def drop_corner_values(face: Face) -> None:
"""
Erase those pixels which are too close or to far to be treated as
valuable data.
"""
depth_median = np.median(face.depth_img[face.mask])
face.depth_img[face.mask] -= depth_median
depth_stdev = np.std(face.depth_img[face.mask])
# |allowed_dist| is the distance from the center in the maximum metric
# which allows the any depth values
allowed_dist = 6
for i in range(IMG_SIZE):
for j in range(IMG_SIZE):
if abs(i - IMG_SIZE // 2) < allowed_dist and abs(j - IMG_SIZE //
2) < allowed_dist:
# Too close to the center of the face to drop those values
# (might be a very pointy nose).
continue
if abs(face.depth_img[i, j]) >= 2. * depth_stdev:
face.depth_img[i, j] = 0
depth_stdev = np.std(face.depth_img[face.mask])
face.depth_img /= depth_stdev * 4
mx = face.depth_img.max()
mi = face.depth_img.min()
if abs(mi) > abs(mx):
face.depth_img = np.maximum(-abs(mx), face.depth_img)
else:
face.depth_img = np.minimum(abs(mi), face.depth_img)
# Scale each dimension into interval 0..1
rescale_one_dim(face.depth_img)
rescale_one_dim(face.gir_img)
face.depth_img *= DEPTH_TO_WIDTH_RATIO
def recentre(face: Face) -> None:
assert face.depth_img.shape == face.gir_img.shape
logging.debug("\n\nRECENTRE")
move_x = int((CENTER_DEST[0] - face.face_center[0]) * IMG_SIZE)
move_y = int((CENTER_DEST[1] - face.face_center[1]) * IMG_SIZE)
logging.debug("MOVE X MOVE Y %d %d" % (move_x, move_y))
face.gir_img = np.roll(face.gir_img, move_x, axis=1)
face.gir_img = np.roll(face.gir_img, move_y, axis=0)
face.depth_img = np.roll(face.depth_img, move_x, axis=1)
face.depth_img = np.roll(face.depth_img, move_y, axis=0)
if move_x >= 0:
face.gir_img[:, move_x] = 0
face.depth_img[:, move_x] = 0
else:
face.gir_img[:, move_x:] = 0
face.depth_img[:, move_x:] = 0
if move_y >= 0:
face.gir_img[:move_y, :] = 0
face.depth_img[:move_y, :] = 0
else:
face.gir_img[move_y:, :] = 0
face.depth_img[move_y:, :] = 0