{
 "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",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8rckz3ZuGhIc",
    "outputId": "6f615f06-759a-4eea-839e-658155df8d36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "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",
      "Saved 1 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n",
      "Found 2 image links\n",
      "Saved 2 images\n"
     ]
    }
   ],
   "source": [
    "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",
    "download_folder = \"./brandnew_images/train/\"\n",
    "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",
    "\n",
    "# Set options\n",
    "options = webdriver.FirefoxOptions()\n",
    "options.add_argument('--headless')\n",
    "\n",
    "# Create Driver\n",
    "driver = webdriver.Firefox(options=options, executable_path=\"/usr/bin/geckodriver\")\n",
    "\n",
    "# create instance of crawler\n",
    "image_crawling = Image_crawling(driver, waittime=waittime)\n",
    "\n",
    "# Find urls and download images\n",
    "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",
   "execution_count": 57,
   "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",
    "import os, datetime\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",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rN_Mp1rmGhI1",
    "outputId": "6417b1f9-e7d4-4d56-a213-191f9d17524a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 420 images belonging to 8 classes.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "\n",
    "plot_img(dir_iter[0][0][1,...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V2fYccc8GhJF"
   },
   "source": [
    "Before you continue, you need to split the downloaded images into a `train` folder and into a `validation` folder."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "colab_type": "raw",
    "id": "VamXG4FoGhJH"
   },
   "source": [
    "./\n",
    "├── train\n",
    "│   ├── brad pitt\n",
    "│   └── johnny deep\n",
    "|   ├── leonardo di caprio\n",
    "|   └── ...\n",
    "│       \n",
    "└── validation\n",
    "    ├── brad pitt\n",
    "    ├── johnny deep\n",
    "    ├── leonardo di caprio\n",
    "    └── ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9322su6vGhJJ"
   },
   "source": [
    "If you want to use the example of this jupyter notebook, you can use the images provided in the ./train and ./validation folders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xPqJWgeAGhJL"
   },
   "source": [
    "## Define a ConvNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UuJV4JBKGhJO"
   },
   "outputs": [],
   "source": [
    "batch_size = 20\n",
    "num_train_images = 480\n",
    "num_valid_images = 80\n",
    "num_classes = 8\n",
    "\n",
    "model_scratch = Sequential()\n",
    "model_scratch.add(Conv2D(32, (3, 3), input_shape=(image_size, image_size, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(32, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model_scratch.add(Conv2D(64, (3, 3)))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "# this converts our 3D feature maps to 1D feature vectors\n",
    "model_scratch.add(Flatten())  \n",
    "model_scratch.add(Dense(64))\n",
    "model_scratch.add(Activation('relu'))\n",
    "model_scratch.add(Dropout(0.5))\n",
    "model_scratch.add(Dense(num_classes))\n",
    "model_scratch.add(Activation('softmax'))\n",
    "\n",
    "model_scratch.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JFdkIokMGhJT",
    "outputId": "63e7d032-4083-4fe0-d970-c10bf0c39a94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 420 images belonging to 8 classes.\n",
      "Found 70 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "# This is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# This is the augmentation configuration we will use for validation:\n",
    "# only rescaling\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# This is a generator that will read pictures found in\n",
    "# subfolers of './train', and indefinitely generate\n",
    "# batches of augmented image data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        './train',  # this is the target directory\n",
    "        target_size=(image_size, image_size),  # all images will be resized to 150x150\n",
    "        classes=class_names,\n",
    "        batch_size=batch_size)  \n",
    "\n",
    "# This is a similar generator, for validation data\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size = (image_size, image_size),\n",
    "        classes = class_names,\n",
    "        batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cytHiQUTGhJb"
   },
   "outputs": [],
   "source": [
    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C7dCbyXPGhJg",
    "outputId": "98b4085e-ed6d-43e2-831f-aec32161583f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "21/21 [==============================] - 7s 306ms/step - loss: 2.0885 - accuracy: 0.1262 - val_loss: 1.9665 - val_accuracy: 0.2000\n",
      "Epoch 2/20\n",
      "21/21 [==============================] - 6s 285ms/step - loss: 1.9984 - accuracy: 0.1738 - val_loss: 1.9472 - val_accuracy: 0.2714\n",
      "Epoch 3/20\n",
      "21/21 [==============================] - 6s 290ms/step - loss: 1.9498 - accuracy: 0.2143 - val_loss: 1.7938 - val_accuracy: 0.3571\n",
      "Epoch 4/20\n",
      "21/21 [==============================] - 6s 292ms/step - loss: 1.8720 - accuracy: 0.2571 - val_loss: 1.6480 - val_accuracy: 0.3714\n",
      "Epoch 5/20\n",
      "21/21 [==============================] - 6s 294ms/step - loss: 1.7373 - accuracy: 0.3071 - val_loss: 1.5296 - val_accuracy: 0.3714\n",
      "Epoch 6/20\n",
      "21/21 [==============================] - 6s 298ms/step - loss: 1.6746 - accuracy: 0.3262 - val_loss: 1.4689 - val_accuracy: 0.4857\n",
      "Epoch 7/20\n",
      "21/21 [==============================] - 6s 269ms/step - loss: 1.5790 - accuracy: 0.3786 - val_loss: 1.4480 - val_accuracy: 0.4571\n",
      "Epoch 8/20\n",
      "21/21 [==============================] - 6s 271ms/step - loss: 1.5066 - accuracy: 0.4024 - val_loss: 1.3394 - val_accuracy: 0.5143\n",
      "Epoch 9/20\n",
      "21/21 [==============================] - 6s 273ms/step - loss: 1.5292 - accuracy: 0.4214 - val_loss: 1.2919 - val_accuracy: 0.5286\n",
      "Epoch 10/20\n",
      "21/21 [==============================] - 6s 289ms/step - loss: 1.4593 - accuracy: 0.4214 - val_loss: 1.4683 - val_accuracy: 0.4286\n",
      "Epoch 11/20\n",
      "21/21 [==============================] - 6s 288ms/step - loss: 1.4929 - accuracy: 0.4405 - val_loss: 1.3502 - val_accuracy: 0.4714\n",
      "Epoch 12/20\n",
      "21/21 [==============================] - 6s 284ms/step - loss: 1.3252 - accuracy: 0.4667 - val_loss: 1.3498 - val_accuracy: 0.5429\n",
      "Epoch 13/20\n",
      "21/21 [==============================] - 6s 301ms/step - loss: 1.3037 - accuracy: 0.4786 - val_loss: 1.3477 - val_accuracy: 0.4857\n",
      "Epoch 14/20\n",
      "21/21 [==============================] - 6s 285ms/step - loss: 1.2823 - accuracy: 0.4952 - val_loss: 1.3954 - val_accuracy: 0.5143\n",
      "Epoch 15/20\n",
      "21/21 [==============================] - 6s 277ms/step - loss: 1.3229 - accuracy: 0.4738 - val_loss: 1.3522 - val_accuracy: 0.4714\n",
      "Epoch 16/20\n",
      "21/21 [==============================] - 6s 266ms/step - loss: 1.2398 - accuracy: 0.5095 - val_loss: 1.3212 - val_accuracy: 0.4857\n",
      "Epoch 17/20\n",
      "21/21 [==============================] - 6s 275ms/step - loss: 1.1783 - accuracy: 0.5714 - val_loss: 1.3451 - val_accuracy: 0.5286\n",
      "Epoch 18/20\n",
      "21/21 [==============================] - 6s 276ms/step - loss: 1.1516 - accuracy: 0.5357 - val_loss: 1.3049 - val_accuracy: 0.5714\n",
      "Epoch 19/20\n",
      "21/21 [==============================] - 6s 269ms/step - loss: 1.1335 - accuracy: 0.5738 - val_loss: 1.2275 - val_accuracy: 0.5286\n",
      "Epoch 20/20\n",
      "21/21 [==============================] - 6s 274ms/step - loss: 1.1489 - accuracy: 0.5452 - val_loss: 1.2404 - val_accuracy: 0.5571\n"
     ]
    }
   ],
   "source": [
    "history = model_scratch.fit(\n",
    "    train_generator,\n",
    "    epochs = 20,\n",
    "    validation_data = validation_generator,\n",
    "    callbacks = [tensorboard_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wt_ONw5PGhJm",
    "outputId": "e75d8a73-da49-4dbe-ffcf-7cb316be39a2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "\n",
    "os.makedirs(logdir, exist_ok=True)\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y8oAT4oUGhJs"
   },
   "source": [
    "# Part II : Transfer Learning\n",
    "\n",
    "\n",
    "Having to train an image-classification model using very little data is a common situation,\n",
    "which you’ll likely encounter in practice if you ever do computer vision in a\n",
    "professional context. A “few” samples can mean anywhere from a few hundred to a\n",
    "few tens of thousands of images. As a practical example, we’ll focus on classifying\n",
    "560 images belongig to 8 actors. We’ll use 480 pictures for training, and 80 for validation.\n",
    "\n",
    "\n",
    "## 2.1 Feature Extraction with a Pretrained Model\n",
    "\n",
    "Feature extraction consists of using the representations learned by a previously\n",
    "trained model to extract interesting features from new samples. These features are\n",
    "then run through a new classifier, which is trained from scratch.\n",
    "\n",
    "\n",
    "As you saw previously, ConvNets used for image classification comprise two parts:\n",
    "they start with a series of pooling and convolution layers, and they end with a densely\n",
    "connected classifier. The first part is called the _convolutional base_ of the model. In the\n",
    "case of convnets, feature extraction consists of taking the convolutional base of a previously\n",
    "trained network, running the new data through it, and training a new classifier\n",
    "on top of the output.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "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",
    "import os, datetime\n",
    "\n",
    "# Shortcuts to keras if (however from tensorflow)\n",
    "from tensorflow import keras\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",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"./Bilder/feature_extraction.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why only reuse the convolutional base? Could we reuse the densely connected\n",
    "classifier as well? In general, doing so should be avoided. The reason is that the representations\n",
    "learned by the convolutional base are likely to be more generic and, therefore,\n",
    "more reusable: the feature maps of a ConvNet are presence maps of generic\n",
    "concepts over a picture, which are likely to be useful regardless of the computer vision\n",
    "problem at hand. But the representations learned by the classifier will necessarily be\n",
    "specific to the set of classes on which the model was trained—they will only contain\n",
    "information about the presence probability of this or that class in the entire picture.\n",
    "Additionally, representations found in densely connected layers no longer contain any information about where objects are located in the input image; these layers get rid of\n",
    "the notion of space, whereas the object location is still described by convolutional feature\n",
    "maps. For problems where object location matters, densely connected features\n",
    "are largely useless.\n",
    "\n",
    "\n",
    "Note that the level of generality (and therefore reusability) of the representations\n",
    "extracted by specific convolution layers depends on the depth of the layer in the\n",
    "model. Layers that come earlier in the model extract local, highly generic feature\n",
    "maps (such as visual edges, colors, and textures), whereas layers that are higher up\n",
    "extract more-abstract concepts (such as “cat ear” or “dog eye”). So if your new dataset\n",
    "differs a lot from the dataset on which the original model was trained, you may be better\n",
    "off using only the first few layers of the model to do feature extraction, rather than\n",
    "using the entire convolutional base.\n",
    "\n",
    "\n",
    "\n",
    "In this case, because the ImageNet class set does not contain images of actors, we’ll \n",
    "choose not to use the densely connected layers, in order to cover\n",
    "the more general case where the class set of the new problem doesn’t overlap the\n",
    "class set of the original model. Let’s put this into practice by using the convolutional\n",
    "base of the VGG16 network, trained on ImageNet, to extract interesting features\n",
    "from actors, and then train a classifier for the 8 actors on top of\n",
    "these features.\n",
    "\n",
    "The VGG16 model, among others, comes prepackaged with Keras. You can import\n",
    "it from the `keras.applications` module. Many other image-classification models (all\n",
    "pretrained on the ImageNet dataset) are available as part of `keras.applications`:\n",
    "\n",
    "\n",
    "-  Xception\n",
    "-  ResNet\n",
    "-  MobileNet\n",
    "-  EfficientNet\n",
    "-  DenseNet\n",
    "-  etc.\n",
    "\n",
    "Let's instantiate the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Luec7pbGhJv",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 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",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eRes_n9BGhJ0"
   },
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
    "                                           include_top=False,\n",
    "                                           input_shape=(image_size, image_size, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vEIWLeqSGhJ5"
   },
   "source": [
    "You pass three arguments to the constructor:\n",
    "\n",
    "- `weights` specifies the weight checkpoint from which to initialize the model.\n",
    "\n",
    "- `include_top` refers to including (or not) the densely connected classifier on\n",
    "top of the network. By default, this densely connected classifier corresponds to\n",
    "the 1'000 classes from ImageNet. Because we intend to use our own densely\n",
    "connected classifier (with 8 classes of actors), we don’t need to\n",
    "include it.\n",
    "\n",
    "- `input_shape` is the shape of the image tensors that we’ll feed to the network.\n",
    "This argument is purely optional: if we don’t pass it, the network will be able to\n",
    "process inputs of any size. Here we pass it so that we can visualize (in the following\n",
    "summary) how the size of the feature maps shrinks with each new convolution\n",
    "and pooling layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here’s the detail of the architecture of the VGG16 convolutional base. It’s similar to\n",
    "the simple convnets you’re already familiar with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M7Bk7t1MGhJ6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_8 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DBSrhVORGhKH"
   },
   "source": [
    "\n",
    "The final feature map (output volume) has shape $(5, 5, 512)$. That's the feature on top of which we will stick a densely connected classifier.\n",
    "\n",
    "At this point, there are two ways how we could proceed:\n",
    "\n",
    "- __Approach 1__: Run the convolutional base over our dataset, record its output to a NumPy array\n",
    "on disk, and then use this data as input to a standalone, densely connected classifier\n",
    "similar to those you saw in Block 4 of this course. This solution is fast and\n",
    "cheap to run, because it only requires running the convolutional base once for\n",
    "every input image, and the convolutional base is by far the most expensive part\n",
    "of the pipeline. But for the same reason, this technique won’t allow us to use\n",
    "data augmentation.\n",
    "\n",
    "- __Approach 2__: Extend the model we have (`conv_base`) by adding `Dense` layers on top, and run\n",
    "the whole thing from end to end on the input data. This will allow us to use\n",
    "data augmentation, because every input image goes through the convolutional\n",
    "base every time it’s seen by the model. But for the same reason, this technique is\n",
    "far more expensive than the first.\n",
    "\n",
    "We’ll cover both techniques. Let’s walk through the code required to set up the first\n",
    "one: recording the output of `conv_base` on our data and using these outputs as inputs\n",
    "to a new model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mlpIDmSCGhKI"
   },
   "source": [
    "### 1. Approach : Fast feature extraction without data augmentation\n",
    "\n",
    "\n",
    "We’ll start by extracting features as NumPy arrays by calling the `predict()` method of\n",
    "the `conv_base` model on our training, and validation datasets.\n",
    "Let’s iterate over our datasets to extract the VGG16 features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 files belonging to 8 classes.\n",
      "Found 80 files belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "\n",
    "train_dataset = image_dataset_from_directory(\n",
    "    './train',\n",
    "    image_size=(150, 150),\n",
    "    batch_size=32,\n",
    "    shuffle=False,\n",
    "    label_mode=\"categorical\")\n",
    "\n",
    "validation_dataset = image_dataset_from_directory(\n",
    "    './validation',\n",
    "    image_size=(150, 150),\n",
    "    batch_size=32,\n",
    "    shuffle=False,\n",
    "    label_mode=\"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def get_features_and_labels(dataset):\n",
    "    all_features = []\n",
    "    all_labels = []\n",
    "    for images, labels in dataset:\n",
    "        preprocessed_images = keras.applications.vgg16.preprocess_input(images)\n",
    "        features = conv_base.predict(preprocessed_images)\n",
    "        all_features.append(features)\n",
    "        all_labels.append(labels)\n",
    "    return np.concatenate(all_features), np.concatenate(all_labels)\n",
    "\n",
    "train_features, train_labels = get_features_and_labels(train_dataset)\n",
    "val_features, val_labels = get_features_and_labels(validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importantly, `predict()` only expects images, not labels, but our current dataset yields\n",
    "batches that contain both images and their labels. Moreover, the VGG16 model expects\n",
    "inputs that are preprocessed with the function `keras.applications.vgg16.preprocess_input`, which scales pixel values to an appropriate range.\n",
    "The extracted features are currently of shape `(samples, 4, 4, 512)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 4, 4, 512)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the labels are now referring to the order of the folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 8)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80, 4, 4, 512)\n",
      "(80, 8)\n"
     ]
    }
   ],
   "source": [
    "print(val_features.shape)\n",
    "print(val_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(4, 4, 512))\n",
    "# Note the use of the Flatten layer before passing the\n",
    "# features to a Dense layer\n",
    "x = layers.Flatten()(inputs)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.7)(x)\n",
    "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_9 (InputLayer)        [(None, 4, 4, 512)]       0         \n",
      "                                                                 \n",
      " flatten_6 (Flatten)         (None, 8192)              0         \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 256)               2097408   \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 8)                 2056      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,099,464\n",
      "Trainable params: 2,099,464\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 42.8625 - accuracy: 0.3500 - val_loss: 15.6536 - val_accuracy: 0.4625\n",
      "Epoch 2/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 17.1892 - accuracy: 0.6083 - val_loss: 21.8502 - val_accuracy: 0.4125\n",
      "Epoch 3/30\n",
      "15/15 [==============================] - 1s 47ms/step - loss: 8.7772 - accuracy: 0.7167 - val_loss: 19.9887 - val_accuracy: 0.5000\n",
      "Epoch 4/30\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 6.2434 - accuracy: 0.8000 - val_loss: 17.7987 - val_accuracy: 0.4875\n",
      "Epoch 5/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 5.6720 - accuracy: 0.8250 - val_loss: 28.0569 - val_accuracy: 0.4750\n",
      "Epoch 6/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 6.5725 - accuracy: 0.8208 - val_loss: 21.5370 - val_accuracy: 0.5000\n",
      "Epoch 7/30\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 3.9265 - accuracy: 0.8458 - val_loss: 22.9832 - val_accuracy: 0.5125\n",
      "Epoch 8/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 2.6559 - accuracy: 0.8958 - val_loss: 35.3841 - val_accuracy: 0.3875\n",
      "Epoch 9/30\n",
      "15/15 [==============================] - 1s 47ms/step - loss: 4.1241 - accuracy: 0.8542 - val_loss: 23.3027 - val_accuracy: 0.5375\n",
      "Epoch 10/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 2.8569 - accuracy: 0.8958 - val_loss: 28.1831 - val_accuracy: 0.5000\n",
      "Epoch 11/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.8484 - accuracy: 0.9312 - val_loss: 27.4439 - val_accuracy: 0.4500\n",
      "Epoch 12/30\n",
      "15/15 [==============================] - 1s 36ms/step - loss: 2.2897 - accuracy: 0.9292 - val_loss: 25.6774 - val_accuracy: 0.5375\n",
      "Epoch 13/30\n",
      "15/15 [==============================] - 1s 36ms/step - loss: 2.3126 - accuracy: 0.9271 - val_loss: 31.6493 - val_accuracy: 0.4750\n",
      "Epoch 14/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.7738 - accuracy: 0.9292 - val_loss: 25.8194 - val_accuracy: 0.5375\n",
      "Epoch 15/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.2570 - accuracy: 0.9458 - val_loss: 25.7935 - val_accuracy: 0.5750\n",
      "Epoch 16/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 2.5836 - accuracy: 0.9167 - val_loss: 29.3070 - val_accuracy: 0.5125\n",
      "Epoch 17/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 1.7006 - accuracy: 0.9563 - val_loss: 35.8275 - val_accuracy: 0.5000\n",
      "Epoch 18/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.7990 - accuracy: 0.9396 - val_loss: 26.9765 - val_accuracy: 0.5500\n",
      "Epoch 19/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.9373 - accuracy: 0.9646 - val_loss: 32.0846 - val_accuracy: 0.5125\n",
      "Epoch 20/30\n",
      "15/15 [==============================] - 0s 34ms/step - loss: 2.0872 - accuracy: 0.9500 - val_loss: 30.0126 - val_accuracy: 0.5875\n",
      "Epoch 21/30\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 1.4677 - accuracy: 0.9604 - val_loss: 32.2186 - val_accuracy: 0.5500\n",
      "Epoch 22/30\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 1.6923 - accuracy: 0.9438 - val_loss: 32.9531 - val_accuracy: 0.5125\n",
      "Epoch 23/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 1.2237 - accuracy: 0.9542 - val_loss: 44.3842 - val_accuracy: 0.4375\n",
      "Epoch 24/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.7080 - accuracy: 0.9833 - val_loss: 32.2801 - val_accuracy: 0.5250\n",
      "Epoch 25/30\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 1.4296 - accuracy: 0.9667 - val_loss: 34.0057 - val_accuracy: 0.5125\n",
      "Epoch 26/30\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 1.3958 - accuracy: 0.9542 - val_loss: 39.1364 - val_accuracy: 0.5500\n",
      "Epoch 27/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.0060 - accuracy: 0.9604 - val_loss: 33.8739 - val_accuracy: 0.4875\n",
      "Epoch 28/30\n",
      "15/15 [==============================] - 0s 34ms/step - loss: 0.8546 - accuracy: 0.9563 - val_loss: 33.2485 - val_accuracy: 0.4750\n",
      "Epoch 29/30\n",
      "15/15 [==============================] - 0s 33ms/step - loss: 0.9944 - accuracy: 0.9563 - val_loss: 35.6355 - val_accuracy: 0.5125\n",
      "Epoch 30/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 0.8821 - accuracy: 0.9604 - val_loss: 36.2544 - val_accuracy: 0.5375\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])\n",
    "\n",
    "\n",
    "logdir = os.path.join(\"logs_feature_extraction\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "train_features, train_labels,\n",
    "epochs=30,\n",
    "validation_data=(val_features, val_labels),\n",
    "callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we’ll also use a `ModelCheckpoint` callback to save the model after each\n",
    "epoch. We’ll configure it with the path specifying where to save the file, as well as the\n",
    "arguments `save_best_only=True` and `monitor=\"val_loss\"`: they tell the callback to\n",
    "only save a new file (overwriting any previous one) when the current value of the\n",
    "`val_loss` metric is lower than at any previous time during training. This guarantees\n",
    "that your saved file will always contain the state of the model corresponding to its bestperforming\n",
    "training epoch, in terms of its performance on the validation data. As a\n",
    "result, we won’t have to retrain a new model for a lower number of epochs if we start\n",
    "overfitting: we can just reload our saved file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the loss and accuracy curves during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 5ms/step - loss: 36.2544 - accuracy: 0.5375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[36.254432678222656, 0.5375000238418579]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(val_features, val_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reach a validation accuracy of about 52% — much worse than we achieved in the\n",
    "previous section with the small model trained from scratch. \n",
    "\n",
    "The learning curves indicate that we’re overfitting almost from the start—\n",
    "despite using dropout with a fairly large rate. That’s because this technique doesn’t\n",
    "use data augmentation, which is essential for preventing overfitting with small image\n",
    "datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DJT-DgHvGhKu"
   },
   "source": [
    "### 2. Approach : Feature Extraction with Data Augmentation\n",
    "\n",
    "\n",
    "Now let’s review the second technique we mentioned for doing feature extraction,\n",
    "which is much slower and more expensive, but which allows us to use data augmentation\n",
    "during training: creating a model that chains the `conv_base` with a new dense\n",
    "classifier, and training it end to end on the inputs.\n",
    "\n",
    "\n",
    "In order to do this, we will first freeze the convolutional base. Freezing a layer or set of\n",
    "layers means preventing their weights from being updated during training. If we don’t\n",
    "do this, the representations that were previously learned by the convolutional base will\n",
    "be modified during training. Because the Dense layers on top are randomly initialized,\n",
    "very large weight updates would be propagated through the network, effectively\n",
    "destroying the representations previously learned.\n",
    "\n",
    "In Keras, we freeze a layer or model by setting its trainable attribute to `False`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "50DF9pH1GhKw"
   },
   "source": [
    "#### Instantiating and freezing the VGG16 convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=False)\n",
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting trainable to `False` empties the list of trainable weights of the layer or model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the list of trainable weights before and after freezing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 26\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights before freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after freezing the conv base: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights after freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a new model that chains together\n",
    "\n",
    "1. A data augmentation stage\n",
    "\n",
    "2. Our frozen convolutional base \n",
    "\n",
    "3. A dense classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a data augmentation stage and a classifier to the convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 420 images belonging to 8 classes.\n",
      "Found 70 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "# This is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# This is the augmentation configuration we will use for validation:\n",
    "# only rescaling\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# This is a generator that will read pictures found in\n",
    "# subfolers of './train', and indefinitely generate\n",
    "# batches of augmented image data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        './train',  # this is the target directory\n",
    "        target_size=(image_size, image_size),  # all images will be resized to 150x150\n",
    "        classes=class_names,\n",
    "        batch_size=batch_size)  \n",
    "\n",
    "# This is a similar generator, for validation data\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size = (image_size, image_size),\n",
    "        classes = class_names,\n",
    "        batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(image_size, image_size, 3))\n",
    "x = conv_base(inputs)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this setup, only the weights from the two Dense layers that we added will be\n",
    "trained. That’s a total of four weight tensors: two per layer (the main weight matrix\n",
    "and the bias vector). \n",
    "\n",
    "Note that in order for these changes to take effect, you must first\n",
    "compile the model. If you ever modify weight trainability after compilation, you\n",
    "should then recompile the model, or these changes will be ignored.\n",
    "\n",
    "Let’s train our model. Thanks to data augmentation, it will take much longer for\n",
    "the model to start overfitting, so we can train for more epochs — let’s do 50.\n",
    "\n",
    "__NOTE__ This technique is expensive enough that you should only attempt it if\n",
    "you have access to a GPU (such as the free GPU available in Colab) — it’s\n",
    "intractable on CPU. If you can’t run your code on GPU, then the previous\n",
    "technique is the way to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = os.path.join(\"logs_feature_extraction_with_augmentation\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction_with_augmentation.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "21/21 [==============================] - 17s 779ms/step - loss: 11.6322 - accuracy: 0.1976 - val_loss: 7.2716 - val_accuracy: 0.3857\n",
      "Epoch 2/50\n",
      "21/21 [==============================] - 16s 752ms/step - loss: 4.2603 - accuracy: 0.4571 - val_loss: 4.1719 - val_accuracy: 0.4000\n",
      "Epoch 3/50\n",
      "21/21 [==============================] - 16s 767ms/step - loss: 3.6926 - accuracy: 0.5143 - val_loss: 4.4105 - val_accuracy: 0.3143\n",
      "Epoch 4/50\n",
      "21/21 [==============================] - 15s 727ms/step - loss: 3.1450 - accuracy: 0.5690 - val_loss: 3.5688 - val_accuracy: 0.4714\n",
      "Epoch 5/50\n",
      "21/21 [==============================] - 16s 770ms/step - loss: 2.6020 - accuracy: 0.5857 - val_loss: 3.0999 - val_accuracy: 0.6000\n",
      "Epoch 6/50\n",
      "21/21 [==============================] - 16s 740ms/step - loss: 1.7227 - accuracy: 0.6976 - val_loss: 3.0574 - val_accuracy: 0.6571\n",
      "Epoch 7/50\n",
      "21/21 [==============================] - 16s 744ms/step - loss: 1.6670 - accuracy: 0.7262 - val_loss: 1.9948 - val_accuracy: 0.6286\n",
      "Epoch 8/50\n",
      "21/21 [==============================] - 16s 741ms/step - loss: 1.5795 - accuracy: 0.7357 - val_loss: 2.1614 - val_accuracy: 0.6571\n",
      "Epoch 9/50\n",
      "21/21 [==============================] - 16s 737ms/step - loss: 1.6403 - accuracy: 0.7595 - val_loss: 1.9888 - val_accuracy: 0.6286\n",
      "Epoch 10/50\n",
      "21/21 [==============================] - 17s 807ms/step - loss: 1.5998 - accuracy: 0.7524 - val_loss: 2.6838 - val_accuracy: 0.6571\n",
      "Epoch 11/50\n",
      "21/21 [==============================] - 16s 770ms/step - loss: 0.9330 - accuracy: 0.8214 - val_loss: 1.6577 - val_accuracy: 0.7286\n",
      "Epoch 12/50\n",
      "21/21 [==============================] - 16s 782ms/step - loss: 1.0512 - accuracy: 0.7881 - val_loss: 1.7717 - val_accuracy: 0.6857\n",
      "Epoch 13/50\n",
      "21/21 [==============================] - 16s 761ms/step - loss: 0.9523 - accuracy: 0.8333 - val_loss: 2.4486 - val_accuracy: 0.6143\n",
      "Epoch 14/50\n",
      "21/21 [==============================] - 17s 811ms/step - loss: 0.8774 - accuracy: 0.8476 - val_loss: 3.4850 - val_accuracy: 0.6571\n",
      "Epoch 15/50\n",
      "21/21 [==============================] - 16s 768ms/step - loss: 1.0266 - accuracy: 0.8190 - val_loss: 2.3278 - val_accuracy: 0.6571\n",
      "Epoch 16/50\n",
      "21/21 [==============================] - 16s 773ms/step - loss: 0.8246 - accuracy: 0.8500 - val_loss: 2.2152 - val_accuracy: 0.6714\n",
      "Epoch 17/50\n",
      "21/21 [==============================] - 16s 757ms/step - loss: 0.8190 - accuracy: 0.8405 - val_loss: 1.8512 - val_accuracy: 0.7286\n",
      "Epoch 18/50\n",
      "21/21 [==============================] - 16s 767ms/step - loss: 1.0406 - accuracy: 0.8429 - val_loss: 3.3484 - val_accuracy: 0.6000\n",
      "Epoch 19/50\n",
      "21/21 [==============================] - 16s 772ms/step - loss: 0.7508 - accuracy: 0.8595 - val_loss: 2.7499 - val_accuracy: 0.6286\n",
      "Epoch 20/50\n",
      "21/21 [==============================] - 16s 749ms/step - loss: 0.7002 - accuracy: 0.8667 - val_loss: 2.1313 - val_accuracy: 0.6714\n",
      "Epoch 21/50\n",
      "21/21 [==============================] - 16s 747ms/step - loss: 0.4617 - accuracy: 0.8810 - val_loss: 4.2319 - val_accuracy: 0.6143\n",
      "Epoch 22/50\n",
      "21/21 [==============================] - 16s 745ms/step - loss: 0.8230 - accuracy: 0.8643 - val_loss: 2.1207 - val_accuracy: 0.6857\n",
      "Epoch 23/50\n",
      "21/21 [==============================] - 17s 827ms/step - loss: 0.4906 - accuracy: 0.9048 - val_loss: 2.3895 - val_accuracy: 0.6714\n",
      "Epoch 24/50\n",
      "21/21 [==============================] - 17s 796ms/step - loss: 0.4801 - accuracy: 0.8881 - val_loss: 1.9410 - val_accuracy: 0.7429\n",
      "Epoch 25/50\n",
      "21/21 [==============================] - 17s 824ms/step - loss: 0.5383 - accuracy: 0.8714 - val_loss: 2.5354 - val_accuracy: 0.7429\n",
      "Epoch 26/50\n",
      "21/21 [==============================] - 16s 778ms/step - loss: 0.3463 - accuracy: 0.9381 - val_loss: 7.8247 - val_accuracy: 0.4429\n",
      "Epoch 27/50\n",
      "21/21 [==============================] - 17s 797ms/step - loss: 0.6099 - accuracy: 0.8976 - val_loss: 3.2540 - val_accuracy: 0.6571\n",
      "Epoch 28/50\n",
      "21/21 [==============================] - 16s 760ms/step - loss: 0.3002 - accuracy: 0.9238 - val_loss: 2.9259 - val_accuracy: 0.6857\n",
      "Epoch 29/50\n",
      "21/21 [==============================] - 17s 792ms/step - loss: 0.5603 - accuracy: 0.8810 - val_loss: 3.5017 - val_accuracy: 0.6571\n",
      "Epoch 30/50\n",
      "21/21 [==============================] - 17s 787ms/step - loss: 0.4138 - accuracy: 0.9071 - val_loss: 2.8042 - val_accuracy: 0.7143\n",
      "Epoch 31/50\n",
      "21/21 [==============================] - 16s 758ms/step - loss: 0.4523 - accuracy: 0.9024 - val_loss: 3.3092 - val_accuracy: 0.6571\n",
      "Epoch 32/50\n",
      "21/21 [==============================] - 16s 777ms/step - loss: 0.3770 - accuracy: 0.9310 - val_loss: 2.6923 - val_accuracy: 0.6571\n",
      "Epoch 33/50\n",
      "21/21 [==============================] - 16s 749ms/step - loss: 0.3377 - accuracy: 0.9214 - val_loss: 2.3103 - val_accuracy: 0.6857\n",
      "Epoch 34/50\n",
      "21/21 [==============================] - 17s 798ms/step - loss: 0.3697 - accuracy: 0.9357 - val_loss: 2.4706 - val_accuracy: 0.7429\n",
      "Epoch 35/50\n",
      "21/21 [==============================] - 16s 778ms/step - loss: 0.3270 - accuracy: 0.9238 - val_loss: 3.1356 - val_accuracy: 0.7143\n",
      "Epoch 36/50\n",
      "21/21 [==============================] - 18s 875ms/step - loss: 0.5009 - accuracy: 0.8929 - val_loss: 2.4079 - val_accuracy: 0.7000\n",
      "Epoch 37/50\n",
      "21/21 [==============================] - 18s 836ms/step - loss: 0.3155 - accuracy: 0.9429 - val_loss: 2.4206 - val_accuracy: 0.7286\n",
      "Epoch 38/50\n",
      "21/21 [==============================] - 17s 829ms/step - loss: 0.5126 - accuracy: 0.9262 - val_loss: 2.4771 - val_accuracy: 0.7143\n",
      "Epoch 39/50\n",
      "21/21 [==============================] - 17s 795ms/step - loss: 0.2267 - accuracy: 0.9500 - val_loss: 2.5547 - val_accuracy: 0.7571\n",
      "Epoch 40/50\n",
      "21/21 [==============================] - 17s 801ms/step - loss: 0.3562 - accuracy: 0.9286 - val_loss: 2.9861 - val_accuracy: 0.7000\n",
      "Epoch 41/50\n",
      "21/21 [==============================] - 16s 769ms/step - loss: 0.4196 - accuracy: 0.9262 - val_loss: 1.9966 - val_accuracy: 0.7286\n",
      "Epoch 42/50\n",
      "21/21 [==============================] - 16s 756ms/step - loss: 0.2527 - accuracy: 0.9595 - val_loss: 1.9996 - val_accuracy: 0.7143\n",
      "Epoch 43/50\n",
      "21/21 [==============================] - 16s 766ms/step - loss: 0.3493 - accuracy: 0.9190 - val_loss: 2.0899 - val_accuracy: 0.7143\n",
      "Epoch 44/50\n",
      "21/21 [==============================] - 16s 746ms/step - loss: 0.2321 - accuracy: 0.9381 - val_loss: 2.1638 - val_accuracy: 0.7429\n",
      "Epoch 45/50\n",
      "21/21 [==============================] - 16s 748ms/step - loss: 0.3123 - accuracy: 0.9429 - val_loss: 2.6326 - val_accuracy: 0.7571\n",
      "Epoch 46/50\n",
      "21/21 [==============================] - 16s 757ms/step - loss: 0.4184 - accuracy: 0.9214 - val_loss: 2.9315 - val_accuracy: 0.6857\n",
      "Epoch 47/50\n",
      "21/21 [==============================] - 19s 934ms/step - loss: 0.2153 - accuracy: 0.9714 - val_loss: 2.2021 - val_accuracy: 0.7286\n",
      "Epoch 48/50\n",
      "21/21 [==============================] - 19s 905ms/step - loss: 0.3776 - accuracy: 0.9333 - val_loss: 3.1044 - val_accuracy: 0.7000\n",
      "Epoch 49/50\n",
      "21/21 [==============================] - 19s 882ms/step - loss: 0.3487 - accuracy: 0.9310 - val_loss: 3.2144 - val_accuracy: 0.7143\n",
      "Epoch 50/50\n",
      "21/21 [==============================] - 18s 839ms/step - loss: 0.2779 - accuracy: 0.9476 - val_loss: 3.0806 - val_accuracy: 0.7000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs = 50,\n",
    "    validation_data = validation_generator,\n",
    "    callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s plot the results again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 2s 438ms/step - loss: 3.0806 - accuracy: 0.7000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.0806334018707275, 0.699999988079071]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we reach a validation accuracy of over 43%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FZYRLtbkGhLV"
   },
   "source": [
    "## 2.2 Fine Tuning\n",
    "\n",
    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
    "Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used\n",
    "for feature extraction, and jointly training both the newly added part of the model (in this case, the\n",
    "fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly \n",
    "adjusts the more abstract representations of the model being reused in order to make them more relevant for the problem at hand.\n",
    "\n",
    "I stated earlier that it’s necessary to freeze the convolution base of VGG16 in order to be able to\n",
    "train a randomly initialized classifier on top. For the same reason, it’s only possible to fine-tune the top\n",
    "layers of the convolutional base once the classifier on top has already been trained. If the classifier isn’t\n",
    "already trained, the error signal propagating through the network during training will be too\n",
    "large, and the representations previously learned by the layers being fine-tuned will be destroyed. Thus\n",
    "the steps for fine-tuning a network are as follows:\n",
    "\n",
    "The steps for fine-tuning are as follows:\n",
    "\n",
    "1. Add our custom network on top of an already-trained base network.\n",
    "2. Freeze the base network.\n",
    "3. Train the part we added.\n",
    "4. Unfreeze some layers in the base network. (Note that you should not unfreeze “batch normalization” layers, which are not relevant here since there are no such layers in VGG16. )\n",
    "5. Jointly train both these layers and the part we added.\n",
    "\n",
    "We already completed the first three steps when doing feature extraction. Let’s proceed with step 4:\n",
    "we’ll unfreeze our `conv_base` and then freeze individual layers inside it.\n",
    "\n",
    "As a reminder, this is what our convolutional base looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cnObzTupGhLV",
    "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_14 (InputLayer)       [(None, None, None, 3)]   0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, None, None, 64)    1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, None, None, 64)    36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, None, None, 64)    0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, None, None, 128)   73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, None, None, 128)   147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, None, None, 128)   0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, None, None, 256)   295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, None, None, 256)   590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, None, None, 256)   590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, None, None, 256)   0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, None, None, 512)   1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aDtcl5X2GhLa"
   },
   "source": [
    "We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable.\n",
    "\n",
    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base?\n",
    "You could. But you need to consider the following:\n",
    "\n",
    "- Earlier layers in the convolutional base encode more generic, reusable features, whereas layers higher up encode more specialized features. It’s more useful to fine-tune the more specialized features, because these are the ones that need to be repurposed on your new problem. There would be fast-decreasing returns in fine-tuning lower layers.\n",
    "\n",
    "- The more parameters you’re training, the more you’re at risk of overfitting. The convolutional base has 15 million parameters, so it would be risky to attempt to train it on your small dataset. \n",
    "\n",
    "Thus, in this situation, it’s a good strategy to fine-tune only the top two or three layers in the convolutional base. Let’s set this up, starting from where we left off in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Freezing all layers until the fourth from the last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tBXYN1t2GhLc",
    "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer name = input_14, shape = [(None, None, None, 3)], trainable = False\n",
      "layer name = block1_conv1, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block1_conv2, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block1_pool, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block2_conv1, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block2_conv2, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block2_pool, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block3_conv1, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_conv2, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_conv3, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_pool, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block4_conv1, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_conv2, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_conv3, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_pool, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block5_conv1, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_conv2, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_conv3, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_pool, shape = (None, None, None, 512), trainable = True\n"
     ]
    }
   ],
   "source": [
    "conv_base.trainable = True\n",
    "for layer in conv_base.layers[:-4]:\n",
    "    layer.trainable = False\n",
    "    \n",
    "for layer in conv_base.layers[0:]:\n",
    "    print('layer name = ' + layer.name + ', shape = ' + repr(layer.output_shape)\n",
    "            + ', trainable = ' + repr(layer.trainable))        \n",
    " \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XWw1mYfUGhLg"
   },
   "source": [
    "Now we can begin fine-tuning the model. We’ll do this with the `RMSprop` optimizer, using a very low learning rate. The reason for using a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the three\n",
    "layers we’re fine-tuning. Updates that are too large may harm these representations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fine-tuning the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4YBjFhSVGhLh",
    "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer name = input_18, shape = [(None, 150, 150, 3)], trainable = True\n",
      "layer name = vgg16, shape = (None, None, None, 512), trainable = True\n",
      "layer name = flatten_12, shape = (None, 8192), trainable = True\n",
      "layer name = dense_24, shape = (None, 256), trainable = True\n",
      "layer name = dropout_12, shape = (None, 256), trainable = True\n",
      "layer name = dense_25, shape = (None, 8), trainable = True\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
    "    metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "21/21 [==============================] - 20s 935ms/step - loss: 2.5061 - accuracy: 0.1548 - val_loss: 1.8825 - val_accuracy: 0.2429\n",
      "Epoch 2/30\n",
      "21/21 [==============================] - 20s 932ms/step - loss: 2.1597 - accuracy: 0.1952 - val_loss: 1.7301 - val_accuracy: 0.3714\n",
      "Epoch 3/30\n",
      "21/21 [==============================] - 20s 938ms/step - loss: 1.8653 - accuracy: 0.3000 - val_loss: 1.5753 - val_accuracy: 0.4143\n",
      "Epoch 4/30\n",
      "21/21 [==============================] - 20s 963ms/step - loss: 1.6819 - accuracy: 0.3738 - val_loss: 1.4570 - val_accuracy: 0.4429\n",
      "Epoch 5/30\n",
      "21/21 [==============================] - 20s 956ms/step - loss: 1.4900 - accuracy: 0.4238 - val_loss: 1.3590 - val_accuracy: 0.4286\n",
      "Epoch 6/30\n",
      "21/21 [==============================] - 20s 938ms/step - loss: 1.2282 - accuracy: 0.5405 - val_loss: 1.2675 - val_accuracy: 0.5000\n",
      "Epoch 7/30\n",
      "21/21 [==============================] - 20s 960ms/step - loss: 1.1882 - accuracy: 0.5548 - val_loss: 1.1840 - val_accuracy: 0.5429\n",
      "Epoch 8/30\n",
      "21/21 [==============================] - 20s 931ms/step - loss: 1.0866 - accuracy: 0.5857 - val_loss: 1.1269 - val_accuracy: 0.5429\n",
      "Epoch 9/30\n",
      "21/21 [==============================] - 19s 921ms/step - loss: 0.9324 - accuracy: 0.6500 - val_loss: 1.0479 - val_accuracy: 0.5714\n",
      "Epoch 10/30\n",
      "21/21 [==============================] - 19s 925ms/step - loss: 0.8240 - accuracy: 0.6833 - val_loss: 1.0298 - val_accuracy: 0.6286\n",
      "Epoch 11/30\n",
      "21/21 [==============================] - 20s 934ms/step - loss: 0.6968 - accuracy: 0.7595 - val_loss: 0.9499 - val_accuracy: 0.5857\n",
      "Epoch 12/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.6573 - accuracy: 0.7690 - val_loss: 0.9385 - val_accuracy: 0.6286\n",
      "Epoch 13/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.6287 - accuracy: 0.7690 - val_loss: 0.8889 - val_accuracy: 0.6429\n",
      "Epoch 14/30\n",
      "21/21 [==============================] - 21s 1s/step - loss: 0.5531 - accuracy: 0.8119 - val_loss: 0.8967 - val_accuracy: 0.7000\n",
      "Epoch 15/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.4807 - accuracy: 0.8524 - val_loss: 0.8767 - val_accuracy: 0.6857\n",
      "Epoch 16/30\n",
      "21/21 [==============================] - 21s 1s/step - loss: 0.4412 - accuracy: 0.8643 - val_loss: 0.8314 - val_accuracy: 0.7000\n",
      "Epoch 17/30\n",
      "21/21 [==============================] - 20s 950ms/step - loss: 0.4161 - accuracy: 0.8571 - val_loss: 0.8314 - val_accuracy: 0.7143\n",
      "Epoch 18/30\n",
      "21/21 [==============================] - 20s 954ms/step - loss: 0.3725 - accuracy: 0.8786 - val_loss: 0.7916 - val_accuracy: 0.7286\n",
      "Epoch 19/30\n",
      "21/21 [==============================] - 20s 929ms/step - loss: 0.3556 - accuracy: 0.8690 - val_loss: 0.7489 - val_accuracy: 0.6714\n",
      "Epoch 20/30\n",
      "21/21 [==============================] - 20s 958ms/step - loss: 0.2972 - accuracy: 0.8929 - val_loss: 0.7240 - val_accuracy: 0.7143\n",
      "Epoch 21/30\n",
      "21/21 [==============================] - 21s 973ms/step - loss: 0.2663 - accuracy: 0.9167 - val_loss: 0.7122 - val_accuracy: 0.7714\n",
      "Epoch 22/30\n",
      "21/21 [==============================] - 19s 922ms/step - loss: 0.2709 - accuracy: 0.9143 - val_loss: 0.7322 - val_accuracy: 0.7571\n",
      "Epoch 23/30\n",
      "21/21 [==============================] - 20s 936ms/step - loss: 0.2267 - accuracy: 0.9357 - val_loss: 0.6977 - val_accuracy: 0.7429\n",
      "Epoch 24/30\n",
      "21/21 [==============================] - 20s 930ms/step - loss: 0.2592 - accuracy: 0.9214 - val_loss: 0.7409 - val_accuracy: 0.7857\n",
      "Epoch 25/30\n",
      "21/21 [==============================] - 20s 929ms/step - loss: 0.2202 - accuracy: 0.9333 - val_loss: 0.7303 - val_accuracy: 0.7429\n",
      "Epoch 26/30\n",
      "21/21 [==============================] - 19s 923ms/step - loss: 0.2009 - accuracy: 0.9429 - val_loss: 0.7513 - val_accuracy: 0.7571\n",
      "Epoch 27/30\n",
      "21/21 [==============================] - 19s 912ms/step - loss: 0.1688 - accuracy: 0.9667 - val_loss: 0.7185 - val_accuracy: 0.7429\n",
      "Epoch 28/30\n",
      "21/21 [==============================] - 20s 976ms/step - loss: 0.1527 - accuracy: 0.9619 - val_loss: 0.7684 - val_accuracy: 0.7571\n",
      "Epoch 29/30\n",
      "21/21 [==============================] - 19s 917ms/step - loss: 0.1290 - accuracy: 0.9690 - val_loss: 0.8116 - val_accuracy: 0.7429\n",
      "Epoch 30/30\n",
      "21/21 [==============================] - 20s 952ms/step - loss: 0.1121 - accuracy: 0.9667 - val_loss: 0.7612 - val_accuracy: 0.7571\n"
     ]
    }
   ],
   "source": [
    "logdir = os.path.join(\"logs_fine_tuning\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"fine_tuning.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "train_generator,\n",
    "epochs=30,\n",
    "validation_data=validation_generator,\n",
    "callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9rwSMMQaGhLx",
    "outputId": "0a58db5a-0f22-45e8-d1fb-0a664fceaf4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 2s 437ms/step - loss: 0.7612 - accuracy: 0.7571\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.7611837387084961, 0.7571428418159485]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix and Missclassified Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(validation_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WoDOi_F8GhL5",
    "outputId": "17c21c92-2a5d-4e21-c367-57e818046762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  6   0   0   0   0   3   0   1 ], angelina jolie\n",
      "[  1   6   0   0   0   1   2   0 ], brad pitt\n",
      "[  2   0   3   0   1   3   0   1 ], catherine deneuve\n",
      "[  3   2   1   0   1   1   0   2 ], johnny depp\n",
      "[  7   0   0   0   2   1   0   0 ], leonardo dicaprio\n",
      "[  6   0   0   0   0   2   0   2 ], marion cotillard\n",
      "[  0   1   1   0   2   0   5   1 ], robert de niro\n",
      "[  4   0   0   0   0   1   0   5 ], sandra bullock\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import sys\n",
    "class_names = [\"angelina jolie\", \"brad pitt\",\"catherine deneuve\" , \"johnny depp\",\"leonardo dicaprio\", \"marion cotillard\", \"robert de niro\",\"sandra bullock\"]\n",
    "\n",
    "\n",
    "Y_valid = np.zeros((num_valid_images,1),dtype=int)\n",
    "\n",
    "step = num_valid_images // num_classes\n",
    "for ind in range(num_classes):\n",
    "    Y_valid[ind*step:(ind+1)*step] = ind\n",
    "    \n",
    "confmat = confusion_matrix(val_labels.argmax(axis=1),np.argmax(prediction,axis=1))   \n",
    "\n",
    "for i0 in range(num_classes):\n",
    "    sys.stdout.write('[')\n",
    "    for i1 in range(num_classes):\n",
    "        sys.stdout.write('{:3d} '.format(confmat[i0,i1]))\n",
    "    \n",
    "    sys.stdout.write('], {}\\n'.format(class_names[i0]))\n",
    "    \n",
    "sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nNp0qChLGhL-",
    "outputId": "f22e9bfe-e5da-4d57-fbdc-2ea55d6681e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrong classification for: sandra bullock\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: angelina jolie\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: angelina jolie\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: marion cotillard\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: angelina jolie\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAADKUlEQVR4nO3UMQEAIAzAMMC/5+GiHCQKenXPzAKgcV4HAPzEdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIGS6ACHTBQiZLkDIdAFCpgsQMl2AkOkChEwXIHQBcjcEy3+fc28AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matched to: angelina jolie\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose the class label you want to check\n",
    "clbl = 7\n",
    "step = num_valid_images // num_classes\n",
    "pred_labels = np.argmax(prediction[clbl*step:(clbl+1)*step],axis=1)\n",
    "wrong_labels = np.transpose(np.nonzero(pred_labels != clbl))\n",
    "\n",
    "\n",
    "# Get the validation images as numpy arrays\n",
    "\n",
    "import numpy as np\n",
    "def get_images_and_labels(dataset):\n",
    "    all_images = []\n",
    "    all_labels = []\n",
    "    for images, labels in dataset:\n",
    "        all_images.append(images)\n",
    "        all_labels.append(labels)\n",
    "    return np.concatenate(all_images), np.concatenate(all_labels)\n",
    "\n",
    "val_images, val_labels = get_images_and_labels(validation_dataset)\n",
    "\n",
    "\n",
    "print('wrong classification for: {}'.format(class_names[clbl]))\n",
    "\n",
    "for i, i0 in enumerate(wrong_labels):\n",
    "    img = val_images[clbl*step + i0]\n",
    "    img = np.squeeze(img, axis=0)\n",
    "    plt.figure(figsize=(4, 4))\n",
    "    plt.imshow(img.astype(\"uint8\"))\n",
    "    plt.show()\n",
    "    plt.axis(\"off\")\n",
    "    print('matched to: {}'.format(class_names[pred_labels[i0][0]]))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part III : Semantic Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we’ll focus on semantic segmentation: we’ll be looking once again at\n",
    "images of cats and dogs, and this time we’ll learn how to tell apart the main subject\n",
    "and its background.\n",
    "\n",
    "We’ll work with the Oxford-IIIT Pets dataset (www.robots.ox.ac.uk/~vgg/data/pets/), which contains 7'390 pictures of various breeds of cats and dogs, together with foreground-background segmentation masks for \n",
    "each picture. A segmentation mask is the image-segmentation equivalent of a label: it’s an image the same size as the input image, with a single color channel where each integer value corresponds to the class of the corresponding pixel in the input image. In our case, the pixels of our segmentation\n",
    "masks can take one of three integer values:\n",
    "1. (foreground)\n",
    "2. (background)\n",
    "3. (contour)\n",
    "\n",
    "Let’s start by downloading and uncompressing our dataset, using the `wget` and `tar`\n",
    "shell utilities:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
    "!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
    "!tar -xf images.tar.gz\n",
    "!tar -xf annotations.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input pictures are stored as JPG files in the `images/` folder \n",
    "(such as images/Abyssinian_1.jpg), and the corresponding segmentation mask is stored as a PNG file with\n",
    "the same name in the `annotations/trimaps/` folder (such as annotations/trimaps/Abyssinian_1.png).\n",
    "Let’s prepare the list of input file paths, as well as the list of the corresponding\n",
    "mask file paths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "input_dir = \"images/\"\n",
    "target_dir = \"annotations/trimaps/\"\n",
    "input_img_paths = sorted(\n",
    "    [os.path.join(input_dir, fname)\n",
    "    for fname in os.listdir(input_dir)\n",
    "    if fname.endswith(\".jpg\")])\n",
    "    \n",
    "target_paths = sorted([os.path.join(target_dir, fname)\n",
    "    for fname in os.listdir(target_dir)\n",
    "    if fname.endswith(\".png\") and not fname.startswith(\".\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, what does one of these inputs and its mask look like? Let’s take a quick look.\n",
    "Here’s a sample image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10ca72a60>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.utils import load_img, img_to_array\n",
    "plt.axis(\"off\")\n",
    "# Display input image number 9\n",
    "plt.imshow(load_img(input_img_paths[9]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is its corresponding target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_target(target_array):\n",
    "    # The original labels are 1, 2, and 3. We subtract 1 so that the\n",
    "    # labels range from 0 to 2, and then we multiply by 127 so that\n",
    "    # the labels become 0 (black), 127 (gray), 254 (near-white).\n",
    "    normalized_array = (target_array.astype(\"uint8\") - 1) * 127\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(normalized_array[:, :, 0])\n",
    "\n",
    "# We use color_mode=\"grayscale\" so that the image we load is treated as\n",
    "# having a single color channel.    \n",
    "img = img_to_array(load_img(target_paths[9], color_mode=\"grayscale\"))\n",
    "display_target(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let’s load our inputs and targets into two NumPy arrays, and let’s split the arrays\n",
    "into a training and a validation set. Since the dataset is very small, we can just load\n",
    "everything into memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "# We resize everything to 160x160\n",
    "img_size = (160, 160)\n",
    "# Total number of samples in the data\n",
    "num_imgs = len(input_img_paths)\n",
    "\n",
    "# Shuffle the file paths (they were originally sorted by breed). We \n",
    "# use the same seed (1337) in both statements to ensure that the input \n",
    "# paths and target paths stay in the same order\n",
    "\n",
    "random.Random(1337).shuffle(input_img_paths)\n",
    "random.Random(1337).shuffle(target_paths)\n",
    "\n",
    "def path_to_input_image(path):\n",
    "    return img_to_array(load_img(path, target_size=img_size))\n",
    "\n",
    "def path_to_target(path):\n",
    "    img = img_to_array(\n",
    "    load_img(path, target_size=img_size, color_mode=\"grayscale\"))\n",
    "    # Subtract 1 so that our labels become 0, 1, and 2\n",
    "    img = img.astype(\"uint8\") - 1\n",
    "    return img\n",
    "\n",
    "# Load all images in the input_imgs float32 array and their masks in the\n",
    "# targets uint8 array (same order). The inputs have three channels (RBG values)\n",
    "# and the targets have a single channel (which contains integer labels)\n",
    "input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\"float32\")\n",
    "targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\"uint8\")\n",
    "for i in range(num_imgs):\n",
    "    input_imgs[i] = path_to_input_image(input_img_paths[i])\n",
    "    targets[i] = path_to_target(target_paths[i])\n",
    "  \n",
    "# Reserve 1000 samples for validation\n",
    "num_val_samples = 1000\n",
    "\n",
    "# Split the data into a training and a\n",
    "# validation set\n",
    "train_input_imgs = input_imgs[:-num_val_samples]\n",
    "train_targets = targets[:-num_val_samples]\n",
    "val_input_imgs = input_imgs[-num_val_samples:]\n",
    "val_targets = targets[-num_val_samples:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it’s time to define our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-10 22:51:00.162044: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 160, 160, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 80, 80, 32)   896         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 80, 80, 32)  128         ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 80, 80, 32)   0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 80, 80, 32)   0           ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " separable_conv2d (SeparableCon  (None, 80, 80, 64)  2400        ['activation_1[0][0]']           \n",
      " v2D)                                                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 80, 80, 64)  256         ['separable_conv2d[0][0]']       \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 80, 80, 64)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " separable_conv2d_1 (SeparableC  (None, 80, 80, 64)  4736        ['activation_2[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 80, 80, 64)  256         ['separable_conv2d_1[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 40, 40, 64)   0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 40, 40, 64)   2112        ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " add (Add)                      (None, 40, 40, 64)   0           ['max_pooling2d[0][0]',          \n",
      "                                                                  'conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 40, 40, 64)   0           ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " separable_conv2d_2 (SeparableC  (None, 40, 40, 128)  8896       ['activation_3[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 40, 40, 128)  512        ['separable_conv2d_2[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 40, 40, 128)  0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " separable_conv2d_3 (SeparableC  (None, 40, 40, 128)  17664      ['activation_4[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 40, 40, 128)  512        ['separable_conv2d_3[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 20, 20, 128)  0          ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 20, 20, 128)  8320        ['add[0][0]']                    \n",
      "                                                                                                  \n",
      " add_1 (Add)                    (None, 20, 20, 128)  0           ['max_pooling2d_1[0][0]',        \n",
      "                                                                  'conv2d_2[0][0]']               \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 20, 20, 128)  0           ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " separable_conv2d_4 (SeparableC  (None, 20, 20, 256)  34176      ['activation_5[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 20, 20, 256)  1024       ['separable_conv2d_4[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 20, 20, 256)  0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " separable_conv2d_5 (SeparableC  (None, 20, 20, 256)  68096      ['activation_6[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 20, 20, 256)  1024       ['separable_conv2d_5[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 10, 10, 256)  0          ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 10, 10, 256)  33024       ['add_1[0][0]']                  \n",
      "                                                                                                  \n",
      " add_2 (Add)                    (None, 10, 10, 256)  0           ['max_pooling2d_2[0][0]',        \n",
      "                                                                  'conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 10, 10, 256)  0           ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTransp  (None, 10, 10, 258)  594690     ['activation_7[0][0]']           \n",
      " ose)                                                                                             \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 10, 10, 258)  1032       ['conv2d_transpose[0][0]']       \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 10, 10, 258)  0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_transpose_1 (Conv2DTran  (None, 10, 10, 258)  599334     ['activation_8[0][0]']           \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 10, 10, 258)  1032       ['conv2d_transpose_1[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " up_sampling2d_1 (UpSampling2D)  (None, 20, 20, 256)  0          ['add_2[0][0]']                  \n",
      "                                                                                                  \n",
      " up_sampling2d (UpSampling2D)   (None, 20, 20, 258)  0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 20, 20, 258)  66306       ['up_sampling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " add_3 (Add)                    (None, 20, 20, 258)  0           ['up_sampling2d[0][0]',          \n",
      "                                                                  'conv2d_4[0][0]']               \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 20, 20, 258)  0           ['add_3[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_transpose_2 (Conv2DTran  (None, 20, 20, 128)  297344     ['activation_9[0][0]']           \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 20, 20, 128)  512        ['conv2d_transpose_2[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 20, 20, 128)  0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_transpose_3 (Conv2DTran  (None, 20, 20, 128)  147584     ['activation_10[0][0]']          \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 20, 20, 128)  512        ['conv2d_transpose_3[0][0]']     \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " up_sampling2d_3 (UpSampling2D)  (None, 40, 40, 258)  0          ['add_3[0][0]']                  \n",
      "                                                                                                  \n",
      " up_sampling2d_2 (UpSampling2D)  (None, 40, 40, 128)  0          ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 40, 40, 128)  33152       ['up_sampling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " add_4 (Add)                    (None, 40, 40, 128)  0           ['up_sampling2d_2[0][0]',        \n",
      "                                                                  'conv2d_5[0][0]']               \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 40, 40, 128)  0           ['add_4[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_transpose_4 (Conv2DTran  (None, 40, 40, 64)  73792       ['activation_11[0][0]']          \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 40, 40, 64)  256         ['conv2d_transpose_4[0][0]']     \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 40, 40, 64)   0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_transpose_5 (Conv2DTran  (None, 40, 40, 64)  36928       ['activation_12[0][0]']          \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 40, 40, 64)  256         ['conv2d_transpose_5[0][0]']     \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " up_sampling2d_5 (UpSampling2D)  (None, 80, 80, 128)  0          ['add_4[0][0]']                  \n",
      "                                                                                                  \n",
      " up_sampling2d_4 (UpSampling2D)  (None, 80, 80, 64)  0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 80, 80, 64)   8256        ['up_sampling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " add_5 (Add)                    (None, 80, 80, 64)   0           ['up_sampling2d_4[0][0]',        \n",
      "                                                                  'conv2d_6[0][0]']               \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 80, 80, 64)   0           ['add_5[0][0]']                  \n",
      "                                                                                                  \n",
      " conv2d_transpose_6 (Conv2DTran  (None, 80, 80, 32)  18464       ['activation_13[0][0]']          \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 80, 80, 32)  128         ['conv2d_transpose_6[0][0]']     \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 80, 80, 32)   0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_transpose_7 (Conv2DTran  (None, 80, 80, 32)  9248        ['activation_14[0][0]']          \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 80, 80, 32)  128         ['conv2d_transpose_7[0][0]']     \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " up_sampling2d_7 (UpSampling2D)  (None, 160, 160, 64  0          ['add_5[0][0]']                  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " up_sampling2d_6 (UpSampling2D)  (None, 160, 160, 32  0          ['batch_normalization_14[0][0]'] \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 160, 160, 32  2080        ['up_sampling2d_7[0][0]']        \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " add_6 (Add)                    (None, 160, 160, 32  0           ['up_sampling2d_6[0][0]',        \n",
      "                                )                                 'conv2d_7[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 160, 160, 3)  867         ['add_6[0][0]']                  \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 2,075,933\n",
      "Trainable params: 2,072,149\n",
      "Non-trainable params: 3,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "\n",
    "def get_model(img_size, num_classes):\n",
    "    inputs = keras.Input(shape=img_size + (3,))\n",
    "    \n",
    "    # Don’t forget to rescale input images to the [0-1] range\n",
    "    x = layers.Rescaling(1./255)(inputs)\n",
    "    ### [First half of the network: downsampling inputs] ###\n",
    "\n",
    "    # Entry block\n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(inputs)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    previous_block_activation = x  # Set aside residual\n",
    "\n",
    "    # Blocks 1, 2, 3 are identical apart from the feature depth.\n",
    "\n",
    "    \n",
    "    for filters in [64, 128, 256]:\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.SeparableConv2D(filters, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.SeparableConv2D(filters, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
    "\n",
    "        # Project residual\n",
    "        residual = layers.Conv2D(filters, 1, strides=2, padding=\"same\")(previous_block_activation)\n",
    "        x = layers.add([x, residual])  # Add back residual\n",
    "        previous_block_activation = x  # Set aside next residual\n",
    "\n",
    "    ### [Second half of the network: upsampling inputs] ###\n",
    "\n",
    "    for filters in [258, 128, 64, 32]:\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.Conv2DTranspose(filters, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.Conv2DTranspose(filters, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.UpSampling2D(2)(x)\n",
    "\n",
    "        # Project residual\n",
    "        residual = layers.UpSampling2D(2)(previous_block_activation)\n",
    "        residual = layers.Conv2D(filters, 1, padding=\"same\")(residual)\n",
    "        x = layers.add([x, residual])  # Add back residual\n",
    "        previous_block_activation = x  # Set aside next residual\n",
    "\n",
    "    # We end the model with a per-pixel three-way\n",
    "    # softmax to classify each output pixel into one of\n",
    "    # our three categories\n",
    "    outputs = layers.Conv2D(num_classes, 3, activation=\"softmax\", padding=\"same\")(x)\n",
    "\n",
    "    # Define the model\n",
    "    model = keras.Model(inputs, outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "# Free up RAM in case the model definition cells were run multiple times\n",
    "keras.backend.clear_session()\n",
    "\n",
    "# Build model\n",
    "model = get_model(img_size, num_classes=3)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first half of the model closely resembles the kind of convnet you’d use for image\n",
    "classification: a stack of `Conv2D` layers, with gradually increasing filter sizes. We downsample\n",
    "our images three times by a factor of two each, ending up with activations of size\n",
    "$(25, 25, 256)$. The purpose of this first half is to encode the images into smaller feature\n",
    "maps, where each spatial location (or pixel) contains information about a large spatial\n",
    "chunk of the original image. You can understand it as a kind of compression.\n",
    "\n",
    "\n",
    "One important difference between the first half of this model and the classification\n",
    "models you’ve seen before is the way we do downsampling: in the classification\n",
    "ConvNets from the last chapter, we used `MaxPooling2D` layers to downsample feature\n",
    "maps. Here, we downsample by adding _strides_ to every other convolution layer. We do \n",
    "this because, in the case of image segmentation, we care a lot about the _spatial location_ of information in the image, since we need to produce per-pixel target masks as output of the \n",
    "model. When you do $2\\times 2$ max pooling, you are completely destroying location information within each pooling window: you return one scalar value per window, with zero knowledge of which of the four locations in the windows the value came from. So while max pooling layers perform\n",
    "well for classification tasks, they would hurt us quite a bit for a segmentation\n",
    "task. Meanwhile, strided convolutions do a better job at downsampling feature maps\n",
    "while retaining location information. Throughout this book, you’ll notice that we\n",
    "tend to use strides instead of max pooling in any model that cares about feature location,\n",
    "such as generative models.\n",
    "\n",
    "The second half of the model is a stack of `Conv2DTranspose` layers. What are those?\n",
    "Well, the output of the first half of the model is a feature map of shape $(20, 20, 128)$, \n",
    "but we want our final output to have the same shape as the target masks, $(160, 160,3)$. Therefore, we need to apply a kind of _inverse_ of the transformations we’ve applied\n",
    "so far — something that will _upsample_ the feature maps instead of downsampling them.\n",
    "That’s the purpose of the `Conv2DTranspose` layer: you can think of it as a kind of convolution\n",
    "layer that _learns to upsample_. If you have an input of shape $(80, 80, 64)$, and you\n",
    "run it through the layer `Conv2D(128, 3, strides=2, padding=\"same\")`, you get an\n",
    "output of shape $(40, 40, 128)$. If you run this output through the layer \n",
    "`Conv2DTranspose(64, 3, strides=2, padding=\"same\")`, you get back an output of shape $(80,\n",
    "80, 64)$, the same as the original. So after compressing our inputs into feature maps of\n",
    "shape $(10, 10, 256)$ via a stack of `Conv2D` layers, we can simply apply the corresponding\n",
    "sequence of `Conv2DTranspose` layers to get back to images of shape $(160, 160, 3)$.\n",
    "\n",
    "We can now compile and fit our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "100/100 [==============================] - 389s 4s/step - loss: 1.4495 - accuracy: 0.6714 - val_loss: 2.0964 - val_accuracy: 0.5844\n",
      "Epoch 2/50\n",
      "100/100 [==============================] - 380s 4s/step - loss: 0.6255 - accuracy: 0.7521 - val_loss: 3.0735 - val_accuracy: 0.5840\n",
      "Epoch 3/50\n",
      "100/100 [==============================] - 359s 4s/step - loss: 0.5015 - accuracy: 0.8007 - val_loss: 2.9807 - val_accuracy: 0.5840\n",
      "Epoch 4/50\n",
      "100/100 [==============================] - 646s 6s/step - loss: 0.4438 - accuracy: 0.8242 - val_loss: 1.8472 - val_accuracy: 0.5843\n",
      "Epoch 5/50\n",
      "100/100 [==============================] - 12058s 122s/step - loss: 0.4025 - accuracy: 0.8410 - val_loss: 0.9695 - val_accuracy: 0.6677\n",
      "Epoch 6/50\n",
      "100/100 [==============================] - 2337s 4s/step - loss: 0.3703 - accuracy: 0.8536 - val_loss: 0.4071 - val_accuracy: 0.8383\n",
      "Epoch 7/50\n",
      "100/100 [==============================] - 3028s 31s/step - loss: 0.3436 - accuracy: 0.8643 - val_loss: 0.4214 - val_accuracy: 0.8389\n",
      "Epoch 8/50\n",
      "100/100 [==============================] - 11752s 119s/step - loss: 0.3198 - accuracy: 0.8733 - val_loss: 0.4524 - val_accuracy: 0.8212\n",
      "Epoch 9/50\n",
      "100/100 [==============================] - 914s 9s/step - loss: 0.2965 - accuracy: 0.8819 - val_loss: 0.4060 - val_accuracy: 0.8426\n",
      "Epoch 10/50\n",
      "100/100 [==============================] - 1213s 12s/step - loss: 0.2772 - accuracy: 0.8892 - val_loss: 0.3871 - val_accuracy: 0.8556\n",
      "Epoch 11/50\n",
      "100/100 [==============================] - 349s 3s/step - loss: 0.2606 - accuracy: 0.8956 - val_loss: 0.3973 - val_accuracy: 0.8490\n",
      "Epoch 12/50\n",
      "100/100 [==============================] - 2942s 30s/step - loss: 0.2451 - accuracy: 0.9013 - val_loss: 0.3932 - val_accuracy: 0.8559\n",
      "Epoch 13/50\n",
      "100/100 [==============================] - 383s 4s/step - loss: 0.2342 - accuracy: 0.9055 - val_loss: 0.4213 - val_accuracy: 0.8513\n",
      "Epoch 14/50\n",
      "100/100 [==============================] - 346s 3s/step - loss: 0.2223 - accuracy: 0.9102 - val_loss: 0.4490 - val_accuracy: 0.8462\n",
      "Epoch 15/50\n",
      "100/100 [==============================] - 2655s 27s/step - loss: 0.2142 - accuracy: 0.9128 - val_loss: 0.4023 - val_accuracy: 0.8603\n",
      "Epoch 16/50\n",
      "100/100 [==============================] - 345s 3s/step - loss: 0.2049 - accuracy: 0.9161 - val_loss: 0.4357 - val_accuracy: 0.8635\n",
      "Epoch 17/50\n",
      "100/100 [==============================] - 451s 5s/step - loss: 0.1959 - accuracy: 0.9196 - val_loss: 0.4202 - val_accuracy: 0.8550\n",
      "Epoch 18/50\n",
      "100/100 [==============================] - 547s 5s/step - loss: 0.1915 - accuracy: 0.9214 - val_loss: 0.4300 - val_accuracy: 0.8577\n",
      "Epoch 19/50\n",
      "100/100 [==============================] - 555s 6s/step - loss: 0.1822 - accuracy: 0.9247 - val_loss: 0.4459 - val_accuracy: 0.8570\n",
      "Epoch 20/50\n",
      "100/100 [==============================] - 531s 5s/step - loss: 0.1773 - accuracy: 0.9268 - val_loss: 0.4104 - val_accuracy: 0.8691\n",
      "Epoch 21/50\n",
      "100/100 [==============================] - 548s 5s/step - loss: 0.1713 - accuracy: 0.9291 - val_loss: 0.4190 - val_accuracy: 0.8654\n",
      "Epoch 22/50\n",
      "100/100 [==============================] - 582s 6s/step - loss: 0.1626 - accuracy: 0.9322 - val_loss: 0.4380 - val_accuracy: 0.8651\n",
      "Epoch 23/50\n",
      "100/100 [==============================] - 398s 4s/step - loss: 0.1599 - accuracy: 0.9334 - val_loss: 0.4456 - val_accuracy: 0.8552\n",
      "Epoch 24/50\n",
      "100/100 [==============================] - 353s 4s/step - loss: 0.1528 - accuracy: 0.9360 - val_loss: 0.4514 - val_accuracy: 0.8675\n",
      "Epoch 25/50\n",
      "100/100 [==============================] - 375s 4s/step - loss: 0.1518 - accuracy: 0.9365 - val_loss: 0.4538 - val_accuracy: 0.8638\n",
      "Epoch 26/50\n",
      "100/100 [==============================] - 370s 4s/step - loss: 0.1484 - accuracy: 0.9379 - val_loss: 0.4353 - val_accuracy: 0.8642\n",
      "Epoch 27/50\n",
      "100/100 [==============================] - 380s 4s/step - loss: 0.1421 - accuracy: 0.9402 - val_loss: 0.4421 - val_accuracy: 0.8676\n",
      "Epoch 28/50\n",
      "100/100 [==============================] - 405s 4s/step - loss: 0.1397 - accuracy: 0.9415 - val_loss: 0.4484 - val_accuracy: 0.8651\n",
      "Epoch 29/50\n",
      "100/100 [==============================] - 416s 4s/step - loss: 0.1340 - accuracy: 0.9435 - val_loss: 0.5394 - val_accuracy: 0.8380\n",
      "Epoch 30/50\n",
      "100/100 [==============================] - 380s 4s/step - loss: 0.1326 - accuracy: 0.9440 - val_loss: 0.4622 - val_accuracy: 0.8654\n",
      "Epoch 31/50\n",
      "100/100 [==============================] - 408s 4s/step - loss: 0.1313 - accuracy: 0.9448 - val_loss: 0.4683 - val_accuracy: 0.8635\n",
      "Epoch 32/50\n",
      "100/100 [==============================] - 401s 4s/step - loss: 0.1268 - accuracy: 0.9465 - val_loss: 0.4691 - val_accuracy: 0.8668\n",
      "Epoch 33/50\n",
      "100/100 [==============================] - 374s 4s/step - loss: 0.1246 - accuracy: 0.9475 - val_loss: 0.4865 - val_accuracy: 0.8624\n",
      "Epoch 34/50\n",
      "100/100 [==============================] - 376s 4s/step - loss: 0.1233 - accuracy: 0.9482 - val_loss: 0.6864 - val_accuracy: 0.8293\n",
      "Epoch 35/50\n",
      "100/100 [==============================] - 381s 4s/step - loss: 0.1224 - accuracy: 0.9488 - val_loss: 0.5263 - val_accuracy: 0.8481\n",
      "Epoch 36/50\n",
      "100/100 [==============================] - 392s 4s/step - loss: 0.1192 - accuracy: 0.9500 - val_loss: 0.4628 - val_accuracy: 0.8639\n",
      "Epoch 37/50\n",
      "100/100 [==============================] - 400s 4s/step - loss: 0.1168 - accuracy: 0.9510 - val_loss: 0.4689 - val_accuracy: 0.8658\n",
      "Epoch 38/50\n",
      "100/100 [==============================] - 389s 4s/step - loss: 0.1109 - accuracy: 0.9529 - val_loss: 0.5279 - val_accuracy: 0.8657\n",
      "Epoch 39/50\n",
      "100/100 [==============================] - 381s 4s/step - loss: 0.1104 - accuracy: 0.9533 - val_loss: 0.4647 - val_accuracy: 0.8699\n",
      "Epoch 40/50\n",
      "100/100 [==============================] - 397s 4s/step - loss: 0.1064 - accuracy: 0.9547 - val_loss: 0.4940 - val_accuracy: 0.8722\n",
      "Epoch 41/50\n",
      "100/100 [==============================] - 378s 4s/step - loss: 0.1067 - accuracy: 0.9547 - val_loss: 0.5408 - val_accuracy: 0.8522\n",
      "Epoch 42/50\n",
      "100/100 [==============================] - 375s 4s/step - loss: 0.1074 - accuracy: 0.9550 - val_loss: 0.4689 - val_accuracy: 0.8725\n",
      "Epoch 43/50\n",
      "100/100 [==============================] - 392s 4s/step - loss: 0.1025 - accuracy: 0.9565 - val_loss: 0.5200 - val_accuracy: 0.8694\n",
      "Epoch 44/50\n",
      "100/100 [==============================] - 397s 4s/step - loss: 0.1051 - accuracy: 0.9560 - val_loss: 0.4728 - val_accuracy: 0.8697\n",
      "Epoch 45/50\n",
      "100/100 [==============================] - 417s 4s/step - loss: 0.0982 - accuracy: 0.9582 - val_loss: 0.5322 - val_accuracy: 0.8588\n",
      "Epoch 46/50\n",
      "100/100 [==============================] - 401s 4s/step - loss: 0.0987 - accuracy: 0.9581 - val_loss: 0.5161 - val_accuracy: 0.8658\n",
      "Epoch 47/50\n",
      "100/100 [==============================] - 389s 4s/step - loss: 0.0956 - accuracy: 0.9593 - val_loss: 0.5350 - val_accuracy: 0.8627\n",
      "Epoch 48/50\n",
      "100/100 [==============================] - 378s 4s/step - loss: 0.0954 - accuracy: 0.9597 - val_loss: 0.5461 - val_accuracy: 0.8542\n",
      "Epoch 49/50\n",
      "100/100 [==============================] - 378s 4s/step - loss: 0.0934 - accuracy: 0.9603 - val_loss: 0.4983 - val_accuracy: 0.8712\n",
      "Epoch 50/50\n",
      "100/100 [==============================] - 2374s 24s/step - loss: 0.0954 - accuracy: 0.9599 - val_loss: 0.5317 - val_accuracy: 0.8689\n"
     ]
    }
   ],
   "source": [
    "import os, datetime\n",
    "import tensorflow as tf\n",
    "\n",
    "model.compile(optimizer=\"rmsprop\", loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"])\n",
    "\n",
    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"unet_segmentation.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "] \n",
    "\n",
    "history = model.fit(train_input_imgs, train_targets,\n",
    "    epochs=50,\n",
    "    callbacks=callbacks,\n",
    "    batch_size=64,\n",
    "    validation_data=(val_input_imgs, val_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s display our training and validation loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that we start overfitting midway, around epoch 25. Let’s reload our best\n",
    "performing model according to the validation loss, and demonstrate how to use it to\n",
    "predict a segmentation mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import array_to_img\n",
    "model = keras.models.load_model(\"unet_segmentation.keras\")\n",
    "i = 4\n",
    "test_image = val_input_imgs[i]\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(array_to_img(test_image))\n",
    "mask = model.predict(np.expand_dims(test_image, 0))[0]\n",
    "\n",
    "# Utility to display a model’s prediction\n",
    "def display_mask(pred):\n",
    "    mask = np.argmax(pred, axis=-1)\n",
    "    mask *= 127\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(mask)\n",
    "    \n",
    "display_mask(mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of small artifacts in our predicted mask, caused by geometric shapes\n",
    "in the foreground and background. Nevertheless, our model appears to work nicely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part IV : Object Detection with Yolo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The section follows the [blog](https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.\n",
    "\n",
    "It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.\n",
    "\n",
    "The “You Only Look Once,” or YOLO, family of models are a series of end-to-end deep learning models designed for fast object detection, developed by Joseph Redmon, et al. and first described in the 2015 paper titled [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640).\n",
    "\n",
    "The approach involves a single deep convolutional neural network (originally a version of GoogLeNet, later updated and called DarkNet based on VGG) that splits the input into a grid of cells and each cell directly predicts a bounding box and object classification. The result is a large number of candidate bounding boxes that are consolidated into a final prediction by a post-processing step.\n",
    "\n",
    "There are three main variations of the approach, at the time of writing; they are YOLOv1, YOLOv2, and YOLOv3. The first version proposed the general architecture, whereas the second version refined the design and made use of predefined anchor boxes to improve bounding box proposal, and version three further refined the model architecture and training process.\n",
    "\n",
    "Although the accuracy of the models is close but not as good as Region-Based Convolutional Neural Networks (R-CNNs), they are popular for object detection because of their detection speed, often demonstrated in real-time on video or with camera feed input.\n",
    "\n",
    "A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object Detection With YOLOv3\n",
    "\n",
    "The keras-yolo3 project provides a lot of capability for using [YOLOv3 models](https://github.com/experiencor/keras-yolo3), including object detection, transfer learning, and training new models from scratch.\n",
    "\n",
    "In this section, we will use a pre-trained model to perform object detection on an unseen photograph. This capability is available in a single Python file in the repository called [yolo3_one_file_to_detect_them_all.py](https://raw.githubusercontent.com/experiencor/keras-yolo3/master/yolo3_one_file_to_detect_them_all.py) that has about 435 lines. This script is, in fact, a program that will use pre-trained weights to prepare a model and use that model to perform object detection and output a model. It also depends upon OpenCV.\n",
    "\n",
    "Instead of using this program directly, we will reuse elements from this program and develop our own scripts to first prepare and save a Keras YOLOv3 model, and then load the model to make a prediction for a new photograph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and Save Model\n",
    "\n",
    "The first step is to download the pre-trained model weights.\n",
    "\n",
    "These were trained using the DarkNet code base on the MSCOCO dataset. Download the model weights and place them into your current working directory with the filename `yolov3.weights`. It is a large file and may take a moment to download depending on the speed of your internet connection.\n",
    "\n",
    "[YOLOv3 Pre-trained Model Weights (yolov3.weights) (237 MB)](https://pjreddie.com/media/files/yolov3.weights)\n",
    "\n",
    "\n",
    "Next, we need to define a Keras model that has the right number and type of layers to match the downloaded model weights. The model architecture is called a _DarkNet_ and was originally loosely based on the VGG-16 model.\n",
    "\n",
    "The `yolo3_one_file_to_detect_them_all.py` script provides the `make_yolov3_model()` function to create the model for us, and the helper function `_conv_block()` that is used to create blocks of layers. These two functions can be copied directly from the script.\n",
    "\n",
    "We can now define the Keras model for YOLOv3."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# define the model\n",
    "model = make_yolov3_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to load the model weights. The model weights are stored in whatever format that was used by \n",
    "_DarkNet_. Rather than trying to decode the file manually, we can use the `WeightReader` class provided in the script.\n",
    "\n",
    "To use the `WeightReader`, it is instantiated with the path to our weights file (e.g. `yolov3.weights`). This will parse the file \n",
    "and load the model weights into memory in a format that we can set into our Keras model."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load the model weights\n",
    "weight_reader = WeightReader('yolov3.weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then call the `load_weights()` function of the `WeightReader` instance, passing in our defined Keras model to set the weights into the layers."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# set the model weights into the model\n",
    "weight_reader.load_weights(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That’s it; we now have a _YOLOv3_ model for use.\n",
    "\n",
    "We can save this model to a Keras compatible `.h5` model file ready for later use."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# save the model to file\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can tie all of this together; the complete code example including functions copied directly from the `yolo3_one_file_to_detect_them_all.py` script is listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading weights of convolution #0\n",
      "loading weights of convolution #1\n",
      "loading weights of convolution #2\n",
      "loading weights of convolution #3\n",
      "no convolution #4\n",
      "loading weights of convolution #5\n",
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      "loading weights of convolution #7\n",
      "no convolution #8\n",
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      "no convolution #11\n",
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      "loading weights of convolution #14\n",
      "no convolution #15\n",
      "loading weights of convolution #16\n",
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      "no convolution #18\n",
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      "loading weights of convolution #20\n",
      "no convolution #21\n",
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      "no convolution #24\n",
      "loading weights of convolution #25\n",
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      "no convolution #27\n",
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      "no convolution #30\n",
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      "loading weights of convolution #35\n",
      "no convolution #36\n",
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      "no convolution #40\n",
      "loading weights of convolution #41\n",
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      "no convolution #43\n",
      "loading weights of convolution #44\n",
      "loading weights of convolution #45\n",
      "no convolution #46\n",
      "loading weights of convolution #47\n",
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      "no convolution #49\n",
      "loading weights of convolution #50\n",
      "loading weights of convolution #51\n",
      "no convolution #52\n",
      "loading weights of convolution #53\n",
      "loading weights of convolution #54\n",
      "no convolution #55\n",
      "loading weights of convolution #56\n",
      "loading weights of convolution #57\n",
      "no convolution #58\n",
      "loading weights of convolution #59\n",
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      "no convolution #61\n",
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      "no convolution #65\n",
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      "no convolution #68\n",
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      "no convolution #82\n",
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      "loading weights of convolution #87\n",
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      "no convolution #94\n",
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      "loading weights of convolution #96\n",
      "no convolution #97\n",
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      "loading weights of convolution #99\n",
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      "loading weights of convolution #103\n",
      "loading weights of convolution #104\n",
      "loading weights of convolution #105\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "# create a YOLOv3 Keras model and save it to file\n",
    "# based on https://github.com/experiencor/keras-yolo3\n",
    "import struct\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import Input\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.layers import ZeroPadding2D\n",
    "from keras.layers import UpSampling2D\n",
    "from keras.layers.merge import add, concatenate\n",
    "from keras.models import Model\n",
    "\n",
    "def _conv_block(inp, convs, skip=True):\n",
    "    x = inp\n",
    "    count = 0\n",
    "    for conv in convs:\n",
    "        if count == (len(convs) - 2) and skip:\n",
    "            skip_connection = x\n",
    "        count += 1\n",
    "        if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top\n",
    "        x = Conv2D(conv['filter'],\n",
    "                   conv['kernel'],\n",
    "                   strides=conv['stride'],\n",
    "                   padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top\n",
    "                   name='conv_' + str(conv['layer_idx']),\n",
    "                   use_bias=False if conv['bnorm'] else True)(x)\n",
    "        if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)\n",
    "        if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)\n",
    "    return add([skip_connection, x]) if skip else x\n",
    "\n",
    "def make_yolov3_model():\n",
    "    input_image = Input(shape=(None, None, 3))\n",
    "    # Layer  0 => 4\n",
    "    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},\n",
    "                                  {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},\n",
    "                                  {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},\n",
    "                                  {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])\n",
    "    # Layer  5 => 8\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},\n",
    "                        {'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])\n",
    "    # Layer  9 => 11\n",
    "    x = _conv_block(x, [{'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},\n",
    "                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])\n",
    "    # Layer 12 => 15\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},\n",
    "                        {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},\n",
    "                        {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])\n",
    "    # Layer 16 => 36\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},\n",
    "                            {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])\n",
    "    skip_36 = x\n",
    "    # Layer 37 => 40\n",
    "    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])\n",
    "    # Layer 41 => 61\n",
    "    for i in range(7):\n",
    "        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},\n",
    "                            {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])\n",
    "    skip_61 = x\n",
    "    # Layer 62 => 65\n",
    "    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])\n",
    "    # Layer 66 => 74\n",
    "    for i in range(3):\n",
    "        x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},\n",
    "                            {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])\n",
    "    # Layer 75 => 79\n",
    "    x = _conv_block(x, [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},\n",
    "                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},\n",
    "                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)\n",
    "    # Layer 80 => 82\n",
    "    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 80},\n",
    "                                {'filter':  255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)\n",
    "    # Layer 83 => 86\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_61])\n",
    "    # Layer 87 => 91\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},\n",
    "                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},\n",
    "                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)\n",
    "    # Layer 92 => 94\n",
    "    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 92},\n",
    "                              {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)\n",
    "    # Layer 95 => 98\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True,   'layer_idx': 96}], skip=False)\n",
    "    x = UpSampling2D(2)(x)\n",
    "    x = concatenate([x, skip_36])\n",
    "    # Layer 99 => 106\n",
    "    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 99},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 100},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 101},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 102},\n",
    "                               {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 103},\n",
    "                               {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 104},\n",
    "                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)\n",
    "    model = Model(input_image, [yolo_82, yolo_94, yolo_106])\n",
    "    return model\n",
    "\n",
    "class WeightReader:\n",
    "    def __init__(self, weight_file):\n",
    "        with open(weight_file, 'rb') as w_f:\n",
    "            major,\t= struct.unpack('i', w_f.read(4))\n",
    "            minor,\t= struct.unpack('i', w_f.read(4))\n",
    "            revision, = struct.unpack('i', w_f.read(4))\n",
    "            if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:\n",
    "                w_f.read(8)\n",
    "            else:\n",
    "                w_f.read(4)\n",
    "            transpose = (major > 1000) or (minor > 1000)\n",
    "            binary = w_f.read()\n",
    "        self.offset = 0\n",
    "        self.all_weights = np.frombuffer(binary, dtype='float32')\n",
    "\n",
    "    def read_bytes(self, size):\n",
    "        self.offset = self.offset + size\n",
    "        return self.all_weights[self.offset-size:self.offset]\n",
    "\n",
    "    def load_weights(self, model):\n",
    "        for i in range(106):\n",
    "            try:\n",
    "                conv_layer = model.get_layer('conv_' + str(i))\n",
    "                print(\"loading weights of convolution #\" + str(i))\n",
    "                if i not in [81, 93, 105]:\n",
    "                    norm_layer = model.get_layer('bnorm_' + str(i))\n",
    "                    size = np.prod(norm_layer.get_weights()[0].shape)\n",
    "                    beta  = self.read_bytes(size) # bias\n",
    "                    gamma = self.read_bytes(size) # scale\n",
    "                    mean  = self.read_bytes(size) # mean\n",
    "                    var   = self.read_bytes(size) # variance\n",
    "                    weights = norm_layer.set_weights([gamma, beta, mean, var])\n",
    "                if len(conv_layer.get_weights()) > 1:\n",
    "                    bias   = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel, bias])\n",
    "                else:\n",
    "                    kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))\n",
    "                    kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))\n",
    "                    kernel = kernel.transpose([2,3,1,0])\n",
    "                    conv_layer.set_weights([kernel])\n",
    "            except ValueError:\n",
    "                print(\"no convolution #\" + str(i))\n",
    "\n",
    "    def reset(self):\n",
    "        self.offset = 0\n",
    "\n",
    "# define the model\n",
    "model = make_yolov3_model()\n",
    "# load the model weights\n",
    "weight_reader = WeightReader('yolov3.weights')\n",
    "# set the model weights into the model\n",
    "weight_reader.load_weights(model)\n",
    "# save the model to file\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the example may take a little less than one minute to execute on modern hardware.\n",
    "\n",
    "As the weight file is loaded, you will see debug information reported about what was loaded, output by the `WeightReader` class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end of the run, the `model.h5` file is saved in your current working directory with approximately the same size as the original weight file (237MB), but ready to be loaded and used directly as a Keras model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a Prediction\n",
    "\n",
    "We need a new photo for object detection, ideally with objects that we know that the model knows about from the MSCOCO dataset.\n",
    "\n",
    "We will use a photograph of two elephants.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='./Bilder/african-elephant.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load the Keras model. This might be the slowest part of making a prediction."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load yolov3 model\n",
    "model = load_model('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to load our new photograph and prepare it as suitable input to the model. The model expects inputs to be color images with the square shape of $416\\times 416$ pixels.\n",
    "\n",
    "We can use the `load_img()` Keras function to load the image and the `target_size` argument to resize the image after loading. We can also use the `img_to_array()` function to convert the loaded `PIL` image object into a NumPy array, and then rescale the pixel values from $0-255$ to $0-1$ 32-bit floating point values.\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load the image with the required size\n",
    "image = load_img('./Bilder/african-elephant.jpg', target_size=(416, 416))\n",
    "# convert to numpy array\n",
    "image = img_to_array(image)\n",
    "# scale pixel values to [0, 1]\n",
    "image = image.astype('float32')\n",
    "image /= 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will want to show the original photo again later, which means we will need to scale the bounding boxes of all detected objects from the square shape back to the original shape. As such, we can load the image and retrieve the original shape."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load the image to get its shape\n",
    "image = load_img('./Bilder/african-elephant.jpg')\n",
    "width, height = image.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can tie all of this together into a convenience function named `load_image_pixels()` that takes the filename and target size and returns the scaled pixel data ready to provide as input to the Keras model, as well as the original width and height of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and prepare an image\n",
    "def load_image_pixels(filename, shape):\n",
    "    # load the image to get its shape\n",
    "    image = load_img(filename)\n",
    "    width, height = image.size\n",
    "    # load the image with the required size\n",
    "    image = load_img(filename, target_size=shape)\n",
    "    # convert to numpy array\n",
    "    image = img_to_array(image)\n",
    "    # scale pixel values to [0, 1]\n",
    "    image = image.astype('float32')\n",
    "    image /= 255.0\n",
    "    # add a dimension so that we have one sample\n",
    "    image = expand_dims(image, 0)\n",
    "    return image, width, height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then call this function to load our photo of elephants."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# define the expected input shape for the model\n",
    "input_w, input_h = 416, 416\n",
    "# define our new photo\n",
    "photo_filename = 'african-elephant.jpg'\n",
    "# load and prepare image\n",
    "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now feed the photo into the Keras model and make a prediction."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# make prediction\n",
    "yhat = model.predict(image)\n",
    "# summarize the shape of the list of arrays\n",
    "print([a.shape for a in yhat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That’s it, at least for making a prediction. The complete example is listed below."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# load yolov3 model and perform object detection\n",
    "# based on https://github.com/experiencor/keras-yolo3\n",
    "from numpy import expand_dims\n",
    "from keras.models import load_model\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    " \n",
    "# load and prepare an image\n",
    "def load_image_pixels(filename, shape):\n",
    "    # load the image to get its shape\n",
    "    image = load_img(filename)\n",
    "    width, height = image.size\n",
    "    # load the image with the required size\n",
    "    image = load_img(filename, target_size=shape)\n",
    "    # convert to numpy array\n",
    "    image = img_to_array(image)\n",
    "    # scale pixel values to [0, 1]\n",
    "    image = image.astype('float32')\n",
    "    image /= 255.0\n",
    "    # add a dimension so that we have one sample\n",
    "    image = expand_dims(image, 0)\n",
    "    return image, width, height\n",
    " \n",
    "# load yolov3 model\n",
    "model = load_model('model.h5')\n",
    "# define the expected input shape for the model\n",
    "input_w, input_h = 416, 416\n",
    "# define our new photo\n",
    "photo_filename = 'zebra.jpg'\n",
    "# load and prepare image\n",
    "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))\n",
    "# make prediction\n",
    "yhat = model.predict(image)\n",
    "# summarize the shape of the list of arrays\n",
    "print([a.shape for a in yhat])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the example returns a list of three NumPy arrays, the shape of which is displayed as output.\n",
    "\n",
    "These arrays predict both the bounding boxes and class labels but are encoded. They must be interpreted."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a Prediction and Interpret Result\n",
    "\n",
    "The output of the model is, in fact, encoded candidate bounding boxes from three different grid sizes, and the boxes are defined the context of anchor boxes, carefully chosen based on an analysis of the size of objects in the MSCOCO dataset.\n",
    "\n",
    "The script provided by experiencor provides a function called `decode_netout()` that will take each one of the NumPy arrays, one at a time, and decode the candidate bounding boxes and class predictions. Further, any bounding boxes that don’t confidently describe an object (e.g. all class probabilities below a threshold) are ignored. We will use a probability of 60% or 0.6. The function returns a list of `BoundBox` instances that define the corners of each bounding box in the context of the input image shape and class probabilities."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# define the anchors\n",
    "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n",
    "# define the probability threshold for detected objects\n",
    "class_threshold = 0.6\n",
    "boxes = list()\n",
    "for i in range(len(yhat)):\n",
    "    # decode the output of the network\n",
    "    boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the bounding boxes can be stretched back into the shape of the original image. This is helpful as it means that later we can plot the original image and draw the bounding boxes, hopefully detecting real objects.\n",
    "\n",
    "The experiencor script provides the `correct_yolo_boxes()` function to perform this translation of bounding box coordinates, taking the list of bounding boxes, the original shape of our loaded photograph, and the shape of the input to the network as arguments. The coordinates of the bounding boxes are updated directly."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# correct the sizes of the bounding boxes for the shape of the image\n",
    "correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has predicted a lot of candidate bounding boxes, and most of the boxes will be referring to the same objects. The list of bounding boxes can be filtered and those boxes that overlap and refer to the same object can be merged. We can define the amount of overlap as a configuration parameter, in this case, 50% or 0.5. This filtering of bounding box regions is generally referred to as non-maximal suppression and is a required post-processing step.\n",
    "\n",
    "The experiencor script provides this via the `do_nms()` function that takes the list of bounding boxes and a threshold parameter. Rather than purging the overlapping boxes, their predicted probability for their overlapping class is cleared. This allows the boxes to remain and be used if they also detect another object type."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# suppress non-maximal boxes\n",
    "do_nms(boxes, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will leave us with the same number of boxes, but only very few of interest. We can retrieve just those boxes that strongly predict the presence of an object: that is are more than 60% confident. This can be achieved by enumerating over all boxes and checking the class prediction values. We can then look up the corresponding class label for the box and add it to the list. Each box must be considered for each class label, just in case the same box strongly predicts more than one object.\n",
    "\n",
    "We can develop a `get_boxes()` function that does this and takes the list of boxes, known labels, and our classification threshold as arguments and returns parallel lists of boxes, labels, and scores."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# get all of the results above a threshold\n",
    "def get_boxes(boxes, labels, thresh):\n",
    "    v_boxes, v_labels, v_scores = list(), list(), list()\n",
    "    # enumerate all boxes\n",
    "    for box in boxes:\n",
    "        # enumerate all possible labels\n",
    "        for i in range(len(labels)):\n",
    "            # check if the threshold for this label is high enough\n",
    "            if box.classes[i] > thresh:\n",
    "                v_boxes.append(box)\n",
    "                v_labels.append(labels[i])\n",
    "                v_scores.append(box.classes[i]*100)\n",
    "                # don't break, many labels may trigger for one box\n",
    "    return v_boxes, v_labels, v_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can call this function with our list of boxes.\n",
    "\n",
    "We also need a list of strings containing the class labels known to the model in the correct order used during training, specifically those class labels from the MSCOCO dataset. Thankfully, this is provided in the experiencor script."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# define the labels\n",
    "labels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"bus\", \"train\", \"truck\",\n",
    "    \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\",\n",
    "    \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\",\n",
    "    \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n",
    "    \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\",\n",
    "    \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\",\n",
    "    \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\",\n",
    "    \"chair\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\",\n",
    "    \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\",\n",
    "    \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]\n",
    "# get the details of the detected objects\n",
    "v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have those few boxes of strongly predicted objects, we can summarize them."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# summarize what we found\n",
    "for i in range(len(v_boxes)):\n",
    "    print(v_labels[i], v_scores[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot our original photograph and draw the bounding box around each detected object. This can be achieved by retrieving the coordinates from each bounding box and creating a Rectangle object."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "box = v_boxes[i]\n",
    "# get coordinates\n",
    "y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "# calculate width and height of the box\n",
    "width, height = x2 - x1, y2 - y1\n",
    "# create the shape\n",
    "rect = Rectangle((x1, y1), width, height, fill=False, color='white')\n",
    "# draw the box\n",
    "ax.add_patch(rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also draw a string with the class label and confidence."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# draw text and score in top left corner\n",
    "label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "pyplot.text(x1, y1, label, color='white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `draw_boxes()` function below implements this, taking the filename of the original photograph and the parallel lists of bounding boxes, labels and scores, and creates a plot showing all detected objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw all results\n",
    "def draw_boxes(filename, v_boxes, v_labels, v_scores):\n",
    "    # load the image\n",
    "    data = pyplot.imread(filename)\n",
    "    # plot the image\n",
    "    pyplot.imshow(data)\n",
    "    # get the context for drawing boxes\n",
    "    ax = pyplot.gca()\n",
    "    # plot each box\n",
    "    for i in range(len(v_boxes)):\n",
    "        box = v_boxes[i]\n",
    "        # get coordinates\n",
    "        y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "        # calculate width and height of the box\n",
    "        width, height = x2 - x1, y2 - y1\n",
    "        # create the shape\n",
    "        rect = Rectangle((x1, y1), width, height, fill=False, color='white')\n",
    "        # draw the box\n",
    "        ax.add_patch(rect)\n",
    "        # draw text and score in top left corner\n",
    "        label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "        pyplot.text(x1, y1, label, color='white')\n",
    "    # show the plot\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then call this function to plot our final result."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# draw what we found\n",
    "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# draw what we found\n",
    "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "[(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]\n",
      "elephant 98.6307144165039\n",
      "elephant 77.56589651107788\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load yolov3 model and perform object detection\n",
    "# based on https://github.com/experiencor/keras-yolo3\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from numpy import expand_dims\n",
    "from keras.models import load_model\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    " \n",
    "class BoundBox:\n",
    "    def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n",
    "        self.xmin = xmin\n",
    "        self.ymin = ymin\n",
    "        self.xmax = xmax\n",
    "        self.ymax = ymax\n",
    "        self.objness = objness\n",
    "        self.classes = classes\n",
    "        self.label = -1\n",
    "        self.score = -1\n",
    " \n",
    "    def get_label(self):\n",
    "        if self.label == -1:\n",
    "            self.label = np.argmax(self.classes)\n",
    " \n",
    "        return self.label\n",
    " \n",
    "    def get_score(self):\n",
    "        if self.score == -1:\n",
    "            self.score = self.classes[self.get_label()]\n",
    " \n",
    "        return self.score\n",
    " \n",
    "def _sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    " \n",
    "def decode_netout(netout, anchors, obj_thresh, net_h, net_w):\n",
    "    grid_h, grid_w = netout.shape[:2]\n",
    "    nb_box = 3\n",
    "    netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n",
    "    nb_class = netout.shape[-1] - 5\n",
    "    boxes = []\n",
    "    netout[..., :2]  = _sigmoid(netout[..., :2])\n",
    "    netout[..., 4:]  = _sigmoid(netout[..., 4:])\n",
    "    netout[..., 5:]  = netout[..., 4][..., np.newaxis] * netout[..., 5:]\n",
    "    netout[..., 5:] *= netout[..., 5:] > obj_thresh\n",
    " \n",
    "    for i in range(grid_h*grid_w):\n",
    "        row = i / grid_w\n",
    "        col = i % grid_w\n",
    "        for b in range(nb_box):\n",
    "            # 4th element is objectness score\n",
    "            objectness = netout[int(row)][int(col)][b][4]\n",
    "            if(objectness.all() <= obj_thresh): continue\n",
    "            # first 4 elements are x, y, w, and h\n",
    "            x, y, w, h = netout[int(row)][int(col)][b][:4]\n",
    "            x = (col + x) / grid_w # center position, unit: image width\n",
    "            y = (row + y) / grid_h # center position, unit: image height\n",
    "            w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n",
    "            h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height\n",
    "            # last elements are class probabilities\n",
    "            classes = netout[int(row)][col][b][5:]\n",
    "            box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)\n",
    "            boxes.append(box)\n",
    "    return boxes\n",
    " \n",
    "def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n",
    "    new_w, new_h = net_w, net_h\n",
    "    for i in range(len(boxes)):\n",
    "        x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n",
    "        y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n",
    "        boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n",
    "        boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n",
    "        boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n",
    "        boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)\n",
    " \n",
    "def _interval_overlap(interval_a, interval_b):\n",
    "    x1, x2 = interval_a\n",
    "    x3, x4 = interval_b\n",
    "    if x3 < x1:\n",
    "        if x4 < x1:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x1\n",
    "    else:\n",
    "        if x2 < x3:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x3\n",
    " \n",
    "def bbox_iou(box1, box2):\n",
    "    intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n",
    "    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n",
    "    intersect = intersect_w * intersect_h\n",
    "    w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n",
    "    w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n",
    "    union = w1*h1 + w2*h2 - intersect\n",
    "    return float(intersect) / union\n",
    " \n",
    "def do_nms(boxes, nms_thresh):\n",
    "    if len(boxes) > 0:\n",
    "        nb_class = len(boxes[0].classes)\n",
    "    else:\n",
    "        return\n",
    "    for c in range(nb_class):\n",
    "        sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n",
    "        for i in range(len(sorted_indices)):\n",
    "            index_i = sorted_indices[i]\n",
    "            if boxes[index_i].classes[c] == 0: continue\n",
    "            for j in range(i+1, len(sorted_indices)):\n",
    "                index_j = sorted_indices[j]\n",
    "                if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n",
    "                    boxes[index_j].classes[c] = 0\n",
    " \n",
    "# load and prepare an image\n",
    "def load_image_pixels(filename, shape):\n",
    "    # load the image to get its shape\n",
    "    image = load_img(filename)\n",
    "    width, height = image.size\n",
    "    # load the image with the required size\n",
    "    image = load_img(filename, target_size=shape)\n",
    "    # convert to numpy array\n",
    "    image = img_to_array(image)\n",
    "    # scale pixel values to [0, 1]\n",
    "    image = image.astype('float32')\n",
    "    image /= 255.0\n",
    "    # add a dimension so that we have one sample\n",
    "    image = expand_dims(image, 0)\n",
    "    return image, width, height\n",
    " \n",
    "# get all of the results above a threshold\n",
    "def get_boxes(boxes, labels, thresh):\n",
    "    v_boxes, v_labels, v_scores = list(), list(), list()\n",
    "    # enumerate all boxes\n",
    "    for box in boxes:\n",
    "        # enumerate all possible labels\n",
    "        for i in range(len(labels)):\n",
    "            # check if the threshold for this label is high enough\n",
    "            if box.classes[i] > thresh:\n",
    "                v_boxes.append(box)\n",
    "                v_labels.append(labels[i])\n",
    "                v_scores.append(box.classes[i]*100)\n",
    "                # don't break, many labels may trigger for one box\n",
    "    return v_boxes, v_labels, v_scores\n",
    " \n",
    "# draw all results\n",
    "def draw_boxes(filename, v_boxes, v_labels, v_scores):\n",
    "    # load the image\n",
    "    data = pyplot.imread(filename)\n",
    "    # plot the image\n",
    "    pyplot.imshow(data)\n",
    "    # get the context for drawing boxes\n",
    "    ax = pyplot.gca()\n",
    "    # plot each box\n",
    "    for i in range(len(v_boxes)):\n",
    "        box = v_boxes[i]\n",
    "        # get coordinates\n",
    "        y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "        # calculate width and height of the box\n",
    "        width, height = x2 - x1, y2 - y1\n",
    "        # create the shape\n",
    "        rect = Rectangle((x1, y1), width, height, fill=False, color='white')\n",
    "        # draw the box\n",
    "        ax.add_patch(rect)\n",
    "        # draw text and score in top left corner\n",
    "        label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "        pyplot.text(x1, y1, label, color='white')\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    " \n",
    "# load yolov3 model\n",
    "model = load_model('model.h5')\n",
    "# define the expected input shape for the model\n",
    "input_w, input_h = 416, 416\n",
    "# define our new photo\n",
    "photo_filename = './Bilder/african-elephant.jpg'\n",
    "# load and prepare image\n",
    "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))\n",
    "# make prediction\n",
    "yhat = model.predict(image)\n",
    "# summarize the shape of the list of arrays\n",
    "print([a.shape for a in yhat])\n",
    "# define the anchors\n",
    "anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]\n",
    "# define the probability threshold for detected objects\n",
    "class_threshold = 0.6\n",
    "boxes = list()\n",
    "for i in range(len(yhat)):\n",
    "    # decode the output of the network\n",
    "    boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)\n",
    "# correct the sizes of the bounding boxes for the shape of the image\n",
    "correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)\n",
    "# suppress non-maximal boxes\n",
    "do_nms(boxes, 0.5)\n",
    "# define the labels\n",
    "labels = [\"person\", \"bicycle\", \"car\", \"motorbike\", \"aeroplane\", \"bus\", \"train\", \"truck\",\n",
    "    \"boat\", \"traffic light\", \"fire hydrant\", \"stop sign\", \"parking meter\", \"bench\",\n",
    "    \"bird\", \"cat\", \"dog\", \"horse\", \"sheep\", \"cow\", \"elephant\", \"bear\", \"zebra\", \"giraffe\",\n",
    "    \"backpack\", \"umbrella\", \"handbag\", \"tie\", \"suitcase\", \"frisbee\", \"skis\", \"snowboard\",\n",
    "    \"sports ball\", \"kite\", \"baseball bat\", \"baseball glove\", \"skateboard\", \"surfboard\",\n",
    "    \"tennis racket\", \"bottle\", \"wine glass\", \"cup\", \"fork\", \"knife\", \"spoon\", \"bowl\", \"banana\",\n",
    "    \"apple\", \"sandwich\", \"orange\", \"broccoli\", \"carrot\", \"hot dog\", \"pizza\", \"donut\", \"cake\",\n",
    "    \"chair\", \"sofa\", \"pottedplant\", \"bed\", \"diningtable\", \"toilet\", \"tvmonitor\", \"laptop\", \"mouse\",\n",
    "    \"remote\", \"keyboard\", \"cell phone\", \"microwave\", \"oven\", \"toaster\", \"sink\", \"refrigerator\",\n",
    "    \"book\", \"clock\", \"vase\", \"scissors\", \"teddy bear\", \"hair drier\", \"toothbrush\"]\n",
    "# get the details of the detected objects\n",
    "v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)\n",
    "# summarize what we found\n",
    "for i in range(len(v_boxes)):\n",
    "    print(v_labels[i], v_scores[i])\n",
    "# draw what we found\n",
    "draw_boxes(photo_filename, v_boxes, v_labels, v_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0dfpdDOGhM2"
   },
   "source": [
    "# Part V : Instance Segmentation with Mask R-CNN\n",
    "\n",
    "### Please run this section on Colab !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vOAEQt-pGhM3"
   },
   "source": [
    "Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.\n",
    "\n",
    "It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).\n",
    "\n",
    "In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.\n",
    "\n",
    "In this section, we will discover how to use the __Mask R-CNN__ model to detect objects in new photographs.\n",
    "\n",
    "After completing this tutorial, you will know:\n",
    "\n",
    "- The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.\n",
    "\n",
    "- The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.\n",
    "    \n",
    "- How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ra-bXlWXGhM4"
   },
   "source": [
    "## Mask R-CNN for Object Detection\n",
    "\n",
    "Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.\n",
    "\n",
    "It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.\n",
    "\n",
    "An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation.\n",
    "\n",
    "The __Region-Based__ Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.\n",
    "\n",
    "There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:\n",
    "\n",
    "- __R-CNN__: Bounding boxes are proposed by the “selective search” algorithm, each of which is stretched and features are extracted via a deep convolutional neural network, such as AlexNet, before a final set of object classifications are made with linear SVMs.\n",
    "\n",
    "- __Fast R-CNN__: Simplified design with a single model, bounding boxes are still specified as input, but a region-of-interest pooling layer is used after the deep CNN to consolidate regions and the model predicts both class labels and regions of interest directly.\n",
    "    \n",
    "- __Faster R-CNN__: Addition of a Region Proposal Network that interprets features extracted from the deep CNN and learns to propose regions-of-interest directly.\n",
    "    \n",
    "- __Mask R-CNN__: Extension of Faster R-CNN that adds an output model for predicting a mask for each detected object.\n",
    "\n",
    "The Mask R-CNN model introduced in the 2018 paper titled [Mask R-CNN](https://arxiv.org/abs/1703.06870) is the most recent variation of the family models and supports both object detection and object segmentation. The paper provides a nice summary of the model linage to that point:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GlXwuVoOGhM7"
   },
   "source": [
    "### Matterport Mask R-CNN Project\n",
    "\n",
    "Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.\n",
    "\n",
    "Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:\n",
    "\n",
    "- R-CNN: [Regions with Convolutional Neural Network Features, GitHub](https://github.com/rbgirshick/rcnn)\n",
    "\n",
    "- Fast R-CNN, [GitHub](https://github.com/rbgirshick/fast-rcnn)\n",
    "\n",
    "- Faster R-CNN Python Code, [GitHub](https://github.com/rbgirshick/py-faster-rcnn)\n",
    "\n",
    "- Detectron, Facebook AI, [GitHub](https://github.com/facebookresearch/Detectron)\n",
    "\n",
    "Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.\n",
    "\n",
    "The best of breed third-party implementations of Mask R-CNN is the [Mask R-CNN](https://github.com/matterport/Mask_RCNN) Project developed by Matterport. The project is open source released under a permissive license (i.e. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.\n",
    "\n",
    "Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project available, just in case there are major changes to the API in the future.\n",
    "\n",
    "The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:\n",
    "\n",
    "- Mask R-CNN Demo, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb)\n",
    "\n",
    "- Mask R-CNN – Inspect Trained Model, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/coco/inspect_model.ipynb)\n",
    "\n",
    "There are perhaps three main use cases for using the Mask R-CNN model with the Matterport library; they are:\n",
    "\n",
    "- __Object Detection Application__: Use a pre-trained model for object detection on new images.\n",
    "\n",
    "- __New Model via Transfer Learning__: Use a pre-trained model as a starting point in developing a model for a new object detection dataset.\n",
    "    \n",
    "- __New Model from Scratch__: Develop a new model from scratch for an object detection dataset.\n",
    "\n",
    "In order to get familiar with the model and the library, we will look at the first example in the next section.\n",
    "\n",
    "#### Object Detection With Mask R-CNN\n",
    "\n",
    "In this section, we will use the Matterport Mask R-CNN library to perform object detection on arbitrary photographs.\n",
    "\n",
    "Much like using a pre-trained deep CNN for image classification, e.g. such as VGG-16 trained on an ImageNet dataset, we can use a pre-trained Mask R-CNN model to detect objects in new photographs. In this case, we will use a Mask R-CNN trained on the [MS COCO object detection problem](http://cocodataset.org/#home).\n",
    "\n",
    "#### Mask R-CNN Installation\n",
    "\n",
    "The first step is to install the library.\n",
    "\n",
    "At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.\n",
    "\n",
    "Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the [installation instructions](https://github.com/matterport/Mask_RCNN#installation) buried in the library’s readme file.\n",
    "\n",
    "#### Step 0. Open Colab and Upload this Notebook\n",
    "\n",
    "#### Step 1. Clone the Mask R-CNN GitHub Repository\n",
    "\n",
    "This is as simple as running the following command from your command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "colab_type": "code",
    "id": "HGiDmuejGhM8",
    "outputId": "ce5ca013-96e5-4766-d2ed-b4cde9b3ca94"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/matterport/Mask_RCNN.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S7uXyFVPGhNA"
   },
   "source": [
    "This will create a new local directory with the name Mask_RCNN that looks as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "DhKn5ytcGhNA"
   },
   "source": [
    "Mask_RCNN\n",
    "├── assets\n",
    "├── build\n",
    "│   ├── bdist.macosx-10.13-x86_64\n",
    "│   └── lib\n",
    "│       └── mrcnn\n",
    "├── dist\n",
    "├── images\n",
    "├── mask_rcnn.egg-info\n",
    "├── mrcnn\n",
    "└── samples\n",
    "    ├── balloon\n",
    "    ├── coco\n",
    "    ├── nucleus\n",
    "    └── shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WvFlDgvJGhNB"
   },
   "source": [
    "#### Step 2. Install the Mask R-CNN Library\n",
    "\n",
    "The library can be installed directly via pip.\n",
    "\n",
    "Change directory into the _Mask_RCNN_ directory and run the installation script.\n",
    "\n",
    "From the command line, type the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "aEUeZhX5GhNB",
    "outputId": "be5de5a1-e821-477c-ce28-91bb9f8c3194"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('./Mask_RCNN')\n",
    "!pip3 install -r requirements.txt\n",
    "!python3 setup.py install "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DlySPeHPGhNE"
   },
   "source": [
    "The library will then install directly and you will see a lot of successful installation messages ending with the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "nAww1LboGhNF"
   },
   "source": [
    "...\n",
    "Finished processing dependencies for mask-rcnn==2.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "55X0zSm7GhNG"
   },
   "source": [
    "#### Step 3: Confirm the Library Was Installed\n",
    "\n",
    "It is always a good idea to confirm that the library was installed correctly.\n",
    "\n",
    "You can confirm that the library was installed correctly by querying it via the pip command; for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "kKXRZ1vTGhNG",
    "outputId": "9f0df55c-755f-4e11-a6c3-e8b7418eefcb"
   },
   "outputs": [],
   "source": [
    "!pip3 show mask-rcnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f0vwUrMcGhNJ"
   },
   "source": [
    "### Example of Object Localization\n",
    "\n",
    "We are going to use a pre-trained Mask R-CNN model to detect objects on a new photograph.\n",
    "\n",
    "#### Step 1. Download Model Weights\n",
    "\n",
    "First, download the weights for the pre-trained model, specifically a Mask R-CNN trained on the MS Coco dataset.\n",
    "\n",
    "The weights are available from the project GitHub project and the file is about 250 megabytes. Download the model weights to a file with the name ‘mask_rcnn_coco.h5‘ in your current working directory.\n",
    "\n",
    "[Download Weights (mask_rcnn_coco.h5)](https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5) (246 megabytes)\n",
    "\n",
    "#### Step 2. Download Sample Photograph\n",
    "\n",
    "We also need a photograph in which to detect objects.\n",
    "\n",
    "Download from Ilias the photograph to your current working directory with the filename ‘african-elephant.jpg‘\n",
    "\n",
    "\n",
    "african-elephant.jpg![grafik.png](attachment:grafik.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n8ccmDSvGhNK"
   },
   "source": [
    "#### Step 3. Load Model and Make Prediction\n",
    "\n",
    "First, the model must be defined via an instance MaskRCNN class.\n",
    "\n",
    "This class requires a configuration object as a parameter. The configuration object defines how the model might be used during training or inference.\n",
    "\n",
    "In this case, the configuration will only specify the number of images per batch, which will be one, and the number of classes to predict.\n",
    "\n",
    "You can see the full extent of the configuration object and the properties that you can override in the [config.py](https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qAfMaOOzGhNL"
   },
   "outputs": [],
   "source": [
    "%tensorflow_version 1.x\n",
    "from mrcnn.config import Config\n",
    "from mrcnn.model import MaskRCNN\n",
    "# define the test configuration\n",
    "class TestConfig(Config):\n",
    "     NAME = \"test\"\n",
    "     GPU_COUNT = 1\n",
    "     IMAGES_PER_GPU = 1\n",
    "     NUM_CLASSES = 1 + 80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1CmHYT4RGhNN"
   },
   "source": [
    "We can now define the MaskRCNN instance.\n",
    "\n",
    "We will define the model as type “inference” indicating that we are interested in making predictions and not training. We must also specify a directory where any log messages could be written, which in this case will be the current working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sg482-mcGhNO"
   },
   "outputs": [],
   "source": [
    "# define the model\n",
    "rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install 'h5py==2.10.0' --force-reinstall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9BtI50MlGhNR"
   },
   "source": [
    "The next step is to load the weights that we downloaded. You should save it on google drive and then load it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_TWgehzsNOSV",
    "outputId": "73225d99-e9df-4d1c-c733-a092c97e336c"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "colab_type": "code",
    "id": "46t9gwLdGhNR",
    "outputId": "842b58f4-2678-4ad9-bbcf-aac4656392b7"
   },
   "outputs": [],
   "source": [
    "# load coco model weights\n",
    "rcnn.load_weights('/content/drive/My Drive/mask_rcnn_coco.h5', by_name=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bTBwZPvBGhNU"
   },
   "source": [
    "Now we can make a prediction for our image. First, we can load the image and convert it to a NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6k8CgLmCGhNW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "# load photograph\n",
    "img = image.load_img('/content/drive/My Drive/african-elephant.jpg')\n",
    "img = image.img_to_array(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h2hsqN-5GhNZ"
   },
   "source": [
    "We can then make a prediction with the model. Instead of calling `predict()` as we would on a normal Keras model, will call the `detect()` function and pass it the single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ubUzpG2lGhNZ"
   },
   "outputs": [],
   "source": [
    "# make prediction\n",
    "results = rcnn.detect([img], verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UfgnKPgSGhNc"
   },
   "source": [
    "The result contains a dictionary for each image that we passed into the `detect()` function, in this case, a list of a single dictionary for the one image.\n",
    "\n",
    "The dictionary has keys for the bounding boxes, masks, and so on, and each key points to a list for multiple possible objects detected in the image.\n",
    "\n",
    "The keys of the dictionary of note are as follows:\n",
    "\n",
    "- __‘rois‘__: The bound boxes or regions-of-interest (ROI) for detected objects.\n",
    "- __‘masks‘__: The masks for the detected objects.\n",
    "- __‘class_ids‘__: The class integers for the detected objects.\n",
    "- __‘scores‘__: The probability or confidence for each predicted class.\n",
    "\n",
    "We can draw each box detected in the image by first getting the dictionary for the first image (e.g. results[0]), and then retrieving the list of bounding boxes (e.g. [‘rois’])."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Gb2Q5QgLGhNc"
   },
   "outputs": [],
   "source": [
    "boxes = results[0]['rois']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qUxs3u4qGhNf"
   },
   "source": [
    "Each bounding box is defined in terms of the bottom left and top right coordinates of the bounding box in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wKPg5GodGhNg"
   },
   "outputs": [],
   "source": [
    "y1, x1, y2, x2 = boxes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Mp9EfU8vGhNj"
   },
   "source": [
    "We can use these coordinates to create a `Rectangle()` from the matplotlib API and draw each rectangle over the top of our image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    "ax = pyplot.gca()\n",
    "# get coordinates\n",
    "y1, x1, y2, x2 = boxes[0]\n",
    "# calculate width and height of the box\n",
    "width, height = x2 - x1, y2 - y1\n",
    "# create the shape\n",
    "rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "# draw the box\n",
    "ax.add_patch(rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "To keep things neat, we can create a function to do this that will take the filename of the photograph and the list of bounding boxes to draw and will show the photo with the boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "# draw an image with detected objects\n",
    "def draw_image_with_boxes(filename, boxes_list):\n",
    "     # load the image\n",
    "     data = pyplot.imread(filename)\n",
    "     # plot the image\n",
    "     pyplot.imshow(data)\n",
    "     # get the context for drawing boxes\n",
    "     ax = pyplot.gca()\n",
    "     # plot each box\n",
    "     for box in boxes_list:\n",
    "          # get coordinates\n",
    "          y1, x1, y2, x2 = box\n",
    "          # calculate width and height of the box\n",
    "          width, height = x2 - x1, y2 - y1\n",
    "          # create the shape\n",
    "          rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "          # draw the box\n",
    "          ax.add_patch(rect)\n",
    "     # show the plot\n",
    "     pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TKjNOnR5GhNq"
   },
   "source": [
    "We can now tie all of this together and load the pre-trained model and use it to detect objects in our photograph of an elephant, then draw the photograph with all detected objects.\n",
    "\n",
    "The complete example is listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "XscAeWiLGhNq",
    "outputId": "8c0f20a6-1ff0-4162-f7a0-d2ed64370872"
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   "outputs": [],
   "source": [
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from mrcnn.config import Config\n",
    "from mrcnn.model import MaskRCNN\n",
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    " \n",
    "# draw an image with detected objects\n",
    "def draw_image_with_boxes(filename, boxes_list):\n",
    "     # load the image\n",
    "     data = pyplot.imread(filename)\n",
    "     # plot the image\n",
    "     pyplot.imshow(data)\n",
    "     # get the context for drawing boxes\n",
    "     ax = pyplot.gca()\n",
    "     # plot each box\n",
    "     for box in boxes_list:\n",
    "          # get coordinates\n",
    "          y1, x1, y2, x2 = box\n",
    "          # calculate width and height of the box\n",
    "          width, height = x2 - x1, y2 - y1\n",
    "          # create the shape\n",
    "          rect = Rectangle((x1, y1), width, height, fill=False, color='red')\n",
    "          # draw the box\n",
    "          ax.add_patch(rect)\n",
    "     # show the plot\n",
    "     pyplot.show()\n",
    " \n",
    "# define the test configuration\n",
    "class TestConfig(Config):\n",
    "     NAME = \"test\"\n",
    "     GPU_COUNT = 1\n",
    "     IMAGES_PER_GPU = 1\n",
    "     NUM_CLASSES = 1 + 80\n",
    " \n",
    "# define the model\n",
    "rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())\n",
    "# load coco model weights\n",
    "rcnn.load_weights('/content/drive/My Drive/mask_rcnn_coco.h5', by_name=True)\n",
    "# load photograph\n",
    "img = load_img('/content/drive/My Drive/african-elephant.jpg')\n",
    "img = img_to_array(img)\n",
    "# make prediction\n",
    "results = rcnn.detect([img], verbose=0)\n",
    "# visualize the results\n",
    "draw_image_with_boxes('/content/drive/My Drive/african-elephant.jpg', results[0]['rois'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gl69hYXeGhNt"
   },
   "source": [
    "Running the example loads the model and performs object detection. More accurately, we have performed object localization, only drawing bounding boxes around detected objects.\n",
    "\n",
    "In this case, we can see that the model has correctly located the single object in the photo, the elephant, and drawn a red box around it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2JHZGM-gGhNt"
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   "source": [
    "## Example of Object Detection\n",
    "\n",
    "Now that we know how to load the model and use it to make a prediction, let’s update the example to perform real object detection.\n",
    "\n",
    "That is, in addition to localizing objects, we want to know what they are.\n",
    "\n",
    "The `Mask_RCNN API` provides a function called `display_instances()` that will take the array of pixel values for the loaded image and the aspects of the prediction dictionary, such as the bounding boxes, scores, and class labels, and will plot the photo with all of these annotations.\n",
    "\n",
    "One of the arguments is the list of predicted class identifiers available in the `class_id` key of the dictionary. The function also needs a mapping of ids to class labels. The pre-trained model was fit with a dataset that had 80 (81 including background) class labels, helpfully provided as a list in the [Mask R-CNN Demo, Notebook Tutorial](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb), listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TLdQQg8gGhNv"
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   "outputs": [],
   "source": [
    "# define 81 classes that the coco model knowns about\n",
    "class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\n",
    "               'bus', 'train', 'truck', 'boat', 'traffic light',\n",
    "               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',\n",
    "               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',\n",
    "               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',\n",
    "               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',\n",
    "               'kite', 'baseball bat', 'baseball glove', 'skateboard',\n",
    "               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',\n",
    "               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\n",
    "               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',\n",
    "               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',\n",
    "               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',\n",
    "               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',\n",
    "               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',\n",
    "               'teddy bear', 'hair drier', 'toothbrush']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "llndXml9GhNz"
   },
   "source": [
    "We can then provide the details of the prediction for the elephant photo to the display_instances() function; for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 632
    },
    "colab_type": "code",
    "id": "mIlhDj57GhNz",
    "outputId": "9e57f9b3-97af-4cb5-c389-6d6f2435ddc7"
   },
   "outputs": [],
   "source": [
    "from mrcnn.visualize import display_instances\n",
    "# get dictionary for first prediction\n",
    "r = results[0]\n",
    "# show photo with bounding boxes, masks, class labels and scores\n",
    "display_instances(img, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S8daLDB4GhN2"
   },
   "source": [
    "The `display_instances()` function is flexible, allowing you to only draw the mask or only the bounding boxes. You can learn more about this function in the `visualize.py` source file.\n",
    "\n",
    "The complete example with this change using the `display_instances()` function is listed below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vorB1IyBGhN3"
   },
   "source": [
    "Running the example shows the photograph of the two elephants with the annotations predicted by the Mask R-CNN model, specifically:\n",
    "\n",
    "- __Bounding Box__: Dotted bounding box around each detected object.\n",
    "- __Class Label__: Class label assigned each detected object written in the top left corner of the bounding box.\n",
    "- __Prediction Confidence__: Confidence of class label prediction for each detected object written in the top left corner of the bounding box.\n",
    "- __Object Mask Outline__: Polygon outline for the mask of each detected object.\n",
    "- __Object Mask__: Polygon fill for the mask of each detected object.\n",
    "\n",
    "The result is very impressive and sparks many ideas for how such a powerful pre-trained model could be used in practice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9zKyjQ7nGhN4"
   },
   "source": [
    "# Suggestions for Your Project in DLV\n",
    "\n",
    "1. Get familiar with different ConvNet architectures such as _EfficientNets_, _MobileNet_, etc. and apply transfer learning to your own dataset. Discuss the resulting confusion matrices and record test set accuracies, F1-scores, etc.\n",
    "\n",
    "2. Scrape your own image dataset and label objects in your images by means of e.g. [labelImg](https://github.com/tzutalin/labelImg). Use [YOLO](https://github.com/Ma-Dan/keras-yolo4), [Retinanet](https://keras.io/examples/vision/retinanet/), and [SSD](https://github.com/pierluigiferrari/ssd_keras) to detect objects in your dataset. Compare your results with respect to speed, Intersection of Union (IoU) and Mean Average Precision (MAP) (see lecture notes).\n",
    "\n",
    "3. Get acquainted with the [Coconut Annotator](https://github.com/jsbroks/coco-annotator) to annotate and segment objects in your images. Use Transfer Learning for object detection and classification. See [Mask RCNN for Object Detection and Segmentation](https://github.com/matterport/Mask_RCNN)\n",
    "\n",
    "4. Discover which parts of an image are relevant for image classification. Apply GradCam and get familiar with [Layer-Wise Relevance Propagation](https://towardsdatascience.com/indepth-layer-wise-relevance-propagation-340f95deb1ea). Use LRP with Keras (https://pypi.org/project/keras-explain/) to your image classification task.\n",
    "\n",
    "5. Label joints of animals in your images by means of [DeepLabCut](http://www.mackenziemathislab.org/deeplabcut). Classify animals or poses of animals by means of (relative) joint coordinates. See as well [Real Time Pose Estimation](https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation)"
   ]
  }
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