import numpy as np import cv2 from typing import Tuple, List import requests import base64 from .base import register_textdetectors, TextDetectorBase, TextBlock, ProjImgTrans from utils.message import create_error_dialog, create_info_dialog import json import time import os @register_textdetectors('stariver_ocr') class StariverDetector(TextDetectorBase): params = { 'User': "填入你的用户名", 'Password': "填入你的密码。请注意，密码会明文保存，请勿在公共电脑上使用", 'expand_ratio': "0.01", "refine": { 'type': 'checkbox', 'value': True }, "filtrate": { 'type': 'checkbox', 'value': True }, "disable_skip_area": { 'type': 'checkbox', 'value': True }, "detect_scale": "3", "merge_threshold": "2.0", "low_accuracy_mode": { 'type': 'checkbox', 'value': False }, "force_expand": { 'type': 'checkbox', 'value': False }, "font_size_offset": "0", "font_size_min(set to -1 to disable)": "-1", "font_size_max(set to -1 to disable)": "-1", "font_size_multiplier": "1.0", 'update_token_btn': { 'type': 'pushbtn', 'value': '', 'description': '删除旧 Token 并重新申请', 'display_name': '更新 Token' }, 'description': '星河云(团子翻译器) OCR 文字检测器' } @property def User(self): return self.params['User'] @property def Password(self): return self.params['Password'] @property def expand_ratio(self): return float(self.params['expand_ratio']) @property def refine(self): return self.params['refine']['value'] @property def filtrate(self): return self.params['filtrate']['value'] @property def disable_skip_area(self): return self.params['disable_skip_area']['value'] @property def detect_scale(self): return int(self.params['detect_scale']) @property def merge_threshold(self): return float(self.params['merge_threshold']) @property def low_accuracy_mode(self): return self.params['low_accuracy_mode']['value'] @property def force_expand(self): return self.params['force_expand']['value'] @property def font_size_offset(self): return int(self.params['font_size_offset']) @property def font_size_min(self): return int(self.params['font_size_min(set to -1 to disable)']) @property def font_size_max(self): return int(self.params['font_size_max(set to -1 to disable)']) @property def font_size_multiplier(self): return float(self.params['font_size_multiplier']) def __init__(self, **params) -> None: super().__init__(**params) self.url = 'https://dl.ap-qz.starivercs.cn/v2/manga_trans/advanced/manga_ocr' self.debug = False self.token = '' self.token_obtained = False # 初始化时设置用户名和密码为空 self.register_username = None self.register_password = None def get_token(self): response = requests.post('https://capiv1.ap-sh.starivercs.cn/OCR/Admin/Login', json={ "User": self.User, "Password": self.Password }).json() if response.get('Status', -1) != "Success": error_msg = f'stariver ocr 登录失败，错误信息：{response.get("ErrorMsg", "")}' raise Exception(error_msg) token = response.get('Token', '') if token != '': self.logger.info(f'stariver detector 登录成功，token前10位：{token[:10]}') return token def adjust_font_size(self, original_font_size): new_font_size = original_font_size + self.font_size_offset if self.font_size_min != -1: new_font_size = max(new_font_size, self.font_size_min) if self.font_size_max != -1: new_font_size = min(new_font_size, self.font_size_max) if self.font_size_multiplier != 1.0: new_font_size = int(new_font_size * self.font_size_multiplier) return new_font_size def _detect(self, img: np.ndarray, proj: ProjImgTrans = None) -> Tuple[np.ndarray, List[TextBlock]]: self.update_token_if_needed() # 在向服务器发送请求前尝试更新 Token if not self.token or self.token == '': self.logger.error( f'stariver detector token 没有设置。当前token：{self.token}') raise ValueError('stariver detector token 没有设置。') if self.low_accuracy_mode: self.logger.info('stariver detector 当前处于低精度模式。') short_side = 768 else: short_side = 1536 # 计算缩放比例 height, width = img.shape[:2] scale = short_side / min(height, width) # 计算新的宽高 new_width = int(width * scale) new_height = int(height * scale) # 按比例缩放图像 if scale < 1: img_scaled = cv2.resize( img, (new_width, new_height), interpolation=cv2.INTER_AREA) else: img_scaled = img # 记录日志 self.logger.debug(f'图像缩放比例：{scale}，图像尺寸：{new_width}x{new_height}') # 编码图像为base64 img_encoded = cv2.imencode('.jpg', img_scaled)[1] img_base64 = base64.b64encode(img_encoded).decode('utf-8') payload = { "token": self.token, "mask": True, "refine": self.refine, "filtrate": self.filtrate, "disable_skip_area": self.disable_skip_area, "detect_scale": self.detect_scale, "merge_threshold": self.merge_threshold, "low_accuracy_mode": self.low_accuracy_mode, "force_expand": self.force_expand, "image": img_base64 } if self.debug: payload_log = {k: v for k, v in payload.items() if k != 'image'} self.logger.debug(f'stariver detector 请求参数：{payload_log}') self.save_debug_json(payload_log, 'request') response = requests.post(self.url, json=payload) if response.status_code != 200: self.logger.error( f'stariver detector 请求失败，状态码：{response.status_code}') if response.json().get('Code', -1) != 0: self.logger.error( f'stariver detector 错误信息：{response.json().get("Message", "")}') with open('stariver_ocr_error.txt', 'w', encoding='utf-8') as f: f.write(response.text) raise ValueError('stariver detector 请求失败。') response_data = response.json()['Data'] if self.debug: self.save_debug_json(response_data, 'response') blk_list = [] for block in response_data.get('text_block', []): if scale < 1: xyxy = [int(min(coord[0] for coord in block['block_coordinate'].values()) / scale), int(min( coord[1] for coord in block['block_coordinate'].values()) / scale), int(max( coord[0] for coord in block['block_coordinate'].values()) / scale), int(max(coord[1] for coord in block['block_coordinate'].values()) / scale)] lines = [np.array([[coord[pos][0] / scale, coord[pos][1] / scale] for pos in ['upper_left', 'upper_right', 'lower_right', 'lower_left']], dtype=np.float32) for coord in block['coordinate']] else: xyxy = [int(min(coord[0] for coord in block['block_coordinate'].values())), int(min(coord[1] for coord in block['block_coordinate'].values())), int(max(coord[0] for coord in block['block_coordinate'].values())), int(max(coord[1] for coord in block['block_coordinate'].values()))] lines = [np.array([[coord[pos][0], coord[pos][1]] for pos in ['upper_left', 'upper_right', 'lower_right', 'lower_left']], dtype=np.float32) for coord in block['coordinate']] texts = [text.replace('', '') for text in block.get('texts', [])] original_font_size = block.get('text_size', 0) scaled_font_size = original_font_size / \ scale if scale < 1 else original_font_size font_size_recalculated = self.adjust_font_size(scaled_font_size) if self.debug: self.logger.debug( f'原始字体大小：{original_font_size}，修正后字体大小：{font_size_recalculated}') blk = TextBlock( xyxy=xyxy, lines=lines, language=block.get('language', 'unknown'), vertical=block.get('is_vertical', False), font_size=font_size_recalculated, text=texts, fg_colors=np.array(block.get('foreground_color', [ 0, 0, 0]), dtype=np.float32), bg_colors=np.array(block.get('background_color', [ 0, 0, 0]), dtype=np.float32) ) blk_list.append(blk) if self.debug: self.logger.debug(f'检测到文本块：{blk.to_dict()}') mask = self._decode_base64_mask( response_data['mask']) if response_data.get('mask', '') != '' else None if mask is None: self.logger.warning(f'stariver detector 未检测到文字') return None, [] mask = self.expand_mask(mask) # scale back to original size if scale < 1: mask = cv2.resize(mask, (width, height), interpolation=cv2.INTER_NEAREST) self.logger.debug(f'检测结果mask尺寸：{mask.shape}') return mask, blk_list @staticmethod def _decode_base64_mask(base64_str: str) -> np.ndarray: img_data = base64.b64decode(base64_str) img_array = np.frombuffer(img_data, dtype=np.uint8) mask = cv2.imdecode(img_array, cv2.IMREAD_GRAYSCALE) return mask def expand_mask(self, mask: np.ndarray, expand_ratio: float = 0.01) -> np.ndarray: """ 在mask的原始部分上扩展mask，以便于提取更大的文字区域。 :param mask: 输入的mask :param expand_ratio: 扩展比例，默认值为0.01 :return: 扩展后的mask """ if expand_ratio == 0: return mask # 确保mask是二值图像（只含0和255） mask = (mask > 0).astype(np.uint8) * 255 # 获得图像的尺寸 height, width = mask.shape # 计算kernel的大小（取图像尺寸的一部分，按比例expand_ratio） kernel_size = int(min(height, width) * expand_ratio) if kernel_size % 2 == 0: kernel_size += 1 # 确保kernel尺寸是奇数 # 创建一个正方形的kernel kernel = np.ones((kernel_size, kernel_size), np.uint8) # 执行膨胀操作 dilated_mask = cv2.dilate(mask, kernel, iterations=1) # 计算扩展后的mask dilated_mask = (dilated_mask > 0).astype(np.uint8) * 255 return dilated_mask def update_token_if_needed(self): token_updated = False if (self.User != self.register_username or self.Password != self.register_password): if self.token_obtained == False: if "填入你的用户名" not in self.User and "填入你的密码。请注意，密码会明文保存，请勿在公共电脑上使用" not in self.Password: if len(self.Password) > 7 and len(self.User) >= 1: new_token = self.get_token() if new_token: # 确保新获取到有效token再更新信息 self.token = new_token self.register_username = self.User self.register_password = self.Password self.token_obtained = True self.logger.info("Token updated due to credential change.") token_updated = True return token_updated def updateParam(self, param_key: str, param_content): super().updateParam(param_key, param_content) if param_key == 'update_token_btn': self.token_obtained = False # 强制刷新token时，将标志位设置为False self.token = '' # 强制刷新token时，将token置空 self.register_username = None # 强制刷新token时，将用户名置空 self.register_password = None # 强制刷新token时，将密码置空 try: if self.update_token_if_needed(): create_info_dialog('Token 更新成功') except Exception as e: create_error_dialog(e, 'Token 更新失败', 'TokenUpdateFailed') def save_debug_json(self, data, prefix='debug'): timestamp = int(time.time()) filename = f"{prefix}_{timestamp}.json" os.makedirs('debug_logs', exist_ok=True) filepath = os.path.join('debug_logs', filename) with open(filepath, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=2) self.logger.debug(f"Debug JSON saved to {filepath}")