Update sam_module.py
Browse files- sam_module.py +1 -15
sam_module.py
CHANGED
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@@ -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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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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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)
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@@ -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(
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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()
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