return dataset, cfg
if __name__ == "__main__":
### let's save some things
### save the images, labels, boxes for all test and train
logger.info("starting.....")
loader = UADetracLoader()
"""
images = loader.load_images(dir='/nethome/jbang36/eva_jaeho/data/ua_detrac/4_images')
labels, boxes = loader.load_labels('/nethome/jbang36/eva_jaeho/data/ua_detrac/4_xml')
assert(len(images) == len(boxes))
loader.save_images(name = 'uad_train_images.npy', vi_name='uad_train_vi.npy')
loader.save_labels(name = 'uad_train_labels.npy')
loader.save_boxes(name = 'uad_train_boxes.npy')
logger.info("Saved all train data!")
"""
test_images = loader.load_images(
dir='/nethome/jbang36/eva_jaeho/data/ua_detrac/5_images')
test_labels, test_boxes = loader.load_labels(
'/nethome/jbang36/eva_jaeho/data/ua_detrac/5_xml')
assert (len(test_images) == len(test_boxes))
loader.save_images(name='uad_test_images.npy', vi_name='uad_test_vi.npy')
loader.save_labels(name='uad_test_labels.npy')
loader.save_boxes(name='uad_test_boxes.npy')
logger.info("Saved all test data!")
return rec, prec, ap
if __name__ == '__main__':
# load net
num_classes = len(labelmap) + 1 # +1 for background
net = build_ssd('test', 300, num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.eval()
print('Finished loading model!')
# load data
loader = UADetracLoader()
images = loader.load_images(
dir=os.path.join(home_dir, 'data', 'ua_detrac', '5_images'))
labels, boxes = loader.load_labels(
dir=os.path.join(home_dir, 'data', 'ua_detrac', '5_xml'))
labels = labels['vehicle']
images, labels, boxes = loader.filter_input3(images, labels, boxes)
dataset = UADDetection(transform=BaseTransform(300, dataset_mean),
target_transform=UADAnnotationTransform())
dataset.set_images(images)
dataset.set_labels(labels)
dataset.set_boxes(boxes)
if args.cuda:
net = net.cuda()
cudnn.benchmark = True
# evaluation
"""
def _loadSegmentedImages(self):
eva_dir = config.eva_dir
dir = os.path.join(eva_dir, 'eva_storage', 'tmp_data', 'segmented_images.npy')
if os.path.exists(dir):
self.segmented_images = np.load(dir)
else:
print("path", dir, "does not exist..")
if __name__ == "__main__":
loader = UADetracLoader()
images = loader.load_images()
labels = loader.load_labels()
boxes = loader.load_boxes()
video_start_indices = loader.get_video_start_indices()
#images loaded as 300x300 - prepare the images
preprocessor = PreprocessingModule()
segmented_images = preprocessor.run(images, video_start_indices)