from data_generator.object_detection_2d_data_generator import DataGenerator train_dataset = DataGenerator(load_images_into_memory=True, hdf5_dataset_path=None) train_images_dirs = ['../datasets/rgbd/'] train_image_set_filenames = ['../datasets/rgbd/train.txt'] train_annotations_dirs = ['../datasets/rgbd/labels/'] train_classes = ['background', 'gate'] val_images_dirs = ['../datasets/rgbd/'] val_image_set_filenames = ['../datasets/rgbd/val.txt'] val_annotations_dirs = ['../datasets/rgbd/labels/'] val_classes = ['background', 'gate'] train_dataset.parse_labelimg_xml(train_images_dirs, train_image_set_filenames, train_annotations_dirs, train_classes)
from data_generator.object_detection_2d_data_generator import DataGenerator val_dataset = DataGenerator(load_images_into_memory=True, hdf5_dataset_path=None) images_dir = '../datasets/rgbd/' val_dataset.parse_labelimg_xml( images_dir=images_dir, labels_filename=val_labels_filename, input_format=['image_name', 'xmin', 'xmax', 'ymin', 'ymax', 'class_id'], include_classes='all')