)
parser.add_argument(
"--epochs", type=float, default=40,
help = "Number of epochs for training last layers and number of epochs for fine-tuning layers. Default is 51."
)
FLAGS = parser.parse_args()
np.random.seed(FLAGS.random_seed)
log_dir = FLAGS.log_dir
class_names = get_classes(FLAGS.classes_file)
num_classes = len(class_names)
anchors = get_anchors(FLAGS.anchors_path)
weights_path = FLAGS.weights_path
input_shape = (416, 416) # multiple of 32, height, width
epoch1, epoch2 = FLAGS.epochs, FLAGS.epochs
is_tiny_version = (len(anchors)==6) # default setting
if FLAGS.is_tiny:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path = weights_path)
else:
model = create_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path = weights_path) # make sure you know what you freeze
log_dir_time = os.path.join(log_dir,'{}'.format(int(time())))
logging = TensorBoard(log_dir=log_dir_time)
)
parser.add_argument(
"--video_out",
type=str,
dest="out_path",
default='./vidout/out.avi',
help="Path to the videos",
)
FLAGS = parser.parse_args()
# Split images and videos
img_endings = (".jpg", ".jpeg", ".png")
vid_endings = (".mp4", ".mpeg", ".mpg", ".avi")
anchors = get_anchors(anchors_path)
yolo = YOLO(
**{
"model_path": FLAGS.model_path,
"anchors_path": anchors_path,
"classes_path": FLAGS.classes_path,
"score": FLAGS.score,
"gpu_num": FLAGS.gpu_num,
"model_image_size": (416, 416),
})
# labels to draw on images
class_file = open(FLAGS.classes_path, "r")
input_labels = [line.rstrip("\n") for line in class_file.readlines()]
FLAGS.output = 'vidout'
