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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, ops | |
| from ultralytics.yolo.utils.plotting import Annotator, colors, 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 | |
| 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 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 | |
| self.annotator = self.get_annotator(im0) | |
| det = results[idx].boxes # TODO: make boxes inherit from tensors | |
| if len(det) == 0: | |
| return f'{log_string}(no detections), ' | |
| 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)}, " | |
| # 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()) | |
| if self.args.save_txt: # Write to file | |
| 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 = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}') | |
| self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) | |
| 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.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() | |