def extract_features_hahog(
image: np.ndarray, config: Dict[str, Any],
features_count: int) -> Tuple[np.ndarray, np.ndarray]:
t = time.time()
points, desc = pyfeatures.hahog(
image.astype(np.float32) /
255, # VlFeat expects pixel values between 0, 1
peak_threshold=config["hahog_peak_threshold"],
edge_threshold=config["hahog_edge_threshold"],
target_num_features=features_count,
use_adaptive_suppression=config["feature_use_adaptive_suppression"],
)
if config["feature_root"]:
desc = np.sqrt(desc)
uchar_scaling = 362 # x * 512 < 256 => sqrt(x) * 362 < 256
else:
uchar_scaling = 512
if config["hahog_normalize_to_uchar"]:
# pyre-fixme [16]: `int` has no attribute `clip`
desc = (uchar_scaling * desc).clip(0, 255).round()
logger.debug("Found {0} points in {1}s".format(len(points),
time.time() - t))
return points, desc
def extract_features_hahog(image, config):
t = time.time()
points, desc = pyfeatures.hahog(image.astype(np.float32) / 255, # VlFeat expects pixel values between 0, 1
peak_threshold=config['hahog_peak_threshold'],
edge_threshold=config['hahog_edge_threshold'],
target_num_features=config['feature_min_frames'],
use_adaptive_suppression=config['feature_use_adaptive_suppression'])
if config['feature_root']:
desc = np.sqrt(desc)
uchar_scaling = 362 # x * 512 < 256 => sqrt(x) * 362 < 256
else:
uchar_scaling = 512
if config['hahog_normalize_to_uchar']:
desc = (uchar_scaling * desc).clip(0, 255).round()
logger.debug('Found {0} points in {1}s'.format(len(points), time.time() - t))
return points, desc
