def _step( self ):
# grab image container from port using traits
optical_c = self.grab_input_using_trait( 'optical_image' )
thermal_c = self.grab_input_using_trait( 'thermal_image' )
# Get python image from conatiner (just for show)
optical_npy = optical_c.image().asarray().astype('uint8')
thermal_npy = thermal_c.image().asarray().astype('uint16')
thermal_norm = normalize_thermal( thermal_npy )
if thermal_norm is not None and optical_npy is not None:
# compute transform
ret, transform, _ = compute_transform(
optical_npy,
thermal_norm,
warp_mode = cv2.MOTION_HOMOGRAPHY,
match_low_res = True,
good_match_percent = self._good_match_percent,
ratio_test = self._ratio_test,
match_height = self._match_height,
min_matches = self._min_matches,
min_inliers = self._min_inliers )
else:
ret = False
if ret:
# TODO: Make all of these computations conditional on port connection
inv_transform = np.linalg.inv( transform )
thermal_warped = cv2.warpPerspective( thermal_npy, transform, \
( optical_npy.shape[1], optical_npy.shape[0] ) )
optical_warped = cv2.warpPerspective( optical_npy, inv_transform, \
( thermal_npy.shape[1], thermal_npy.shape[0] ) )
#self.push_to_port_using_trait( 'thermal_to_optical_homog',
# Homography.from_matrix( transform, 'd' )
#self.push_to_port_using_trait( 'optical_to_thermal_homog',
# Homography.from_matrix( inv_transform, 'd' )
self.push_to_port_using_trait( 'warped_thermal_image',
ImageContainer.fromarray( thermal_warped ) )
self.push_to_port_using_trait( 'warped_optical_image',
ImageContainer.fromarray( optical_warped ) )
else:
print( 'alignment failed!' )
#self.push_to_port_using_trait( "thermal_to_optical_homog", Homography() )
#self.push_to_port_using_trait( "optical_to_thermal_homog", Homography() )
self.push_to_port_using_trait( 'warped_optical_image', ImageContainer() )
self.push_to_port_using_trait( 'warped_thermal_image', ImageContainer() )
self._base_step()
def _test_numpy(dtype_name, nchannels, order='c'):
np_img = create_numpy_image(dtype_name, nchannels, order)
img_container = ImageContainer.fromarray(np_img)
recast = img_container.asarray()
# asarray always returns 3 channels
np_img = np.atleast_3d(np_img)
vital_img = img_container.image()
pixel_type_name = vital_img.pixel_type_name()
pixel_type_name = vital_img.pixel_type_name()
want = map_dtype_name_to_pixel_type(dtype_name)
assert pixel_type_name == want, 'want={} but got={}'.format(
want, pixel_type_name)
if not np.all(np_img == recast):
raise AssertionError(
'Failed dtype={}, nchannels={}, order={}'.format(
dtype_name, nchannels, order))
