Mencari Doujin dan Manga Dengan Potongan Gambar

Cari.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cari.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqq7McHGVEG_",
        "colab_type": "text"
      },
      "source": [
        "# Mencari Doujin atau Manga dengan Gambar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3alvY3HpVi9v",
        "colab_type": "text"
      },
      "source": [
        "##### Ingat ini hanya untuk pembelajaran\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wfBb8c9WDcT",
        "colab_type": "text"
      },
      "source": [
        "## 1. Mempersiapkan Dependencies "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tc0LOUheWSbj",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   numpy\n",
        "*   opencv-python\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqhOQ7OVWk9I",
        "colab_type": "text"
      },
      "source": [
        "## 2. Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fB-ExiCXW_BD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pip install numpy opencv-python"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYAE21PKXBPM",
        "colab_type": "text"
      },
      "source": [
        "## 3. Silahkan copy Folder FAKKU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afoH806lXUwk",
        "colab_type": "text"
      },
      "source": [
        "https://drive.google.com/drive/folders/1-_JkK-AE6JBlcp9vR-eGlyBPdITo1-yh?usp=sharing\n",
        "\n",
        "*jika memerlukan ijin silahkan PM [Iqbal Rifai](https://m.me/composer.json)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1q5pJY8Y3wk",
        "colab_type": "text"
      },
      "source": [
        "## 4. Pindah ke Folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7jcLr64XUFn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cd FAKKU"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyVGfQ7NZKL4",
        "colab_type": "text"
      },
      "source": [
        "## 5. Upload Gambar yang ingin di cari ke Folder FAKKU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biOnNtjTZk1Y",
        "colab_type": "text"
      },
      "source": [
        "## 6. Eksekusi dengan menjalankan Script di bawah ini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJTbqAoUZwYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys, os\n",
        "import argparse\n",
        "import numpy as np\n",
        "import zipfile\n",
        "import cv2\n",
        "import datetime\n",
        "\n",
        "#Import Image to be searched for\n",
        "#parser = argparse.ArgumentParser()\n",
        "#parser.add_argument('-i', type=argparse.FileType('r', encoding='UTF-8'), required=True)\n",
        "#args = parser.parse_args()\n",
        "#small_image = cv2.imread(args)\n",
        "\n",
        "\n",
        "startimage = \"image.png\" #Ganti dengan nama gambar yang tadi di Upload\n",
        "small_image = cv2.imread(startimage)\n",
        "\n",
        "#create vars\n",
        "filelist = []\n",
        "similarity = []\n",
        "bestMatch = sys.float_info.max\n",
        "counter = -1\n",
        "\n",
        "##get all zips in subdirectories\n",
        "for subdir, dirs, files in os.walk(\".\"):\n",
        "    for dir in dirs:\n",
        "        path = os.path.join(subdir, dir)\n",
        "        for file in os.listdir(path):\n",
        "            if file.endswith(\".zip\"):                \n",
        "                filelist.append(os.path.join(path, file))\n",
        "\n",
        "#go through all found zips\n",
        "for file in filelist:\n",
        "    counter += 1\n",
        "    print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + \": progress: \" \n",
        "          + str(counter) + \"/\" + str(len(filelist)))\n",
        "    zippedImgs = zipfile.ZipFile(file)\n",
        "\n",
        "    if(bestMatch == 0.0):\n",
        "        break\n",
        "\n",
        "    #go through all images in all zips\n",
        "    for img in zippedImgs.namelist():\n",
        "        if(\".jpg\" in img or \".png\" in img or \".gif\" in img):\n",
        "            #transform zipped files to images\n",
        "            data = zippedImgs.read(img)\n",
        "            nparr = np.fromstring(data, np.uint8)\n",
        "            large_image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)\n",
        "\n",
        "            #check if image is a closer match than all previous images\n",
        "            if(large_image.shape[0] < small_image.shape[0] or large_image.shape[1] < small_image.shape[1]):\n",
        "                continue\n",
        "\n",
        "            resultData = cv2.matchTemplate(large_image, small_image, cv2.TM_SQDIFF_NORMED)\n",
        "            result = cv2.minMaxLoc(resultData);\n",
        "            similarity.append([result, file, img])\n",
        "\n",
        "            #Draw result rectangle\n",
        "            if(result[0] < bestMatch):\n",
        "                bestMatch = result[0]\n",
        "                # We want the minimum squared difference\n",
        "                mn,_,mnLoc,_ = cv2.minMaxLoc(resultData)\n",
        "                # Draw the rectangle:\n",
        "                # Step 1: Extract the coordinates of our best match\n",
        "                MPx,MPy = mnLoc\n",
        "                # Step 2: Get the size of the template. This is the same size as the match.\n",
        "                trows,tcols = small_image.shape[:2]\n",
        "                # Step 3: Draw the rectangle on large_image\n",
        "                cv2.rectangle(large_image, (MPx,MPy),(MPx+tcols,MPy+trows),(0,0,255),2)\n",
        "                cv2.imwrite(\"hasil.png\", large_image)\n",
        "                print(\"Hasil Yang Sesuai:\")\n",
        "                print([result, file, img])\n",
        "\n",
        "similarity.sort(key=lambda x: x[0][0])\n",
        "\n",
        "print(\"\\nTotal Hasil yang sesuai:\")\n",
        "print(similarity[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cfz8_Sc2Z7EU",
        "colab_type": "text"
      },
      "source": [
        "Tungu hingga Selesai"
      ]
    }
  ]
}