Update postprocessing (output settings, allow no marker)

deepliif/models/__init__.py

@@ -427,18 +427,18 @@ def get_net_tiles(n):
  raise Exception(f'inference() not implemented for model {opt.model}')
 
 
-def postprocess(orig, images, tile_size, seg_thresh=150, size_thresh='default', marker_thresh='default', size_thresh_upper=None, opt=None):
- if opt.model == 'DeepLIIF':
+def postprocess(orig, images, tile_size, model, seg_thresh=150, size_thresh='default', marker_thresh='default', size_thresh_upper=None):
+ if model == 'DeepLIIF':
  resolution = '40x' if tile_size > 384 else ('20x' if tile_size > 192 else '10x')
  overlay, refined, scoring = compute_results(np.array(orig), np.array(images['Seg']),
- np.array(images['Marker'].convert('L')), resolution,
- seg_thresh, size_thresh, marker_thresh, size_thresh_upper)
+ np.array(images['Marker'].convert('L')) if 'Marker' in images else None,
+ resolution, seg_thresh, size_thresh, marker_thresh, size_thresh_upper)
  processed_images = {}
  processed_images['SegOverlaid'] = Image.fromarray(overlay)
  processed_images['SegRefined'] = Image.fromarray(refined)
  return processed_images, scoring
 
- elif opt.model == 'DeepLIIFExt':
+ elif model == 'DeepLIIFExt':
  resolution = '40x' if tile_size > 768 else ('20x' if tile_size > 384 else '10x')
  processed_images = {}
  scoring = {}
@@ -455,7 +455,7 @@ def postprocess(orig, images, tile_size, seg_thresh=150, size_thresh='default',
  return processed_images, scoring
 
  else:
- raise Exception(f'postprocess() not implemented for model {opt.model}')
+ raise Exception(f'postprocess() not implemented for model {model}')
 
 
 def infer_modalities(img, tile_size, model_dir, eager_mode=False,
@@ -489,7 +489,7 @@ def infer_modalities(img, tile_size, model_dir, eager_mode=False,
  )
 
  if not hasattr(opt,'seg_gen') or (hasattr(opt,'seg_gen') and opt.seg_gen): # the first condition accounts for old settings of deepliif; the second refers to deepliifext models
- post_images, scoring = postprocess(img, images, tile_size, opt=opt)
+ post_images, scoring = postprocess(img, images, tile_size, opt.model)
  images = {**images, **post_images}
  return images, scoring
  else:

deepliif/postprocessing.py

@@ -407,7 +407,10 @@ def compute_results(orig, seg, marker, resolution=None, seg_thresh=150, size_thr
 
  if size_thresh == 'default':
  size_thresh = calc_default_size_thresh(mask, resolution)
- if marker_thresh == 'default':
+ if marker_thresh is None:
+ marker_thresh = 0
+ marker = None
+ elif marker_thresh == 'default':
  marker_thresh = calc_default_marker_thresh(marker)
 
  counts = compute_cell_classification(mask, marker, size_thresh, marker_thresh, size_thresh_upper=None)
@@ -417,7 +420,11 @@ def compute_results(orig, seg, marker, resolution=None, seg_thresh=150, size_thr
  'num_total': counts['num_total'],
  'num_pos': counts['num_pos'],
  'num_neg': counts['num_neg'],
- 'percent_pos': round(counts['num_pos'] / counts['num_total'] * 100, 1) if counts['num_pos'] > 0 else 0
+ 'percent_pos': round(counts['num_pos'] / counts['num_total'] * 100, 1) if counts['num_pos'] > 0 else 0,
+ 'prob_thresh': seg_thresh,
+ 'size_thresh': size_thresh,
+ 'size_thresh_upper': size_thresh_upper,
+ 'marker_thresh': marker_thresh if marker is not None else None,
  }
 
  overlay = np.copy(orig)