def __call__(self, frame: Node) -> List[ObjectPrediction]:
import tensorflow as tf
tiles = list(
node_to_tiles(
frame.resize(STREAM_WIDTH // SCALE, STREAM_HEIGHT // SCALE)))
y_pred = self.model(
tf.stack([process_image(tile.image) for (_, _, tile) in tiles]))
pred_class = [self.classes[i] for i in np.argmax(y_pred, axis=1)]
confidence = np.max(y_pred, axis=1)
out = []
for i in range(len(tiles)):
x_offset, y_offset, tile = tiles[i]
rect = Rectangle(x_offset * SCALE, y_offset * SCALE, TILE_WIDTH,
TILE_HEIGHT)
out.append(
ObjectPrediction(pred_class[i], confidence[i], rect, tile))
return out
def load_relevant_backgrounds() -> Iterable[Node]:
root = Path(os.environ['GENERATIVE_BGS_SRC'])
for path in root.glob('*.png'):
img = cv2.imread(path.as_posix())
node = Node(img)
yield node.resize(node.width // 2, node.height // 2)