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Model coverage cleanup (#4585)

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Glenn Jocher 2023-08-27 04:19:41 +02:00 • committed by GitHub
parent c635418a27
commit deac7575b1
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12 changed files with 132 additions and 175 deletions
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35
ultralytics/models/fastsam/prompt.py
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@ -51,32 +51,16 @@ class FastSAMPrompt:
n = len(result.masks.data)
for i in range(n):
mask = result.masks.data[i] == 1.0
if torch.sum(mask) < filter:
continue
annotation = {
'id': i,
'segmentation': mask.cpu().numpy(),
'bbox': result.boxes.data[i],
'score': result.boxes.conf[i]}
annotation['area'] = annotation['segmentation'].sum()
annotations.append(annotation)
if torch.sum(mask) >= filter:
annotation = {
'id': i,
'segmentation': mask.cpu().numpy(),
'bbox': result.boxes.data[i],
'score': result.boxes.conf[i]}
annotation['area'] = annotation['segmentation'].sum()
annotations.append(annotation)
return annotations
@staticmethod
def filter_masks(annotations): # filter the overlap mask
annotations.sort(key=lambda x: x['area'], reverse=True)
to_remove = set()
for i in range(len(annotations)):
a = annotations[i]
for j in range(i + 1, len(annotations)):
b = annotations[j]
if i != j and j not in to_remove and b['area'] < a['area'] and \
(a['segmentation'] & b['segmentation']).sum() / b['segmentation'].sum() > 0.8:
to_remove.add(j)
return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove
@staticmethod
def _get_bbox_from_mask(mask):
mask = mask.astype(np.uint8)
@ -242,15 +226,12 @@ class FastSAMPrompt:
cropped_images = []
not_crop = []
filter_id = []
# annotations, _ = filter_masks(annotations)
# filter_id = list(_)
for _, mask in enumerate(annotations):
if np.sum(mask['segmentation']) <= 100:
filter_id.append(_)
continue
bbox = self._get_bbox_from_mask(mask['segmentation']) # mask 的 bbox
cropped_boxes.append(self._segment_image(image, bbox)) # 保存裁剪的图片
# cropped_boxes.append(segment_image(image,mask["segmentation"]))
cropped_images.append(bbox) # 保存裁剪的图片的bbox
return cropped_boxes, cropped_images, not_crop, filter_id, annotations