Bounding Box to OBB conversion (#7572)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>

ultralytics/data/converter.py

@@ -474,3 +474,64 @@ def merge_multi_segment(segments):
                     nidx = abs(idx[1] - idx[0])
                     s.append(segments[i][nidx:])
     return s
+
+
+def yolo_bbox2segment(im_dir, save_dir=None, sam_model="sam_b.pt"):
+    """
+    Converts existing object detection dataset (bounding boxes) to segmentation dataset or oriented bounding box (OBB)
+    in YOLO format. Generates segmentation data using SAM auto-annotator as needed.
+
+    Args:
+        im_dir (str | Path): Path to image directory to convert.
+        save_dir (str | Path): Path to save the generated labels, labels will be saved
+            into `labels-segment` in the same directory level of `im_dir` if save_dir is None. Default: None.
+        sam_model (str): Segmentation model to use for intermediate segmentation data; optional.
+
+    Notes:
+        The input directory structure assumed for dataset:
+            - im_dir
+                ├─ 001.jpg
+                ├─ ..
+                ├─ NNN.jpg
+            - labels
+                ├─ 001.txt
+                ├─ ..
+                ├─ NNN.txt
+    """
+    from ultralytics.data import YOLODataset
+    from ultralytics.utils.ops import xywh2xyxy
+    from ultralytics.utils import LOGGER
+    from ultralytics import SAM
+    from tqdm import tqdm
+
+    # NOTE: add placeholder to pass class index check
+    dataset = YOLODataset(im_dir, data=dict(names=list(range(1000))))
+    if len(dataset.labels[0]["segments"]) > 0:  # if it's segment data
+        LOGGER.info("Segmentation labels detected, no need to generate new ones!")
+        return
+
+    LOGGER.info("Detection labels detected, generating segment labels by SAM model!")
+    sam_model = SAM(sam_model)
+    for l in tqdm(dataset.labels, total=len(dataset.labels), desc="Generating segment labels"):
+        h, w = l["shape"]
+        boxes = l["bboxes"]
+        boxes[:, [0, 2]] *= w
+        boxes[:, [1, 3]] *= h
+        im = cv2.imread(l["im_file"])
+        sam_results = sam_model(im, bboxes=xywh2xyxy(boxes), verbose=False, save=False)
+        l["segments"] = sam_results[0].masks.xyn
+
+    save_dir = Path(save_dir) if save_dir else Path(im_dir).parent / "labels-segment"
+    save_dir.mkdir(parents=True, exist_ok=True)
+    for l in dataset.labels:
+        texts = []
+        lb_name = Path(l["im_file"]).with_suffix(".txt").name
+        txt_file = save_dir / lb_name
+        cls = l["cls"]
+        for i, s in enumerate(l["segments"]):
+            line = (int(cls[i]), *s.reshape(-1))
+            texts.append(("%g " * len(line)).rstrip() % line)
+        if texts:
+            with open(txt_file, "a") as f:
+                f.writelines(text + "\n" for text in texts)
+    LOGGER.info(f"Generated segment labels saved in {save_dir}")