DeepLearning-語義分割資料處理例項
阿新 • • 發佈:2022-04-22
資料集:Pascal VOC2012, 參考材料:動手學深度學習
以下示例實現了對資料的預讀取,處理等操作
import os from random import shuffle from turtle import width import torch import torchvision from d2l import torch as d2l voc_dir = "./dataset/VOC2012/"# 資料讀取 def read_voc_images(voc_dir, is_train=True): 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) 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'] def voc_colormap2label(): """構建從RGB到VOC類別索引的對映""" 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 #@save def voc_label_indices(colormap, colormap2label): """將VOC標籤中的RGB值對映到它們的類別索引""" 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()) def voc_rand_crop(feature, labek, height, weight): 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] #@save class VOCSegDataset(torch.utils.data.Dataset): """一個用於載入VOC資料集的自定義資料集""" def __init__(self, is_train, crop_size, voc_dir): 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 = [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.) 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) 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