128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
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# cvat_xml_to_yolo_obb.py
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# 仅在有标注时生成 .txt 文件
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import xml.etree.ElementTree as ET
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import numpy as np
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from pathlib import Path
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def rotate_box(x_center, y_center, w, h, angle_deg):
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"""
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将旋转框转为 4 个角点坐标（未归一化）
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"""
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angle_rad = np.radians(angle_deg)
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# 四个角点相对于中心的偏移
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corners = np.array([
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[-w/2, -h/2],
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[ w/2, -h/2],
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[ w/2, h/2],
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[-w/2, h/2]
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])
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# 旋转矩阵（顺时针）
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cos_a, sin_a = np.cos(angle_rad), np.sin(angle_rad)
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rotation_matrix = np.array([[cos_a, -sin_a],
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[sin_a, cos_a]])
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# 旋转并平移
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rotated_corners = np.dot(corners, rotation_matrix.T) + np.array([x_center, y_center])
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return rotated_corners # 返回 (4, 2) 数组
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def cvat_xml_to_yolo_obb(cvat_xml_path, output_dir, class_name_to_id=None):
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"""
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将 CVAT annotations.xml 转为 YOLO-OBB 格式
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- 仅在有有效标注时创建 .txt 文件
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"""
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if class_name_to_id is None:
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class_name_to_id = {"clamp": 0} # ✅ 请根据你的实际类别修改
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tree = ET.parse(cvat_xml_path)
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root = tree.getroot()
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# 创建 labels 输出目录
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labels_dir = Path(output_dir) / "labels"
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labels_dir.mkdir(parents=True, exist_ok=True)
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# 统计信息
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processed_images = 0
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saved_files = 0
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skipped_classes = 0
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for image_elem in root.findall("image"):
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image_name = image_elem.get("name")
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img_w = float(image_elem.get("width"))
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img_h = float(image_elem.get("height"))
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label_file = (labels_dir / Path(image_name).stem).with_suffix(".txt")
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boxes = image_elem.findall("box")
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valid_annotations = []
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for box in boxes:
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label = box.get("label")
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if label not in class_name_to_id:
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print(f"⚠️ 跳过未知类别: {label} (图片: {image_name})")
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skipped_classes += 1
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continue
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class_id = class_name_to_id[label]
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xtl = float(box.get("xtl"))
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ytl = float(box.get("ytl"))
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xbr = float(box.get("xbr"))
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ybr = float(box.get("ybr"))
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# 计算中心点和宽高
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x_center = (xtl + xbr) / 2
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y_center = (ytl + ybr) / 2
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w = xbr - xtl
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h = ybr - ytl
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# 获取旋转角度
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angle = float(box.get("rotation", 0.0))
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# 计算 4 个角点
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corners = rotate_box(x_center, y_center, w, h, angle)
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# 归一化到 [0,1]
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corners[:, 0] /= img_w
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corners[:, 1] /= img_h
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# 展平并生成行
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points = corners.flatten()
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line = str(class_id) + " " + " ".join(f"{coord:.6f}" for coord in points)
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valid_annotations.append(line)
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# ✅ 只有存在有效标注时才写入文件
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if valid_annotations:
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with open(label_file, 'w') as f:
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f.write("\n".join(valid_annotations) + "\n")
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saved_files += 1
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print(f"✅ 已生成: {label_file}")
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# else: # 无标注，不创建文件
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# print(f"🟡 无标注，跳过: {image_name}")
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processed_images += 1
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print(f"\n🎉 转换完成！")
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print(f"📊 处理图片: {processed_images}")
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print(f"✅ 生成标签: {saved_files}")
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if skipped_classes:
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print(f"⚠️ 跳过类别: {skipped_classes} 个标注")
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# ==================== 使用示例 ====================
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if __name__ == "__main__":
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# ✅ 请修改以下路径和类别
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CVAT_XML_PATH = "annotations.xml" # 你的 annotations.xml 文件
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OUTPUT_DIR = "yolo_obb_dataset" # 输出目录
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CLASS_MAPPING = {
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"clamp": 0, # 请根据你的实际类别修改
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# "other_class": 1,
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}
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# 执行转换
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cvat_xml_to_yolo_obb(
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cvat_xml_path=CVAT_XML_PATH,
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output_dir=OUTPUT_DIR,
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class_name_to_id=CLASS_MAPPING
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)
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