# Copyright (c) Opendatalab. All rights reserved.
import time
from collections import Counter
from uuid import uuid4

import numpy as np
import torch
from loguru import logger
from ultralytics import YOLO

language_dict = {
    "ch": "中文简体",
    "en": "英语",
    "japan": "日语",
    "korean": "韩语",
    "fr": "法语",
    "german": "德语",
    "ar": "阿拉伯语",
    "ru": "俄语"
}


def split_images(image, result_images=None):
    """
    对输入文件夹内的图片进行处理,若图片竖向(y方向)分辨率超过400,则进行拆分，
    每次平分图片,直至拆分出的图片竖向分辨率都满足400以下,将处理后的图片(拆分后的子图片)保存到输出文件夹。
    避免保存因裁剪区域超出图片范围导致出现的无效黑色图片部分。
    """
    if result_images is None:
        result_images = []

    width, height = image.size
    long_side = max(width, height)  # 获取较长边长度

    if long_side <= 400:
        result_images.append(image)
        return result_images

    new_long_side = long_side // 2
    sub_images = []

    if width >= height:  # 如果宽度是较长边
        for x in range(0, width, new_long_side):
            # 判断裁剪区域是否超出图片范围，如果超出则不进行裁剪保存操作
            if x + new_long_side > width:
                continue
            box = (x, 0, x + new_long_side, height)
            sub_image = image.crop(box)
            sub_images.append(sub_image)
    else:  # 如果高度是较长边
        for y in range(0, height, new_long_side):
            # 判断裁剪区域是否超出图片范围，如果超出则不进行裁剪保存操作
            if y + new_long_side > height:
                continue
            box = (0, y, width, y + new_long_side)
            sub_image = image.crop(box)
            sub_images.append(sub_image)

    for sub_image in sub_images:
        split_images(sub_image, result_images)

    return result_images


def resize_images_to_224(image):
    """
    若分辨率小于224则用黑色背景补齐到224*224大小,若大于等于224则调整为224*224大小。
    Works directly with NumPy arrays.
    """
    try:
        # Handle numpy array directly
        if len(image.shape) == 3:  # Color image
            height, width, channels = image.shape
        else:  # Grayscale image
            height, width = image.shape
            image = np.stack([image] * 3, axis=2)  # Convert to RGB

        if width < 224 or height < 224:
            # Create black background
            new_image = np.zeros((224, 224, 3), dtype=np.uint8)
            # Calculate paste position
            paste_x = (224 - width) // 2
            paste_y = (224 - height) // 2
            # Paste original image onto black background
            new_image[paste_y:paste_y + height, paste_x:paste_x + width] = image
            image = new_image
        else:
            # Resize using cv2 functionality or numpy interpolation
            # Method 1: Using cv2 (preferred for better quality)
            import cv2
            image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LANCZOS4)

        return image
    except Exception as e:
        logger.exception(e)


class YOLOv11LangDetModel(object):
    def __init__(self, langdetect_model_weight, device):

        self.model = YOLO(langdetect_model_weight)

        if str(device).startswith("npu"):
            self.device = torch.device(device)
        else:
            self.device = device
    def do_detect(self, images: list):
        all_images = []
        for image in images:
            height, width = image.shape[:2]
            if width < 100 and height < 100:
                continue
            temp_images = split_images(image)
            for temp_image in temp_images:
                all_images.append(resize_images_to_224(temp_image))
        # langdetect_start = time.time()
        images_lang_res = self.batch_predict(all_images, batch_size=256)
        # logger.info(f"image number of langdetect: {len(images_lang_res)}, langdetect time: {round(time.time() - langdetect_start, 2)}")
        if len(images_lang_res) > 0:
            count_dict = Counter(images_lang_res)
            language = max(count_dict, key=count_dict.get)
        else:
            language = None
        return language

    def predict(self, image):
        results = self.model.predict(image, verbose=False, device=self.device)
        predicted_class_id = int(results[0].probs.top1)
        predicted_class_name = self.model.names[predicted_class_id]
        return predicted_class_name


    def batch_predict(self, images: list, batch_size: int) -> list:
        images_lang_res = []

        for index in range(0, len(images), batch_size):
            lang_res = [
                image_res.cpu()
                for image_res in self.model.predict(
                    images[index: index + batch_size],
                    verbose = False,
                    device=self.device,
                )
            ]
            for res in lang_res:
                predicted_class_id = int(res.probs.top1)
                predicted_class_name = self.model.names[predicted_class_id]
                images_lang_res.append(predicted_class_name)

        return images_lang_res