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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.v8.detect.predict import DetectionPredictor |
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class PosePredictor(DetectionPredictor): |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
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super().__init__(cfg, overrides, _callbacks) |
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self.args.task = 'pose' |
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def postprocess(self, preds, img, orig_imgs): |
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"""Return detection results for a given input image or list of images.""" |
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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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nc=len(self.model.names)) |
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results = [] |
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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs |
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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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pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:] |
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pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, shape) |
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path = self.batch[0] |
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img_path = path[i] if isinstance(path, list) else path |
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results.append( |
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Results(orig_img=orig_img, |
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path=img_path, |
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names=self.model.names, |
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boxes=pred[:, :6], |
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keypoints=pred_kpts)) |
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return results |
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def predict(cfg=DEFAULT_CFG, use_python=False): |
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"""Runs YOLO to predict objects in an image or video.""" |
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model = cfg.model or 'yolov8n-pose.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 = PosePredictor(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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