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119 lines
4.9 KiB
119 lines
4.9 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import sys
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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_CFG, 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, classes=None):
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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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classes=self.args.classes)
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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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imc = im0.copy() if self.args.save_crop else im0
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if self.source_type.webcam or self.source_type.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)) if self.args.boxes else None
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if self.args.save_crop:
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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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def predict(cfg=DEFAULT_CFG, use_python=False):
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model = cfg.model or "yolov8n-seg.pt"
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source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \
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else "https://ultralytics.com/images/bus.jpg"
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args = dict(model=model, source=source)
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if use_python:
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from ultralytics import YOLO
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YOLO(model)(**args)
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else:
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predictor = SegmentationPredictor(overrides=args)
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predictor.predict_cli()
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if __name__ == "__main__":
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predict()
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