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')
