def load_data(self):
cfg = basic_config(self.newDir)
if self.method == 'faster':
cfg.faster_rcnn_config()
elif self.method == 'fast':
cfg.fast_rcnn_config()
elif self.method == 'normal':
cfg.rcnn_config()
cfg = mcfg.rcnn_hoi_classes(cfg)
cfg = set_config(cfg)
cfg.get_args()
cfg.dataset = 'HICO'
cfg.update_paths()
trainMeta = utils.load_dict(cfg.data_path + 'train')
testMeta = utils.load_dict(cfg.data_path + 'test')
trainGTMeta = utils.load_dict(cfg.data_path + 'train_GT')
testGTMeta = utils.load_dict(cfg.data_path + 'test_GT')
labels = utils.load_dict(cfg.data_path + 'labels')
class_mapping = utils.load_dict(cfg.data_path + 'class_mapping')
if cfg.max_classes is not None:
# Reduce data to include only max_classes number of different classes
_, counts = utils.getLabelStats(trainGTMeta, labels)
reduced_idxs = utils.getReducedIdxs(counts, cfg.max_classes,
labels)
trainGTMeta = utils.reduceData(trainGTMeta, reduced_idxs)
testGTMeta = utils.reduceData(testGTMeta, reduced_idxs)
trainMeta = utils.reduceData(trainMeta, reduced_idxs)
testMeta = utils.reduceData(testMeta, reduced_idxs)
labels = utils.idxs2labels(reduced_idxs, labels)
cfg.nb_classes = len(labels)
cfg.set_class_weights(labels, trainGTMeta)
_, valMeta = utils.splitData(list(trainMeta.keys()), trainMeta)
self.cfg = cfg
if cfg.move:
self.move_data()
print('Data:', cfg.data_path)
print('Path:', cfg.my_results_path)
self.labels = labels
self.class_mapping = class_mapping
self.trainMeta = trainMeta
self.valMeta = valMeta
self.testMeta = testMeta
self.trainGTMeta = trainGTMeta
self.testGTMeta = testGTMeta
def load_data(self):
cfg = basic_config(self.newDir)
cfg = set_config(cfg)
cfg.get_args()
cfg.update_paths()
trainMeta = utils.load_dict(cfg.data_path + 'train')
testMeta = utils.load_dict(cfg.data_path + 'test')
trainGTMeta = utils.load_dict(cfg.data_path + 'train_GT')
testGTMeta = utils.load_dict(cfg.data_path + 'test_GT')
labels = utils.load_dict(cfg.data_path + 'labels')
if cfg.max_classes is not None:
# Reduce data to include only max_classes number of different classes
_, counts = utils.getLabelStats(trainGTMeta, labels)
trainGTMeta, reduced_idxs = utils.reduceTrainData(
trainGTMeta, counts, cfg.max_classes)
testGTMeta = utils.reduceTestData(testGTMeta, reduced_idxs)
trainMeta = utils.reduceTestData(trainMeta, reduced_idxs)
testMeta = utils.reduceTestData(testMeta, reduced_idxs)
labels = utils.idxs2labels(reduced_idxs, labels)
cfg.nb_classes = len(labels)
cfg.set_class_weights(labels, trainGTMeta)
_, valMeta = utils.splitData(list(trainMeta.keys()), trainMeta)
self.cfg = cfg
if cfg.move:
self.move_data()
print('Path:', cfg.my_results_path)
self.labels = labels
self.trainMeta = trainMeta
self.valMeta = valMeta
self.testMeta = testMeta
self.trainGTMeta = trainGTMeta
self.testGTMeta = testGTMeta
new_imagesMeta[imageID] = {
'imageName': imageMeta['imageName'],
'rels': rels
}
return new_imagesMeta
if __name__ == "__main__":
# metaData = sio.loadmat(url + 'anno.mat', struct_as_record=False, squeeze_me=True)
bbData = sio.loadmat(url + 'anno_bbox.mat',
struct_as_record=False,
squeeze_me=True)
# actions = bbData['list_action']
# trainYMatrix = metaData['anno_train']
bbDataTrain = bbData['bbox_train']
cfg = basic_config()
cfg = set_config(cfg)
cfg.dataset = 'HICO'
cfg.get_data_path()
cfg.get_results_paths()
labels = utils.load_dict(cfg.data_path + 'labels')
print("Extract meta data")
tmpTrainMeta = extractMetaData(bbDataTrain)
print("Combine similar BBs")
newTrainMeta = combineSimilarBBs(tmpTrainMeta, labels, 0.4)
newTrainMetaID = list(newTrainMeta.keys())
newTrainMetaID.sort()
# imagesID = imagesID[6490:7000]
# images = pp.loadImages(imagesID, imagesMeta, url+"images/train2015/")
# [dataXP, dataXB, dataY, dataMeta] = pp.getData(imagesID, imagesMeta, images, (224,244))
# trainYMatrix = pp.getMatrixLabels(len(actions), dataY)
utils.save_dict(tmpTrainMeta, url + 'HICO_train_GT')