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

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


def process_obb_images_for_angle_distribution(model_path, image_dir, conf_thresh=0.15, imgsz=640):
    """
    批量处理图像的 OBB 推理，计算目标主方向夹角分布（每10°一个区间），
    并统计最大和最小夹角。

    输入：
        model_path: YOLO 权重路径
        image_dir: 图像文件夹路径
        conf_thresh: 置信度阈值
        imgsz: 输入图像大小
    输出：
        distribution: 每10°一个区间的夹角统计字典
        max_angle: 所有图像的最大夹角
        min_angle: 所有图像的最小夹角
    """
    # 初始化每10°统计字典
    angle_distribution = {f"{i}-{i + 10}": 0 for i in range(0, 180, 10)}
    max_angle = -1
    min_angle = 181

    print("加载 YOLO 模型...")
    model = YOLO(model_path)
    print("✅ 模型加载完成")

    # 获取图像文件
    image_files = [f for f in os.listdir(image_dir) if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS]
    if not image_files:
        print(f"❌ 未找到图像文件：{image_dir}")
        return angle_distribution, max_angle, min_angle

    print(f"发现 {len(image_files)} 张图像待处理")

    for img_filename in image_files:
        img_path = os.path.join(image_dir, img_filename)
        print(f"\n正在处理：{img_filename}")

        img = cv2.imread(img_path)
        if img is None:
            print(f"❌ 跳过：无法读取图像 {img_path}")
            continue

        # 推理 OBB
        results = model(img, save=False, imgsz=imgsz, conf=conf_thresh, mode='obb')
        result = results[0]

        # 提取旋转角
        boxes = result.obb
        angles_deg = []
        if boxes is None or len(boxes) == 0:
            print("❌ 该图像中未检测到任何目标")
            continue
        else:
            for box in boxes:
                cx, cy, w, h, r_rad = box.xywhr.cpu().numpy()[0]
                direction = r_rad if w >= h else r_rad + np.pi / 2
                direction = direction % np.pi
                angle_deg = np.degrees(direction)
                angles_deg.append(angle_deg)

        # 两两夹角
        if len(angles_deg) >= 2:
            for i in range(len(angles_deg)):
                for j in range(i + 1, len(angles_deg)):
                    diff_rad = abs(np.radians(angles_deg[i]) - np.radians(angles_deg[j]))
                    min_diff_rad = min(diff_rad, np.pi - diff_rad)
                    angle_diff_deg = np.degrees(min_diff_rad)

                    # 更新每10°分布
                    bin_index = int(angle_diff_deg // 10) * 10
                    bin_key = f"{bin_index}-{bin_index + 10}"
                    if bin_key in angle_distribution:
                        angle_distribution[bin_key] += 1

                    # 更新全局最大最小夹角
                    if angle_diff_deg > max_angle:
                        max_angle = angle_diff_deg
                    if angle_diff_deg < min_angle:
                        min_angle = angle_diff_deg

    print("\n所有图像处理完成！")
    return angle_distribution, max_angle, min_angle


# ------------------- 测试调用 -------------------
if __name__ == "__main__":
    MODEL_PATH = r'obb.pt'
    IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb3/train"

    distribution, max_angle, min_angle = process_obb_images_for_angle_distribution(MODEL_PATH, IMAGE_SOURCE_DIR)

    print("\n夹角分布统计（每10°）:")
    for k, v in distribution.items():
        print(f"{k}°: {v}")

    print(f"\n最大夹角: {max_angle:.2f}°")
    print(f"最小夹角: {min_angle:.2f}°")