97jdR8AkCgBcAtmY\noWgGi2dbbtBKpwEBznwetKBZ7BTHpAoAPBzIRsLe/10QmFU3DWgmbNFhxGfOYutjFO0VHje5F2wZ\nTLIAwHsCnVn5VviDu+CLrw0WCOXjTgOG5zWEyZAWwBmPwt77I0Xp6pwRMOkCAO8JZBITJ/f9ga+B\n3/xv2AACAIoRQJQM9mnhfuiDMax/HvZ5cVMAzuiYBgGASyCTiIhIAmJ426fQfsswQdzugBGmAQ3D\nZNjy34APGXzgKSQlP/7eP3KmRQDg4UB3NJNkBv5wB3wmIjKwKNzzhS8IAiYDLYAPPYca3+DrjJVp\nuvkbeE+gB4YlN+o7/zcWqasAgIEWBAERv/tmOCt2AThjx3sCPRAhj/+6NcS3bUSP3QtY9k2aMwSw\niOYcBD74HDazVwg/khqdcpjGXgB4TyA3hqEz7sYW7NN6n3aZCZglgDiK4JcOwj4yCx+OiRcsSAnA\nKYtpFQB4T6B/Pvo0dulCJGuGA51mAjYihxjQ6T+GxYcRaQZIZRz9nb90plkA4BLITTMlpxl46a9h\nj96Z7LeFAIbBxiSmt6PPQW+7yLtbFWbaBQAugb4xDP7TZviTX4a3nAtHrm89YeMMdvqdaMlriDyX\nX2lcAAkugT4RSZxvH/xp5kWkj8Xewa8BLoA5cr9VSZqR9ENJ14b9QyXdJmmrpG9IWhjK9w77W8Px\nQ0bT9HLxi6i++N+ulX76q2cBW1L7lwKXmdlhwBPA2lC+FngilF8WznOcSuACmE8uCUhaAfxb4C/C\nvoBjgW+HU64ETgrbJ4Z9wvHj5K+8UwH8Mswmb0/gj4CPk2S7AJYAu81sT9jfBiwP28uBRwDC8SfD\n+Y5TGi6AzvSUgKR3AjvN7I4iK5a0TtImSZt27dpV5FM7TgsugO7k6Qm8FXiXpIeBq0jCgD8GFklq\nZBdWANvD9nZgJUA4/hLgsfYnNbPLzWyVma1aunTpUP8Jx+mEC6A3PSVgZmeb2QozOwQ4BbjJzH4X\nuBk4OZy2Brg6bF8T9gnHbzKzjM/aOs5ocQHkY5jZLJ8APiJpK0nMf0UovwJYEso/AmwYromO0z8u\ngPz0NVnIzL4LfDdsPwQclXHOvwDvLqBtjjMQLoD+8HmtzkThAugfl4AzMbgABsMl4EwELoDBcQk4\ntccFMBwuAafWuACGxyXg1BYXQDG4BJxa4gIoDpeAUztcAMXiEnBqhQugeFwCTm1wAYwGl4BTC1wA\no8Ml4FQeF8BocQk4lcYFMHpcAk5lcQGMB5eAU0lcAOPDJeBUDhfAeHEJOJXCBTB+XAJOZXABlINL\nwKkELoDycAk4peMCKBeXgFMqLoDycQk4peECqAYuAacUXADVwSXgjB0XQLVwCThjxQVQPVwCzthw\nAVQTl4AzFlwA1cUl4IwcF0C1cQk4I8UFUH1cAs7IcAHUA5eAMxJcAPXBJeAUjgugXrgEnEJxAdQP\nl4BTGC6AeuIScArBBVBfXALO0LgA6o1LwBkKF0D9cQk4A+MCmAxkZmW3AUk/B+4rux19cCDwaNmN\nyEmd2gr1am+d2grwcjNb2l64oIyWZHCfma0quxF5kbSpLu2tU1uhXu2tU1u74eGA40w5LgHHmXKq\nIoHLy25An9SpvXVqK9SrvXVqa0cqMTDoOE55VKUn4DhOSZQuAUmrJd0naaukDRVoz5ck7ZR0d6rs\nAEk3SHog/F4cyiXpC6Htd0p6YwntXSnpZkn3SrpH0llVbbOkfSTdLulHoa3nh/JDJd0W2vQNSQtD\n+d5hf2s4fsi42ppq84ykH0q6tuptHZRSJSBpBvgz4B3Aq4H3SHp1mW0CvgysbivbANxoZocDN4Z9\nSNp9ePhZB3xxTG1Mswf4qJm9GjgaODO8hlVs83PAsWb2euANwGpJRwOXApeZ2WHAE8DacP5a4IlQ\nflk4b9ycBWxJ7Ve5rYNhZqX9AMcA16f2zwbOLrNNoR2HAHen9u8DDgrbB5HMawD4X8B7ss4rse1X\nAydUvc3Ai4AfAG8mmXCzoP2aAK4HjgnbC8J5GmMbV5AI9FjgWkBVbeswP2WHA8uBR1L720JZ1Vhm\nZjvC9k+BZWG7Uu0PXdAjgNuoaJtD93ozsBO4AXgQ2G1mezLa02xrOP4ksGRcbQX+CPg4EIf9JVS3\nrQNTtgRqhyWqr1xKRdJ+wHeAD5vZU+ljVWqzmc2a2RtI3mWPAl5VcpMykfROYKeZ3VF2W0ZN2RLY\nDqxM7a8IZVXjZ5IOAgi/d4bySrRf0l4kAvgrM/vrUFzpNpvZbuBmki71IkmNKezp9jTbGo6/BHhs\nTE18K/AuSQ8DV5GEBH9c0bYORdkS+D5weBhxXQicAlxTcpuyuAZYE7bXkMTdjfLTwoj70cCTqS74\nWFDyUb4rgC1m9vnUocq1WdJSSYvC9r4kYxdbSGRwcoe2Nv4PJwM3hV7NyDGzs81shZkdQnJd3mRm\nv1vFtg5N2YMSwG8B95PEhp+sQHu+DuwAXiCJ+daSxHY3Ag8Afw8cEM4VSXbjQeAuYFUJ7X0bSVf/\nTmBz+PmtKrYZeB3ww9DWu4FPhfJXALcDW4FvAXuH8n3C/tZw/BUlXRO/CVxbh7YO8uMzBh1nyik7\nHHAcp2RcAo4z5bgEHGfKcQk4zpTjEnCcKccl4DhTjkvAcaYcl4DjTDn/H8uriv6cJG31AAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O79BrK-bC8oh"
      },
      "source": [
        "## Random HSV in YIQ\n",
        "This operation changes color scale of a given RGB image to YIQ but here delta hue and saturation values are picked randomly from the given range."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zZBI-9XvBSuh",
        "outputId": "452460e3-13ee-4eb7-ef4e-dd8a86c897eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "delta = 0.5\n",
        "lower_saturation = 0.1\n",
        "upper_saturation = 0.9\n",
        "lower_value = 0.2\n",
        "upper_value = 0.8\n",
        "rand_hsvinyiq = tfa.image.random_hsv_in_yiq(img, delta, lower_saturation, upper_saturation, lower_value, upper_value)\n",
        "_ = plt.imshow(rand_hsvinyiq)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ruyvVnmCDBgj"
      },
      "source": [
        "## Adjust HSV in YIQ\n",
        "This operation changes color scale of a given RGB image to YIQ but here instead of choosing randomly, delta hue and saturation values are inputs form the user."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vbCdwGtYChnQ",
        "outputId": "430b5cf5-8a0a-497b-fa77-af6792bd5bf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "delta = 0.5\n",
        "saturation = 0.3\n",
        "value = 0.6\n",
        "adj_hsvinyiq = tfa.image.adjust_hsv_in_yiq(img, delta, saturation, value)\n",
        "_ = plt.imshow(adj_hsvinyiq)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3gFu7ad9HZv9A1/ZPgC8XpDoRKTqdMSgSOYWASOQUAiKRUwiIRE4hIBI5hYBI5BQCIpFT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\nEjmFgEjkFAIikft/x2XfotEpnLcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fdbCDYJkG8Gv"
      },
      "source": [
        "## Dense Image Warp\n",
        "This operation is for non-linear warp of any image specified by the flow field of the offset vector (here used random values for example). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dG557eQDDtSK",
        "outputId": "641b79fd-e23a-4bb5-d162-eb670b2b88d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "input_img = tf.image.convert_image_dtype(tf.expand_dims(img, 0), tf.dtypes.float32)\n",
        "\n",
        "flow_shape = [1, input_img.shape[1], input_img.shape[2], 2]\n",
        "init_flows = np.float32(np.random.normal(size=flow_shape) * 2.0)\n",
        "dense_img_warp = tfa.image.dense_image_warp(input_img, init_flows)\n",
        "dense_img_warp = tf.squeeze(dense_img_warp, 0)\n",
        "_ = plt.imshow(dense_img_warp)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CKPzLBPZv7oXpRgZKPOxKappT0kdXKSqcvGYJ3rg0f4B++G62Dd30qbygBTGSZ4ufL6uV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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FcLMnSKYPcjA"
      },
      "source": [
        "## Euclidian Distance Transform\n",
        "This operation updates the pixel value with the euclidian distance from the foreground pixel to the background one.\n",
        "* Note : It takes only binary image and results in transformed image. If a different image is given it results in a image with single value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-OMh6oeRQaYQ",
        "outputId": "4f7e9aca-ed78-4e4d-8c06-6833ec899797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "gray = tf.image.convert_image_dtype(bw_img,tf.uint8)\n",
        "# The op expects a batch of images, so add a batch dimension\n",
        "gray = tf.expand_dims(gray, 0)\n",
        "eucid = tfa.image.euclidean_dist_transform(gray)\n",
        "eucid = tf.squeeze(eucid, (0, -1))\n",
        "_ = plt.imshow(eucid, cmap='gray')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9kLebmf1Jbvt3zOwXdmDv3Wb2NTP7vpn9o5l9fF9tNrOGmf2Dmf2/3Nb/mre/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\nCo5EQIiCIxEQouBIBIQoOP8fIPbL/VrfJikAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}