def preprocess(self, mode): assert mode in ['train', 'val', 'test'] pascal_data_path = get_data_path('pascal') sbd_data_path = get_data_path('sbd') items = [] if mode == 'train': p = open( os.path.join(pascal_data_path, 'ImageSets', 'Person', 'train.txt')).readlines() s = open(os.path.join(sbd_data_path, 'dataset', 'train.txt')).readlines() lines = list(set(p).intersection(s)) data_list = [l.strip('\n') for l in lines] elif mode == 'val': p = open( os.path.join(pascal_data_path, 'ImageSets', 'Person', 'val.txt')).readlines() s = open(os.path.join(sbd_data_path, 'dataset', 'val.txt')).readlines() lines = list(set(p).intersection(s)) data_list = [l.strip('\n') for l in lines] img_path = os.path.join(sbd_data_path, 'dataset', 'img') semseg_mask_path = os.path.join(sbd_data_path, 'dataset', 'cls') parts_mask_path = os.path.join(pascal_data_path, 'ImageSets', 'Person', 'gt') for it in data_list: item = (os.path.join(img_path, it + '.jpg'), os.path.join(semseg_mask_path, it + '.mat'), os.path.join(parts_mask_path, it + '.png')) items.append(item) return items
def preprocess(self, mode): assert mode in ['train', 'val', 'test'] items = [] data_path = get_data_path('lip') if mode == 'train': img_path = os.path.join(data_path, 'multi-person', 'Training', 'Images') mask_path = os.path.join(data_path, 'multi-person', 'Training', 'Category_ids') data_list = [ l.strip('\n') for l in open( os.path.join(data_path, 'multi-person', 'Training', 'train_id.txt')).readlines() ] for it in data_list: item = (os.path.join(img_path, it + '.jpg'), os.path.join(mask_path, it + '.png')) items.append(item) elif mode == 'val': img_path = os.path.join(data_path, 'multi-person', 'Validation', 'Images') mask_path = os.path.join(data_path, 'multi-person', 'Validation', 'Category_ids') data_list = [ l.strip('\n') for l in open( os.path.join(data_path, 'multi-person', 'Validation', 'val_id.txt')).readlines() ] for it in data_list: item = (os.path.join(img_path, it + '.jpg'), os.path.join(mask_path, it + '.png')) items.append(item) return items[0:11716]
def preprocess(self, mode): assert mode in ['train', 'val', 'test'] data_path = get_data_path('pascalparts') if mode == 'train': data_list = [ l.strip('\n') for l in open( os.path.join(data_path, 'ImageSets', 'Person', 'train.txt')).readlines() ] elif mode == 'val': data_list = [ l.strip('\n') for l in open( os.path.join(data_path, 'ImageSets', 'Person', 'val.txt')).readlines() ] items = [] img_path = os.path.join(data_path, 'JPEGImages') mask_path = os.path.join(data_path, 'ImageSets', 'Person', 'gt') for it in data_list: item = (os.path.join(img_path, it + '.jpg'), os.path.join(mask_path, it + '.png')) items.append(item) return items
def preprocess(self, mode): assert mode in ['train', 'val', 'test'] items = [] sbd_path = get_data_path('sbd') sbd_img_path = os.path.join(sbd_path, 'dataset', 'img') sbd_mask_path = os.path.join(sbd_path, 'dataset', 'cls') voc_path = get_data_path('pascal') voc_test_path = get_data_path('pascal_test') voc_img_path = os.path.join(voc_path, 'JPEGImages') voc_mask_path = os.path.join(voc_path, 'SegmentationClass') # Train data = VOC_train + SBD_train + SBD_val if mode == 'train': sbd_data_list = [ l.strip('\n') for l in open(os.path.join(sbd_path, 'dataset', 'trainval.txt')).readlines() ] # SBD dataset contains some of the voc_val samples, so we have to remove them voc_val_data_list = [ l.strip('\n') for l in open( os.path.join(voc_path, 'ImageSets', 'Segmentation', 'val.txt')).readlines() ] sbd_data_list = list(set(sbd_data_list) - set(voc_val_data_list)) for it in sbd_data_list: item = (os.path.join(sbd_img_path, it + '.jpg'), os.path.join(sbd_mask_path, it + '.mat')) items.append(item) voc_data_list = [ l.strip('\n') for l in open( os.path.join(voc_path, 'ImageSets', 'Segmentation', 'train.txt')).readlines() ] for it in voc_data_list: item = (os.path.join(voc_img_path, it + '.jpg'), os.path.join(voc_mask_path, it + '.png')) items.append(item) # Val data = VOC_val elif mode == 'val': data_list = [ l.strip('\n') for l in open( os.path.join(voc_path, 'ImageSets', 'Segmentation', 'val.txt')).readlines() ] for it in data_list: item = (os.path.join(voc_img_path, it + '.jpg'), os.path.join(voc_mask_path, it + '.png')) items.append(item) # Test data = VOC_test else: img_path = os.path.join(voc_test_path, 'JPEGImages') data_list = [ l.strip('\n') for l in open( os.path.join(voc_path, 'ImageSets', 'Segmentation', 'test.txt')).readlines() ] for it in data_list: items.append((img_path, it)) return items