import cv2
import numpy as np

def resize_and_paste_back_with_repair_mask(
    image_path,
    mask_path,
    scale_width=1.0,
    scale_height=1.0,
    output_image_path="output_with_resized_obj.jpg",
    output_repair_mask_path="repair_mask.png"
):
    """
    抠出物体 → 缩放 → 贴回原图，并生成用于 AI 修复的 mask。
    
    Returns:
        output_img: 合成后的图像
        repair_mask: 修复用二值 mask（白色=需修复）
    """
    # 1. 加载原图和掩码
    image = cv2.imread(image_path)
    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
    if image is None or mask is None:
        raise FileNotFoundError("Image or mask not found!")
    
    h, w = image.shape[:2]
    assert mask.shape == (h, w), "Mask and image size mismatch!"

    # 2. 找到物体边界框
    coords = cv2.findNonZero(mask)
    if coords is None:
        raise ValueError("No object in mask!")
    x, y, obj_w, obj_h = cv2.boundingRect(coords)

    # 3. 计算原物体中心
    center_x = x + obj_w // 2
    center_y = y + obj_h // 2

    # 4. 抠出 RGBA 物体
    rgba = np.dstack([image, mask])
    obj_rgba = rgba[y:y+obj_h, x:x+obj_w]

    # 5. 缩放物体
    new_w = max(1, int(obj_w * scale_width))
    new_h = max(1, int(obj_h * scale_height))
    resized_obj = cv2.resize(obj_rgba, (new_w, new_h), interpolation=cv2.INTER_AREA)

    # 6. 计算新位置（保持中心对齐）
    new_x = center_x - new_w // 2
    new_y = center_y - new_h // 2

    # 7. 计算贴图的有效区域（保证 src 和 dst 尺寸一致）
    dst_x1 = max(0, new_x)
    dst_y1 = max(0, new_y)
    dst_x2 = min(w, new_x + new_w)
    dst_y2 = min(h, new_y + new_h)

    src_x1 = dst_x1 - new_x
    src_y1 = dst_y1 - new_y
    src_x2 = src_x1 + (dst_x2 - dst_x1)
    src_y2 = src_y1 + (dst_y2 - dst_y1)

    # Clamp to resized_obj bounds
    src_x1 = max(0, int(src_x1))
    src_y1 = max(0, int(src_y1))
    src_x2 = min(new_w, int(src_x2))
    src_y2 = min(new_h, int(src_y2))

    # Update final coordinates
    dst_x_start, dst_y_start = dst_x1, dst_y1
    dst_x_end, dst_y_end = dst_x2, dst_y2
    src_x_start, src_y_start = src_x1, src_y1
    src_x_end, src_y_end = src_x2, src_y2

    # 8. 创建修复 mask
    repair_mask = np.zeros((h, w), dtype=np.uint8)
    repair_mask[mask > 0] = 255  # 原物体区域需修复

    # 9. 合成图像 & 构建新物体 mask
    background = image.copy()
    background[mask > 0] = [255, 255, 255]  # 白色填充原物体区域
    output_img = background.copy()
    new_obj_mask = np.zeros((h, w), dtype=np.uint8)

    if src_x_end > src_x_start and src_y_end > src_y_start:
        # 提取有效区域
        obj_part = resized_obj[src_y_start:src_y_end, src_x_start:src_x_end]
        alpha = obj_part[:, :, 3].astype(np.float32) / 255.0
        alpha_uint8 = (alpha * 255).astype(np.uint8)

        # Alpha blending
        bg_part = output_img[dst_y_start:dst_y_end, dst_x_start:dst_x_end]
        fg_part = obj_part[:, :, :3]
        blended = fg_part * alpha[..., None] + bg_part * (1 - alpha[..., None])
        output_img[dst_y_start:dst_y_end, dst_x_start:dst_x_end] = blended.astype(np.uint8)

        # 记录新物体覆盖区域
        new_obj_mask[dst_y_start:dst_y_end, dst_x_start:dst_x_end] = alpha_uint8

    # 10. 生成 repair mask：原区域 - 新物体覆盖区域
    kernel = np.ones((5, 5), np.uint8)
    new_obj_mask_dilated = cv2.dilate(new_obj_mask, kernel, iterations=1)
    repair_mask = cv2.subtract(repair_mask, new_obj_mask_dilated)
    repair_mask = np.clip(repair_mask, 0, 255).astype(np.uint8)
    repair_mask = cv2.dilate(repair_mask, np.ones((3, 3), np.uint8), iterations=1)

    # 11. 保存
    cv2.imwrite(output_image_path, output_img)
    cv2.imwrite(output_repair_mask_path, repair_mask)
    print(f"✅ Image saved to: {output_image_path}")
    print(f"✅ Repair mask saved to: {output_repair_mask_path}")
    
    return output_img, repair_mask

img, repair_mask = resize_and_paste_back_with_repair_mask(
    image_path="/mnt/prev_nas/qhy_1/datasets/flux_gen_images/allocentric_002.png",
    mask_path="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_masks/allocentric_002.png",
    scale_width=1.0,
    scale_height=1.4,
    output_image_path="/mnt/prev_nas/qhy_1/GenSpace/example/imageedit/29/allocentric_002.png",
    output_repair_mask_path="fatter_repair_mask.png"
)