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ultralytics 8.1.39 add YOLO-World training (#9268)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-03-31 22:30:17 +08:00 • committed by GitHub
parent 18036908d4
commit e9187c1296
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34 changed files with 2166 additions and 100 deletions
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26
ultralytics/data/converter.py
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@ -219,6 +219,7 @@ def convert_coco(
use_segments=False,
use_keypoints=False,
cls91to80=True,
lvis=False,
):
"""
Converts COCO dataset annotations to a YOLO annotation format suitable for training YOLO models.
@ -229,12 +230,14 @@ def convert_coco(
use_segments (bool, optional): Whether to include segmentation masks in the output.
use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
lvis (bool, optional): Whether to convert data in lvis dataset way.
Example:
```python
from ultralytics.data.converter import convert_coco
convert_coco('../datasets/coco/annotations/', use_segments=True, use_keypoints=False, cls91to80=True)
convert_coco('../datasets/lvis/annotations/', use_segments=True, use_keypoints=False, cls91to80=False, lvis=True)
```
Output:
@ -251,8 +254,14 @@ def convert_coco(
# Import json
for json_file in sorted(Path(labels_dir).resolve().glob("*.json")):
fn = Path(save_dir) / "labels" / json_file.stem.replace("instances_", "") # folder name
lname = "" if lvis else json_file.stem.replace("instances_", "")
fn = Path(save_dir) / "labels" / lname # folder name
fn.mkdir(parents=True, exist_ok=True)
if lvis:
# NOTE: create folders for both train and val in advance,
# since LVIS val set contains images from COCO 2017 train in addition to the COCO 2017 val split.
(fn / "train2017").mkdir(parents=True, exist_ok=True)
(fn / "val2017").mkdir(parents=True, exist_ok=True)
with open(json_file) as f:
data = json.load(f)
@ -263,16 +272,20 @@ def convert_coco(
for ann in data["annotations"]:
imgToAnns[ann["image_id"]].append(ann)
image_txt = []
# Write labels file
for img_id, anns in TQDM(imgToAnns.items(), desc=f"Annotations {json_file}"):
img = images[f"{img_id:d}"]
h, w, f = img["height"], img["width"], img["file_name"]
h, w = img["height"], img["width"]
f = str(Path(img["coco_url"]).relative_to("http://images.cocodataset.org")) if lvis else img["file_name"]
if lvis:
image_txt.append(str(Path("./images") / f))
bboxes = []
segments = []
keypoints = []
for ann in anns:
if ann["iscrowd"]:
if ann.get("iscrowd", False):
continue
# The COCO box format is [top left x, top left y, width, height]
box = np.array(ann["bbox"], dtype=np.float64)
@ -314,7 +327,12 @@ def convert_coco(
) # cls, box or segments
file.write(("%g " * len(line)).rstrip() % line + "\n")
LOGGER.info(f"COCO data converted successfully.\nResults saved to {save_dir.resolve()}")
if lvis:
with open((Path(save_dir) / json_file.name.replace("lvis_v1_", "").replace(".json", ".txt")), "a") as f:
for l in image_txt:
f.write(f"{l}\n")
LOGGER.info(f"{'LVIS' if lvis else 'COCO'} data converted successfully.\nResults saved to {save_dir.resolve()}")
def convert_dota_to_yolo_obb(dota_root_path: str):