def map_coordinates(cls, image, dx, dy, order=1, cval=0, mode="constant"):
# small debug message to be sure that the right function is executed
print("map_coordinates() with cv2")
if order == 0 and image.dtype.name in ["uint64", "int64"]:
raise Exception((
"dtypes uint64 and int64 are only supported in ElasticTransformation for order=0, "
+ "got order=%d with dtype=%s.") % (order, image.dtype.name))
input_dtype = image.dtype
if image.dtype.name == "bool":
image = image.astype(np.float32)
elif order == 1 and image.dtype.name in ["int8", "int16", "int32"]:
image = image.astype(np.float64)
elif order >= 2 and image.dtype.name == "int8":
image = image.astype(np.int16)
elif order >= 2 and image.dtype.name == "int32":
image = image.astype(np.float64)
shrt_max = 32767
backend = "cv2"
if order == 0:
bad_dtype_cv2 = (image.dtype.name in [
"uint32", "uint64", "int64", "float128", "bool"
])
elif order == 1:
bad_dtype_cv2 = (image.dtype.name in [
"uint32", "uint64", "int8", "int16", "int32", "int64",
"float128", "bool"
])
else:
bad_dtype_cv2 = (image.dtype.name in [
"uint32", "uint64", "int8", "int32", "int64", "float128",
"bool"
])
bad_dx_shape_cv2 = (dx.shape[0] >= shrt_max or dx.shape[1] >= shrt_max)
bad_dy_shape_cv2 = (dy.shape[0] >= shrt_max or dy.shape[1] >= shrt_max)
if bad_dtype_cv2 or bad_dx_shape_cv2 or bad_dy_shape_cv2:
backend = "scipy"
ia.do_assert(image.ndim == 3)
result = np.copy(image)
height, width = image.shape[0:2]
# False was added here, only difference to usual code
if False or backend == "scipy":
h, w = image.shape[0:2]
y, x = np.meshgrid(np.arange(h).astype(np.float32),
np.arange(w).astype(np.float32),
indexing='ij')
x_shifted = x + (-1) * dx
y_shifted = y + (-1) * dy
for c in sm.xrange(image.shape[2]):
remapped_flat = ndimage.interpolation.map_coordinates(
image[..., c], (x_shifted.flatten(), y_shifted.flatten()),
order=order,
cval=cval,
mode=mode)
remapped = remapped_flat.reshape((height, width))
result[..., c] = remapped
else:
h, w, nb_channels = image.shape
y, x = np.meshgrid(np.arange(h).astype(np.float32),
np.arange(w).astype(np.float32),
indexing='ij')
x_shifted = x + (-1) * dx
y_shifted = y + (-1) * dy
if image.dtype.kind == "f":
cval = float(cval)
else:
cval = int(cval)
border_mode = cls._MAPPING_MODE_SCIPY_CV2[mode]
interpolation = cls._MAPPING_ORDER_SCIPY_CV2[order]
is_nearest_neighbour = (interpolation == cv2.INTER_NEAREST)
map1, map2 = cv2.convertMaps(x_shifted,
y_shifted,
cv2.CV_16SC2,
nninterpolation=is_nearest_neighbour)
# remap only supports up to 4 channels
if nb_channels <= 4:
result = cv2.remap(image,
map1,
map2,
interpolation=interpolation,
borderMode=border_mode,
borderValue=cval)
if image.ndim == 3 and result.ndim == 2:
result = result[..., np.newaxis]
else:
current_chan_idx = 0
result = []
while current_chan_idx < nb_channels:
channels = image[...,
current_chan_idx:current_chan_idx + 4]
result_c = cv2.remap(channels,
map1,
map2,
interpolation=interpolation,
borderMode=border_mode,
borderValue=cval)
if result_c.ndim == 2:
result_c = result_c[..., np.newaxis]
result.append(result_c)
current_chan_idx += 4
result = np.concatenate(result, axis=2)
if result.dtype.name != input_dtype.name:
result = meta.restore_dtypes_(result, input_dtype)
return result
def map_coordinates(cls, image, dx, dy, order=1, cval=0, mode="constant"):
# small debug message to be sure that the right function is executed
print("map_coordinates() with cv2")
if order == 0 and image.dtype.name in ["uint64", "int64"]:
raise Exception(("dtypes uint64 and int64 are only supported in ElasticTransformation for order=0, "
+ "got order=%d with dtype=%s.") % (order, image.dtype.name))
input_dtype = image.dtype
if image.dtype.name == "bool":
image = image.astype(np.float32)
elif order == 1 and image.dtype.name in ["int8", "int16", "int32"]:
image = image.astype(np.float64)
elif order >= 2 and image.dtype.name == "int8":
image = image.astype(np.int16)
elif order >= 2 and image.dtype.name == "int32":
image = image.astype(np.float64)
shrt_max = 32767
backend = "cv2"
if order == 0:
bad_dtype_cv2 = (image.dtype.name in ["uint32", "uint64", "int64", "float128", "bool"])
elif order == 1:
bad_dtype_cv2 = (image.dtype.name in ["uint32", "uint64", "int8", "int16", "int32", "int64", "float128",
"bool"])
else:
bad_dtype_cv2 = (image.dtype.name in ["uint32", "uint64", "int8", "int32", "int64", "float128", "bool"])
bad_dx_shape_cv2 = (dx.shape[0] >= shrt_max or dx.shape[1] >= shrt_max)
bad_dy_shape_cv2 = (dy.shape[0] >= shrt_max or dy.shape[1] >= shrt_max)
if bad_dtype_cv2 or bad_dx_shape_cv2 or bad_dy_shape_cv2:
backend = "scipy"
ia.do_assert(image.ndim == 3)
result = np.copy(image)
height, width = image.shape[0:2]
# False was added here, only difference to usual code
if False or backend == "scipy":
h, w = image.shape[0:2]
y, x = np.meshgrid(np.arange(h).astype(np.float32), np.arange(w).astype(np.float32), indexing='ij')
x_shifted = x + (-1) * dx
y_shifted = y + (-1) * dy
for c in sm.xrange(image.shape[2]):
remapped_flat = ndimage.interpolation.map_coordinates(
image[..., c],
(x_shifted.flatten(), y_shifted.flatten()),
order=order,
cval=cval,
mode=mode
)
remapped = remapped_flat.reshape((height, width))
result[..., c] = remapped
else:
h, w, nb_channels = image.shape
y, x = np.meshgrid(np.arange(h).astype(np.float32), np.arange(w).astype(np.float32), indexing='ij')
x_shifted = x + (-1) * dx
y_shifted = y + (-1) * dy
if image.dtype.kind == "f":
cval = float(cval)
else:
cval = int(cval)
border_mode = cls._MAPPING_MODE_SCIPY_CV2[mode]
interpolation = cls._MAPPING_ORDER_SCIPY_CV2[order]
is_nearest_neighbour = (interpolation == cv2.INTER_NEAREST)
map1, map2 = cv2.convertMaps(x_shifted, y_shifted, cv2.CV_16SC2, nninterpolation=is_nearest_neighbour)
# remap only supports up to 4 channels
if nb_channels <= 4:
result = cv2.remap(image, map1, map2, interpolation=interpolation, borderMode=border_mode, borderValue=cval)
if image.ndim == 3 and result.ndim == 2:
result = result[..., np.newaxis]
else:
current_chan_idx = 0
result = []
while current_chan_idx < nb_channels:
channels = image[..., current_chan_idx:current_chan_idx+4]
result_c = cv2.remap(channels, map1, map2, interpolation=interpolation, borderMode=border_mode,
borderValue=cval)
if result_c.ndim == 2:
result_c = result_c[..., np.newaxis]
result.append(result_c)
current_chan_idx += 4
result = np.concatenate(result, axis=2)
if result.dtype.name != input_dtype.name:
result = meta.restore_dtypes_(result, input_dtype)
return result
