tif_loader.ipynb

{
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  "metadata": {
    "colab": {
      "name": "tif_loader.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "swift",
      "display_name": "Swift"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZRlD4utdPuX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "// Helper function for downloading the tif.\n",
        "import Foundation\n",
        "import FoundationNetworking\n",
        "func download(from sourceString: String, to destinationString: String) {\n",
        "    let source = URL(string: sourceString)!\n",
        "    let destination = URL(fileURLWithPath: destinationString)\n",
        "    let data = try! Data.init(contentsOf: source)\n",
        "    try! data.write(to: destination)\n",
        "}\n",
        "\n",
        "// Thanks to https://www.nightprogrammer.org/development/multipage-tiff-example-download-test-image-file/ for this example.\n",
        "download(\n",
        "    from: \"http://www.nightprogrammer.org/wp-uploads/2013/02/multipage_tiff_example.tif\",\n",
        "    to: \"multipage_tiff_example.tif\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sz13-SHy6zhR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import Python\n",
        "let Image = Python.import(\"PIL.Image\")\n",
        "let np = Python.import(\"numpy\")\n",
        "let im = Image.open(\"multipage_tiff_example.tif\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jeXkaw67X_f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import TensorFlow\n",
        "func tensor(image: PythonObject, frame: Int) -> Tensor<UInt8>? {\n",
        "    image.seek(frame)\n",
        "    return Tensor(numpy: np.array(image))\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsoQwKip7iUL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d1eadb7d-ec4f-4f66-9166-d586a5692ed3"
      },
      "source": [
        "tensor(image: im, frame: 0)?.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "▿ Optional<TensorShape>\n",
              "  ▿ some : [600, 800, 3]\n",
              "    ▿ dimensions : 3 elements\n",
              "      - 0 : 600\n",
              "      - 1 : 800\n",
              "      - 2 : 3\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHBb7yBI8al0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d760479f-cf4a-4da6-91c0-8fa753907eac"
      },
      "source": [
        "tensor(image: im, frame: 1)?.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "▿ Optional<TensorShape>\n",
              "  ▿ some : [600, 800, 3]\n",
              "    ▿ dimensions : 3 elements\n",
              "      - 0 : 600\n",
              "      - 1 : 800\n",
              "      - 2 : 3\n"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kdz6FVTT8dEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}