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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
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308
ultralytics/data/converter.py
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@ -20,10 +20,98 @@ def coco91_to_coco80_class():
corresponding 91-index class ID.
"""
return [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, None, 24, 25, None,
None, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, None, 60, None, None, 61, None, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
None, 73, 74, 75, 76, 77, 78, 79, None]
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
None,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
None,
24,
25,
None,
None,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
None,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
None,
60,
None,
None,
61,
None,
62,
63,
64,
65,
66,
67,
68,
69,
70,
71,
72,
None,
73,
74,
75,
76,
77,
78,
79,
None,
]
def coco80_to_coco91_class():
@ -42,16 +130,96 @@ def coco80_to_coco91_class():
```
"""
return [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
27,
28,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
67,
70,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
84,
85,
86,
87,
88,
89,
90,
]
def convert_coco(labels_dir='../coco/annotations/',
save_dir='coco_converted/',
use_segments=False,
use_keypoints=False,
cls91to80=True):
def convert_coco(
labels_dir="../coco/annotations/",
save_dir="coco_converted/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
):
"""
Converts COCO dataset annotations to a YOLO annotation format suitable for training YOLO models.
@ -75,76 +243,78 @@ def convert_coco(labels_dir='../coco/annotations/',
# Create dataset directory
save_dir = increment_path(save_dir) # increment if save directory already exists
for p in save_dir / 'labels', save_dir / 'images':
for p in save_dir / "labels", save_dir / "images":
p.mkdir(parents=True, exist_ok=True) # make dir
# Convert classes
coco80 = coco91_to_coco80_class()
# 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
for json_file in sorted(Path(labels_dir).resolve().glob("*.json")):
fn = Path(save_dir) / "labels" / json_file.stem.replace("instances_", "") # folder name
fn.mkdir(parents=True, exist_ok=True)
with open(json_file) as f:
data = json.load(f)
# Create image dict
images = {f'{x["id"]:d}': x for x in data['images']}
images = {f'{x["id"]:d}': x for x in data["images"]}
# Create image-annotations dict
imgToAnns = defaultdict(list)
for ann in data['annotations']:
imgToAnns[ann['image_id']].append(ann)
for ann in data["annotations"]:
imgToAnns[ann["image_id"]].append(ann)
# 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']
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"]
bboxes = []
segments = []
keypoints = []
for ann in anns:
if ann['iscrowd']:
if ann["iscrowd"]:
continue
# The COCO box format is [top left x, top left y, width, height]
box = np.array(ann['bbox'], dtype=np.float64)
box = np.array(ann["bbox"], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
continue
cls = coco80[ann['category_id'] - 1] if cls91to80 else ann['category_id'] - 1 # class
cls = coco80[ann["category_id"] - 1] if cls91to80 else ann["category_id"] - 1 # class
box = [cls] + box.tolist()
if box not in bboxes:
bboxes.append(box)
if use_segments and ann.get('segmentation') is not None:
if len(ann['segmentation']) == 0:
if use_segments and ann.get("segmentation") is not None:
if len(ann["segmentation"]) == 0:
segments.append([])
continue
elif len(ann['segmentation']) > 1:
s = merge_multi_segment(ann['segmentation'])
elif len(ann["segmentation"]) > 1:
s = merge_multi_segment(ann["segmentation"])
s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
else:
s = [j for i in ann['segmentation'] for j in i] # all segments concatenated
s = [j for i in ann["segmentation"] for j in i] # all segments concatenated
s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
s = [cls] + s
segments.append(s)
if use_keypoints and ann.get('keypoints') is not None:
keypoints.append(box + (np.array(ann['keypoints']).reshape(-1, 3) /
np.array([w, h, 1])).reshape(-1).tolist())
if use_keypoints and ann.get("keypoints") is not None:
keypoints.append(
box + (np.array(ann["keypoints"]).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
)
# Write
with open((fn / f).with_suffix('.txt'), 'a') as file:
with open((fn / f).with_suffix(".txt"), "a") as file:
for i in range(len(bboxes)):
if use_keypoints:
line = *(keypoints[i]), # cls, box, keypoints
line = (*(keypoints[i]),) # cls, box, keypoints
else:
line = *(segments[i]
if use_segments and len(segments[i]) > 0 else bboxes[i]), # cls, box or segments
file.write(('%g ' * len(line)).rstrip() % line + '\n')
line = (
*(segments[i] if use_segments and len(segments[i]) > 0 else bboxes[i]),
) # 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()}')
LOGGER.info(f"COCO data converted successfully.\nResults saved to {save_dir.resolve()}")
def convert_dota_to_yolo_obb(dota_root_path: str):
@ -184,31 +354,32 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
# Class names to indices mapping
class_mapping = {
'plane': 0,
'ship': 1,
'storage-tank': 2,
'baseball-diamond': 3,
'tennis-court': 4,
'basketball-court': 5,
'ground-track-field': 6,
'harbor': 7,
'bridge': 8,
'large-vehicle': 9,
'small-vehicle': 10,
'helicopter': 11,
'roundabout': 12,
'soccer-ball-field': 13,
'swimming-pool': 14,
'container-crane': 15,
'airport': 16,
'helipad': 17}
"plane": 0,
"ship": 1,
"storage-tank": 2,
"baseball-diamond": 3,
"tennis-court": 4,
"basketball-court": 5,
"ground-track-field": 6,
"harbor": 7,
"bridge": 8,
"large-vehicle": 9,
"small-vehicle": 10,
"helicopter": 11,
"roundabout": 12,
"soccer-ball-field": 13,
"swimming-pool": 14,
"container-crane": 15,
"airport": 16,
"helipad": 17,
}
def convert_label(image_name, image_width, image_height, orig_label_dir, save_dir):
"""Converts a single image's DOTA annotation to YOLO OBB format and saves it to a specified directory."""
orig_label_path = orig_label_dir / f'{image_name}.txt'
save_path = save_dir / f'{image_name}.txt'
orig_label_path = orig_label_dir / f"{image_name}.txt"
save_path = save_dir / f"{image_name}.txt"
with orig_label_path.open('r') as f, save_path.open('w') as g:
with orig_label_path.open("r") as f, save_path.open("w") as g:
lines = f.readlines()
for line in lines:
parts = line.strip().split()
@ -218,20 +389,21 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
class_idx = class_mapping[class_name]
coords = [float(p) for p in parts[:8]]
normalized_coords = [
coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)]
formatted_coords = ['{:.6g}'.format(coord) for coord in normalized_coords]
coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)
]
formatted_coords = ["{:.6g}".format(coord) for coord in normalized_coords]
g.write(f"{class_idx} {' '.join(formatted_coords)}\n")
for phase in ['train', 'val']:
image_dir = dota_root_path / 'images' / phase
orig_label_dir = dota_root_path / 'labels' / f'{phase}_original'
save_dir = dota_root_path / 'labels' / phase
for phase in ["train", "val"]:
image_dir = dota_root_path / "images" / phase
orig_label_dir = dota_root_path / "labels" / f"{phase}_original"
save_dir = dota_root_path / "labels" / phase
save_dir.mkdir(parents=True, exist_ok=True)
image_paths = list(image_dir.iterdir())
for image_path in TQDM(image_paths, desc=f'Processing {phase} images'):
if image_path.suffix != '.png':
for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):
if image_path.suffix != ".png":
continue
image_name_without_ext = image_path.stem
img = cv2.imread(str(image_path))
@ -293,7 +465,7 @@ def merge_multi_segment(segments):
s.append(segments[i])
else:
idx = [0, idx[1] - idx[0]]
s.append(segments[i][idx[0]:idx[1] + 1])
s.append(segments[i][idx[0] : idx[1] + 1])
else:
for i in range(len(idx_list) - 1, -1, -1):