import os
import cv2
import numpy as np
from ultralytics import YOLO
import matplotlib.pyplot as plt

# ================== 配置参数 ==================
MODEL_PATH = r"obb.pt"
IMAGE_SOURCE_DIR = r"/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/obb4/train"
LABEL_SOURCE_DIR = IMAGE_SOURCE_DIR  # 假设标签和图像在同一目录
OUTPUT_DIR = "./inference_results"
VISUAL_DIR = os.path.join(OUTPUT_DIR, "visual_errors_gt5deg")  # 保存误差 >5° 的可视化图
IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff', '.webp'}

os.makedirs(OUTPUT_DIR, exist_ok=True)
os.makedirs(VISUAL_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
    img_shape: (height, width) 用于去归一化
    """
    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)


def draw_boxes_on_image(image, pred_boxes=None, true_boxes=None):
    """在图像上绘制预测框（绿色）和真实框（红色）"""
    img_vis = image.copy()

    # 绘制真实框（红色）
    if true_boxes is not None:
        for box in true_boxes:
            pts = np.int32(box['points']).reshape((-1, 1, 2))
            cv2.polylines(img_vis, [pts], isClosed=True, color=(0, 0, 255), thickness=2)

    # 绘制预测框（绿色）
    if pred_boxes is not None:
        for box in pred_boxes:
            xyxyxyxy = box.xyxyxyxy.cpu().numpy()[0]
            pts = xyxyxyxy.reshape(4, 2).astype(int)
            pts = pts.reshape((-1, 1, 2))
            cv2.polylines(img_vis, [pts], isClosed=True, color=(0, 255, 0), thickness=2)

    return img_vis

# ================== 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]:
            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, 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}°")

    # === 可视化误差 >5° 的情况 ===
    if error_deg > 3:
        print(f"  🎯 误差 >5°，生成可视化图像...")
        img_with_boxes = draw_boxes_on_image(img, pred_boxes=pred_boxes, true_boxes=true_boxes)
        # 添加文字
        cv2.putText(img_with_boxes, f"Error: {error_deg:.2f}°", (20, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        vis_output_path = os.path.join(VISUAL_DIR, f"error_{error_deg:.2f}deg_{img_filename}")
        cv2.imwrite(vis_output_path, img_with_boxes)
        print(f"  ✅ 已保存可视化图像: {vis_output_path}")

# ================== 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！")