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add yolo v10 and modify pipeline

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王庆刚
2025-03-28 13:19:54 +08:00
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ultralytics/data/converter.py
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@ -1,31 +1,120 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import json
import shutil
from collections import defaultdict
from pathlib import Path
import cv2
import numpy as np
from ultralytics.utils import TQDM
from ultralytics.utils import LOGGER, TQDM
from ultralytics.utils.files import increment_path
def coco91_to_coco80_class():
"""Converts 91-index COCO class IDs to 80-index COCO class IDs.
"""
Converts 91-index COCO class IDs to 80-index COCO class IDs.
Returns:
(list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
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(): #
def coco80_to_coco91_class():
"""
Converts 80-index (val2014) to 91-index (paper).
For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
@ -41,16 +130,102 @@ 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/', use_segments=False, use_keypoints=False, cls91to80=True):
"""Converts COCO dataset annotations to a format suitable for training YOLOv5 models.
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.
Args:
labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
save_dir (str, optional): Path to directory to save results to.
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.
@ -67,78 +242,79 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
"""
# Create dataset directory
save_dir = Path('yolo_labels')
if save_dir.exists():
shutil.rmtree(save_dir) # delete dir
for p in save_dir / 'labels', save_dir / 'images':
save_dir = increment_path(save_dir) # increment if save directory already exists
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:
segments.append([])
continue
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 = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
s = [cls] + s
if s not in segments:
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"])
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 = (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()}")
def convert_dota_to_yolo_obb(dota_root_path: str):
@ -160,48 +336,52 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
Notes:
The directory structure assumed for the DOTA dataset:
- DOTA
- images
- train
- val
- labels
- train_original
- val_original
After the function execution, the new labels will be saved in:
- DOTA
- labels
- train
- val
├─ images
│ ├─ train
│ └─ val
└─ labels
├─ train_original
└─ val_original
After execution, the function will organize the labels into:
- DOTA
└─ labels
├─ train
└─ val
"""
dota_root_path = Path(dota_root_path)
# 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):
orig_label_path = orig_label_dir / f'{image_name}.txt'
save_path = save_dir / f'{image_name}.txt'
"""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"
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()
@ -211,20 +391,21 @@ 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))
@ -237,8 +418,8 @@ def min_index(arr1, arr2):
Find a pair of indexes with the shortest distance between two arrays of 2D points.
Args:
arr1 (np.array): A NumPy array of shape (N, 2) representing N 2D points.
arr2 (np.array): A NumPy array of shape (M, 2) representing M 2D points.
arr1 (np.ndarray): A NumPy array of shape (N, 2) representing N 2D points.
arr2 (np.ndarray): A NumPy array of shape (M, 2) representing M 2D points.
Returns:
(tuple): A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively.
@ -263,31 +444,30 @@ def merge_multi_segment(segments):
segments = [np.array(i).reshape(-1, 2) for i in segments]
idx_list = [[] for _ in range(len(segments))]
# record the indexes with min distance between each segment
# Record the indexes with min distance between each segment
for i in range(1, len(segments)):
idx1, idx2 = min_index(segments[i - 1], segments[i])
idx_list[i - 1].append(idx1)
idx_list[i].append(idx2)
# use two round to connect all the segments
# Use two round to connect all the segments
for k in range(2):
# forward connection
# Forward connection
if k == 0:
for i, idx in enumerate(idx_list):
# middle segments have two indexes
# reverse the index of middle segments
# Middle segments have two indexes, reverse the index of middle segments
if len(idx) == 2 and idx[0] > idx[1]:
idx = idx[::-1]
segments[i] = segments[i][::-1, :]
segments[i] = np.roll(segments[i], -idx[0], axis=0)
segments[i] = np.concatenate([segments[i], segments[i][:1]])
# deal with the first segment and the last one
# Deal with the first segment and the last one
if i in [0, len(idx_list) - 1]:
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):
@ -296,3 +476,67 @@ 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"]
if len(boxes) == 0: # skip empty labels
continue
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}")