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# Ultralytics YOLO 🚀, GPL-3.0 license
import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT
from ultralytics.yolo.utils.plotting import Annotator
class ClassificationPredictor(BasePredictor):
def get_annotator(self, img):
return Annotator(img, example=str(self.model.names), pil=True)
def preprocess(self, img):
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
def postprocess(self, preds, img, orig_imgs):
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
path, _, _, _, _ = self.batch
img_path = path[i] if isinstance(path, list) else path
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
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
im0 = im0.copy()
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
# save_path = str(self.save_dir / p.name) # im.jpg
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)
result = results[idx]
if len(result) == 0:
return log_string
prob = result.probs
# Print results
n5 = min(len(self.model.names), 5)
top5i = prob.argsort(0, descending=True)[:n5].tolist() # top 5 indices
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
# write
text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
if self.args.save or self.args.show: # Add bbox to image
self.annotator.text((32, 32), text, txt_color=(255, 255, 255))
if self.args.save_txt: # Write to file
with open(f'{self.txt_path}.txt', 'a') as f:
f.write(text + '\n')
return log_string
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n-cls.pt' # or "resnet18"
source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
else 'https://ultralytics.com/images/bus.jpg'
args = dict(model=model, source=source)
if use_python:
from ultralytics import YOLO
YOLO(model)(**args)
else:
predictor = ClassificationPredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()
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