bushu

yolo11_obb/divid_dataset.py

@@ -0,0 +1,83 @@
+import os
+import shutil
+import random
+
+def split_train_val_test(train_dir, val_dir, test_dir, val_ratio=0.1, test_ratio=0.1):
+    """
+    将 train 目录中的数据按比例划分为 train/val/test 三部分。
+    val 和 test 各自获得不同的一部分数据。
+    """
+    # 检查目录
+    if not os.path.exists(train_dir):
+        raise FileNotFoundError(f"训练目录不存在: {train_dir}")
+
+    os.makedirs(val_dir, exist_ok=True)
+    os.makedirs(test_dir, exist_ok=True)
+
+    # 支持的图片格式
+    img_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+
+    # 获取所有图片文件
+    all_files = os.listdir(train_dir)
+    image_files = [f for f in all_files if os.path.splitext(f.lower())[1] in img_extensions]
+
+    # 配对图片和标签
+    pairs = []
+    for img in image_files:
+        name_no_ext = os.path.splitext(img)[0]
+        txt = name_no_ext + '.txt'
+        if txt in all_files:
+            pairs.append((img, txt))
+        else:
+            print(f"⚠️ 忽略 {img}：缺少对应标签文件")
+
+    if len(pairs) == 0:
+        print("❌ 未找到有效数据对")
+        return
+
+    total = len(pairs)
+    num_test = int(total * test_ratio)
+    num_val = int(total * val_ratio)
+
+    print(f"✅ 共找到 {total} 组有效数据")
+    print(f"✅ 将移动：val={num_val}, test={num_test}")
+
+    # 打乱并抽样
+    random.shuffle(pairs)
+    test_sample = pairs[:num_test]
+    val_sample = pairs[num_test:num_test + num_val]
+    # 剩下的留在 train
+
+    # 移动到 test
+    for img, txt in test_sample:
+        try:
+            shutil.move(os.path.join(train_dir, img), os.path.join(test_dir, img))
+            shutil.move(os.path.join(train_dir, txt), os.path.join(test_dir, txt))
+        except Exception as e:
+            print(f"❌ 移动到 test 失败: {img}, {e}")
+
+    # 移动到 val
+    for img, txt in val_sample:
+        try:
+            shutil.move(os.path.join(train_dir, img), os.path.join(val_dir, img))
+            shutil.move(os.path.join(train_dir, txt), os.path.join(val_dir, txt))
+        except Exception as e:
+            print(f"❌ 移动到 val 失败: {img}, {e}")
+
+    print(f"\n✅ 划分完成！")
+    print(f"   train 保留: {total - num_val - num_test}")
+    print(f"   val       : {num_val}")
+    print(f"   test      : {num_test}")
+    print(f"   所有 val 和 test 数据已从 train 移出。")
+
+
+# ========================
+# 使用示例
+# ========================
+if __name__ == "__main__":
+    train_dir = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb3/train"
+    val_dir   = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb3/val"
+    test_dir  = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb3/test"
+
+    split_train_val_test(train_dir, val_dir, test_dir, val_ratio=0.1, test_ratio=0.1)
+# ========================

yolo11_obb/image_lianghua/image_list.txt

@@ -0,0 +1,49 @@
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150642365.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153132629.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144341882.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014154328204.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153839977.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014145006232.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150210497.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144500393.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153257985.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150246105.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153420611.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153918385.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144421259.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153506692.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144222222.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150720686.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014145128164.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144538968.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014151150768.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014145321687.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144303840.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_202510141514352.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_2025101415103290.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_2025101415152183.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150444617.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014145045330.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144814974.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153338300.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150918102.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014151311222.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153215762.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_2025101415111060.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014151922555.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144144937.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014154037360.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014151232368.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144619387.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014154121106.jpg
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+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150325231.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014151353357.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014154203390.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014145244471.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153957167.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014150406560.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_2025101415052359.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153632704.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014144929890.jpg
+/home/hx/yolo/yolo11_obb/image_lianghua/192.168.0.234_01_20251014153800800.jpg

yolo11_obb/move_photo_label.py

@@ -0,0 +1,53 @@
+import os
+import shutil
+
+
+def move_images_to_labels(image_dir, label_dir):
+    """
+    将标签对应的图片从图片文件夹移动到标签文件夹。
+
+    :param image_dir: 存放图片的文件夹路径
+    :param label_dir: 存放标签的文件夹路径，同时也是目标文件夹
+    """
+    # 支持的图片扩展名集合
+    IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
+
+    # 遍历标签文件夹中的所有文件
+    for label_filename in os.listdir(label_dir):
+        if not os.path.isfile(os.path.join(label_dir, label_filename)):
+            continue  # 跳过非文件项（如目录）
+
+        # 获取文件的基础名称（不带扩展名）
+        base_name, _ = os.path.splitext(label_filename)
+
+        # 在图片文件夹中查找对应的所有可能扩展名的图片文件
+        found_image = False
+        for ext in IMG_EXTENSIONS:
+            image_filename = base_name + ext
+            image_path = os.path.join(image_dir, image_filename)
+
+            if os.path.exists(image_path):
+                # 构造目标路径
+                target_path = os.path.join(label_dir, image_filename)
+
+                # 移动图片文件到标签文件夹
+                print(f"移动 {image_path} 到 {target_path}")
+                shutil.move(image_path, target_path)
+
+                found_image = True
+                break
+
+        if not found_image:
+            print(f"未找到与标签 {label_filename} 对应的图片")
+
+
+if __name__ == "__main__":
+    IMAGE_SOURCE_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb4/images"  # 替换为你的图片文件夹路径
+    LABEL_TARGET_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb4/labels"  # 替换为你的标签文件夹路径
+
+    if not os.path.isdir(IMAGE_SOURCE_DIR):
+        print(f"图片文件夹不存在: {IMAGE_SOURCE_DIR}")
+    elif not os.path.isdir(LABEL_TARGET_DIR):
+        print(f"标签文件夹不存在: {LABEL_TARGET_DIR}")
+    else:
+        move_images_to_labels(IMAGE_SOURCE_DIR, LABEL_TARGET_DIR)

yolo11_obb/trans_cvattoobb.py

@@ -116,8 +116,7 @@ if __name__ == "__main__":
     CVAT_XML_PATH = "annotations.xml"      # 你的 annotations.xml 文件
     OUTPUT_DIR = "yolo_obb_dataset1"        # 输出目录
     CLASS_MAPPING = {
-        "clamp": 0,                        # 请根据你的实际类别修改
-        # "other_class": 1,
+        "clamp": 0,
     }
 
     # 执行转换

yolo11_obb/txt_1.py

@@ -2,10 +2,10 @@ import os
 
 # ================== 配置参数 ==================
 # 图片所在的文件夹路径
-image_folder = '/home/hx/yolo/yolo11_obb/yolo_obb_dataset'  # 修改为你的图片文件夹路径
+image_folder = '/home/hx/yolo/yolo11_obb/image_lianghua'  # 修改为你的图片文件夹路径
 
 # 输出的txt文件路径
-output_txt = '/home/hx/yolo/yolo11_obb/yolo_obb_dataset/image_list.txt'  # 修改为你想保存的路径
+output_txt = '/home/hx/yolo/yolo11_obb/image_lianghua/image_list.txt'  # 修改为你想保存的路径
 
 # 支持的图片格式
 image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.webp', '.tiff', '.gif'}