photo_to_painting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HasyimAP/SandBox/blob/main/photo_to_painting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8tzin82-AFEL"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "from google.colab.patches import cv2_imshow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "9L8b0SkZAGLu"
      },
      "outputs": [],
      "source": [
        "def read_file(filename):\n",
        "  img = cv2.imread(filename)\n",
        "  cv2_imshow(img)\n",
        "  return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "52SoupfWBjQk"
      },
      "outputs": [],
      "source": [
        "def edge_mask(img, line_size, blur_value):\n",
        "  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  gray_blur = cv2.medianBlur(gray, blur_value)\n",
        "  edges = cv2.adaptiveThreshold(gray_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, line_size, blur_value)\n",
        "  return edges\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qyUlneIIB2kb"
      },
      "outputs": [],
      "source": [
        "def color_quantization(img, k):\n",
        "# Transform the image\n",
        "  data = np.float32(img).reshape((-1, 3))\n",
        "\n",
        "# Determine criteria\n",
        "  criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 0.001)\n",
        "\n",
        "# Implementing K-Means\n",
        "  ret, label, center = cv2.kmeans(data, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)\n",
        "  center = np.uint8(center)\n",
        "  result = center[label.flatten()]\n",
        "  result = result.reshape(img.shape)\n",
        "  return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2KbI7xyqCW3T"
      },
      "outputs": [],
      "source": [
        "#upload image\n",
        "uploaded = files.upload()\n",
        "filename = next(iter(uploaded))\n",
        "img = read_file(filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NZTAvaitCLAD"
      },
      "outputs": [],
      "source": [
        "line_size = 5\n",
        "blur_value = 5\n",
        "edges = edge_mask(img, line_size, blur_value)\n",
        "cv2_imshow(edges)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_color = 10\n",
        "quantized_img = color_quantization(img, total_color)\n",
        "blurred = cv2.bilateralFilter(quantized_img, d=9, sigmaColor=250,sigmaSpace=250)\n",
        "cv2_imshow(blurred)"
      ],
      "metadata": {
        "id": "VYPLb2OsXSWA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "painting = cv2.bitwise_and(blurred, blurred, mask=edges)\n",
        "cv2_imshow(painting)"
      ],
      "metadata": {
        "id": "jOrq9ZMRXUf6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2>Note</h2>\n",
        "\n",
        "If you use a higher resolution picture it will take longer time to finish"
      ],
      "metadata": {
        "id": "z7A09oDYVYZq"
      }
    }
  ],
  "metadata": {
    "colab": {
      "name": "photo to painting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP5vfmJnm3f5X+8gbjKg8Be",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}