notebooks/11_image_segmentation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4v1OB9yWQeyr"
      },
      "source": [
        "# Image segmentation\n",
        "\n",
        "In this notebook we try a different type of computer vision task, which is to take in images and draw a line around objects of interest, so that they are very precicely located. Finding different objects in an image is typically just called object detection, but when we also highlight the detailed shape, it's known as _image segmentation_."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6cW8ZbXwxOW"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FVkqpQUjQHMf"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "import numpy as np\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05VYa3-rRVXn"
      },
      "source": [
        "## Download the data\n",
        "\n",
        "We use the [_Oxford Pets_](https://www.robots.ox.ac.uk/~vgg/data/pets/) dataset, which contains annotated images of... pets. This is a is a 773MB download.\n",
        "\n",
        "In case you want to try out image segmentation at a bigger scale, the best-known dataset is called [COCO](https://cocodataset.org/#home) and contains some 330K images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLOZUarFRr9h",
        "outputId": "0b2ccd2b-e79c-4fe3-bac0-13dd87a46c86"
      },
      "outputs": [],
      "source": [
        "!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
        "!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
        "\n",
        "!tar -xf images.tar.gz\n",
        "!tar -xf annotations.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgBHu5nSS7DO",
        "outputId": "c8f29455-6b7f-42fc-aed2-96ca652d8b48"
      },
      "outputs": [],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04EFmVptR0R1"
      },
      "source": [
        "Make lists of the input files."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSA0nXifR2E1",
        "outputId": "b0d87e9c-785a-4755-9cb8-43739fdd2e04"
      },
      "outputs": [],
      "source": [
        "\n",
        "input_dir = \"images/\"\n",
        "target_dir = \"annotations/trimaps/\"\n",
        "img_size = (160, 160)\n",
        "num_classes = 3\n",
        "batch_size = 32\n",
        "\n",
        "input_img_paths = sorted(\n",
        "    [\n",
        "        os.path.join(input_dir, fname)\n",
        "        for fname in os.listdir(input_dir)\n",
        "        if fname.endswith(\".jpg\")\n",
        "    ]\n",
        ")\n",
        "target_img_paths = sorted(\n",
        "    [\n",
        "        os.path.join(target_dir, fname)\n",
        "        for fname in os.listdir(target_dir)\n",
        "        if fname.endswith(\".png\") and not fname.startswith(\".\")\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(\"Number of samples:\", len(input_img_paths))\n",
        "\n",
        "for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):\n",
        "    print(input_path, \"|\", target_path)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnU3lY86UT3h"
      },
      "source": [
        "In the `annotations` directory we find the segmentation masks. The mask defines the area in the image where the object is.\n",
        "\n",
        "Plot an example to see what they look like:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "Yc8IBi4mVAZM",
        "outputId": "fac5a0f9-12d3-4abe-c557-49f40aec5d88"
      },
      "outputs": [],
      "source": [
        "from IPython.display import Image, display\n",
        "from keras.utils import load_img\n",
        "from PIL import ImageOps\n",
        "\n",
        "# Display input image\n",
        "display(Image(filename=input_img_paths[0]))\n",
        "\n",
        "# Display auto-contrast version of corresponding target (per-pixel categories)\n",
        "img = ImageOps.autocontrast(load_img(target_img_paths[0]))\n",
        "display(img)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXJogaGRVN7L"
      },
      "source": [
        "So this is the thing we want to make our model predict.\n",
        "\n",
        "These data are a little more demanding than usual when it comes to data loading, and we need some custom code to prepare them as a `tf.data.Dataset`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFiBA2pBVkuk"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "from tensorflow import data as tf_data\n",
        "from tensorflow import image as tf_image\n",
        "from tensorflow import io as tf_io\n",
        "\n",
        "\n",
        "def get_dataset(\n",
        "    batch_size,\n",
        "    img_size,\n",
        "    input_img_paths,\n",
        "    target_img_paths,\n",
        "    max_dataset_len=None,\n",
        "):\n",
        "    \"\"\"Returns a TF Dataset.\"\"\"\n",
        "\n",
        "    def load_img_masks(input_img_path, target_img_path):\n",
        "        input_img = tf_io.read_file(input_img_path)\n",
        "        input_img = tf_io.decode_png(input_img, channels=3)\n",
        "        input_img = tf_image.resize(input_img, img_size)\n",
        "        input_img = tf_image.convert_image_dtype(input_img, \"float32\")\n",
        "\n",
        "        target_img = tf_io.read_file(target_img_path)\n",
        "        target_img = tf_io.decode_png(target_img, channels=1)\n",
        "        target_img = tf_image.resize(target_img, img_size, method=\"nearest\")\n",
        "        target_img = tf_image.convert_image_dtype(target_img, \"uint8\")\n",
        "\n",
        "        # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:\n",
        "        target_img -= 1\n",
        "        return input_img, target_img\n",
        "\n",
        "    # For faster debugging, limit the size of data\n",
        "    if max_dataset_len:\n",
        "        input_img_paths = input_img_paths[:max_dataset_len]\n",
        "        target_img_paths = target_img_paths[:max_dataset_len]\n",
        "    dataset = tf_data.Dataset.from_tensor_slices((input_img_paths, target_img_paths))\n",
        "    dataset = dataset.map(load_img_masks, num_parallel_calls=tf_data.AUTOTUNE)\n",
        "    return dataset.batch(batch_size)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G9tfHeDUwXnw"
      },
      "source": [
        "Now create the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SsSk6Hq_wZew"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "# Split our img paths into a training and a validation set\n",
        "val_samples = 1000\n",
        "random.Random(1337).shuffle(input_img_paths)\n",
        "random.Random(1337).shuffle(target_img_paths)\n",
        "train_input_img_paths = input_img_paths[:-val_samples]\n",
        "train_target_img_paths = target_img_paths[:-val_samples]\n",
        "val_input_img_paths = input_img_paths[-val_samples:]\n",
        "val_target_img_paths = target_img_paths[-val_samples:]\n",
        "\n",
        "# Instantiate dataset for each split\n",
        "# Limit input files in `max_dataset_len` for faster epoch training time.\n",
        "# Remove the `max_dataset_len` arg when running with full dataset.\n",
        "train_dataset = get_dataset(\n",
        "    batch_size,\n",
        "    img_size,\n",
        "    train_input_img_paths,\n",
        "    train_target_img_paths,\n",
        "    max_dataset_len=1000,\n",
        ")\n",
        "valid_dataset = get_dataset(\n",
        "    batch_size, img_size, val_input_img_paths, val_target_img_paths\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npyAfGlMw39N"
      },
      "source": [
        "## Create the model\n",
        "\n",
        "Our task is to make a (smaller) version of the U-Net shown above. Since we have smaller resolution images than the original U-Net, we need fewer blocks to get down to the smallest resolution, before going back up."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZ7MiadQylcG"
      },
      "source": [
        "### <span style=\"color: red; font-weight: bold;\">Exercise:<span>\n",
        "\n",
        "Fill in the missing pieces below to build the U-Net model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2daQYs3-yoZm"
      },
      "outputs": [],
      "source": [
        "def encoder_block(inputs, filters, stride):\n",
        "    \"\"\"\n",
        "    A block that applies 2 times convolutional layers with batch normalisation,\n",
        "    then returns 1) the output, and 2) the downsampled output, after max pooling.\n",
        "    \"\"\"\n",
        "\n",
        "    # TODO:\n",
        "    # Add convolutional layers, batch norm, and activations.\n",
        "\n",
        "    x = ...\n",
        "\n",
        "    # Pooling layer\n",
        "    pool = keras.layers.MaxPooling2D(pool_size=2)(x)\n",
        "\n",
        "    return x, pool\n",
        "\n",
        "\n",
        "def decoder_block(inputs, inputs_skip, filters, stride):\n",
        "    \"\"\"\n",
        "    A block that receives two inputs -- one from the previous convolutional\n",
        "    block \"below\", and one from the skip connection. The shapes of these\n",
        "    don't match, so we upsample the input from the previous convolution.\n",
        "    Then the two inputs are merged, and we do 2x convolutions on that.\n",
        "\n",
        "    The upsampling does \"the opposite\" of the Conv2D layers, and is achieved\n",
        "    with the Conv2DTranspose layer.\n",
        "    \"\"\"\n",
        "\n",
        "    # Upsample the inputs\n",
        "    x = keras.layers.Conv2DTranspose(filters, kernel_size=2, strides=2, padding='same')(inputs)\n",
        "\n",
        "    # In case you need to do some debugging\n",
        "    # print('In decoder block: x.shape:', x.shape, 'inputs_skip.shape:', inputs_skip.shape)\n",
        "\n",
        "    # Concatenate with the skip connection\n",
        "    x = keras.layers.Concatenate()([x, inputs_skip])\n",
        "\n",
        "    # TODO:\n",
        "    # Apply 2x convolutions\n",
        "    x = ...\n",
        "\n",
        "\n",
        "    return x\n",
        "\n",
        "\n",
        "def create_unet(image_shape):\n",
        "\n",
        "    inputs = keras.layers.Input(image_shape)\n",
        "\n",
        "    # Build the encoder, which is the left part of the U shape in the network\n",
        "    # architecture. Here we need to keep track of the \"skip\" connections.\n",
        "    conv1, pool1 = encoder_block(inputs, 64, 1)\n",
        "    conv2, pool2 = encoder_block(pool1, 64, 1)\n",
        "    conv3, pool3 = encoder_block(pool2, 64, 1)\n",
        "\n",
        "    # The lowermost convolutions -- has no pooling layer at the end.\n",
        "    x = keras.layers.Conv2D(128, 3, padding=\"same\")(pool3)\n",
        "    x = keras.layers.BatchNormalization()(x)\n",
        "    x = keras.layers.Activation(\"relu\")(x)\n",
        "    x = keras.layers.Conv2D(128, 3, padding=\"same\")(x)\n",
        "    x = keras.layers.BatchNormalization()(x)\n",
        "    x = keras.layers.Activation(\"relu\")(x)\n",
        "\n",
        "    # Build the decoder, which is the right-hand part if the U-Net, where we\n",
        "    # increase the resolution again (while adding in the skip connections).\n",
        "    x = decoder_block(x, conv3, 64, 1)\n",
        "    x = decoder_block(x, conv2, 64, 1)\n",
        "    x = decoder_block(x, conv1, 64, 1)\n",
        "\n",
        "    # The output -- a per-pixel classification layer\n",
        "    outputs = keras.layers.Conv2D(num_classes, 3, activation=\"softmax\", padding=\"same\")(x)\n",
        "\n",
        "    # Finalise the model\n",
        "    model = keras.Model(inputs, outputs)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kS00inW6umm"
      },
      "source": [
        "Plot the model to see if it looks as expected:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tolvi4oI61CR",
        "outputId": "9a55c99f-7ce7-4603-88b4-f3e34ccd9b52"
      },
      "outputs": [],
      "source": [
        "model = create_unet(img_size + (3,))    # image shape plus RGB channels\n",
        "\n",
        "keras.utils.plot_model(model, dpi=75, show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gT0MnDe7T1k"
      },
      "source": [
        "If yes, then we are ready to train it!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LEZcPMnU7XP-",
        "outputId": "a390c0f9-e6e7-4061-9595-a6c54c2f2477"
      },
      "outputs": [],
      "source": [
        "# We use the \"sparse\" version of categorical_crossentropy because the target\n",
        "# data is integers.\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(1e-4), loss=\"sparse_categorical_crossentropy\"\n",
        ")\n",
        "\n",
        "# TODO:\n",
        "# If needed, implement measures to improve the training (e.g. learning rate\n",
        "# scheduling)\n",
        "\n",
        "# Train the model, doing validation at the end of each epoch.\n",
        "epochs = 25\n",
        "model.fit(\n",
        "    train_dataset,\n",
        "    epochs=epochs,\n",
        "    validation_data=valid_dataset,\n",
        "    verbose=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLOk-j5mKxXQ"
      },
      "source": [
        "Plot some predictions:\n",
        "\n",
        "(remember we are working on 160x160 pixel versions, so the images are smaller than original):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "id": "nTJY9UErKyxO",
        "outputId": "ddbb0900-2389-47a9-834b-c0fad243a7f6"
      },
      "outputs": [],
      "source": [
        "for images, targets in valid_dataset.take(1):\n",
        "\n",
        "    preds = model.predict(images, verbose=0)\n",
        "\n",
        "    for i in range(3):\n",
        "\n",
        "        display(keras.utils.array_to_img(images[i]))\n",
        "\n",
        "        mask = np.argmax(preds[i], axis=-1)\n",
        "        mask = np.expand_dims(mask, axis=-1)\n",
        "        img = ImageOps.autocontrast(keras.utils.array_to_img(mask))\n",
        "        display(img)\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}