部署文件+resize_cls+resize_seg

angle_base_obb/anger_caculate_file.py

@@ -4,8 +4,8 @@ import numpy as np
 import os
 
 # ================== 配置参数 ==================
-MODEL_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb2/weights/best.pt"
-IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/f13"  # 图像文件夹路径
+MODEL_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb4/weights/best.pt"
+IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb2/test"  # 图像文件夹路径
 OUTPUT_DIR = "./inference_results"                # 输出结果保存路径
 IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
 

angle_base_obb/error_E.py

@@ -0,0 +1,154 @@
+import os
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+# ================== 配置参数 ==================
+MODEL_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb4/weights/best.pt"
+IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb2/test"
+LABEL_SOURCE_DIR = IMAGE_SOURCE_DIR  # 假设标签和图像在同一目录
+OUTPUT_DIR = "./inference_results"
+
+IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# 加载模型
+print("🔄 加载 YOLO OBB 模型...")
+model = YOLO(MODEL_PATH)
+print("✅ 模型加载完成")
+
+# 获取图像列表
+image_files = [
+    f for f in os.listdir(IMAGE_SOURCE_DIR)
+    if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
+]
+
+if not image_files:
+    print(f"❌ 错误：未找到图像文件")
+    exit(1)
+print(f"📁 发现 {len(image_files)} 张图像待处理")
+all_angle_errors = []  # 存储每张图的夹角误差（度）
+
+# ================== 工具函数 ==================
+
+def parse_obb_label_file(label_path):
+    """解析 OBB 标签文件，返回 [{'cls': int, 'points': (4,2)}]"""
+    boxes = []
+    if not os.path.exists(label_path):
+        return boxes
+    with open(label_path, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) != 9:
+                continue
+            cls_id = int(parts[0])
+            coords = list(map(float, parts[1:]))
+            points = np.array(coords).reshape(4, 2)
+            boxes.append({'cls': cls_id, 'points': points})
+    return boxes
+
+
+def compute_main_direction(points):
+    """
+    根据四个顶点计算旋转框的主方向（长边方向），
+    返回 [0, π) 范围内的弧度值。
+    """
+    edges = []
+    for i in range(4):
+        p1 = points[i]
+        p2 = points[(i + 1) % 4]
+        vec = p2 - p1
+        length = np.linalg.norm(vec)
+        if length > 1e-6:
+            edges.append((length, vec))
+
+    if not edges:
+        return 0.0
+
+    # 找最长边
+    longest_edge = max(edges, key=lambda x: x[0])[1]
+    angle_rad = np.arctan2(longest_edge[1], longest_edge[0])
+
+    # 归一化到 [0, π)
+    angle_rad = angle_rad % np.pi
+    return angle_rad
+
+
+def compute_min_angle_between_two_dirs(dir1_rad, dir2_rad):
+    """计算两个方向之间的最小夹角（0 ~ 90°），返回角度制"""
+    diff = abs(dir1_rad - dir2_rad)
+    min_diff_rad = min(diff, np.pi - diff)
+    return np.degrees(min_diff_rad)  # 返回 0~90°
+
+
+# ================== 主循环 ==================
+for img_filename in image_files:
+    img_path = os.path.join(IMAGE_SOURCE_DIR, img_filename)
+    label_path = os.path.join(LABEL_SOURCE_DIR, os.path.splitext(img_filename)[0] + ".txt")
+
+    print(f"\n🖼️ 处理: {img_filename}")
+
+    # 读图
+    img = cv2.imread(img_path)
+    if img is None:
+        print("❌ 无法读取图像")
+        continue
+
+    # 推理
+    results = model(img, imgsz=640, conf=0.15, verbose=False)
+    result = results[0]
+    pred_boxes = result.obb
+
+    # === 提取预测框主方向 ===
+    pred_dirs = []
+    if pred_boxes is not None and len(pred_boxes) >= 2:
+        for box in pred_boxes[:2]:  # 只取前两个
+            xywhr = box.xywhr.cpu().numpy()[0]
+            cx, cy, w, h, r_rad = xywhr
+            main_dir = r_rad if w >= h else r_rad + np.pi / 2
+            pred_dirs.append(main_dir % np.pi)
+        pred_angle = compute_min_angle_between_two_dirs(pred_dirs[0], pred_dirs[1])
+    else:
+        print("❌ 预测框不足两个")
+        continue
+
+    # === 提取真实框主方向 ===
+    true_boxes = parse_obb_label_file(label_path)
+    if len(true_boxes) < 2:
+        print("❌ 标签框不足两个")
+        continue
+
+    true_dirs = []
+    for tb in true_boxes[:2]:  # 取前两个
+        d = compute_main_direction(tb['points'])
+        true_dirs.append(d)
+    true_angle = compute_min_angle_between_two_dirs(true_dirs[0], true_dirs[1])
+
+    # === 计算夹角误差 ===
+    error_deg = abs(pred_angle - true_angle)
+    all_angle_errors.append(error_deg)
+
+    print(f"  🔹 预测夹角: {pred_angle:.2f}°")
+    print(f"  🔹 真实夹角: {true_angle:.2f}°")
+    print(f"  🔺 夹角误差: {error_deg:.2f}°")
+
+# ================== 输出统计 ==================
+print("\n" + "=" * 60)
+print("📊 夹角误差统计（基于两框间最小夹角）")
+print("=" * 60)
+if all_angle_errors:
+    mean_error = np.mean(all_angle_errors)
+    std_error = np.std(all_angle_errors)
+    max_error = np.max(all_angle_errors)
+    min_error = np.min(all_angle_errors)
+
+    print(f"有效图像数: {len(all_angle_errors)}")
+    print(f"平均夹角误差: {mean_error:.2f}°")
+    print(f"标准差: {std_error:.2f}°")
+    print(f"最大误差: {max_error:.2f}°")
+    print(f"最小误差: {min_error:.2f}°")
+else:
+    print("❌ 无有效数据用于统计")
+
+print("=" * 60)
+print("🎉 所有图像处理完成！")