์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

์ฐธ์กฐ ultralytics/models/yolo/detect/predict.py

์ฐธ๊ณ 

์ด ํŒŒ์ผ์€ https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/models/ yolo/detect/predict .py์—์„œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฌธ์ œ๋ฅผ ๋ฐœ๊ฒฌํ•˜๋ฉด ํ’€ ๋ฆฌํ€˜์ŠคํŠธ ๐Ÿ› ๏ธ ์— ๊ธฐ์—ฌํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋„๋ก ๋„์™€์ฃผ์„ธ์š”. ๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค ๐Ÿ™!



ultralytics.models.yolo.detect.predict.DetectionPredictor

๊ธฐ์ง€: BasePredictor

ํƒ์ง€ ๋ชจ๋ธ์— ๊ธฐ๋ฐ˜ํ•œ ์˜ˆ์ธก์„ ์œ„ํ•ด BasePredictor ํด๋ž˜์Šค๋ฅผ ํ™•์žฅํ•˜๋Š” ํด๋ž˜์Šค์ž…๋‹ˆ๋‹ค.

์˜ˆ์ œ
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.detect import DetectionPredictor

args = dict(model='yolov8n.pt', source=ASSETS)
predictor = DetectionPredictor(overrides=args)
predictor.predict_cli()
์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/models/yolo/detect/predict.py
class DetectionPredictor(BasePredictor):
    """
    A class extending the BasePredictor class for prediction based on a detection model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.detect import DetectionPredictor

        args = dict(model='yolov8n.pt', source=ASSETS)
        predictor = DetectionPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def postprocess(self, preds, img, orig_imgs):
        """Post-processes predictions and returns a list of Results objects."""
        preds = ops.non_max_suppression(
            preds,
            self.args.conf,
            self.args.iou,
            agnostic=self.args.agnostic_nms,
            max_det=self.args.max_det,
            classes=self.args.classes,
        )

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
            img_path = self.batch[0][i]
            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
        return results

postprocess(preds, img, orig_imgs)

์˜ˆ์ธก์„ ์‚ฌํ›„ ์ฒ˜๋ฆฌํ•˜๊ณ  ๊ฒฐ๊ณผ ๊ฐœ์ฒด ๋ชฉ๋ก์„ ๋ฐ˜ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

์˜ ์†Œ์Šค ์ฝ”๋“œ ultralytics/models/yolo/detect/predict.py
def postprocess(self, preds, img, orig_imgs):
    """Post-processes predictions and returns a list of Results objects."""
    preds = ops.non_max_suppression(
        preds,
        self.args.conf,
        self.args.iou,
        agnostic=self.args.agnostic_nms,
        max_det=self.args.max_det,
        classes=self.args.classes,
    )

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

    results = []
    for i, pred in enumerate(preds):
        orig_img = orig_imgs[i]
        pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
        img_path = self.batch[0][i]
        results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
    return results





์ƒ์„ฑ๋จ 2023-11-12, ์—…๋ฐ์ดํŠธ๋จ 2023-11-25
์ž‘์„ฑ์ž: glenn-jocher (3)