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ultralytics/models/yolo/obb/__init__.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from .predict import OBBPredictor
+from .train import OBBTrainer
+from .val import OBBValidator
+
+__all__ = "OBBPredictor", "OBBTrainer", "OBBValidator"

ultralytics/models/yolo/obb/predict.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class OBBPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.obb import OBBPredictor
+
+        args = dict(model='yolov8n-obb.pt', source=ASSETS)
+        predictor = OBBPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes OBBPredictor with optional model and data configuration overrides."""
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "obb"
+
+    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,
+            nc=len(self.model.names),
+            classes=self.args.classes,
+            rotated=True,
+        )
+
+        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 pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]):
+            rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
+            rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
+            # xywh, r, conf, cls
+            obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
+            results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb))
+        return results

ultralytics/models/yolo/obb/train.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from copy import copy
+
+from ultralytics.models import yolo
+from ultralytics.nn.tasks import OBBModel
+from ultralytics.utils import DEFAULT_CFG, RANK
+
+
+class OBBTrainer(yolo.detect.DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on an Oriented Bounding Box (OBB) model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.obb import OBBTrainer
+
+        args = dict(model='yolov8n-obb.pt', data='dota8.yaml', epochs=3)
+        trainer = OBBTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initialize a OBBTrainer object with given arguments."""
+        if overrides is None:
+            overrides = {}
+        overrides["task"] = "obb"
+        super().__init__(cfg, overrides, _callbacks)
+
+    def get_model(self, cfg=None, weights=None, verbose=True):
+        """Return OBBModel initialized with specified config and weights."""
+        model = OBBModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+
+        return model
+
+    def get_validator(self):
+        """Return an instance of OBBValidator for validation of YOLO model."""
+        self.loss_names = "box_loss", "cls_loss", "dfl_loss"
+        return yolo.obb.OBBValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))

ultralytics/models/yolo/obb/val.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from pathlib import Path
+
+import torch
+
+from ultralytics.models.yolo.detect import DetectionValidator
+from ultralytics.utils import LOGGER, ops
+from ultralytics.utils.metrics import OBBMetrics, batch_probiou
+from ultralytics.utils.plotting import output_to_rotated_target, plot_images
+
+
+class OBBValidator(DetectionValidator):
+    """
+    A class extending the DetectionValidator class for validation based on an Oriented Bounding Box (OBB) model.
+
+    Example:
+        ```python
+        from ultralytics.models.yolo.obb import OBBValidator
+
+        args = dict(model='yolov8n-obb.pt', data='dota8.yaml')
+        validator = OBBValidator(args=args)
+        validator(model=args['model'])
+        ```
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """Initialize OBBValidator and set task to 'obb', metrics to OBBMetrics."""
+        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
+        self.args.task = "obb"
+        self.metrics = OBBMetrics(save_dir=self.save_dir, plot=True, on_plot=self.on_plot)
+
+    def init_metrics(self, model):
+        """Initialize evaluation metrics for YOLO."""
+        super().init_metrics(model)
+        val = self.data.get(self.args.split, "")  # validation path
+        self.is_dota = isinstance(val, str) and "DOTA" in val  # is COCO
+
+    def postprocess(self, preds):
+        """Apply Non-maximum suppression to prediction outputs."""
+        return ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            labels=self.lb,
+            nc=self.nc,
+            multi_label=True,
+            agnostic=self.args.single_cls,
+            max_det=self.args.max_det,
+            rotated=True,
+        )
+
+    def _process_batch(self, detections, gt_bboxes, gt_cls):
+        """
+        Return correct prediction matrix.
+
+        Args:
+            detections (torch.Tensor): Tensor of shape [N, 7] representing detections.
+                Each detection is of the format: x1, y1, x2, y2, conf, class, angle.
+            gt_bboxes (torch.Tensor): Tensor of shape [M, 5] representing rotated boxes.
+                Each box is of the format: x1, y1, x2, y2, angle.
+            labels (torch.Tensor): Tensor of shape [M] representing labels.
+
+        Returns:
+            (torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
+        """
+        iou = batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))
+        return self.match_predictions(detections[:, 5], gt_cls, iou)
+
+    def _prepare_batch(self, si, batch):
+        """Prepares and returns a batch for OBB validation."""
+        idx = batch["batch_idx"] == si
+        cls = batch["cls"][idx].squeeze(-1)
+        bbox = batch["bboxes"][idx]
+        ori_shape = batch["ori_shape"][si]
+        imgsz = batch["img"].shape[2:]
+        ratio_pad = batch["ratio_pad"][si]
+        if len(cls):
+            bbox[..., :4].mul_(torch.tensor(imgsz, device=self.device)[[1, 0, 1, 0]])  # target boxes
+            ops.scale_boxes(imgsz, bbox, ori_shape, ratio_pad=ratio_pad, xywh=True)  # native-space labels
+        return {"cls": cls, "bbox": bbox, "ori_shape": ori_shape, "imgsz": imgsz, "ratio_pad": ratio_pad}
+
+    def _prepare_pred(self, pred, pbatch):
+        """Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""
+        predn = pred.clone()
+        ops.scale_boxes(
+            pbatch["imgsz"], predn[:, :4], pbatch["ori_shape"], ratio_pad=pbatch["ratio_pad"], xywh=True
+        )  # native-space pred
+        return predn
+
+    def plot_predictions(self, batch, preds, ni):
+        """Plots predicted bounding boxes on input images and saves the result."""
+        plot_images(
+            batch["img"],
+            *output_to_rotated_target(preds, max_det=self.args.max_det),
+            paths=batch["im_file"],
+            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
+            names=self.names,
+            on_plot=self.on_plot,
+        )  # pred
+
+    def pred_to_json(self, predn, filename):
+        """Serialize YOLO predictions to COCO json format."""
+        stem = Path(filename).stem
+        image_id = int(stem) if stem.isnumeric() else stem
+        rbox = torch.cat([predn[:, :4], predn[:, -1:]], dim=-1)
+        poly = ops.xywhr2xyxyxyxy(rbox).view(-1, 8)
+        for i, (r, b) in enumerate(zip(rbox.tolist(), poly.tolist())):
+            self.jdict.append(
+                {
+                    "image_id": image_id,
+                    "category_id": self.class_map[int(predn[i, 5].item())],
+                    "score": round(predn[i, 4].item(), 5),
+                    "rbox": [round(x, 3) for x in r],
+                    "poly": [round(x, 3) for x in b],
+                }
+            )
+
+    def save_one_txt(self, predn, save_conf, shape, file):
+        """Save YOLO detections to a txt file in normalized coordinates in a specific format."""
+        gn = torch.tensor(shape)[[1, 0]]  # normalization gain whwh
+        for *xywh, conf, cls, angle in predn.tolist():
+            xywha = torch.tensor([*xywh, angle]).view(1, 5)
+            xyxyxyxy = (ops.xywhr2xyxyxyxy(xywha) / gn).view(-1).tolist()  # normalized xywh
+            line = (cls, *xyxyxyxy, conf) if save_conf else (cls, *xyxyxyxy)  # label format
+            with open(file, "a") as f:
+                f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+    def eval_json(self, stats):
+        """Evaluates YOLO output in JSON format and returns performance statistics."""
+        if self.args.save_json and self.is_dota and len(self.jdict):
+            import json
+            import re
+            from collections import defaultdict
+
+            pred_json = self.save_dir / "predictions.json"  # predictions
+            pred_txt = self.save_dir / "predictions_txt"  # predictions
+            pred_txt.mkdir(parents=True, exist_ok=True)
+            data = json.load(open(pred_json))
+            # Save split results
+            LOGGER.info(f"Saving predictions with DOTA format to {pred_txt}...")
+            for d in data:
+                image_id = d["image_id"]
+                score = d["score"]
+                classname = self.names[d["category_id"]].replace(" ", "-")
+                p = d["poly"]
+
+                with open(f'{pred_txt / f"Task1_{classname}"}.txt', "a") as f:
+                    f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
+            # Save merged results, this could result slightly lower map than using official merging script,
+            # because of the probiou calculation.
+            pred_merged_txt = self.save_dir / "predictions_merged_txt"  # predictions
+            pred_merged_txt.mkdir(parents=True, exist_ok=True)
+            merged_results = defaultdict(list)
+            LOGGER.info(f"Saving merged predictions with DOTA format to {pred_merged_txt}...")
+            for d in data:
+                image_id = d["image_id"].split("__")[0]
+                pattern = re.compile(r"\d+___\d+")
+                x, y = (int(c) for c in re.findall(pattern, d["image_id"])[0].split("___"))
+                bbox, score, cls = d["rbox"], d["score"], d["category_id"]
+                bbox[0] += x
+                bbox[1] += y
+                bbox.extend([score, cls])
+                merged_results[image_id].append(bbox)
+            for image_id, bbox in merged_results.items():
+                bbox = torch.tensor(bbox)
+                max_wh = torch.max(bbox[:, :2]).item() * 2
+                c = bbox[:, 6:7] * max_wh  # classes
+                scores = bbox[:, 5]  # scores
+                b = bbox[:, :5].clone()
+                b[:, :2] += c
+                # 0.3 could get results close to the ones from official merging script, even slightly better.
+                i = ops.nms_rotated(b, scores, 0.3)
+                bbox = bbox[i]
+
+                b = ops.xywhr2xyxyxyxy(bbox[:, :5]).view(-1, 8)
+                for x in torch.cat([b, bbox[:, 5:7]], dim=-1).tolist():
+                    classname = self.names[int(x[-1])].replace(" ", "-")
+                    p = [round(i, 3) for i in x[:-2]]  # poly
+                    score = round(x[-2], 3)
+
+                    with open(f'{pred_merged_txt / f"Task1_{classname}"}.txt', "a") as f:
+                        f.writelines(f"{image_id} {score} {p[0]} {p[1]} {p[2]} {p[3]} {p[4]} {p[5]} {p[6]} {p[7]}\n")
+
+        return stats