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# Ultralytics YOLO 🚀, AGPL-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, ops
class DetectionPredictor(BasePredictor):
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
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def postprocess(self, preds, img, orig_imgs):
preds = ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
agnostic=self.args.agnostic_nms,
max_det=self.args.max_det,
classes=self.args.classes)
results = []
for i, pred in enumerate(preds):
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
if not isinstance(orig_imgs, torch.Tensor):
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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, boxes=pred))
return results
def predict(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n.pt'
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 = DetectionPredictor(overrides=args)
predictor.predict_cli()
if __name__ == '__main__':
predict()