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ultralytics 8.0.73 minor fixes (#1929)

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Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: joseliraGB <122470533+joseliraGB@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-04-11 01:00:09 +02:00
committed by GitHub
parent 95f96dc5bc
commit 5629ed0bb7
16 changed files with 224 additions and 198 deletions
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50
ultralytics/yolo/v8/segment/predict.py
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@ -4,7 +4,6 @@ import torch
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import save_one_box
from ultralytics.yolo.v8.detect.predict import DetectionPredictor
@ -40,55 +39,6 @@ class SegmentationPredictor(DetectionPredictor):
Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
return results
def write_results(self, idx, results, batch):
p, im, im0 = batch
log_string = ''
if len(im.shape) == 3:
im = im[None] # expand for batch dim
self.seen += 1
imc = im0.copy() if self.args.save_crop else im0
if self.source_type.webcam or self.source_type.from_img: # batch_size >= 1
log_string += f'{idx}: '
frame = self.dataset.count
else:
frame = getattr(self.dataset, 'frame', 0)
self.data_path = p
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string
result = results[idx]
if len(result) == 0:
return f'{log_string}(no detections), '
det, mask = result.boxes, result.masks # getting tensors TODO: mask mask,box inherit for tensor
# Print results
for c in det.cls.unique():
n = (det.cls == c).sum() # detections per class
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
# Mask plotting
if self.args.save or self.args.show:
im_gpu = torch.as_tensor(im0, dtype=torch.float16, device=mask.masks.device).permute(
2, 0, 1).flip(0).contiguous() / 255 if self.args.retina_masks else im[idx]
self.plotted_img = result.plot(line_width=self.args.line_thickness, im_gpu=im_gpu, boxes=self.args.boxes)
# Write results
for j, d in enumerate(reversed(det)):
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
if self.args.save_txt: # Write to file
seg = mask.xyn[len(det) - j - 1].copy().reshape(-1) # reversed mask.xyn, (n,2) to (n*2)
line = (c, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
with open(f'{self.txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if self.args.save_crop:
save_one_box(d.xyxy,
imc,
file=self.save_dir / 'crops' / self.model.names[c] / f'{self.data_path.stem}.jpg',
BGR=True)
return log_string
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n-seg.pt'