83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
# convert_cvat_to_yolo_keypoints.py
|
||
import xml.etree.ElementTree as ET
|
||
import os
|
||
|
||
# =================== 配置 ===================
|
||
xml_file = 'annotations.xml' # 你的 CVAT XML 文件路径
|
||
images_dir = 'images' # 图像文件夹（用于读取宽高）
|
||
output_dir = 'labels_keypoints' # 输出 YOLO 标签目录
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
# 类别映射（根据你的 XML 中的 label name）
|
||
class_mapping = {
|
||
'clamp1': 0,
|
||
'clamp0': 1,
|
||
'kongliao': 2,
|
||
'duiliao': 3
|
||
}
|
||
# ============================================
|
||
|
||
def parse_points(points_str):
|
||
"""解析 CVAT 的 points 字符串，返回 [(x, y), ...]"""
|
||
return [(float(p.split(',')[0]), float(p.split(',')[1])) for p in points_str.split(';')]
|
||
|
||
def normalize_bbox_and_kpts(image_w, image_h, bbox, keypoints):
|
||
"""归一化 bbox 和关键点"""
|
||
cx, cy, w, h = bbox
|
||
cx_n, cy_n, w_n, h_n = cx/image_w, cy/image_h, w/image_w, h/image_h
|
||
kpts_n = []
|
||
for x, y in keypoints:
|
||
kpts_n.append(x / image_w)
|
||
kpts_n.append(y / image_h)
|
||
kpts_n.append(2) # v=2: visible
|
||
return (cx_n, cy_n, w_n, h_n), kpts_n
|
||
|
||
def points_to_bbox(points):
|
||
"""从点集生成最小外接矩形 (x_center, y_center, width, height)"""
|
||
xs = [p[0] for p in points]
|
||
ys = [p[1] for p in points]
|
||
x_min, x_max = min(xs), max(xs)
|
||
y_min, y_max = min(ys), max(ys)
|
||
cx = (x_min + x_max) / 2
|
||
cy = (y_min + y_max) / 2
|
||
w = x_max - x_min
|
||
h = y_max - y_min
|
||
return cx, cy, w, h
|
||
|
||
# 解析 XML
|
||
tree = ET.parse(xml_file)
|
||
root = tree.getroot()
|
||
|
||
for image_elem in root.findall('image'):
|
||
image_name = image_elem.get('name')
|
||
image_w = int(image_elem.get('width'))
|
||
image_h = int(image_elem.get('height'))
|
||
label_file = os.path.splitext(image_name)[0] + '.txt'
|
||
label_path = os.path.join(output_dir, label_file)
|
||
|
||
with open(label_path, 'w') as f_out:
|
||
# 查找 points 标注
|
||
points_elem = image_elem.find("points[@label='clamp1']")
|
||
if points_elem is None:
|
||
print(f"⚠️ 警告：图像 {image_name} 缺少 clamp1 关键点标注")
|
||
continue
|
||
|
||
points_str = points_elem.get('points')
|
||
keypoints = parse_points(points_str)
|
||
|
||
if len(keypoints) != 4:
|
||
print(f"⚠️ 警告：图像 {image_name} 的关键点数量不是 4，实际为 {len(keypoints)}")
|
||
continue
|
||
|
||
# 生成包围框
|
||
bbox = points_to_bbox(keypoints)
|
||
|
||
# 归一化
|
||
(cx_n, cy_n, w_n, h_n), kpts_n = normalize_bbox_and_kpts(image_w, image_h, bbox, keypoints)
|
||
|
||
# 写入 YOLO 格式
|
||
line = [str(class_mapping['clamp1']), str(cx_n), str(cy_n), str(w_n), str(h_n)] + [str(k) for k in kpts_n]
|
||
f_out.write(' '.join(line) + '\n')
|
||
|
||
print("✅ 所有标注已成功转换为 YOLO 关键点格式！")
|
||
print(f"📌 输出目录: {output_dir}") |