bulentsiyah/toplu_veri_artirma.ipynb

## toplu_veri_artirma.ipynb
{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "toplu_veri_artirma.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/bulentsiyah/5147fc9f090e5f1bad93f400ebd54b36/toplu_veri_artirma.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "MSzO-1myS-AZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2519
        },
        "outputId": "bdc1693f-f458-419c-fc0c-0e602a08f5b1"
      },
      "cell_type": "code",
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Done.\n",
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            "gpg: keybox '/tmp/tmp4td159p_/pubring.gpg' created\n",
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            "Setting up fuse (2.9.7-1ubuntu1) ...\n",
            "Setting up google-drive-ocamlfuse (0.6.21-0ubuntu2) ...\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "muHqdyJUZeU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "71d97e28-95e5-4bb4-c0cb-c606cd004694"
      },
      "cell_type": "code",
      "source": [
        "!google-drive-ocamlfuse drive\n",
        "!ls\n",
        "\n",
        "import os\n",
        "os.chdir('/content/drive/zzColab')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Error: Mountpoint drive should be an existing directory.\n",
            "gans_images\t\t\t\ttest.csv\t\t     train.csv\n",
            "Generative_Adversarial_Networks .ipynb\ttoplu_veri_artirma.ipynb     udemymerve\n",
            "mnist_cnn_keras.ipynb\t\t\ttoplu_veri_artirma_kaynak\n",
            "result_end_keras.csv\t\t\ttoplu_veri_artirma_sonuclar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Zg-tcS52Ttb2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"**Paketlerin Yüklenmesi**\"\"\"\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MvOCQ3SrT-cY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"### Kullanılacak veri artırma yönteminin seçilmesi ve hiper parametrelerinin belirlenmesi işlemleri\"\"\"\n",
        "# kullanılacak veri artırma tekniklerini tanımla\n",
        "datagen = ImageDataGenerator(rotation_range=30,\n",
        "                             width_shift_range=0.2,\n",
        "                             height_shift_range=0.2,\n",
        "                             shear_range=0.2,\n",
        "                             zoom_range=0.2,\n",
        "                             horizontal_flip=True,\n",
        "                             vertical_flip=True,\n",
        "                             fill_mode='nearest')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Dl-e8qSNZF9k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def resim_cogaltma(resim_adi, kopya_sayisi):\n",
        "  \"\"\"**Artırma işlemi yapılacak olan görüntünün dosyadan okunması işlemi**\"\"\"\n",
        "  print('işlem yapılan:', ''+resim_adi)\n",
        "\n",
        "  img = load_img('/content/drive/zzColab/toplu_veri_artirma_kaynak/'+resim_adi)\n",
        "  x = img_to_array(img)\n",
        "  x = x.reshape((1,) + x.shape)\n",
        "  # tek resimden 50 tane farklı resim üret ve Artirilmis_Veri klasörüne .jpeg formatında kaydet.\n",
        "  i = 0\n",
        "\n",
        "  for batch in datagen.flow(x, batch_size=1,\n",
        "                            save_to_dir='toplu_veri_artirma_sonuclar', \n",
        "                            save_format='jpeg'):\n",
        "      i += 1\n",
        "      if i > kopya_sayisi:\n",
        "          break"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pDKEd_NmV48P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "0e0fab2e-94d6-46e3-8da1-33d91251b414"
      },
      "cell_type": "code",
      "source": [
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "listOfFiles = os.listdir('toplu_veri_artirma_kaynak') \n",
        "print(listOfFiles)\n",
        "for i in listOfFiles:\n",
        "  resim_cogaltma(i, 10)\n",
        "  print(i)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['gs.jpg', 'kedi.jpg']\n",
            "işlem yapılan: gs.jpg\n",
            "gs.jpg\n",
            "işlem yapılan: kedi.jpg\n",
            "kedi.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QvleKY1eIFGX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "bf00bfaf-52ca-4703-d207-ea4edfd382ec"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\"\"\"### CIFAR 10 veri kümesi ile veri artırma örnekleri ve görselleştirme\"\"\"\n",
        "\n",
        "from keras.datasets import cifar10\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from matplotlib import pyplot\n",
        "from keras import backend as K\n",
        "import numpy as np\n",
        "\n",
        "# Veriyi indirme ve eğitim test kümelerini rastgele oluşturma\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
        "\n",
        "\n",
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "\n",
        "X_train/=255\n",
        "X_test/=255\n",
        "\n",
        "\"\"\"# Orijinal Görüntüler\"\"\"\n",
        "\n",
        "datagen = ImageDataGenerator()\n",
        "datagen.fit(X_train)\n",
        "\n",
        "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=4, seed=499):\n",
        "  for i in range(0,4):\n",
        "    pyplot.subplot(220 +1 +i)\n",
        "    pyplot.imshow(X_batch[i])\n",
        "  pyplot.show()\n",
        "  break\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 29s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1xnYmw4sXjoU+4SiKoiiBMKUnnDvuuAPPP/88yuUyrr/+epxzzjm4+eab4bou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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf0b671ac8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WrH5TaNrIJ1m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "c23986f3-4751-4aef-b1d2-cb0d8d1d361f"
      },
      "cell_type": "code",
      "source": [
        "\"\"\"# DÖNDÜRME\"\"\"\n",
        "\n",
        "datagen = ImageDataGenerator(rotation_range=359)\n",
        "datagen.fit(X_train)\n",
        "\n",
        "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=4, seed=499):\n",
        "  for i in range(0,4):\n",
        "    pyplot.subplot(220 +1 +i)\n",
        "    pyplot.imshow(X_batch[i])\n",
        "  pyplot.show()\n",
        "  break"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7ixKow9TslNPPqRjLADDYlERz8PAhWEIv9D0zELBUZ81xzTkcuFxoE2PRNcfR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\nhP8CmGWG9re1DmwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf0b6b8780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G2cpnuAkIKqJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "aa7f5107-44d1-48a8-9e00-3f6f984e716b"
      },
      "cell_type": "code",
      "source": [
        "\"\"\"# DİKEYDE KAYDIRMA\"\"\"\n",
        "\n",
        "datagen = ImageDataGenerator(height_shift_range=0.5)\n",
        "datagen.fit(X_train)\n",
        "\n",
        "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=4, seed=499):\n",
        "  for i in range(0,4):\n",
        "    pyplot.subplot(220 +1 +i)\n",
        "    pyplot.imshow(X_batch[i])\n",
        "  pyplot.show()\n",
        "  break"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IcZIUaTtgNByvRKZnGI3GFmmLhNlOJlxdYO5sTrR58QUryL7i8ivIXrBwIdn/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\nwhFCCOELmnCEEEL4wv8PMss9F7hSIaoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf0b732358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "BoECV9XOIOQJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "457991c8-c0c2-4bfd-fda1-27c349acb5b2"
      },
      "cell_type": "code",
      "source": [
        "\"\"\"# YATAYDA SİMETRİĞİNİ ALMA\"\"\"\n",
        "\n",
        "datagen = ImageDataGenerator(horizontal_flip=True)\n",
        "datagen.fit(X_train)\n",
        "\n",
        "for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=4, seed=499):\n",
        "  for i in range(0,4):\n",
        "    pyplot.subplot(220 +1 +i)\n",
        "    pyplot.imshow(X_batch[i])\n",
        "  pyplot.show()\n",
        "  break"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9t4cXErniPmtZFSk739wO6aIBRVEUZbIxpiecW265Bc8++yzK5TKuvPJKnHji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\no1IXCYUAz/ONgoGpIXHcgtBsw6zHAIAtNNdiWWi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mcHAuy9daIsFjrSwWHmSzGWEXUSpx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\no/68OCwXf15MX7ZlwhkbG6NWrVVVVUY11KXE7XbD5+OKp8lkcmaJWF1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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbf08c3b0f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}