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Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb 312 KiB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWyPGNkCGhIX"
   },
   "source": [
    "# Part I : Create Your Own Dataset and Train it with ConvNets\n",
    "\n",
    "In this part of the notebook, you will set up your own dataset for image classification. Please specify \n",
    "under `queries` the image categories you are interested in. Under `limit` specify the number of images \n",
    "you want to download for each image category. \n",
    "\n",
    "You do not need to understand the class `simple_image_download`, just execute the cell after you have specified \n",
    "the download folder.\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rckz3ZuGhIc",
    "outputId": "6f615f06-759a-4eea-839e-658155df8d36"
   },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
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      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
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      "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/thumb/c/c0/Robert_De_Niro_KVIFF_portrait.jpg/1200px-Robert_De_Niro_KVIFF_portrait.jpg - cannot identify image file <_io.BytesIO object at 0x7fae297b5770>\n",
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      "Saved 1 images\n",
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      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
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      "Saved 2 images\n",
      "Found 2 image links\n",
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      "Saved 2 images\n"
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     ]
   "source": [
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    "from selenium import webdriver\n",
    "from selenium.webdriver.firefox.options import Options\n",
    "from Image_crawling import Image_crawling\n",
    "\n",
    "# Specifiy the queries\n",
    "queries = [\"brad pitt\",\"johnny depp\", \"leonardo dicaprio\", \"robert de niro\", \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
    "#queries = [\"Bart Simpson\",\"Homer Simpson\"]\n",
    "limit = 2\n",
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    "download_folder = \"./brandnew_images/train/\"\n",
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    "waittime = 0.1  # Time to wait between actions, depends on the number of pictures you want to crawl. More pictures means you need to wait longer for them to load. \n",
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    "# Set options\n",
    "options = webdriver.FirefoxOptions()\n",
    "options.add_argument('--headless')\n",
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    "# Create Driver\n",
    "driver = webdriver.Firefox(options=options, executable_path=\"/usr/bin/geckodriver\")\n",
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    "# create instance of crawler\n",
    "image_crawling = Image_crawling(driver, waittime=waittime)\n",
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    "# Find urls and download images\n",
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    "for query in queries:\n",
    "    # Craws image urls:\n",
    "    image_urls = image_crawling.fetch_image_urls(query, limit)\n",
    "      \n",
    "    # download images\n",
    "    image_crawling.download_image(download_folder + query)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CRHl9UX6GhIs"
   },
   "source": [
    "Please check carefully the downloaded images, there may be a lot of garbage! You definitely need to \n",
    "clean the data.\n",
    "\n",
    "In the following, you will apply data augmentation to your data set."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3SX21FtcGhIu"
   },
   "outputs": [],
   "source": [
    "# General imports\n",
    "import tensorflow as tf\n",
    "tf.compat.v1.enable_eager_execution(\n",
    "    config=None, device_policy=None, execution_mode=None\n",
    ")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
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    "import os, datetime\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
    "from tensorflow.keras.callbacks import TensorBoard \n",
    "\n",
    "# Shortcut for displaying images\n",
    "def plot_img(img):\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "    \n",
    "# The target image size can be fixed here (quadratic)\n",
    "# the ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
    "image_size = 150"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rN_Mp1rmGhI1",
    "outputId": "6417b1f9-e7d4-4d56-a213-191f9d17524a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Found 480 images belonging to 8 classes.\n"
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     "data": {
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
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     "execution_count": 8,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Class ImageDataGenerator() returns an iterator holding one batch of images\n",
    "# the constructor takes arguments defining the different image transformations\n",
    "# for augmentation purposes (rotation, x-/y-shift, intensity scaling - here 1./255 \n",
    "# to scale range to [0, 1], shear, zoom, flip, ... )\n",
    "\n",
    "class_names = [\"angelina jolie\", \"brad pitt\",\"catherine deneuve\" , \"johnny depp\",\"leonardo dicaprio\", \"marion cotillard\", \"robert de niro\",\"sandra bullock\"]\n",
    "\n",
    "\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rotation_range=10,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest')\n",
    "\n",
    "\n",
    "dir_iter = train_datagen.flow_from_directory('./train/', \n",
    "                                         target_size=(image_size, image_size),\n",
    "                                         classes=class_names,\n",
    "                                         batch_size=25, class_mode='sparse', shuffle=False)\n",
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