Implement smoothing as option

rt_utils/image_helper.py

@@ -100,6 +100,7 @@ def find_mask_contours(mask: np.ndarray, approximate_contours: bool, scaling_fac
  contours
  ) # Open-CV updated contours to be a tuple so we convert it back into a list here
 
+ # Coordinates are rescaled to image grid by dividing with scaling factor
  for i, contour in enumerate(contours):
  contours[i] = [[(contour[i][0][0] / scaling_factor), (contour[i][0][1] / scaling_factor)] for i in
  range(0, len(contour))]

rt_utils/smoothing.py

@@ -5,17 +5,18 @@
 import logging
 
 # A set of parameters that is know to work well
-default_smoothing_parameters = {
- "iterations": 2,
+default_smoothing_parameters_2d = {
+ "scaling_iterations": 2,
+ "filter_iterations": 1,
  "crop_margins": [20, 20, 1],
  "np_kron": {"scaling_factor": 3},
  "ndimage_gaussian_filter": {"sigma": 2,
  "radius": 3},
  "threshold": {"threshold": 0.4},
 }
 # A set of parameters that is know to work well
-default_smoothing_parameters_2 = {
- "iterations": 3,
+default_smoothing_parameters_2d_2 = {
+ "scaling_iterations": 3,
  "crop_margins": [20, 20, 1],
  "np_kron": {"scaling_factor": 2},
  "ndimage_gaussian_filter": {"sigma": 2,
@@ -87,28 +88,30 @@ def restore_mask_dimensions(cropped_mask: np.ndarray, new_shape, bbox):
  new_mask[bbox[0]: bbox[1], bbox[2]: bbox[3], bbox[4]: bbox[5]] = cropped_mask
  return new_mask.astype(bool)
 
-def iteration_2d(mask: np.ndarray, np_kron, ndimage_gaussian_filter, threshold):
+def iteration_2d(mask: np.ndarray, np_kron, ndimage_gaussian_filter, threshold, filter_iterations):
  """
  This is the actual set of filters. Applied iterative over z direction
  """
  cropped_mask = kron_upscale(mask=mask, params=np_kron)
 
- for z_idx in range(cropped_mask.shape[2]):
- slice = cropped_mask[:, :, z_idx] 
- slice = gaussian_blur(mask=slice, params=ndimage_gaussian_filter)
- slice = binary_threshold(mask=slice, params=threshold)
+ for filter_iteration in range(filter_iterations):
+ for z_idx in range(cropped_mask.shape[2]):
+ slice = cropped_mask[:, :, z_idx] 
+ slice = gaussian_blur(mask=slice, params=ndimage_gaussian_filter)
+ slice = binary_threshold(mask=slice, params=threshold)
 
- cropped_mask[:, :, z_idx] = slice
+  cropped_mask[:, :, z_idx] = slice
 
  return cropped_mask
 
-def iteration_3d(mask: np.ndarray, np_kron, ndimage_gaussian_filter, threshold):
+def iteration_3d(mask: np.ndarray, np_kron, ndimage_gaussian_filter, threshold, filter_iterations):
  """
  This is the actual filters applied iteratively in 3d.
  """
- cropped_mask = kron_upscale(mask=mask, params=np_kron)
- cropped_mask = gaussian_blur(mask=cropped_mask, params=ndimage_gaussian_filter)
- cropped_mask = binary_threshold(mask=cropped_mask, params=threshold)
+ for filter_iteration in range(filter_iterations):
+ cropped_mask = kron_upscale(mask=mask, params=np_kron)
+ cropped_mask = gaussian_blur(mask=cropped_mask, params=ndimage_gaussian_filter)
+ cropped_mask = binary_threshold(mask=cropped_mask, params=threshold)
 
  return cropped_mask
 
@@ -119,9 +122,11 @@ def pipeline(mask: np.ndarray,
  This is the entrypoint for smoothing a mask.
  """
  if not smoothing_parameters:
- smoothing_parameters = default_smoothing_parameters
+ smoothing_parameters = default_smoothing_parameters_2d
+
+ scaling_iterations = smoothing_parameters["scaling_iterations"]
+ filter_iterations = smoothing_parameters["filter_iterations"]
 
- iterations = smoothing_parameters["iterations"]
  crop_margins = np.array(smoothing_parameters["crop_margins"])
  np_kron = smoothing_parameters["np_kron"]
  ndimage_gaussian_filter = smoothing_parameters["ndimage_gaussian_filter"]
@@ -132,22 +137,24 @@ def pipeline(mask: np.ndarray,
  cropped_mask, bbox = crop_mask(mask, crop_margins=crop_margins)
  final_shape, final_bbox = get_final_mask_shape_and_bbox(mask=mask,
  scaling_factor=np_kron["scaling_factor"],
- iterations=iterations,
+ scaling_iterations=scaling_iterations,
  bbox=bbox)
- logging.info(f"Final scaling with factor of {np_kron['scaling_factor']} for {iterations} iterations")
- for i in range(iterations):
- logging.info(f"Iteration {i+1} out of {iterations}")
+ logging.info(f"Final scaling with factor of {np_kron['scaling_factor']} for {scaling_iterations} scaling_iterations")
+ for i in range(scaling_iterations):
+ logging.info(f"Iteration {i+1} out of {scaling_iterations}")
  logging.info(f"Applying filters")
  if apply_smoothing == "2d":
  cropped_mask = iteration_2d(cropped_mask,
  np_kron=np_kron,
  ndimage_gaussian_filter=ndimage_gaussian_filter,
- threshold=threshold)
+ threshold=threshold,
+ filter_iterations=filter_iterations)
  elif apply_smoothing == "3d":
  cropped_mask = iteration_3d(cropped_mask,
  np_kron=np_kron,
  ndimage_gaussian_filter=ndimage_gaussian_filter,
- threshold=threshold)
+ threshold=threshold,
+ filter_iterations=filter_iterations)
  else:
  raise Exception("Wrong dimension parameter. Use '2d' or '3d'.")
 
@@ -156,12 +163,12 @@ def pipeline(mask: np.ndarray,
  mask = restore_mask_dimensions(cropped_mask, final_shape, final_bbox)
  return mask
 
-def get_final_mask_shape_and_bbox(mask, bbox, scaling_factor, iterations):
+def get_final_mask_shape_and_bbox(mask, bbox, scaling_factor, scaling_iterations):
  """
  This function scales image shape and the bounding box which should be used for the final mask
  """
 
- final_scaling_factor = pow(scaling_factor, iterations)
+ final_scaling_factor = pow(scaling_factor, scaling_iterations)
 
  final_shape = np.array(mask.shape)
  final_shape[:2] *= final_scaling_factor