126 lines
5.3 KiB
Python
126 lines
5.3 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from ultralytics.engine.predictor import BasePredictor
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from ultralytics.engine.results import Results
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from ultralytics.utils import nms, ops
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class DetectionPredictor(BasePredictor):
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"""
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A class extending the BasePredictor class for prediction based on a detection model.
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This predictor specializes in object detection tasks, processing model outputs into meaningful detection results
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with bounding boxes and class predictions.
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Attributes:
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args (namespace): Configuration arguments for the predictor.
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model (nn.Module): The detection model used for inference.
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batch (list): Batch of images and metadata for processing.
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Methods:
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postprocess: Process raw model predictions into detection results.
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construct_results: Build Results objects from processed predictions.
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construct_result: Create a single Result object from a prediction.
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get_obj_feats: Extract object features from the feature maps.
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Examples:
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>>> from ultralytics.utils import ASSETS
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>>> from ultralytics.models.yolo.detect import DetectionPredictor
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>>> args = dict(model="yolo11n.pt", source=ASSETS)
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>>> predictor = DetectionPredictor(overrides=args)
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>>> predictor.predict_cli()
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"""
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def postprocess(self, preds, img, orig_imgs, **kwargs):
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"""
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Post-process predictions and return a list of Results objects.
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This method applies non-maximum suppression to raw model predictions and prepares them for visualization and
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further analysis.
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Args:
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preds (torch.Tensor): Raw predictions from the model.
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img (torch.Tensor): Processed input image tensor in model input format.
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orig_imgs (torch.Tensor | list): Original input images before preprocessing.
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**kwargs (Any): Additional keyword arguments.
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Returns:
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(list): List of Results objects containing the post-processed predictions.
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Examples:
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>>> predictor = DetectionPredictor(overrides=dict(model="yolo11n.pt"))
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>>> results = predictor.predict("path/to/image.jpg")
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>>> processed_results = predictor.postprocess(preds, img, orig_imgs)
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"""
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save_feats = getattr(self, "_feats", None) is not None
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preds = nms.non_max_suppression(
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preds,
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self.args.conf,
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self.args.iou,
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self.args.classes,
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self.args.agnostic_nms,
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max_det=self.args.max_det,
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nc=0 if self.args.task == "detect" else len(self.model.names),
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end2end=getattr(self.model, "end2end", False),
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rotated=self.args.task == "obb",
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return_idxs=save_feats,
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)
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
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if save_feats:
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obj_feats = self.get_obj_feats(self._feats, preds[1])
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preds = preds[0]
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results = self.construct_results(preds, img, orig_imgs, **kwargs)
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if save_feats:
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for r, f in zip(results, obj_feats):
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r.feats = f # add object features to results
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return results
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def get_obj_feats(self, feat_maps, idxs):
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"""Extract object features from the feature maps."""
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import torch
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s = min(x.shape[1] for x in feat_maps) # find shortest vector length
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obj_feats = torch.cat(
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[x.permute(0, 2, 3, 1).reshape(x.shape[0], -1, s, x.shape[1] // s).mean(dim=-1) for x in feat_maps], dim=1
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) # mean reduce all vectors to same length
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return [feats[idx] if idx.shape[0] else [] for feats, idx in zip(obj_feats, idxs)] # for each img in batch
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def construct_results(self, preds, img, orig_imgs):
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"""
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Construct a list of Results objects from model predictions.
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Args:
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preds (list[torch.Tensor]): List of predicted bounding boxes and scores for each image.
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img (torch.Tensor): Batch of preprocessed images used for inference.
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orig_imgs (list[np.ndarray]): List of original images before preprocessing.
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Returns:
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(list[Results]): List of Results objects containing detection information for each image.
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"""
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return [
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self.construct_result(pred, img, orig_img, img_path)
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for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
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]
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def construct_result(self, pred, img, orig_img, img_path):
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"""
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Construct a single Results object from one image prediction.
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Args:
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pred (torch.Tensor): Predicted boxes and scores with shape (N, 6) where N is the number of detections.
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img (torch.Tensor): Preprocessed image tensor used for inference.
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orig_img (np.ndarray): Original image before preprocessing.
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img_path (str): Path to the original image file.
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Returns:
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(Results): Results object containing the original image, image path, class names, and scaled bounding boxes.
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"""
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6])
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