Deep learning notes - semantic segmentation and data sets

Semantic segmentation only cares about which class the pixel belongs to , Instance segmentation concerns which instance of which class the pixel belongs to （ It can be considered as an evolutionary version of target detection ）.

import os
import torch
import torchvision
from d2l import torch as d2l
import matplotlib.pyplot as plt

d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar',
                           '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')

voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')


#  Read all input images and labels into memory 
def read_voc_images(voc_dir, is_train=True):
    """ Read all VOC Image and label """
    txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation',
                             'train.txt' if is_train else 'val.txt')
    mode = torchvision.io.image.ImageReadMode.RGB
    with open(txt_fname, 'r') as f:
        images = f.read().split()
    features, labels = [], []
    for i, fname in enumerate(images):
        features.append(torchvision.io.read_image(os.path.join(
            voc_dir, 'JPEGImages', f'{fname}.jpg')))
        labels.append(torchvision.io.read_image(os.path.join(
            voc_dir, 'SegmentationClass', f'{fname}.png'), mode))
    return features, labels


train_features, train_labels = read_voc_images(voc_dir, True)

#  Before drawing 5 An input image and its label 
n = 5
imgs = train_features[0:n] + train_labels[0:n]
imgs = [img.permute(1, 2, 0) for img in imgs]
d2l.show_images(imgs, 2, n)
plt.show()

#  list RGB Color value and class name （ Each label corresponds to pixel Of RGB value ）
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]]

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']


#  Find the class index of each pixel in the label 
def voc_colormap2label():
    """ Build from RGB To VOC Mapping of category indexes """
    colormap2label = torch.zeros(256 ** 3, dtype=torch.long)
    for i, colormap in enumerate(VOC_COLORMAP):
        colormap2label[
            (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i
    return colormap2label


def voc_label_indices(colormap, colormap2label):
    """ take VOC In the tag RGB Values map to their category index """
    colormap = colormap.permute(1, 2, 0).numpy().astype('int32')
    idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256
           + colormap[:, :, 2])
    return colormap2label[idx]


y = voc_label_indices(train_labels[0], voc_colormap2label())
print(y[105:115, 130:140], VOC_CLASSES[1])


#  Use random clipping in image augmentation , Crop the same area of the input image and label 
def voc_rand_crop(feature, label, height, width):
    """ Randomly crop features and label images """
    rect = torchvision.transforms.RandomCrop.get_params(
        feature, (height, width))
    feature = torchvision.transforms.functional.crop(feature, *rect)
    label = torchvision.transforms.functional.crop(label, *rect)
    return feature, label


imgs = []
for _ in range(n):
    imgs += voc_rand_crop(train_features[0], train_labels[0], 200, 300)

imgs = [img.permute(1, 2, 0) for img in imgs]
d2l.show_images(imgs[::2] + imgs[1::2], 2, n)
plt.show()


#  Custom semantic segmentation dataset class 
#  When doing small batch training , You need to make the picture the same size , So we need to crop, Don't be casual resize
class VOCSegDataset(torch.utils.data.Dataset):
    """ One for loading VOC Custom datasets for datasets """

    def __init__(self, is_train, crop_size, voc_dir):
        #  I want to use it later ImageNet Upper pre-train model
        self.transform = torchvision.transforms.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        self.crop_size = crop_size
        features, labels = read_voc_images(voc_dir, is_train=is_train)
        self.features = [
            #  do RGB Normalization
            self.normalize_image(feature)
            for feature in self.filter(features)]
        self.labels = self.filter(labels)
        self.colormap2label = voc_colormap2label()
        print('read ' + str(len(self.features)) + ' examples')

    def normalize_image(self, img):
        return self.transform(img.float() / 255)

    #  If the picture is better than crop_size If your height and width are smaller , Then discard 
    def filter(self, imgs):
        return [img for img in imgs if (
                img.shape[1] >= self.crop_size[0] and
                img.shape[2] >= self.crop_size[1])]

    def __getitem__(self, idx):
        feature, label = voc_rand_crop(self.features[idx], self.labels[idx],
                                       *self.crop_size)
        return (feature, voc_label_indices(label, self.colormap2label))

    def __len__(self):
        return len(self.features)


#  Reading data sets 
crop_size = (320, 480)
voc_train = VOCSegDataset(True, crop_size, voc_dir)
voc_test = VOCSegDataset(False, crop_size, voc_dir)

batch_size = 64
train_iter = torch.utils.data.DataLoader(voc_train, batch_size, shuffle=True,
                                         drop_last=True,
                                         num_workers=d2l.get_dataloader_workers())
for X, Y in train_iter:
    print(X.shape)
    print(Y.shape)
    break


#  Integrate all components 
def load_data_voc(batch_size, crop_size):
    """ load VOC Semantic segmentation dataset """
    voc_dir = d2l.download_extract('voc2012', os.path.join(
        'VOCdevkit', 'VOC2012'))
    num_workers = d2l.get_dataloader_workers()
    train_iter = torch.utils.data.DataLoader(
        VOCSegDataset(True, crop_size, voc_dir), batch_size,
        shuffle=True, drop_last=True, num_workers=num_workers)
    test_iter = torch.utils.data.DataLoader(
        VOCSegDataset(False, crop_size, voc_dir), batch_size,
        drop_last=True, num_workers=num_workers)
    return train_iter, test_iter