lowpolypy.ipynb

lowpolypy.ipynb

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  "metadata": {
    "colab": {
      "name": "lowpolypy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOPEXI1y96rAsxvE5J/3e20",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/maschere/6c789d70bbdaed2d89e1742f9d50a508/lowpolypy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lv6TJQvp1-jT",
        "colab_type": "text"
      },
      "source": [
        "# Setup environment\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0IVWk1L3u5Ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/tasercake/lowpolypy --depth=1\n",
        "%cd lowpolypy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_auTNwZfvjZR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install -r requirements.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4vzv4DUwyUi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from lowpolypy import run\n",
        "from PIL import Image\n",
        "from google.colab import files\n",
        "from glob import glob\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1geg0aM2Ghh",
        "colab_type": "text"
      },
      "source": [
        "# select and process image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M68-r3wC2tvr",
        "colab_type": "text"
      },
      "source": [
        "### choose image by url"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cP557BeC2TFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -rf /image-download\n",
        "!wget -P /image-download 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/bf/Mona_Lisa-restored.jpg/1200px-Mona_Lisa-restored.jpg'\n",
        "img_file = glob(\"/image-download/*\")[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrjs39ji20IQ",
        "colab_type": "text"
      },
      "source": [
        "### upload an image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfSh30Ng24oh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_file = list(files.upload().keys())[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov9zt85m5h-o",
        "colab_type": "text"
      },
      "source": [
        "### process it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ag8Q411sy3ws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f = run.run({'mode': 'run', 'image_paths': [img_file],\n",
        " 'output_dir': None, 'longest_edge': 800,\n",
        " 'output_size': 2500, 'polygon_method': 'delaunay',\n",
        " 'num_canny_points': 2500, 'num_laplace_points': 0,\n",
        " 'num_random_points': 0, 'canny_low_threshold': 150,\n",
        " 'canny_high_threshold': 200,\n",
        " 'random_replace_ratio': 0.01, 'jiggle_ratio': 0.003,\n",
        " 'gradient': 'none', 'post_saturation': 0.1, 'post_brightness': 0.0,\n",
        " 'post_contrast': 0.25, 'visualize_canny': False,\n",
        " 'visualize_laplace': False, 'visualize_points': False})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdOCecUD2Nyp",
        "colab_type": "text"
      },
      "source": [
        "# Show output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SW6BIaSu0Bwt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Image.open((list(f.keys())[0]))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}

Fantastic work!

I faced an issue when trying to upload images.
Should it be files.upload().keys() instead of .values()?

Thank you.

And yes, I fixed the upload code

Would it be alright if I linked to this gist from the main repo's Readme? That would help more people get started quickly.
Thanks again for putting this notebook together :)

Absolutely, go ahead!