zjsh_yolov11/angle_base_obb/yanzheng_move.py

import os
import cv2
import numpy as np
from ultralytics import YOLO
import shutil

# ================== 配置参数 ==================
MODEL_PATH = r"/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_obb_new/weights/best.pt"
IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/zjdata16"
# IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb5/val"

LABEL_SOURCE_DIR = IMAGE_SOURCE_DIR  # 标签与图像同目录
TEST_OUTPUT_DIR = os.path.join(IMAGE_SOURCE_DIR, "test")  # 错误样本移动到此目录

IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}

# 创建 test 目录
os.makedirs(TEST_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, img_shape):
    """解析 OBB 标签文件，并将归一化坐标转换为像素坐标"""
    boxes = []
    h, w = img_shape[:2]
    if not os.path.exists(label_path):
        print(f"⚠️  标签文件不存在: {label_path}")
        return boxes
    with open(label_path, 'r') as f:
        for line in f:
            parts = line.strip().split()
            if len(parts) != 9:
                print(f"⚠️  标签行格式错误 (期望9列): {parts}")
                continue
            cls_id = int(parts[0])
            coords = list(map(float, parts[1:]))
            points = np.array(coords).reshape(4, 2)
            points[:, 0] *= w  # x * width
            points[:, 1] *= h  # y * height
            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

    longest_edge = max(edges, key=lambda x: x[0])[1]
    angle_rad = np.arctan2(longest_edge[1], longest_edge[0])
    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)


# ================== 主循环 ==================
for img_filename in image_files:
    stem = os.path.splitext(img_filename)[0]
    img_path = os.path.join(IMAGE_SOURCE_DIR, img_filename)
    label_path = os.path.join(LABEL_SOURCE_DIR, stem + ".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（最多前两个）===
    pred_dirs = []
    if pred_boxes is not None and len(pred_boxes) >= 2:
        for box in pred_boxes[:2]:
            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（最多前两个）===
    true_boxes = parse_obb_label_file(label_path, img.shape)
    if len(true_boxes) < 2:
        print("❌ 标签框不足两个")
        continue

    true_dirs = []
    for tb in true_boxes[:2]:
        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}°")

    # === 如果误差 > 1.5°，移动原图和原 txt 到 test/ ===
    if error_deg > 1.5:
        print(f"  🚩 误差 >1.5°，移动原文件到 test/ ...")

        # 构建目标路径
        img_dst = os.path.join(TEST_OUTPUT_DIR, img_filename)
        txt_dst = os.path.join(TEST_OUTPUT_DIR, stem + ".txt")

        try:
            # 移动图像
            shutil.move(img_path, img_dst)
            print(f"    ✅ 移动图像: {img_path} → {img_dst}")

            # 移动标签（如果存在）
            if os.path.exists(label_path):
                shutil.move(label_path, txt_dst)
                print(f"    ✅ 移动标签: {label_path} → {txt_dst}")
            else:
                print(f"    ⚠️  标签不存在，仅移动图像")

        except Exception as e:
            print(f"    ❌ 移动失败: {e}")

# ================== 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！")
print(f"⚠️  误差 >1.5° 的样本已移至: {TEST_OUTPUT_DIR}")