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ultralytics 8.0.70 minor fixes and improvements (#1892)

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Co-authored-by: feicccccccc <49809204+feicccccccc@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
This commit is contained in:
Glenn Jocher
2023-04-08 00:27:33 +02:00
committed by GitHub
parent c2cd3fd20e
commit c38b17a0d8
17 changed files with 71 additions and 90 deletions
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18
ultralytics/yolo/v8/detect/predict.py
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@ -5,14 +5,11 @@ import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
from ultralytics.yolo.utils.plotting import save_one_box
class DetectionPredictor(BasePredictor):
def get_annotator(self, img):
return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
def preprocess(self, img):
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
@ -52,15 +49,18 @@ class DetectionPredictor(BasePredictor):
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
self.annotator = self.get_annotator(im0)
det = results[idx].boxes # TODO: make boxes inherit from tensors
if len(det) == 0:
result = results[idx] # TODO: make boxes inherit from tensors
if len(result) == 0:
return f'{log_string}(no detections), '
det = result.boxes
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)}, "
if self.args.save or self.args.show: # Add bbox to image
self.plotted_img = result.plot(line_width=self.args.line_thickness)
# write
for d in reversed(det):
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
@ -68,10 +68,6 @@ class DetectionPredictor(BasePredictor):
line = (c, *d.xywhn.view(-1)) + (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 or self.args.show: # Add bbox to image
name = ('' if id is None else f'id:{id} ') + self.model.names[c]
label = (f'{name} {conf:.2f}' if self.args.show_conf else name) if self.args.show_labels else None
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if self.args.save_crop:
save_one_box(d.xyxy,
imc,