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102 lines
4.3 KiB
102 lines
4.3 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import torch
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from ultralytics.yolo.engine.predictor import BasePredictor
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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 Annotator, colors, save_one_box
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class DetectionPredictor(BasePredictor):
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def get_annotator(self, img):
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return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
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def preprocess(self, img):
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img = torch.from_numpy(img).to(self.model.device)
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img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
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img /= 255 # 0 - 255 to 0.0 - 1.0
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return img
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def postprocess(self, preds, img, orig_img):
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preds = ops.non_max_suppression(preds,
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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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classes=self.args.classes)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
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shape = orig_img.shape
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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path, _, _, _, _ = self.batch
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img_path = path[i] if isinstance(path, list) else path
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
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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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det = results[idx].boxes # TODO: make boxes inherit from tensors
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if len(det) == 0:
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return log_string
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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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# write
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for d in 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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line = (cls, *(d.xywhn.view(-1).tolist()), conf) \
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if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist())) # 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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name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]
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label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {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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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.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 = DetectionPredictor(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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