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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| from ultralytics.engine.predictor import BasePredictor | |
| from ultralytics.engine.results import Results | |
| from ultralytics.utils import ops | |
| class DetectionPredictor(BasePredictor): | |
| """ | |
| A class extending the BasePredictor class for prediction based on a detection model. | |
| This predictor specializes in object detection tasks, processing model outputs into meaningful detection results | |
| with bounding boxes and class predictions. | |
| Attributes: | |
| args (namespace): Configuration arguments for the predictor. | |
| model (nn.Module): The detection model used for inference. | |
| batch (list): Batch of images and metadata for processing. | |
| Methods: | |
| postprocess: Process raw model predictions into detection results. | |
| construct_results: Build Results objects from processed predictions. | |
| construct_result: Create a single Result object from a prediction. | |
| Examples: | |
| >>> from ultralytics.utils import ASSETS | |
| >>> from ultralytics.models.yolo.detect import DetectionPredictor | |
| >>> args = dict(model="yolo11n.pt", source=ASSETS) | |
| >>> predictor = DetectionPredictor(overrides=args) | |
| >>> predictor.predict_cli() | |
| """ | |
| def postprocess(self, preds, img, orig_imgs, **kwargs): | |
| """Post-processes predictions and returns a list of Results objects.""" | |
| preds = ops.non_max_suppression( | |
| preds, | |
| self.args.conf, | |
| self.args.iou, | |
| self.args.classes, | |
| self.args.agnostic_nms, | |
| max_det=self.args.max_det, | |
| nc=len(self.model.names), | |
| end2end=getattr(self.model, "end2end", False), | |
| rotated=self.args.task == "obb", | |
| ) | |
| if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list | |
| orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) | |
| return self.construct_results(preds, img, orig_imgs, **kwargs) | |
| def construct_results(self, preds, img, orig_imgs): | |
| """ | |
| Construct a list of Results objects from model predictions. | |
| Args: | |
| preds (List[torch.Tensor]): List of predicted bounding boxes and scores for each image. | |
| img (torch.Tensor): Batch of preprocessed images used for inference. | |
| orig_imgs (List[np.ndarray]): List of original images before preprocessing. | |
| Returns: | |
| (List[Results]): List of Results objects containing detection information for each image. | |
| """ | |
| return [ | |
| self.construct_result(pred, img, orig_img, img_path) | |
| for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]) | |
| ] | |
| def construct_result(self, pred, img, orig_img, img_path): | |
| """ | |
| Construct a single Results object from one image prediction. | |
| Args: | |
| pred (torch.Tensor): Predicted boxes and scores with shape (N, 6) where N is the number of detections. | |
| img (torch.Tensor): Preprocessed image tensor used for inference. | |
| orig_img (np.ndarray): Original image before preprocessing. | |
| img_path (str): Path to the original image file. | |
| Returns: | |
| (Results): Results object containing the original image, image path, class names, and scaled bounding boxes. | |
| """ | |
| pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) | |
| return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6]) | |