{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 0
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   "source": [
    "# Semantic Segmentation and the Dataset\n",
    ":label:`sec_semantic_segmentation`\n",
    "\n",
    "When discussing object detection tasks\n",
    "in :numref:`sec_bbox`--:numref:`sec_rcnn`,\n",
    "rectangular bounding boxes\n",
    "are used to label and predict objects in images.\n",
    "This section will discuss the problem of *semantic segmentation*,\n",
    "which focuses on how to divide an image into regions belonging to different semantic classes.\n",
    "Different from object detection,\n",
    "semantic segmentation\n",
    "recognizes and understands\n",
    "what are in images in pixel level:\n",
    "its labeling and prediction of semantic regions are\n",
    "in pixel level.\n",
    ":numref:`fig_segmentation` shows the labels\n",
    "of the dog, cat, and background of the image in semantic segmentation.\n",
    "Compared with in object detection,\n",
    "the pixel-level borders labeled\n",
    "in semantic segmentation are obviously more fine-grained.\n",
    "\n",
    "\n",
    "![Labels of the dog, cat, and background of the image in semantic segmentation.](../img/segmentation.svg)\n",
    ":label:`fig_segmentation`\n",
    "\n",
    "\n",
    "## Image Segmentation and Instance Segmentation\n",
    "\n",
    "There are also two important tasks\n",
    "in the field of computer vision that are similar to semantic segmentation,\n",
    "namely image segmentation and instance segmentation.\n",
    "We will briefly\n",
    "distinguish them from semantic segmentation as follows.\n",
    "\n",
    "* *Image segmentation* divides an image into several constituent regions. The methods for this type of problem usually make use of the correlation between pixels in the image. It does not need label information about image pixels during training, and it cannot guarantee that the segmented regions will have the semantics that we hope to obtain during prediction. Taking the image in :numref:`fig_segmentation` as input, image segmentation may divide the dog into two regions: one covers the mouth and eyes which are mainly black, and the other covers the rest of the body which is mainly yellow.\n",
    "* *Instance segmentation* is also called *simultaneous detection and segmentation*. It studies how to recognize the pixel-level regions of each object instance in an image. Different from semantic segmentation, instance segmentation needs to distinguish not only semantics, but also different object instances. For example, if there are two dogs in the image, instance segmentation needs to distinguish which of the two dogs a pixel belongs to.\n",
    "\n",
    "\n",
    "\n",
    "## The Pascal VOC2012 Semantic Segmentation Dataset\n",
    "\n",
    "[**On of the most important semantic segmentation dataset\n",
    "is [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/).**]\n",
    "In the following,\n",
    "we will take a look at this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "origin_pos": 1,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from mxnet import gluon, image, np, npx\n",
    "from d2l import mxnet as d2l\n",
    "\n",
    "npx.set_np()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 3
   },
   "source": [
    "The tar file of the dataset is about 2 GB,\n",
    "so it may take a while to download the file.\n",
    "The extracted dataset is located at `../data/VOCdevkit/VOC2012`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "origin_pos": 4,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ../data/VOCtrainval_11-May-2012.tar from http://d2l-data.s3-accelerate.amazonaws.com/VOCtrainval_11-May-2012.tar...\n"
     ]
    }
   ],
   "source": [
    "#@save\n",
    "d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',\n",
    "                           '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')\n",
    "\n",
    "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 5
   },
   "source": [
    "After entering the path `../data/VOCdevkit/VOC2012`,\n",
    "we can see the different components of the dataset.\n",
    "The `ImageSets/Segmentation` path contains text files\n",
    "that specify training and test samples,\n",
    "while the `JPEGImages` and `SegmentationClass` paths\n",
    "store the input image and label for each example, respectively.\n",
    "The label here is also in the image format,\n",
    "with the same size\n",
    "as its labeled input image.\n",
    "Besides,\n",
    "pixels with the same color in any label image belong to the same semantic class.\n",
    "The following defines the `read_voc_images` function to [**read all the input images and labels into the memory**].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 6,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def read_voc_images(voc_dir, is_train=True):\n",
    "    \"\"\"Read all VOC feature and label images.\"\"\"\n",
    "    txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',\n",
    "                             'train.txt' if is_train else 'val.txt')\n",
    "    with open(txt_fname, 'r') as f:\n",
    "        images = f.read().split()\n",
    "    features, labels = [], []\n",
    "    for i, fname in enumerate(images):\n",
    "        features.append(image.imread(os.path.join(\n",
    "            voc_dir, 'JPEGImages', f'{fname}.jpg')))\n",
    "        labels.append(image.imread(os.path.join(\n",
    "            voc_dir, 'SegmentationClass', f'{fname}.png')))\n",
    "    return features, labels\n",
    "\n",
    "train_features, train_labels = read_voc_images(voc_dir, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 8
   },
   "source": [
    "We [**draw the first five input images and their labels**].\n",
    "In the label images, white and black represent borders and  background, respectively, while the other colors correspond to different classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 9,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "imgs = train_features[0:n] + train_labels[0:n]\n",
    "d2l.show_images(imgs, 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 11
   },
   "source": [
    "Next, we [**enumerate\n",
    "the RGB color values and class names**]\n",
    "for all the labels in this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 12,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],\n",
    "                [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],\n",
    "                [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],\n",
    "                [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],\n",
    "                [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],\n",
    "                [0, 64, 128]]\n",
    "\n",
    "#@save\n",
    "VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair', 'cow',\n",
    "               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n",
    "               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 13
   },
   "source": [
    "With the two constants defined above,\n",
    "we can conveniently\n",
    "[**find the class index for each pixel in a label**].\n",
    "We define the `voc_colormap2label` function\n",
    "to build the mapping from the above RGB color values\n",
    "to class indices,\n",
    "and the `voc_label_indices` function\n",
    "to map any RGB values to their class indices in this Pascal VOC2012 dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "origin_pos": 14,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def voc_colormap2label():\n",
    "    \"\"\"Build the mapping from RGB to class indices for VOC labels.\"\"\"\n",
    "    colormap2label = np.zeros(256 ** 3)\n",
    "    for i, colormap in enumerate(VOC_COLORMAP):\n",
    "        colormap2label[\n",
    "            (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n",
    "    return colormap2label\n",
    "\n",
    "#@save\n",
    "def voc_label_indices(colormap, colormap2label):\n",
    "    \"\"\"Map any RGB values in VOC labels to their class indices.\"\"\"\n",
    "    colormap = colormap.astype(np.int32)\n",
    "    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n",
    "           + colormap[:, :, 2])\n",
    "    return colormap2label[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 16
   },
   "source": [
    "[**For example**], in the first example image,\n",
    "the class index for the front part of the airplane is 1,\n",
    "while the background index is 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 17,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
       "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.]]),\n",
       " 'aeroplane')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = voc_label_indices(train_labels[0], voc_colormap2label())\n",
    "y[105:115, 130:140], VOC_CLASSES[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 18
   },
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "In previous experiments\n",
    "such as in :numref:`sec_alexnet`--:numref:`sec_googlenet`,\n",
    "images are rescaled\n",
    "to fit the model's required input shape.\n",
    "However, in semantic segmentation,\n",
    "doing so\n",
    "requires rescaling the predicted pixel classes\n",
    "back to the original shape of the input image.\n",
    "Such rescaling may be inaccurate,\n",
    "especially for segmented regions with different classes. To avoid this issue,\n",
    "we crop the image to a *fixed* shape instead of rescaling. Specifically, [**using random cropping from image augmentation, we crop the same area of\n",
    "the input image and the label**].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "origin_pos": 19,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def voc_rand_crop(feature, label, height, width):\n",
    "    \"\"\"Randomly crop both feature and label images.\"\"\"\n",
    "    feature, rect = image.random_crop(feature, (width, height))\n",
    "    label = image.fixed_crop(label, *rect)\n",
    "    return feature, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "origin_pos": 21,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imgs = []\n",
    "for _ in range(n):\n",
    "    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)\n",
    "d2l.show_images(imgs[::2] + imgs[1::2], 2, n);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 23
   },
   "source": [
    "### [**Custom Semantic Segmentation Dataset Class**]\n",
    "\n",
    "We define a custom semantic segmentation dataset class `VOCSegDataset` by inheriting the `Dataset` class provided by high-level APIs.\n",
    "By implementing the `__getitem__` function,\n",
    "we can arbitrarily access the input image indexed as `idx` in the dataset and the class index of each pixel in this image.\n",
    "Since some images in the dataset\n",
    "have a smaller size\n",
    "than the output size of random cropping,\n",
    "these examples are filtered out\n",
    "by a custom `filter` function.\n",
    "In addition, we also\n",
    "define the `normalize_image` function to\n",
    "standardize the values of the three RGB channels of input images.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "origin_pos": 24,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "class VOCSegDataset(gluon.data.Dataset):\n",
    "    \"\"\"A customized dataset to load the VOC dataset.\"\"\"\n",
    "    def __init__(self, is_train, crop_size, voc_dir):\n",
    "        self.rgb_mean = np.array([0.485, 0.456, 0.406])\n",
    "        self.rgb_std = np.array([0.229, 0.224, 0.225])\n",
    "        self.crop_size = crop_size\n",
    "        features, labels = read_voc_images(voc_dir, is_train=is_train)\n",
    "        self.features = [self.normalize_image(feature)\n",
    "                         for feature in self.filter(features)]\n",
    "        self.labels = self.filter(labels)\n",
    "        self.colormap2label = voc_colormap2label()\n",
    "        print('read ' + str(len(self.features)) + ' examples')\n",
    "\n",
    "    def normalize_image(self, img):\n",
    "        return (img.astype('float32') / 255 - self.rgb_mean) / self.rgb_std\n",
    "\n",
    "    def filter(self, imgs):\n",
    "        return [img for img in imgs if (\n",
    "            img.shape[0] >= self.crop_size[0] and\n",
    "            img.shape[1] >= self.crop_size[1])]\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],\n",
    "                                       *self.crop_size)\n",
    "        return (feature.transpose(2, 0, 1),\n",
    "                voc_label_indices(label, self.colormap2label))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 26
   },
   "source": [
    "### [**Reading the Dataset**]\n",
    "\n",
    "We use the custom `VOCSegDatase`t class to\n",
    "create instances of the training set and test set, respectively.\n",
    "Suppose that\n",
    "we specify that the output shape of randomly cropped images is $320\\times 480$.\n",
    "Below we can view the number of examples\n",
    "that are retained in the training set and test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "origin_pos": 27,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1114 examples\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 1078 examples\n"
     ]
    }
   ],
   "source": [
    "crop_size = (320, 480)\n",
    "voc_train = VOCSegDataset(True, crop_size, voc_dir)\n",
    "voc_test = VOCSegDataset(False, crop_size, voc_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 28
   },
   "source": [
    "Setting the batch size to 64,\n",
    "we define the data iterator for the training set.\n",
    "Let us print the shape of the first minibatch.\n",
    "Different from in image classification or object detection, labels here are three-dimensional tensors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "origin_pos": 29,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 3, 320, 480)\n",
      "(64, 320, 480)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "train_iter = gluon.data.DataLoader(voc_train, batch_size, shuffle=True,\n",
    "                                   last_batch='discard',\n",
    "                                   num_workers=d2l.get_dataloader_workers())\n",
    "for X, Y in train_iter:\n",
    "    print(X.shape)\n",
    "    print(Y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 31
   },
   "source": [
    "### [**Putting All Things Together**]\n",
    "\n",
    "Finally, we define the following `load_data_voc` function\n",
    "to download and read the Pascal VOC2012 semantic segmentation dataset.\n",
    "It returns data iterators for both the training and test datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "origin_pos": 32,
    "tab": [
     "mxnet"
    ]
   },
   "outputs": [],
   "source": [
    "#@save\n",
    "def load_data_voc(batch_size, crop_size):\n",
    "    \"\"\"Load the VOC semantic segmentation dataset.\"\"\"\n",
    "    voc_dir = d2l.download_extract('voc2012', os.path.join(\n",
    "        'VOCdevkit', 'VOC2012'))\n",
    "    num_workers = d2l.get_dataloader_workers()\n",
    "    train_iter = gluon.data.DataLoader(\n",
    "        VOCSegDataset(True, crop_size, voc_dir), batch_size,\n",
    "        shuffle=True, last_batch='discard', num_workers=num_workers)\n",
    "    test_iter = gluon.data.DataLoader(\n",
    "        VOCSegDataset(False, crop_size, voc_dir), batch_size,\n",
    "        last_batch='discard', num_workers=num_workers)\n",
    "    return train_iter, test_iter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 34
   },
   "source": [
    "## Summary\n",
    "\n",
    "* Semantic segmentation recognizes and understands what are in an image in pixel level by dividing the image into regions belonging to different semantic classes.\n",
    "* On of the most important semantic segmentation dataset is Pascal VOC2012.\n",
    "* In semantic segmentation, since the input image and  label correspond one-to-one on the pixel, the input image is randomly cropped to a fixed shape rather than rescaled.\n",
    "\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. How can semantic segmentation be applied in autonomous vehicles and medical image diagnostics? Can you think of other applications?\n",
    "1. Recall the descriptions of data augmentation in :numref:`sec_image_augmentation`. Which of the image augmentation methods used in image classification would be infeasible to be applied in semantic segmentation?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "origin_pos": 35,
    "tab": [
     "mxnet"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/375)\n"
   ]
  }
 ],
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