def create_mb_tiny_fd_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=None):
predictor = Predictor(net, config.image_size, config.image_mean_test,
config.image_std,
nms_method=nms_method,
iou_threshold=config.iou_threshold,
candidate_size=candidate_size,
sigma=sigma,
device=device)
return predictor
def create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200, nms_method=None, sigma=0.5, device=torch.device('cpu')):
predictor = Predictor(net, config.image_size, config.image_mean,
config.image_std,
nms_method=nms_method,
iou_threshold=config.iou_threshold,
candidate_size=candidate_size,
sigma=sigma,
device=device)
return predictor
def create_mb_tiny_fd_predictor(net,
model_path=None,
candidate_size=200,
nms_method=None,
sigma=0.5,
device=None,
fuse=False):
predictor = Predictor(net,
config,
model_path=model_path,
nms_method=nms_method,
candidate_size=candidate_size,
sigma=sigma,
device=device,
fuse=fuse)
return predictor
# if net_type == 'vgg16-ssd':
# predictor = create_vgg_ssd_predictor(net, candidate_size=200)
# elif net_type == 'mb1-ssd':
# predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
# elif net_type == 'mb1-ssd-lite':
# predictor = create_mobilenetv1_ssd_lite_predictor(net, candidate_size=200)
# elif net_type == 'mb2-ssd-lite':
# predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200)
# elif net_type == 'sq-ssd-lite':
# predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
# elif net_type == 'resnet-18':
# predictor = create_resnet18_ssd_predictor(net, candidate_size=200)
# if net_type == 'lstm-ssd':
predictor = Predictor(net, config.image_size, config.image_mean,
config.image_std,
nms_method=None,
iou_threshold=config.iou_threshold,
candidate_size=200,
sigma=None)
dir_path = './data/sample/'
imgs = []
for img_name in os.listdir(dir_path):
img_path = os.path.join(dir_path, img_name)
print(img_path)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
imgs.append(img)
video = np.array(imgs)