Astridkraft commited on
Commit
6322e6f
·
verified ·
1 Parent(s): 139a75d

Update sam_module.py

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Files changed (1) hide show
  1. sam_module.py +1 -15
sam_module.py CHANGED
@@ -65,20 +65,6 @@ def create_sam_mask(self, image, bbox_coords, mode):
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  num_masks = outputs.pred_masks.shape[2]
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  print(f" SAM lieferte {num_masks} verschiedene Masken")
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- # Sammlung der Masken
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- all_masks = []
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-
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- for i in range(num_masks):
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- single_mask = outputs.pred_masks[:, :, i, :, :]
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- resized_mask = F.interpolate(
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- single_mask,
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- size=(image.height, image.width),
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- mode='bilinear',
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- align_corners=False
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- ).squeeze()
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-
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- mask_np = resized_mask.sigmoid().cpu().numpy()
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- all_masks.append(mask_np)
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  bbox_center = ((x1 + x2) // 2, (y1 + y2) // 2)
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  bbox_area = (x2 - x1) * (y2 - y1)
@@ -90,7 +76,7 @@ def create_sam_mask(self, image, bbox_coords, mode):
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  best_score = -1
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  # Alle 3 Masken analysieren (OHNE sie alle zu skalieren!)
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- for i in range(3):
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  # Maske in Original-SAM-Größe (256x256) analysieren
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  mask_256 = outputs.pred_masks[:, :, i, :, :]
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  mask_np_256 = mask_256.sigmoid().squeeze().cpu().numpy()
 
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  num_masks = outputs.pred_masks.shape[2]
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  print(f" SAM lieferte {num_masks} verschiedene Masken")
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  bbox_center = ((x1 + x2) // 2, (y1 + y2) // 2)
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  bbox_area = (x2 - x1) * (y2 - y1)
 
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  best_score = -1
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  # Alle 3 Masken analysieren (OHNE sie alle zu skalieren!)
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+ for i in range(num_masks):
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  # Maske in Original-SAM-Größe (256x256) analysieren
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  mask_256 = outputs.pred_masks[:, :, i, :, :]
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  mask_np_256 = mask_256.sigmoid().squeeze().cpu().numpy()