From cd848d218d324dcac935647eeaee4598607bff0d Mon Sep 17 00:00:00 2001 From: ShusenTang Date: Sun, 29 Dec 2019 15:15:26 +0800 Subject: [PATCH] add 9.9 --- ....9_semantic-segmentation-and-dataset.ipynb | 425 ++++++++++++++++++ code/d2lzh_pytorch/utils.py | 76 ++++ docs/README.md | 2 +- docs/_sidebar.md | 2 +- .../9.9_semantic-segmentation-and-dataset.md | 264 +++++++++++ docs/img/chapter09/9.9_output1.png | Bin 0 -> 289549 bytes docs/img/chapter09/9.9_output2.png | Bin 0 -> 247702 bytes docs/img/chapter09/9.9_segmentation.svg | 45 ++ 8 files changed, 812 insertions(+), 2 deletions(-) create mode 100644 code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb create mode 100644 docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md create mode 100644 docs/img/chapter09/9.9_output1.png create mode 100644 docs/img/chapter09/9.9_output2.png create mode 100644 docs/img/chapter09/9.9_segmentation.svg diff --git a/code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb b/code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb new file mode 100644 index 0000000..da39380 --- /dev/null +++ b/code/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.ipynb @@ -0,0 +1,425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 9.9 语义分割和数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2.0 0.4.0a0+6b959ee\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import time\n", + "import torch\n", + "import torch.nn.functional as F\n", + "import torchvision\n", + "import numpy as np\n", + "from PIL import Image\n", + "from tqdm import tqdm\n", + "\n", + "import sys\n", + "sys.path.append(\"..\") \n", + "import d2lzh_pytorch as d2l\n", + "\n", + "print(torch.__version__, torchvision.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9.9.2 Pascal VOC2012语义分割数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[36mAnnotations\u001b[m\u001b[m \u001b[1m\u001b[36mJPEGImages\u001b[m\u001b[m \u001b[1m\u001b[36mSegmentationObject\u001b[m\u001b[m\r\n", + "\u001b[1m\u001b[36mImageSets\u001b[m\u001b[m \u001b[1m\u001b[36mSegmentationClass\u001b[m\u001b[m\r\n" + ] + } + ], + "source": [ + "!ls ../../data/VOCdevkit/VOC2012" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 本函数已保存在d2lzh_pytorch中方便以后使用\n", + "def read_voc_images(root=\"../../data/VOCdevkit/VOC2012\", \n", + " is_train=True, max_num=None):\n", + " txt_fname = '%s/ImageSets/Segmentation/%s' % (\n", + " root, 'train.txt' if is_train else 'val.txt')\n", + " with open(txt_fname, 'r') as f:\n", + " images = f.read().split()\n", + " if max_num is not None:\n", + " images = images[:min(max_num, len(images))]\n", + " features, labels = [None] * len(images), [None] * len(images)\n", + " for i, fname in tqdm(enumerate(images)):\n", + " features[i] = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert(\"RGB\")\n", + " labels[i] = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert(\"RGB\")\n", + " return features, labels # PIL image" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100it [00:01, 54.94it/s]\n" + ] + } + ], + "source": [ + "voc_dir = \"../../data/VOCdevkit/VOC2012\"\n", + "train_features, train_labels = read_voc_images(voc_dir, max_num=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Zm29T5RUEhvl2k15eYq2p3ZKA9zmCCCE0Upq6jBZACnQYTaupBMIDuCkpEBhb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\n+M1/8zskRkyQRnheHm1GjA4P0NtnsnX0Hpa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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "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": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 本函数已保存在d2lzh_pytorch中方便以后使用\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", + "# 本函数已保存在d2lzh_pytorch中方便以后使用\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": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8)\n", + "for i, colormap in enumerate(VOC_COLORMAP):\n", + " colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i\n", + "\n", + "# 本函数已保存在d2lzh_pytorch中方便以后使用\n", + "def voc_label_indices(colormap, colormap2label):\n", + " \"\"\"\n", + " convert colormap (PIL image) to colormap2label (uint8 tensor).\n", + " \"\"\"\n", + " colormap = np.array(colormap.convert(\"RGB\")).astype('int32')\n", + " idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256\n", + " + colormap[:, :, 2])\n", + " return colormap2label[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([[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]], dtype=torch.uint8), 'aeroplane')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = voc_label_indices(train_labels[0], colormap2label)\n", + "y[105:115, 130:140], VOC_CLASSES[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.9.2.1 预处理数据" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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wLvjyV57m/P3fjG89n/3c56MXFjoef+UC5zZPsdzeZlnEDpzb11w0jKLghE5j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\npmEr8DzZxXjacCPw5QWh5kplRX68mGI1aKhcLmtzYz2uN/J82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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 本函数已保存在d2lzh_pytorch中方便以后使用\n", + "def voc_rand_crop(feature, label, height, width):\n", + " \"\"\"\n", + " Random crop feature (PIL image) and label (PIL image).\n", + " \"\"\"\n", + " i, j, h, w = torchvision.transforms.RandomCrop.get_params(\n", + " feature, output_size=(height, width))\n", + " \n", + " feature = torchvision.transforms.functional.crop(feature, i, j, h, w)\n", + " label = torchvision.transforms.functional.crop(label, i, j, h, w) \n", + "\n", + " return feature, label\n", + "\n", + "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": {}, + "source": [ + "### 9.9.2.2 自定义语义分割数据集类" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 本函数已保存在d2lzh_pytorch中方便以后使用\n", + "class VOCSegDataset(torch.utils.data.Dataset):\n", + " def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None):\n", + " \"\"\"\n", + " crop_size: (h, w)\n", + " \"\"\"\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.tsf = torchvision.transforms.Compose([\n", + " torchvision.transforms.ToTensor(),\n", + " torchvision.transforms.Normalize(mean=self.rgb_mean, \n", + " std=self.rgb_std)\n", + " ])\n", + " \n", + " self.crop_size = crop_size # (h, w)\n", + " features, labels = read_voc_images(root=voc_dir, \n", + " is_train=is_train, \n", + " max_num=max_num)\n", + " self.features = self.filter(features) # PIL image\n", + " self.labels = self.filter(labels) # PIL image\n", + " self.colormap2label = colormap2label\n", + " print('read ' + str(len(self.features)) + ' valid examples')\n", + "\n", + " def filter(self, imgs):\n", + " return [img for img in imgs if (\n", + " img.size[1] >= self.crop_size[0] and\n", + " img.size[0] >= 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", + " \n", + " return (self.tsf(feature),\n", + " voc_label_indices(label, self.colormap2label))\n", + "\n", + " def __len__(self):\n", + " return len(self.features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9.9.2.3 读取数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100it [00:00, 104.07it/s]\n", + "6it [00:00, 56.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read 75 valid examples\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100it [00:01, 56.74it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read 77 valid examples\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "crop_size = (320, 480)\n", + "max_num = 100\n", + "voc_train = VOCSegDataset(True, crop_size, voc_dir, colormap2label, max_num)\n", + "voc_test = VOCSegDataset(False, crop_size, voc_dir, colormap2label, max_num)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "batch_size = 64\n", + "num_workers = 0 if sys.platform.startswith('win32') else 4\n", + "train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,\n", + " drop_last=True, num_workers=num_workers)\n", + "test_iter = torch.utils.data.DataLoader(voc_test, batch_size, drop_last=True,\n", + " num_workers=num_workers)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.float32 torch.Size([64, 3, 320, 480])\n", + "torch.uint8 torch.Size([64, 320, 480])\n" + ] + } + ], + "source": [ + "for X, Y in train_iter:\n", + " print(X.dtype, X.shape)\n", + " print(y.dtype, Y.shape)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/d2lzh_pytorch/utils.py b/code/d2lzh_pytorch/utils.py index cd54bad..46bc3ae 100644 --- a/code/d2lzh_pytorch/utils.py +++ b/code/d2lzh_pytorch/utils.py @@ -1080,6 +1080,82 @@ def load_data_pikachu(batch_size, edge_size=256, data_dir = '../../data/pikachu' return train_iter, val_iter +# ################################# 9.9 ######################### +def read_voc_images(root="../../data/VOCdevkit/VOC2012", + is_train=True, max_num=None): + txt_fname = '%s/ImageSets/Segmentation/%s' % ( + root, 'train.txt' if is_train else 'val.txt') + with open(txt_fname, 'r') as f: + images = f.read().split() + if max_num is not None: + images = images[:min(max_num, len(images))] + features, labels = [None] * len(images), [None] * len(images) + for i, fname in tqdm(enumerate(images)): + features[i] = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert("RGB") + labels[i] = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert("RGB") + return features, labels # PIL image + +# colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8) +# for i, colormap in enumerate(VOC_COLORMAP): +# colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i +def voc_label_indices(colormap, colormap2label): + """ + convert colormap (PIL image) to colormap2label (uint8 tensor). + """ + colormap = np.array(colormap.convert("RGB")).astype('int32') + idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256 + + colormap[:, :, 2]) + return colormap2label[idx] + +def voc_rand_crop(feature, label, height, width): + """ + Random crop feature (PIL image) and label (PIL image). + """ + i, j, h, w = torchvision.transforms.RandomCrop.get_params( + feature, output_size=(height, width)) + + feature = torchvision.transforms.functional.crop(feature, i, j, h, w) + label = torchvision.transforms.functional.crop(label, i, j, h, w) + + return feature, label + +class VOCSegDataset(torch.utils.data.Dataset): + def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None): + """ + crop_size: (h, w) + """ + self.rgb_mean = np.array([0.485, 0.456, 0.406]) + self.rgb_std = np.array([0.229, 0.224, 0.225]) + self.tsf = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize(mean=self.rgb_mean, + std=self.rgb_std) + ]) + + self.crop_size = crop_size # (h, w) + features, labels = read_voc_images(root=voc_dir, + is_train=is_train, + max_num=max_num) + self.features = self.filter(features) # PIL image + self.labels = self.filter(labels) # PIL image + self.colormap2label = colormap2label + print('read ' + str(len(self.features)) + ' valid examples') + + def filter(self, imgs): + return [img for img in imgs if ( + img.size[1] >= self.crop_size[0] and + img.size[0] >= self.crop_size[1])] + + def __getitem__(self, idx): + feature, label = voc_rand_crop(self.features[idx], self.labels[idx], + *self.crop_size) + + return (self.tsf(feature), + voc_label_indices(label, self.colormap2label)) + + def __len__(self): + return len(self.features) + # ############################# 10.7 ########################## diff --git a/docs/README.md b/docs/README.md index 1f5e620..eca3786 100644 --- a/docs/README.md +++ b/docs/README.md @@ -114,7 +114,7 @@ docsify serve docs * [9.6 目标检测数据集(皮卡丘)](chapter09_computer-vision/9.6_object-detection-dataset.md) - [ ] 9.7 单发多框检测(SSD) * [9.8 区域卷积神经网络(R-CNN)系列](chapter09_computer-vision/9.8_rcnn.md) - - [ ] 9.9 语义分割和数据集 + * [9.9 语义分割和数据集](chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md) - [ ] 9.10 全卷积网络(FCN) * [9.11 样式迁移](chapter09_computer-vision/9.11_neural-style.md) - [ ] 9.12 实战Kaggle比赛:图像分类(CIFAR-10) diff --git a/docs/_sidebar.md b/docs/_sidebar.md index d2aa096..7d5f18a 100644 --- a/docs/_sidebar.md +++ b/docs/_sidebar.md @@ -76,7 +76,7 @@ * [9.6 目标检测数据集(皮卡丘)](chapter09_computer-vision/9.6_object-detection-dataset.md) * 9.7 单发多框检测(SSD) * [9.8 区域卷积神经网络(R-CNN)系列](chapter09_computer-vision/9.8_rcnn.md) - * 9.9 语义分割和数据集 + * [9.9 语义分割和数据集](chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md) * 9.10 全卷积网络(FCN) * [9.11 样式迁移](chapter09_computer-vision/9.11_neural-style.md) * 9.12 实战Kaggle比赛:图像分类(CIFAR-10) diff --git a/docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md b/docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md new file mode 100644 index 0000000..60fed7d --- /dev/null +++ b/docs/chapter09_computer-vision/9.9_semantic-segmentation-and-dataset.md @@ -0,0 +1,264 @@ +# 9.9 语义分割和数据集 + +在前几节讨论的目标检测问题中,我们一直使用方形边界框来标注和预测图像中的目标。本节将探讨语义分割(semantic segmentation)问题,它关注如何将图像分割成属于不同语义类别的区域。值得一提的是,这些语义区域的标注和预测都是像素级的。图9.10展示了语义分割中图像有关狗、猫和背景的标签。可以看到,与目标检测相比,语义分割标注的像素级的边框显然更加精细。 + +
+ +
+
图9.10 语义分割中图像有关狗、猫和背景的标签
+ +## 9.9.1 图像分割和实例分割 + +计算机视觉领域还有2个与语义分割相似的重要问题,即图像分割(image segmentation)和实例分割(instance segmentation)。我们在这里将它们与语义分割简单区分一下。 + +* 图像分割将图像分割成若干组成区域。这类问题的方法通常利用图像中像素之间的相关性。它在训练时不需要有关图像像素的标签信息,在预测时也无法保证分割出的区域具有我们希望得到的语义。以图9.10的图像为输入,图像分割可能将狗分割成两个区域:一个覆盖以黑色为主的嘴巴和眼睛,而另一个覆盖以黄色为主的其余部分身体。 +* 实例分割又叫同时检测并分割(simultaneous detection and segmentation)。它研究如何识别图像中各个目标实例的像素级区域。与语义分割有所不同,实例分割不仅需要区分语义,还要区分不同的目标实例。如果图像中有两只狗,实例分割需要区分像素属于这两只狗中的哪一只。 + + +## 9.9.2 Pascal VOC2012语义分割数据集 + +语义分割的一个重要数据集叫作Pascal VOC2012 [1]。为了更好地了解这个数据集,我们先导入实验所需的包或模块。 + +``` python +%matplotlib inline +import time +import torch +import torch.nn.functional as F +import torchvision +import numpy as np +from PIL import Image +from tqdm import tqdm + +import sys +sys.path.append("..") +import d2lzh_pytorch as d2l +``` + +我们先下载这个数据集的压缩包([下载地址](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar))。压缩包大小是2 GB左右,下载需要一定时间。下载后解压得到`VOCdevkit/VOC2012`文件夹,然后将其放置在`data`文件夹下。 + +``` python +!ls ../../data/VOCdevkit/VOC2012 +``` +``` +Annotations JPEGImages SegmentationObject +ImageSets SegmentationClass +``` + +进入`../../data/VOCdevkit/VOC2012`路径后,我们可以获取数据集的不同组成部分。其中`ImageSets/Segmentation`路径包含了指定训练和测试样本的文本文件,而`JPEGImages`和`SegmentationClass`路径下分别包含了样本的输入图像和标签。这里的标签也是图像格式,其尺寸和它所标注的输入图像的尺寸相同。标签中颜色相同的像素属于同一个语义类别。下面定义`read_voc_images`函数将输入图像和标签读进内存。 + +``` python +# 本函数已保存在d2lzh_pytorch中方便以后使用 +def read_voc_images(root="../../data/VOCdevkit/VOC2012", + is_train=True, max_num=None): + txt_fname = '%s/ImageSets/Segmentation/%s' % ( + root, 'train.txt' if is_train else 'val.txt') + with open(txt_fname, 'r') as f: + images = f.read().split() + if max_num is not None: + images = images[:min(max_num, len(images))] + features, labels = [None] * len(images), [None] * len(images) + for i, fname in tqdm(enumerate(images)): + features[i] = Image.open('%s/JPEGImages/%s.jpg' % (root, fname)).convert("RGB") + labels[i] = Image.open('%s/SegmentationClass/%s.png' % (root, fname)).convert("RGB") + return features, labels # PIL image + +voc_dir = "../../data/VOCdevkit/VOC2012" +train_features, train_labels = read_voc_images(voc_dir, max_num=100) +``` + +我们画出前5张输入图像和它们的标签。在标签图像中,白色和黑色分别代表边框和背景,而其他不同的颜色则对应不同的类别。 + +``` python +n = 5 +imgs = train_features[0:n] + train_labels[0:n] +d2l.show_images(imgs, 2, n); +``` +
+ +
+ +接下来,我们列出标签中每个RGB颜色的值及其标注的类别。 + +``` python +# 本函数已保存在d2lzh_pytorch中方便以后使用 +VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], + [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], + [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], + [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], + [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], + [0, 64, 128]] +# 本函数已保存在d2lzh_pytorch中方便以后使用 +VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', + 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', + 'diningtable', 'dog', 'horse', 'motorbike', 'person', + 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor'] +``` + +有了上面定义的两个常量以后,我们可以很容易地查找标签中每个像素的类别索引。 + +``` python +colormap2label = torch.zeros(256 ** 3, dtype=torch.uint8) +for i, colormap in enumerate(VOC_COLORMAP): + colormap2label[(colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i + +# 本函数已保存在d2lzh_pytorch中方便以后使用 +def voc_label_indices(colormap, colormap2label): + """ + convert colormap (PIL image) to colormap2label (uint8 tensor). + """ + colormap = np.array(colormap.convert("RGB")).astype('int32') + idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256 + + colormap[:, :, 2]) + return colormap2label[idx] +``` + +例如,第一张样本图像中飞机头部区域的类别索引为1,而背景全是0。 + +``` python +y = voc_label_indices(train_labels[0], colormap2label) +y[105:115, 130:140], VOC_CLASSES[1] +``` +输出: +``` +(tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 0, 0, 0, 1, 1, 1], + [0, 0, 0, 0, 0, 0, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0, 1, 1, 1, 1], + [0, 0, 0, 0, 0, 0, 0, 0, 1, 1]], dtype=torch.uint8), 'aeroplane') +``` + +### 9.9.2.1 预处理数据 + +在之前的章节中,我们通过缩放图像使其符合模型的输入形状。然而在语义分割里,这样做需要将预测的像素类别重新映射回原始尺寸的输入图像。这样的映射难以做到精确,尤其在不同语义的分割区域。为了避免这个问题,我们将图像裁剪成固定尺寸而不是缩放。具体来说,我们使用图像增广里的随机裁剪,并对输入图像和标签裁剪相同区域。 + +``` python +# 本函数已保存在d2lzh_pytorch中方便以后使用 +def voc_rand_crop(feature, label, height, width): + """ + Random crop feature (PIL image) and label (PIL image). + """ + i, j, h, w = torchvision.transforms.RandomCrop.get_params( + feature, output_size=(height, width)) + + feature = torchvision.transforms.functional.crop(feature, i, j, h, w) + label = torchvision.transforms.functional.crop(label, i, j, h, w) + + return feature, label + +imgs = [] +for _ in range(n): + imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300) +d2l.show_images(imgs[::2] + imgs[1::2], 2, n); +``` +
+ +
+ +### 9.9.2.2 自定义语义分割数据集类 + +我们通过继承PyTorch提供的`Dataset`类自定义了一个语义分割数据集类`VOCSegDataset`。通过实现`__getitem__`函数,我们可以任意访问数据集中索引为`idx`的输入图像及其每个像素的类别索引。由于数据集中有些图像的尺寸可能小于随机裁剪所指定的输出尺寸,这些样本需要通过自定义的`filter`函数所移除。此外,我们还对输入图像的RGB三个通道的值分别做标准化。 + +``` python +# 本函数已保存在d2lzh_pytorch中方便以后使用 +class VOCSegDataset(torch.utils.data.Dataset): + def __init__(self, is_train, crop_size, voc_dir, colormap2label, max_num=None): + """ + crop_size: (h, w) + """ + self.rgb_mean = np.array([0.485, 0.456, 0.406]) + self.rgb_std = np.array([0.229, 0.224, 0.225]) + self.tsf = torchvision.transforms.Compose([ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize(mean=self.rgb_mean, + std=self.rgb_std) + ]) + + self.crop_size = crop_size # (h, w) + features, labels = read_voc_images(root=voc_dir, + is_train=is_train, + max_num=max_num) + self.features = self.filter(features) # PIL image + self.labels = self.filter(labels) # PIL image + self.colormap2label = colormap2label + print('read ' + str(len(self.features)) + ' valid examples') + + def filter(self, imgs): + return [img for img in imgs if ( + img.size[1] >= self.crop_size[0] and + img.size[0] >= self.crop_size[1])] + + def __getitem__(self, idx): + feature, label = voc_rand_crop(self.features[idx], self.labels[idx], + *self.crop_size) + + return (self.tsf(feature), # float32 tensor + voc_label_indices(label, self.colormap2label)) # uint8 tensor + + def __len__(self): + return len(self.features) +``` + +### 9.9.2.3 读取数据集 + +我们通过自定义的`VOCSegDataset`类来分别创建训练集和测试集的实例。假设我们指定随机裁剪的输出图像的形状为$320\times 480$。下面我们可以查看训练集和测试集所保留的样本个数。 + +``` python +crop_size = (320, 480) +max_num = 100 +voc_train = VOCSegDataset(True, crop_size, voc_dir, colormap2label, max_num) +voc_test = VOCSegDataset(False, crop_size, voc_dir, colormap2label, max_num) +``` +输出: +``` +read 75 valid examples +read 77 valid examples +``` + +设批量大小为64,分别定义训练集和测试集的迭代器。 + +``` python +batch_size = 64 +num_workers = 0 if sys.platform.startswith('win32') else 4 +train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True, + drop_last=True, num_workers=num_workers) +test_iter = torch.utils.data.DataLoader(voc_test, batch_size, drop_last=True, + num_workers=num_workers) +``` + +打印第一个小批量的类型和形状。不同于图像分类和目标识别,这里的标签是一个三维数组。 + +``` python +for X, Y in train_iter: + print(X.dtype, X.shape) + print(y.dtype, Y.shape) + break +``` +输出: +``` +torch.float32 torch.Size([64, 3, 320, 480]) +torch.uint8 torch.Size([64, 320, 480]) +``` + +## 小结 + +* 语义分割关注如何将图像分割成属于不同语义类别的区域。 +* 语义分割的一个重要数据集叫作Pascal VOC2012。 +* 由于语义分割的输入图像和标签在像素上一一对应,所以将图像随机裁剪成固定尺寸而不是缩放。 + +## 练习 + +* 回忆9.1节(图像增广)中的内容。哪些在图像分类中使用的图像增广方法难以用于语义分割? + +## 参考文献 + +[1] Pascal VOC2012数据集。http://host.robots.ox.ac.uk/pascal/VOC/voc2012/ + + +----------- +> 注:除代码外本节与原书基本相同,[原书传送门](http://zh.d2l.ai/chapter_computer-vision/semantic-segmentation-and-dataset.html) + diff --git a/docs/img/chapter09/9.9_output1.png b/docs/img/chapter09/9.9_output1.png new file mode 100644 index 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