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4258af6 b4e654e 4258af6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ops
class SegmentationPredictor(DetectionPredictor):
"""
A class extending the DetectionPredictor class for prediction based on a segmentation model.
Example:
```python
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.segment import SegmentationPredictor
args = dict(model="yolo11n-seg.pt", source=ASSETS)
predictor = SegmentationPredictor(overrides=args)
predictor.predict_cli()
```
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
"""Initializes the SegmentationPredictor with the provided configuration, overrides, and callbacks."""
super().__init__(cfg, overrides, _callbacks)
self.args.task = "segment"
def postprocess(self, preds, img, orig_imgs):
"""Applies non-max suppression and processes detections for each image in an input batch."""
# tuple if PyTorch model or array if exported
protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1] # preds[1][-1].shape :-> torch.Size([1, 32, 120, 160])
return super().postprocess(preds[0], img, orig_imgs, protos=protos) # preds[0].shape :-> torch.Size([1, 43, 6300])
def construct_results(self, preds, img, orig_imgs, protos):
"""
Constructs a list of result objects from the predictions.
Args:
preds (List[torch.Tensor]): List of predicted bounding boxes, scores, and masks.
img (torch.Tensor): The image after preprocessing.
orig_imgs (List[np.ndarray]): List of original images before preprocessing.
protos (List[torch.Tensor]): List of prototype masks.
Returns:
(list): List of result objects containing the original images, image paths, class names, bounding boxes, and masks.
"""
print(f'***** Calling def construct_results() in models/yolo/segment.py *****')
return [
self.construct_result(pred, img, orig_img, img_path, proto)
for pred, orig_img, img_path, proto in zip(preds, orig_imgs, self.batch[0], protos)
]
def construct_result(self, pred, img, orig_img, img_path, proto):
"""
Constructs the result object from the prediction.
Args:
pred (np.ndarray): The predicted bounding boxes, scores, and masks.
img (torch.Tensor): The image after preprocessing.
orig_img (np.ndarray): The original image before preprocessing.
img_path (str): The path to the original image.
proto (torch.Tensor): The prototype masks.
Returns:
(Results): The result object containing the original image, image path, class names, bounding boxes, and masks.
"""
# assert 0, 'in def construct_result '
print(f'len(pred): {len(pred)} in def construct_result')
if not len(pred): # save empty boxes
masks = None
print(f'Len of masks == 0 in def construct_results in yolo segment predict.py')
elif self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
masks = ops.process_mask_native(proto, pred[:, 7:], pred[:, :4], orig_img.shape[:2]) # HWC # '6' changed to '7' since masks start at one index later than previously
else:
masks = ops.process_mask(proto, pred[:, 7:], pred[:, :4], img.shape[2:], upsample=True) # HWC # HWC # '6' changed to '7' since masks start at one index later than previously
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :7], masks=masks) # '6' changed to '7' since a box now contains
# 4*coords, growth_val, conf, class
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