更新了diff的rknn3568

ailai_pc/change_label.py

@@ -0,0 +1,69 @@
+import os
+import glob
+
+# ======================
+# 配置部分（你要改这里）
+# ======================
+
+# 标签文件所在文件夹
+LABEL_DIR = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/2"
+
+# 新的关键点顺序 (0-based)
+# 例如：原顺序 1,2,3,4 → 新顺序 1,2,4,3
+#new_order = [0, 1, 3, 2]
+new_order = [0, 2, 3, 1]
+
+# ======================
+# 程序部分
+# ======================
+
+def reorder_keypoints(line, new_order):
+    parts = line.strip().split()
+
+    if len(parts) < 17:
+        print(f"数据格式不对，字段数为 {len(parts)}，应 ≥ 17 ：", line)
+        return line
+
+    # 前 5 个保持不变
+    head = parts[:5]
+
+    # 后 12 个是 4 个关键点 (4 * 3)
+    kps = parts[5:]
+
+    # 分组成 [(x1,y1,v1),(x2,y2,v2)...]
+    keypoints = [kps[i:i+3] for i in range(0, 12, 3)]
+
+    # 按新顺序重排
+    reordered = []
+    for idx in new_order:
+        reordered.extend(keypoints[idx])
+
+    return " ".join(head + reordered)
+
+
+def process_all_labels(label_dir, new_order):
+    txt_files = glob.glob(os.path.join(label_dir, "*.txt"))
+
+    print(f"找到 {len(txt_files)} 个标签文件\n")
+
+    for txt_path in txt_files:
+        lines_out = []
+
+        with open(txt_path, "r") as f:
+            lines = f.readlines()
+
+        for line in lines:
+            if line.strip():
+                line = reorder_keypoints(line, new_order)
+            lines_out.append(line)
+
+        # 覆写文件
+        with open(txt_path, "w") as f:
+            f.write("\n".join(lines_out))
+
+        print(f"✓ 已处理：{txt_path}")
+
+
+if __name__ == "__main__":
+    process_all_labels(LABEL_DIR, new_order)
+    print("\n全部处理完成！")

ailai_pc/point_error_test.py

@@ -4,7 +4,7 @@ import os
 from ultralytics import YOLO
 
 # ====================== 用户配置 ======================
-MODEL_PATH = '11.pt'
+MODEL_PATH = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai2/weights/best.pt'
 IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train'   # 验证集图片目录
 LABEL_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/train'          # 标签目录（与图片同名 .txt）
 OUTPUT_DIR = './output_images'
@@ -96,12 +96,14 @@ if __name__ == "__main__":
             skipped += 1
             continue
 
-        # 计算误差
-        errors = np.linalg.norm(pred_kpts - gt_kpts, axis=1)
+        # ====================== 只计算关键点 0 和 1 ======================
+        selected_idx = [0, 1]  # 只算前两个点
+
+        errors = np.linalg.norm(pred_kpts[selected_idx] - gt_kpts[selected_idx], axis=1)
         mean_error = np.mean(errors)
         total_errors.append(mean_error)
 
-        print(f"📸 {img_filename}: 每点误差={np.round(errors, 2)} 像素, 平均误差={mean_error:.2f}px")
+        print(f"📸 {img_filename}: 点0&1误差={np.round(errors, 2)} 像素, 平均误差={mean_error:.2f}px")
 
         # 可视化
         vis_img = img.copy()

ailai_pc/point_test.py

@@ -4,8 +4,8 @@ from ultralytics import YOLO
 import os
 
 # ====================== 用户配置 ======================
-MODEL_PATH = 'best.pt'
-IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2/val'  # 👈 修改为你的图像文件夹路径
+MODEL_PATH = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai/weights/best.pt'
+IMAGE_SOURCE_DIR = '/home/hx/开发/ailai_image_obb/ailai_pc/yolo_obb_dataset/test'  # 👈 修改为你的图像文件夹路径
 OUTPUT_DIR = './output_images'  # 保存结果的文件夹
 
 # 支持的图像扩展名