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111 lines
4.6 KiB
111 lines
4.6 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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import hydra |
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import torch |
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from ultralytics.yolo.engine.results import Results |
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from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops |
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from ultralytics.yolo.utils.plotting import colors, save_one_box |
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from ultralytics.yolo.v8.detect.predict import DetectionPredictor |
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class SegmentationPredictor(DetectionPredictor): |
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def postprocess(self, preds, img, orig_img): |
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# TODO: filter by classes |
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p = ops.non_max_suppression(preds[0], |
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self.args.conf, |
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self.args.iou, |
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agnostic=self.args.agnostic_nms, |
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max_det=self.args.max_det, |
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nm=32) |
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results = [] |
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proto = preds[1][-1] |
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for i, pred in enumerate(p): |
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shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape |
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if not len(pred): |
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results.append(Results(boxes=pred[:, :6], orig_shape=shape[:2])) # save empty boxes |
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continue |
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if self.args.retina_masks: |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() |
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masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC |
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else: |
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masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round() |
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results.append(Results(boxes=pred[:, :6], masks=masks, orig_shape=shape[:2])) |
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return results |
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def write_results(self, idx, results, batch): |
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p, im, im0 = batch |
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log_string = "" |
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if len(im.shape) == 3: |
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im = im[None] # expand for batch dim |
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self.seen += 1 |
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if self.webcam or self.from_img: # batch_size >= 1 |
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log_string += f'{idx}: ' |
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frame = self.dataset.count |
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else: |
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frame = getattr(self.dataset, 'frame', 0) |
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self.data_path = p |
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self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') |
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log_string += '%gx%g ' % im.shape[2:] # print string |
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self.annotator = self.get_annotator(im0) |
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result = results[idx] |
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if len(result) == 0: |
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return log_string |
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det, mask = result.boxes, result.masks # getting tensors TODO: mask mask,box inherit for tensor |
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# Print results |
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for c in det.cls.unique(): |
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n = (det.cls == c).sum() # detections per class |
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log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " |
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# Mask plotting |
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self.annotator.masks( |
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mask.masks, |
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colors=[colors(x, True) for x in det.cls], |
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() / |
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255 if self.args.retina_masks else im[idx]) |
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# Segments |
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if self.args.save_txt: |
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segments = mask.segments |
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# Write results |
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for j, d in enumerate(reversed(det)): |
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cls, conf = d.cls.squeeze(), d.conf.squeeze() |
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if self.args.save_txt: # Write to file |
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seg = segments[j].copy() |
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seg = seg.reshape(-1) # (n,2) to (n*2) |
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line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # label format |
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with open(f'{self.txt_path}.txt', 'a') as f: |
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f.write(('%g ' * len(line)).rstrip() % line + '\n') |
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if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image |
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c = int(cls) # integer class |
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label = None if self.args.hide_labels else ( |
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self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}') |
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self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) |
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if self.args.save_crop: |
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imc = im0.copy() |
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save_one_box(d.xyxy, |
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imc, |
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file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg', |
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BGR=True) |
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return log_string |
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name) |
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def predict(cfg): |
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cfg.model = cfg.model or "yolov8n-seg.pt" |
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cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" |
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predictor = SegmentationPredictor(cfg) |
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predictor.predict_cli() |
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if __name__ == "__main__": |
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predict()
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