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| # Ultralytics YOLO 🚀, GPL-3.0 license | |
| import torch | |
| from ultralytics.yolo.engine.results import Results | |
| from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops | |
| from ultralytics.yolo.utils.plotting import colors, save_one_box | |
| from ultralytics.yolo.v8.detect.predict import DetectionPredictor | |
| class SegmentationPredictor(DetectionPredictor): | |
| def postprocess(self, preds, img, orig_imgs): | |
| # TODO: filter by classes | |
| p = ops.non_max_suppression(preds[0], | |
| self.args.conf, | |
| self.args.iou, | |
| agnostic=self.args.agnostic_nms, | |
| max_det=self.args.max_det, | |
| nc=len(self.model.names), | |
| classes=self.args.classes) | |
| results = [] | |
| proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported | |
| for i, pred in enumerate(p): | |
| 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 | |
| if not len(pred): # save empty boxes | |
| results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])) | |
| continue | |
| if self.args.retina_masks: | |
| if not isinstance(orig_imgs, torch.Tensor): | |
| pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) | |
| masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC | |
| else: | |
| masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC | |
| if not isinstance(orig_imgs, torch.Tensor): | |
| pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) | |
| results.append( | |
| 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 | |
| self.annotator = self.get_annotator(im0) | |
| 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.annotator.masks(masks=mask.masks, colors=[colors(x, True) for x in det.cls], im_gpu=im_gpu) | |
| # 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 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}') | |
| if self.args.boxes: | |
| 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-seg.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 = SegmentationPredictor(overrides=args) | |
| predictor.predict_cli() | |
| if __name__ == '__main__': | |
| predict() | |