import json, os, sys, time, io
import os.path as osp

from PIL import Image
import PIL
import cv2
import numpy as np

# see utils\io_utils.py
def imread(imgpath, read_type=cv2.IMREAD_COLOR, max_retry_limit=5, retry_interval=0.1):
    if not osp.exists(imgpath):
        return None
    
    num_tries = 0
    while True:
        try:
            img = Image.open(imgpath)
            if read_type == cv2.IMREAD_GRAYSCALE:
                img = img.convert('L')
            img = np.array(img)
            if read_type != cv2.IMREAD_GRAYSCALE:
                if img.ndim == 3 and img.shape[-1] == 1:
                    img = img[..., :2]
                if img.ndim == 2:
                    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)

            if img.ndim == 3 and img.shape[-1] == 4:
                if np.all(img[..., -1] == 255):
                    img = np.ascontiguousarray(img[..., :3])
            break
        except PIL.UnidentifiedImageError as e:
            # IMG I/O thread might not finished yet
            num_tries += 1
            if max_retry_limit is not None and num_tries >= max_retry_limit:
                return None
            time.sleep(retry_interval)
    
    return img

def chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

if __name__ == '__main__':

    from modules.textdetector.ctd.inference import TextDetector as CTDModel
    from modules.ocr.mit48px import Model48pxOCR
   
    

    CTD_ONNX_PATH = 'data/models/comictextdetector.pt.onnx'
    device = 'cpu'
    detect_size = 1280

    ctd_model = CTDModel(CTD_ONNX_PATH, detect_size=detect_size, device=device)


    OCR48PXMODEL_PATH = 'data/models/ocr_ar_48px.ckpt'
    ocr_model = Model48pxOCR(OCR48PXMODEL_PATH, device)


    img = imread('E:/huggingface/BallonsTranslator/assets/kcc-0010.jpg')
    
    # All text detectors only support 3 channels input 
    if img.ndim == 3 and img.shape[2] == 4:
        img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)

    _, mask, blk_list = ctd_model(img)

    fnt_rsz = 1.0
    fnt_max = -1
    fnt_min = -1
    for blk in blk_list:
        sz = blk._detected_font_size * fnt_rsz
        if fnt_max > 0:
            sz = min(fnt_max, sz)
        if fnt_min > 0:
            sz = max(fnt_min, sz)
        blk.font_size = sz
        blk._detected_font_size = sz

    ksize = 2
    if ksize > 0:
        element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * ksize + 1, 2 * ksize + 1),(ksize, ksize)) # 创建一个椭圆形的结构元素（kernel），用于后续的形态学操作  # 元素的尺寸 # (ksize, ksize) ：椭圆的锚点（中心点）
        mask = cv2.dilate(mask, element) # 对 mask 图像进行膨胀操作（dilate），使用上面创建的椭圆结构元素。膨胀操作可以让白色区域（通常是前景或目标区域）变大，常用于去除小的黑洞、连接断开的区域等。

    for blk in blk_list:
        blk.det_model = 'ctd'

    need_save_mask = True
    detect_counter = 0

    detect_counter += 1

    # self.ocr.run_ocr(img, blk_list)


    for blk in blk_list:
        blk.text = []

    split_textblk = False
    seg_func = None

    model_text_height = 48
    model_maxwidth = 8100

    from utils.textblock import collect_textblock_regions

    chunk_size = 16

    regions, textblk_lst_indices = collect_textblock_regions(img, blk_list, model_text_height, model_maxwidth, split_textblk, seg_func)


    ocr_model(blk_list, regions, textblk_lst_indices, chunk_size=chunk_size)


    img_draw = img.copy()


    # from qtpy.QtWidgets import QApplication
    # from qtpy.QtGui import QIcon, QFontDatabase, QGuiApplication, QFont, QFontMetrics

    # ui\mainwindow.py
    for blk in blk_list:
        text = blk.get_text()
        for line in blk.lines:
            img_draw = cv2.rectangle(img_draw, line[0], line[3], (0, 0, 255), 2)  # 在一行坚排文字的左边画一条红线
        
        # app_font = QFont('Microsoft YaHei UI')
        # fontMetrics = QFontMetrics(app_font)

        # rect = fontMetrics.boundingRect(text[0])
        # textWidth = rect.width()
        
        pass
        # blk.text = self.ocrSubWidget.sub_text(text)


    cv2.imwrite("E:/xxxxxxxxxxxxxxxx.jpg", img_draw)


    pass