增加obb

angle_base_obb/test.py

@@ -0,0 +1,43 @@
+from ultralytics import YOLO
+import cv2
+
+# 1. 加载模型
+model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb2/weights/best.pt')
+
+# 2. 读取图像
+img_path = r"/home/hx/桌面/image/images/test/1.jpg"
+img = cv2.imread(img_path)
+
+# ✅ 检查图像是否加载成功
+if img is None:
+    print(f"❌ 错误：无法读取图像！请检查路径：{img_path}")
+    print("💡 提示：可能是文件不存在、路径错误或图像损坏")
+    exit(1)
+
+# 3. 预测（使用 OBB 模式！）
+results = model(
+    img,
+    save=False,
+    imgsz=1280,      # 必须和训练时一致（你训练日志中是 1280）
+    conf=0.25,
+    mode='obb'       # 👈👈👈 关键！必须加这一行才能启用旋转框模式
+)
+
+# 4. 在原图上绘制检测结果
+result = results[0]
+annotated_img = result.plot()  # OBB 模式下会自动画旋转框
+
+# 5. 显示图像
+cv2.imshow("YOLO OBB Prediction", annotated_img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+# ✅ 可选：打印检测结果
+if result.boxes is None or len(result.boxes) == 0:
+    print("❌ No objects detected.")
+else:
+    print(f"✅ Detected {len(result.boxes)} object(s):")
+    for box in result.boxes:
+        cls = int(box.cls.cpu().numpy()[0])
+        conf = box.conf.cpu().numpy()[0]
+        print(f"  Class: {cls}, Confidence: {conf:.3f}")

test_cuda.py

@@ -0,0 +1,100 @@
+# test_cuda.py
+"""
+CUDA 环境全面检测脚本
+Author: Qwen
+功能：检测 NVIDIA 驱动、CUDA、PyTorch 与 GPU 是否正常工作
+"""
+
+import torch
+import sys
+import os
+
+
+def print_separator():
+    print("\n" + "=" * 60 + "\n")
+
+
+def check_nvidia_smi():
+    print("🔍 正在检查 nvidia-smi（NVIDIA 驱动和 GPU 状态）...")
+    try:
+        result = os.popen("nvidia-smi").read()
+        print(result)
+    except Exception as e:
+        print(f"❌ 执行 nvidia-smi 失败: {e}")
+
+
+def check_torch_cuda():
+    print("🔍 正在检查 PyTorch 与 CUDA 的集成...")
+    print(f"➡️  PyTorch 版本: {torch.__version__}")
+
+    # 检查 CUDA 是否可用
+    is_available = torch.cuda.is_available()
+    print(f"➡️  torch.cuda.is_available(): {is_available}")
+
+    if is_available:
+        print(f"➡️  CUDA 版本 (PyTorch 使用): {torch.version.cuda}")
+        print(f"➡️  GPU 数量: {torch.cuda.device_count()}")
+        print(f"➡️  当前设备: {torch.cuda.current_device()}")
+        print(f"➡️  设备名称: {torch.cuda.get_device_name(0)}")
+
+        # 尝试创建一个 GPU 张量
+        try:
+            x = torch.randn(3, 3).cuda()
+            print("✅ GPU 张量创建成功！CUDA 可用且正常工作。")
+        except Exception as e:
+            print(f"❌ GPU 张量创建失败: {e}")
+    else:
+        print("❌ CUDA 不可用！PyTorch 无法使用 GPU。")
+        print("💡 常见原因：")
+        print("   - 没有安装 NVIDIA 驱动")
+        print("   - PyTorch 安装的是 CPU 版本")
+        print("   - CUDA Toolkit 与 PyTorch 不匹配")
+        print("   - 环境变量 CUDA_VISIBLE_DEVICES 设置错误")
+
+
+def check_environment():
+    print("🔍 检查环境变量...")
+    cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", None)
+    print(f"➡️  CUDA_VISIBLE_DEVICES: {cuda_visible_devices}")
+
+    print(f"➡️  Python 路径: {sys.executable}")
+    print(f"➡️  Python 版本: {sys.version}")
+
+
+def check_torch_install_type():
+    print("🔍 检查 PyTorch 安装类型...")
+    if "cpu" in torch.__version__ or "cpu" in str(torch.version.cuda):
+        print("⚠️  PyTorch 可能是 CPU 版本")
+    else:
+        print("✅ PyTorch 应该是 GPU 版本（含 CUDA 支持）")
+
+
+def main():
+    print("🚀 开始检测 CUDA 与 PyTorch 环境...\n")
+
+    print_separator()
+    check_environment()
+
+    print_separator()
+    check_nvidia_smi()
+
+    print_separator()
+    check_torch_install_type()
+
+    print_separator()
+    check_torch_cuda()
+
+    print_separator()
+    if torch.cuda.is_available():
+        print("🎉 恭喜！CUDA 和 PyTorch 集成正常，可以使用 GPU 训练！")
+    else:
+        print("💔 问题严重：CUDA 不可用，请按以下步骤排查：")
+        print("   1. 运行 `nvidia-smi` 看是否显示 GPU 和驱动版本")
+        print("   2. 如果没有，安装 NVIDIA 驱动")
+        print("   3. 重新安装 PyTorch GPU 版本：")
+        print("      pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121")
+        print("   4. 确保没有设置 CUDA_VISIBLE_DEVICES= 或设为无效值")
+
+
+if __name__ == "__main__":
+    main()

ultralytics_yolo11-main/obb_data.yaml

@@ -0,0 +1,16 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8  ← downloads here (1MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+train: /home/hx/桌面/image/images/train
+val: /home/hx/桌面/image/images/val
+
+nc: 1
+names: ['clamp']
+

ultralytics_yolo11-main/train_obb_main.py

@@ -0,0 +1,18 @@
+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-obb.yaml')
+    results = model.train(
+        data='obb_data.yaml',
+        epochs=1000,
+        imgsz=1280,
+        batch=4,
+        workers=10,
+        device='0',
+        project='runs/train',
+        name='exp_obb',
+        exist_ok=False,
+        optimizer='AdamW',
+        lr0=0.001,
+        patience=20,
+    )

ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-obb.yaml

@@ -2,7 +2,7 @@
 # YOLO11 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/obb
 
 # Parameters
-nc: 80 # number of classes
+nc: 1 # number of classes
 scales: # model compound scaling constants, i.e. 'model=yolo11n-obb.yaml' will call yolo11-obb.yaml with scale 'n'
   # [depth, width, max_channels]
   n: [0.50, 0.25, 1024] # summary: 344 layers, 2695747 parameters, 2695731 gradients, 6.9 GFLOPs

ultralytics_yolo11-main/ultralytics/data/converter.py

@@ -455,24 +455,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
 
     # Class names to indices mapping
     class_mapping = {
-        "plane": 0,
+        "clamp": 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):
@@ -504,7 +487,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
 
         image_paths = list(image_dir.iterdir())
         for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):
-            if image_path.suffix != ".png":
+            if image_path.suffix not in [".png" ,".jpg" , ".jpeg"]:
                 continue
             image_name_without_ext = image_path.stem
             img = cv2.imread(str(image_path))

yolo11_obb/d_data.py

@@ -0,0 +1,94 @@
+import os
+import random
+import shutil
+from tqdm import tqdm
+
+def split_dataset(
+    images_dir: str,  # 图像文件夹路径
+    labels_dir: str,  # 标签文件夹路径
+    output_root: str,  # 输出根目录
+    train_ratio: float = 0.7,  # 训练集比例
+    val_ratio: float = 0.2,  # 验证集比例
+    image_extensions: tuple = (".jpg", ".png", ".jpeg"),  # 支持的图像文件格式
+    seed: int = 42,  # 随机种子
+):
+    """
+    将数据集按照 7:2:1 的比例划分为训练集、验证集和测试集，并将图像和标签文件分别复制到相应的文件夹中。
+
+    参数:
+        images_dir (str): 图像文件夹路径。
+        labels_dir (str): 标签文件夹路径。
+        output_root (str): 输出根目录。
+        train_ratio (float): 训练集比例，默认为 0.7。
+        val_ratio (float): 验证集比例，默认为 0.2。
+        image_extensions (tuple): 支持的图像文件格式，默认为 (".jpg", ".png", ".jpeg")。
+        seed (int): 随机种子，默认为 42。
+    """
+    # 设置随机种子
+    random.seed(seed)
+
+    # 创建输出文件夹
+    output_images_dir = os.path.join(output_root, "images")
+    output_labels_dir = os.path.join(output_root, "labels")
+
+    # 创建训练集、验证集和测试集的图像文件夹
+    for subset in ["train", "val", "test"]:
+        os.makedirs(os.path.join(output_images_dir, subset), exist_ok=True)  # 图像文件夹
+
+    # 创建训练集和验证集的标签文件夹（指定路径）
+    os.makedirs(os.path.join(output_labels_dir, "train_original"), exist_ok=True)  # 训练集标签文件夹
+    os.makedirs(os.path.join(output_labels_dir, "val_original"), exist_ok=True)  # 验证集标签文件夹
+    os.makedirs(os.path.join(output_labels_dir, "test"), exist_ok=True)  # 测试集标签文件夹
+
+    # 获取所有图像文件的文件名列表
+    image_files = [f for f in os.listdir(images_dir) if f.endswith(image_extensions)]
+    random.shuffle(image_files)  # 随机打乱文件列表
+
+    # 计算训练集、验证集和测试集的大小
+    total_files = len(image_files)
+    train_size = int(total_files * train_ratio)  # 训练集大小
+    val_size = int(total_files * val_ratio)  # 验证集大小
+    test_size = total_files - train_size - val_size  # 测试集大小
+
+    # 复制图像和标签文件到相应的子集文件夹中
+    for i, image_file in enumerate(tqdm(image_files, desc="分割数据集中")):
+        base_file_name = os.path.splitext(image_file)[0]  # 获取文件名（不包括扩展名）
+        image_path = os.path.join(images_dir, image_file)  # 图像文件路径
+        label_path = os.path.join(labels_dir, base_file_name + ".txt")  # 标签文件路径
+
+        # 根据索引判断文件应复制到训练集、验证集还是测试集
+        if i < train_size:
+            # 复制到训练集
+            shutil.copy(image_path, os.path.join(output_images_dir, "train", image_file))  # 复制图像
+            shutil.copy(label_path, os.path.join(output_labels_dir, "train_original", base_file_name + ".txt"))  # 复制标签
+        elif i < train_size + val_size:
+            # 复制到验证集
+            shutil.copy(image_path, os.path.join(output_images_dir, "val", image_file))  # 复制图像
+            shutil.copy(label_path, os.path.join(output_labels_dir, "val_original", base_file_name + ".txt"))  # 复制标签
+        else:
+            # 复制到测试集
+            shutil.copy(image_path, os.path.join(output_images_dir, "test", image_file))  # 复制图像
+            shutil.copy(label_path, os.path.join(output_labels_dir, "test", base_file_name + ".txt"))  # 复制标签
+
+    print(f"数据集分割完成！训练集: {train_size} 个样本，验证集: {val_size} 个样本，测试集: {test_size} 个样本。")
+
+
+# 示例调用
+if __name__ == "__main__":
+    # 数据集路径
+    images_dir = r"/home/hx/桌面/image/image"  # 需要读取的所有图像文件夹路径
+    labels_dir = r"/home/hx/桌面/image/2"  # 需要读取的所有图像与之对应的txt标签文件夹路径
+
+    # 输出路径
+    output_root = r"/home/hx/桌面/image" # 保存最终数据集的根目录
+
+    # 调用函数
+    split_dataset(
+        images_dir=images_dir,
+        labels_dir=labels_dir,
+        output_root=output_root,
+        train_ratio=0.7,  # 训练集比例
+        val_ratio=0.2,  # 验证集比例
+        image_extensions=(".jpg", ".png", ".jpeg"),  # 支持的图像文件格式
+        seed=42,  # 随机种子
+    )

yolo11_obb/d_data_converter .py

@@ -0,0 +1,4 @@
+from ultralytics.data.converter import convert_dota_to_yolo_obb
+
+# 输入需要转换TXT标签文件的数据集文件夹名字的路径
+convert_dota_to_yolo_obb(r"/home/hx/桌面/image")

yolo11_obb/data_trans_dota.py

@@ -0,0 +1,225 @@
+import os
+import xml.etree.ElementTree as ET
+import math
+
+"""
+obb转换代码使用：
+    只用修改if __name__ == '__main__': 下面的三条路径即可：
+
+    roxml_path = r"path/已经存放着原始XML文件的文件夹名字"  
+    dotaxml_path = r"path/准备要存放DOTA格式的XML文件的文件夹名字"  
+    			# （小建议：先手动创建该文件夹，然后路径放在这里）
+    out_path = r"path/准备要存放DOTA格式的TXT文件的文件夹名字"  
+    			# （小建议：先手动创建该文件夹，然后路径放在这里）
+
+"""
+
+
+def edit_xml(xml_file, dotaxml_file):
+    """
+    修改 XML 文件，将 bndbox 或 robndbox 转换为四点坐标格式。
+
+    :param  xml_file: 原始 XML 文件路径
+    :param  dotaxml_file: 转换后的 XML 文件保存路径
+    """
+    tree = ET.parse(xml_file)
+    objs = tree.findall('object')
+    for ix, obj in enumerate(objs):
+        x0 = ET.Element("x0")  # 创建节点
+        y0 = ET.Element("y0")
+        x1 = ET.Element("x1")
+        y1 = ET.Element("y1")
+        x2 = ET.Element("x2")
+        y2 = ET.Element("y2")
+        x3 = ET.Element("x3")
+        y3 = ET.Element("y3")
+
+        if obj.find('robndbox') is None:
+            # 处理 bndbox
+            obj_bnd = obj.find('bndbox')
+            obj_xmin = obj_bnd.find('xmin')
+            obj_ymin = obj_bnd.find('ymin')
+            obj_xmax = obj_bnd.find('xmax')
+            obj_ymax = obj_bnd.find('ymax')
+            xmin = float(obj_xmin.text)
+            ymin = float(obj_ymin.text)
+            xmax = float(obj_xmax.text)
+            ymax = float(obj_ymax.text)
+            obj_bnd.remove(obj_xmin)  # 删除节点
+            obj_bnd.remove(obj_ymin)
+            obj_bnd.remove(obj_xmax)
+            obj_bnd.remove(obj_ymax)
+            x0.text = str(xmin)
+            y0.text = str(ymax)
+            x1.text = str(xmax)
+            y1.text = str(ymax)
+            x2.text = str(xmax)
+            y2.text = str(ymin)
+            x3.text = str(xmin)
+            y3.text = str(ymin)
+        else:
+            # 处理 robndbox
+            obj_bnd = obj.find('robndbox')
+            obj_bnd.tag = 'bndbox'  # 修改节点名
+            obj_cx = obj_bnd.find('cx')
+            obj_cy = obj_bnd.find('cy')
+            obj_w = obj_bnd.find('w')
+            obj_h = obj_bnd.find('h')
+            obj_angle = obj_bnd.find('angle')
+            cx = float(obj_cx.text)
+            cy = float(obj_cy.text)
+            w = float(obj_w.text)
+            h = float(obj_h.text)
+            angle = float(obj_angle.text)
+            obj_bnd.remove(obj_cx)  # 删除节点
+            obj_bnd.remove(obj_cy)
+            obj_bnd.remove(obj_w)
+            obj_bnd.remove(obj_h)
+            obj_bnd.remove(obj_angle)
+
+            x0.text, y0.text = rotatePoint(cx, cy, cx - w / 2, cy - h / 2, -angle)
+            x1.text, y1.text = rotatePoint(cx, cy, cx + w / 2, cy - h / 2, -angle)
+            x2.text, y2.text = rotatePoint(cx, cy, cx + w / 2, cy + h / 2, -angle)
+            x3.text, y3.text = rotatePoint(cx, cy, cx - w / 2, cy + h / 2, -angle)
+
+        obj_bnd.append(x0)  # 新增节点
+        obj_bnd.append(y0)
+        obj_bnd.append(x1)
+        obj_bnd.append(y1)
+        obj_bnd.append(x2)
+        obj_bnd.append(y2)
+        obj_bnd.append(x3)
+        obj_bnd.append(y3)
+
+    tree.write(dotaxml_file, method='xml', encoding='utf-8')  # 更新 XML 文件
+
+
+def rotatePoint(xc, yc, xp, yp, theta):
+    """
+    计算旋转后的点坐标。
+
+    :param xc: 旋转中心 x 坐标
+    :param yc: 旋转中心 y 坐标
+    :param xp: 点 x 坐标
+    :param yp: 点 y 坐标
+    :param theta: 旋转角度（弧度）
+    :return: 旋转后的点坐标 (x, y)
+    """
+    xoff = xp - xc
+    yoff = yp - yc
+    cosTheta = math.cos(theta)
+    sinTheta = math.sin(theta)
+    pResx = cosTheta * xoff + sinTheta * yoff
+    pResy = -sinTheta * xoff + cosTheta * yoff
+    return str(int(xc + pResx)), str(int(yc + pResy))
+
+
+def get_unique_classes(xml_path):
+    """
+    从 XML 文件中提取所有唯一的类别名称。
+
+    :param xml_path: XML 文件所在的目录
+    :return: 包含所有唯一类别名称的集合
+    """
+    unique_classes = set()  # 使用集合存储唯一的类别名称
+    files = os.listdir(xml_path)
+    for file in files:
+        if not file.endswith('.xml'):
+            continue
+        tree = ET.parse(os.path.join(xml_path, file))
+        root = tree.getroot()
+        for obj in root.findall('object'):
+            cls = obj.find('name').text
+            unique_classes.add(cls)
+    return unique_classes
+
+
+def generate_class_mapping(unique_classes):
+    """
+    根据唯一的类别名称生成类别映射字典。
+
+    :param unique_classes: 包含所有唯一类别名称的集合
+    :return: 类别名称到编号的映射字典
+    """
+    class_mapping = {}
+    for index, cls in enumerate(sorted(unique_classes)):  # 按字母顺序排序
+        class_mapping[cls] = index
+    return class_mapping
+
+
+def totxt(xml_path, out_path, class_mapping):
+    """
+    将 XML 文件转换为 TXT 文件，并根据类别名称动态生成编号。
+
+    :param xml_path: XML 文件所在的目录
+    :param out_path: 保存 TXT 文件的目录
+    :param class_mapping: 类别名称到编号的映射字典
+    """
+    # 确保输出目录存在
+    if not os.path.exists(out_path):
+        os.makedirs(out_path)
+
+    # 遍历 xml_path 下的所有文件
+    files = os.listdir(xml_path)
+    for file in files:
+        # 只处理 .xml 文件
+        if not file.endswith('.xml'):
+            continue
+
+        # 解析 XML 文件
+        tree = ET.parse(os.path.join(xml_path, file))
+        root = tree.getroot()
+
+        # 获取文件名（不带扩展名）
+        name = os.path.splitext(file)[0]
+
+        # 设置输出文件路径
+        output = os.path.join(out_path, name + '.txt')  # 使用 os.path.join 确保路径正确
+        with open(output, 'w') as f:  # 使用 with 打开文件，确保文件正确关闭
+            objs = tree.findall('object')
+            for obj in objs:
+                cls = obj.find('name').text
+                box = obj.find('bndbox')
+                x0 = int(float(box.find('x0').text))
+                y0 = int(float(box.find('y0').text))
+                x1 = int(float(box.find('x1').text))
+                y1 = int(float(box.find('y1').text))
+                x2 = int(float(box.find('x2').text))
+                y2 = int(float(box.find('y2').text))
+                x3 = int(float(box.find('x3').text))
+                y3 = int(float(box.find('y3').text))
+
+                # 根据类别名称获取对应的编号
+                cls_index = class_mapping.get(cls, -1)  # 如果类别不存在，默认返回 -1
+                if cls_index == -1:
+                    print(f"Warning: Class '{cls}' not found in class_mapping. Skipping this object.")
+                    continue
+
+                # 写入文件
+                f.write("{} {} {} {} {} {} {} {} {} {}\n".format(x0, y0, x1, y1, x2, y2, x3, y3, cls, cls_index))
+        print(f"Generated: {output}")
+
+
+if __name__ == '__main__':
+    # 设置路径
+    roxml_path = r"/home/hx/桌面/image/1"  # 存放原始 XML 文件夹名字的路径
+    dotaxml_path = r"/home/hx/桌面/image/2"  # 存放 DOTA格式的 XML文件夹名字（小建议：先手动创建该文件夹，然后路径放在这里）
+    out_path = r"/home/hx/桌面/image/2"  # 存放 DOTA格式的 TXT 文件夹名字（小建议：先手动创建该文件夹，然后路径放在这里）
+
+    # 第一步：将 XML 文件统一转换成旋转框的 XML 文件
+    filelist = os.listdir(roxml_path)
+    for file in filelist:
+        print(f"Processing: {os.path.join(roxml_path, file)}")
+        edit_xml(os.path.join(roxml_path, file), os.path.join(dotaxml_path, file))
+
+
+    # 第二步：从 XML 文件中提取所有唯一的类别名称
+    unique_classes = get_unique_classes(dotaxml_path)
+    print(f"Unique classes found: {unique_classes}")
+
+    # 第三步：生成类别映射字典
+    class_mapping = generate_class_mapping(unique_classes)
+    print(f"Generated class mapping: {class_mapping}")
+
+    # 第四步：将旋转框 XML 文件转换成 TXT 格式
+    totxt(dotaxml_path, out_path, class_mapping)

yolo11_obb/final_trans_obb.py

@@ -0,0 +1,73 @@
+# convert_dota_to_yolo_obb_manual.py
+from pathlib import Path
+
+def convert_dota_to_yolo_obb_manual(dota_root: str):
+    dota_root = Path(dota_root)
+    class_mapping = {"clamp": 0}  # 你可以加更多类别
+
+    def convert_file(txt_path: Path, image_width: int, image_height: int, save_path: Path):
+        with open(txt_path, 'r') as f:
+            lines = f.readlines()
+
+        with open(save_path, 'w') as g:
+            for line in lines:
+                parts = line.strip().split()
+                if len(parts) < 9:
+                    continue
+                x1, y1, x2, y2, x3, y3, x4, y4 = map(float, parts[:8])
+                class_name = parts[8]
+                # difficult = parts[9]  # 可选，一般不用
+
+                class_id = class_mapping.get(class_name.lower(), -1)
+                if class_id == -1:
+                    print(f"⚠️ 未知类别: {class_name}, 文件: {txt_path}")
+                    continue
+
+                # 归一化坐标
+                coords = [x1, y1, x2, y2, x3, y3, x4, y4]
+                normalized = [
+                    coords[i] / (image_width if i % 2 == 0 else image_height)
+                    for i in range(8)
+                ]
+
+                # 写入 YOLO-OBB 格式：class_id x1 y1 x2 y2 x3 y3 x4 y4
+                line_str = f"{class_id} " + " ".join(f"{v:.6g}" for v in normalized)
+                g.write(line_str + "\n")
+
+    for phase in ["train", "val"]:
+        image_dir = dota_root / "images" / phase
+        label_dir = dota_root / "labels" / phase
+        save_dir = dota_root / "labels_obb" / phase
+        save_dir.mkdir(parents=True, exist_ok=True)
+
+        for img_file in image_dir.iterdir():
+            if img_file.suffix.lower() not in [".jpg", ".jpeg", ".png"]:
+                continue
+
+            # 获取图像宽高
+            from PIL import Image
+            img = Image.open(img_file)
+            w, h = img.size
+
+            # 找对应标签文件
+            txt_file = label_dir / f"{img_file.stem}.txt"
+            if not txt_file.exists():
+                print(f"⚠️ 找不到标签文件: {txt_file}")
+                continue
+
+            save_file = save_dir / f"{img_file.stem}.txt"
+            convert_file(txt_file, w, h, save_file)
+
+    # 自动生成 dataset.yaml
+    dataset_yaml = dota_root / "dataset.yaml"
+    with open(dataset_yaml, 'w') as f:
+        f.write(f"""train: {dota_root / 'images' / 'train'}\n""")
+        f.write(f"""val: {dota_root / 'images' / 'val'}\n\n""")
+        f.write(f"nc: {len(class_mapping)}\n")
+        f.write(f"names: {list(class_mapping.keys())}\n")
+
+    print(f"✅ 转换完成！标签已保存到: {dota_root / 'labels_obb'}")
+    print(f"✅ dataset.yaml 已生成: {dataset_yaml}")
+
+# 开始转换
+convert_dota_to_yolo_obb_manual("/home/hx/桌面/image")

yolo11_seg/labels_save/frame_20250805_124426_68520.txt

@@ -1,2 +0,0 @@
-1 0.388309 0.226612 0.348864 0.329582 0.301162 0.471663 0.274101 0.560265 0.270431 0.573835 0.264469 0.623324 0.278229 0.644875 0.415830 0.717513 0.443808 0.553879 0.432800 0.522749 0.467201 0.222621
-1 0.561228 0.226612 0.645164 0.231402 0.685527 0.399026 0.706167 0.459690 0.735981 0.562660 0.740109 0.610552 0.730018 0.629709 0.604343 0.705540 0.588748 0.637692 0.574529 0.543503 0.583244 0.517162

yolo11_seg/labels_save/frame_20250805_124426_68535.txt

@@ -1,2 +0,0 @@
-1 0.388309 0.226612 0.348864 0.329582 0.301162 0.471663 0.274101 0.560265 0.270431 0.573835 0.264469 0.623324 0.278229 0.644875 0.415830 0.717513 0.443808 0.553879 0.432800 0.522749 0.467201 0.222621
-1 0.561228 0.226612 0.645164 0.231402 0.685527 0.399026 0.706167 0.459690 0.735981 0.562660 0.740109 0.610552 0.730018 0.629709 0.604343 0.705540 0.588748 0.637692 0.574529 0.543503 0.583244 0.517162

yolo11_seg/labels_save/frame_20250805_124427_68550.txt

@@ -1,2 +0,0 @@
-1 0.382805 0.221823 0.329600 0.435744 0.295200 0.580220 0.290154 0.593790 0.286944 0.639288 0.297952 0.664033 0.441056 0.719109 0.461697 0.553879 0.450688 0.522749 0.464449 0.221823
-1 0.558476 0.223420 0.565356 0.513969 0.553430 0.541906 0.574988 0.709531 0.706626 0.652059 0.717634 0.633701 0.714423 0.585808 0.710754 0.575431 0.637367 0.232200

yolo11_seg/labels_save/frame_20250805_124427_68565.txt

@@ -1,2 +0,0 @@
-1 0.391061 0.227411 0.351616 0.330380 0.303914 0.472462 0.276853 0.561063 0.273183 0.574633 0.267221 0.624122 0.280981 0.645674 0.418582 0.718311 0.446560 0.554678 0.435552 0.523547 0.469953 0.223420
-1 0.568566 0.225814 0.652503 0.230603 0.692866 0.398228 0.713506 0.458892 0.743319 0.561861 0.747448 0.609754 0.737357 0.628911 0.611681 0.704741 0.596087 0.636893 0.581868 0.542704 0.590583 0.516363

yolo11_seg/labels_save/frame_20250805_124428_68580.txt

@@ -1,2 +0,0 @@
-1 0.554806 0.225814 0.638743 0.230603 0.679106 0.398228 0.699746 0.458892 0.729559 0.561861 0.733687 0.609754 0.723597 0.628911 0.597921 0.704741 0.582327 0.636893 0.568108 0.542704 0.576823 0.516363
-1 0.375008 0.220227 0.321802 0.434148 0.287402 0.578624 0.282357 0.592193 0.279146 0.637692 0.290154 0.662436 0.433259 0.717513 0.453899 0.552283 0.442891 0.521153 0.456651 0.220227

yolo11_seg/labels_save/frame_20250805_124428_68595.txt

@@ -1,2 +0,0 @@
-1 0.391979 0.227411 0.338773 0.441331 0.304373 0.585808 0.299328 0.599377 0.296117 0.644875 0.307125 0.669620 0.450230 0.724697 0.470870 0.559467 0.459862 0.528337 0.473622 0.227411
-1 0.558476 0.222621 0.557100 0.510776 0.544716 0.542704 0.567649 0.707136 0.698370 0.663234 0.707543 0.644077 0.707085 0.597781 0.701581 0.577826 0.641954 0.235393

yolo11_seg/labels_save/frame_20250805_124429_68610.txt

@@ -1,2 +0,0 @@
-1 0.387392 0.221823 0.320885 0.604167 0.318133 0.619333 0.315381 0.671216 0.327765 0.686383 0.473622 0.715916 0.486923 0.545897 0.473622 0.514767 0.469953 0.224218
-1 0.552054 0.221823 0.631404 0.231402 0.680023 0.600974 0.685068 0.619333 0.686903 0.660840 0.672684 0.681593 0.535542 0.705540 0.524076 0.534722 0.536918 0.507583

yolo11_seg/labels_save/frame_20250805_124429_68625.txt

@@ -1,2 +0,0 @@
-1 0.388768 0.227411 0.320885 0.604167 0.317674 0.622526 0.315381 0.672813 0.330976 0.692768 0.474998 0.717513 0.486465 0.549888 0.474539 0.521153 0.471329 0.229007
-1 0.554806 0.226612 0.538294 0.505987 0.524993 0.534722 0.536460 0.708732 0.673143 0.682391 0.684610 0.670418 0.687362 0.616938 0.680023 0.597781 0.626359 0.230603

yolo11_seg/labels_save/frame_20250805_124430_68640.txt

@@ -1,2 +0,0 @@
-1 0.388768 0.227411 0.320885 0.604167 0.317674 0.622526 0.315381 0.672813 0.330976 0.692768 0.474998 0.717513 0.486465 0.549888 0.474539 0.521153 0.471329 0.229007
-1 0.554806 0.226612 0.538294 0.505987 0.524993 0.534722 0.536460 0.708732 0.673143 0.682391 0.684610 0.670418 0.687362 0.616938 0.680023 0.597781 0.626359 0.230603

yolo11_seg/labels_save/frame_20250805_124430_68655.txt

@@ -1,2 +0,0 @@
-1 0.388768 0.227411 0.320885 0.604167 0.317674 0.622526 0.315381 0.672813 0.330976 0.692768 0.474998 0.717513 0.486465 0.549888 0.474539 0.521153 0.471329 0.229007
-1 0.554806 0.226612 0.538294 0.505987 0.524993 0.534722 0.536460 0.708732 0.673143 0.682391 0.684610 0.670418 0.687362 0.616938 0.680023 0.597781 0.626359 0.230603

yolo11_seg/labels_save/frame_20250805_124431_68670.txt

@@ -1,2 +0,0 @@
-1 0.390144 0.223420 0.338773 0.615342 0.334186 0.632902 0.333728 0.683988 0.349323 0.698356 0.496097 0.709531 0.501142 0.541108 0.486923 0.512372 0.473163 0.224218
-1 0.553430 0.222621 0.627735 0.229805 0.675436 0.628113 0.676354 0.669620 0.658924 0.688777 0.524076 0.703943 0.515361 0.527538 0.529580 0.504390

yolo11_seg/labels_save/frame_20250805_124431_68685.txt

@@ -1,2 +0,0 @@
-1 0.396107 0.220227 0.341984 0.615342 0.339690 0.634499 0.339690 0.684786 0.357579 0.702347 0.501601 0.707136 0.504811 0.536319 0.490134 0.508381 0.474081 0.222621
-1 0.549761 0.224218 0.623607 0.229805 0.667180 0.610552 0.671767 0.631306 0.671308 0.675208 0.654796 0.691172 0.517654 0.699952 0.513985 0.528337 0.526828 0.501996

yolo11_seg/labels_save/frame_20250805_124432_68700.txt

@@ -1,3 +0,0 @@
-1 0.396107 0.220227 0.341984 0.615342 0.339690 0.634499 0.339690 0.684786 0.357579 0.702347 0.501601 0.707136 0.504811 0.536319 0.490134 0.508381 0.474081 0.222621
-1 0.549761 0.224218 0.623607 0.229805 0.667180 0.610552 0.671767 0.631306 0.671308 0.675208 0.654796 0.691172 0.517654 0.699952 0.513985 0.528337 0.526828 0.501996
-1 0.396107 0.220227 0.341984 0.615342 0.339690 0.634499 0.339690 0.684786 0.357579 0.702347 0.501601 0.707136 0.504811 0.536319 0.490134 0.508381 0.474081 0.222621

yolo11_seg/labels_save/frame_20250805_124436_68820.txt

@@ -1,2 +0,0 @@
-1 0.396107 0.220227 0.341984 0.615342 0.339690 0.634499 0.339690 0.684786 0.357579 0.702347 0.501601 0.707136 0.504811 0.536319 0.490134 0.508381 0.474081 0.222621
-1 0.549761 0.224218 0.623607 0.229805 0.667180 0.610552 0.671767 0.631306 0.671308 0.675208 0.654796 0.691172 0.517654 0.699952 0.513985 0.528337 0.526828 0.501996

yolo11_seg/labels_save/frame_20250805_124437_68835.txt

@@ -1,2 +0,0 @@
-1 0.396107 0.222621 0.340149 0.616140 0.337397 0.632902 0.336938 0.688777 0.353451 0.700750 0.498849 0.710329 0.503894 0.538713 0.489675 0.511574 0.475457 0.223420
-1 0.548844 0.225016 0.527745 0.503592 0.516278 0.528337 0.519489 0.703145 0.658466 0.693566 0.673602 0.676804 0.674060 0.632902 0.671308 0.612149 0.625441 0.231402

yolo11_seg/labels_save/frame_20250805_124437_68850.txt

@@ -1,2 +0,0 @@
-1 0.393355 0.223420 0.314464 0.596983 0.310336 0.612947 0.307584 0.664033 0.320885 0.680795 0.466283 0.718311 0.480043 0.547494 0.467201 0.518758 0.470870 0.223420
-1 0.554348 0.224218 0.635074 0.229007 0.702498 0.577826 0.708002 0.596983 0.709837 0.639288 0.696077 0.663234 0.566732 0.708732 0.547468 0.542704 0.557100 0.513969

yolo11_seg/labels_save/frame_20250805_124438_68865.txt

@@ -1,2 +0,0 @@
-1 0.385557 0.224218 0.293365 0.575431 0.289696 0.592992 0.284650 0.629709 0.296117 0.664033 0.438763 0.719109 0.461238 0.552283 0.449771 0.519556 0.467659 0.224218
-1 0.558934 0.224218 0.580951 0.510776 0.571318 0.541108 0.602967 0.703943 0.726807 0.630508 0.738733 0.609754 0.733687 0.561063 0.728183 0.553879 0.634615 0.230603

yolo11_seg/labels_save/frame_20250805_124438_68880.txt

@@ -1,2 +0,0 @@
-1 0.381888 0.225814 0.276853 0.555476 0.270890 0.574633 0.264010 0.621727 0.275935 0.643279 0.415371 0.714320 0.443350 0.557072 0.432342 0.522749 0.463990 0.224218
-1 0.563521 0.223420 0.601132 0.510776 0.591959 0.545099 0.630946 0.695961 0.750658 0.600974 0.759373 0.578624 0.752952 0.533924 0.745154 0.521951 0.650210 0.234595

yolo11_seg/labels_save/frame_20250805_124439_68895.txt

@@ -1,2 +0,0 @@
-1 0.383264 0.225814 0.297034 0.580220 0.292448 0.595386 0.287402 0.642481 0.301621 0.664033 0.441515 0.721504 0.463073 0.550686 0.453440 0.521153 0.465825 0.225016
-1 0.561686 0.225814 0.574529 0.513170 0.563521 0.545897 0.590583 0.707136 0.716258 0.639288 0.730018 0.621727 0.725431 0.575431 0.718551 0.561063 0.642871 0.237787

yolo11_seg/labels_save/frame_20250805_124439_68910.txt

@@ -1,2 +0,0 @@
-1 0.384181 0.223420 0.291530 0.572238 0.284650 0.592193 0.280981 0.641683 0.294741 0.660042 0.435094 0.723100 0.458486 0.555476 0.448395 0.522749 0.467201 0.223420
-1 0.562145 0.229007 0.581868 0.512372 0.569942 0.544301 0.601591 0.707934 0.724973 0.631306 0.738274 0.608956 0.734146 0.562660 0.727725 0.549888 0.638743 0.234595

yolo11_seg/labels_save/frame_20250805_124440_68925.txt

@@ -1,2 +0,0 @@
-1 0.384181 0.223420 0.277770 0.555476 0.271807 0.572238 0.265386 0.616938 0.280063 0.645674 0.414454 0.719109 0.443350 0.553879 0.433718 0.521951 0.467659 0.222621
-1 0.559852 0.225016 0.602508 0.513969 0.593793 0.541906 0.631404 0.696759 0.752034 0.599377 0.757538 0.579422 0.751117 0.531529 0.742861 0.517162 0.643330 0.234595

yolo11_seg/labels_save/frame_20250805_124440_68940.txt

@@ -1,2 +0,0 @@
-1 0.378677 0.223420 0.290613 0.573835 0.284192 0.589799 0.279605 0.640884 0.291072 0.658445 0.433259 0.720706 0.455275 0.554678 0.446102 0.523547 0.466283 0.223420
-1 0.561686 0.225814 0.581868 0.512372 0.572236 0.545897 0.602049 0.707934 0.725890 0.631306 0.736898 0.611351 0.734605 0.567449 0.726807 0.549090 0.645623 0.236191

yolo11_seg/labels_save/frame_20250805_124441_68955.txt

@@ -1,2 +0,0 @@
-1 0.385557 0.225016 0.309418 0.596983 0.305290 0.610552 0.302080 0.660840 0.316757 0.678400 0.462155 0.717513 0.475457 0.548292 0.464449 0.518758 0.467201 0.224218
-1 0.559393 0.227411 0.556182 0.511574 0.544716 0.543503 0.564438 0.711925 0.694242 0.664033 0.706167 0.646472 0.706626 0.596185 0.701122 0.581817 0.638284 0.233796

yolo11_seg/labels_save/frame_20250805_124441_68970.txt

@@ -1,2 +0,0 @@
-1 0.385099 0.221823 0.300704 0.589001 0.297493 0.600176 0.293824 0.649665 0.307584 0.673611 0.451606 0.719907 0.469494 0.551485 0.457569 0.521153 0.469953 0.221823
-1 0.559393 0.225016 0.566732 0.510776 0.557558 0.546695 0.581868 0.710329 0.708919 0.652059 0.722679 0.628911 0.719010 0.581817 0.710754 0.566651 0.640119 0.232200

yolo11_seg/labels_save/frame_20250805_124442_68985.txt

@@ -1,2 +0,0 @@
-1 0.380971 0.222621 0.286944 0.568247 0.281439 0.585010 0.276394 0.629709 0.288778 0.652858 0.428672 0.722302 0.454358 0.553081 0.444267 0.523547 0.469035 0.221823
-1 0.562604 0.225016 0.588748 0.515565 0.578657 0.547494 0.612599 0.704741 0.734146 0.622526 0.747906 0.600176 0.740109 0.552283 0.733687 0.538713 0.643330 0.232200

yolo11_seg/labels_save/frame_20250805_124442_69000.txt

@@ -1,2 +0,0 @@
-1 0.377760 0.224218 0.270431 0.545099 0.264927 0.564256 0.258047 0.608956 0.269973 0.633701 0.404822 0.718311 0.436011 0.556274 0.427755 0.523547 0.469494 0.222621
-1 0.566732 0.229007 0.610305 0.505987 0.602967 0.541906 0.646082 0.691970 0.759373 0.587404 0.769922 0.561861 0.761666 0.517960 0.754786 0.504390 0.655255 0.236989

yolo11_seg/labels_save/frame_20250805_124446_69105.txt

@@ -1,2 +0,0 @@
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