mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-11-29 11:03:18 +00:00
437 lines
14 KiB
Python
437 lines
14 KiB
Python
import re
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import time
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from datetime import timedelta
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from typing import Optional
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import cv2
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import numpy as np
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from pponnxcr.predict_system import BoxedResult
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import module.config.server as server
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from module.base.button import ButtonWrapper
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from module.base.decorator import cached_property
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from module.base.utils import area_pad, corner2area, crop, extract_white_letters, float2str
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from module.exception import ScriptError
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from module.logger import logger
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from module.ocr.keyword import Keyword
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from module.ocr.models import OCR_MODEL, TextSystem
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from module.ocr.utils import merge_buttons
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class OcrResultButton:
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def __init__(self, boxed_result: BoxedResult, matched_keyword: Optional[Keyword]):
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"""
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Args:
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boxed_result: BoxedResult from ppocr-onnx
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matched_keyword: Keyword object or None
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"""
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self.area = boxed_result.box
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self.search = area_pad(self.area, pad=-20)
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# self.color =
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self.button = boxed_result.box
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if matched_keyword is not None:
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self.matched_keyword = matched_keyword
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self.name = str(matched_keyword)
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else:
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self.matched_keyword = None
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self.name = boxed_result.ocr_text
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self.text = boxed_result.ocr_text
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self.score = boxed_result.score
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def __str__(self):
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return self.name
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__repr__ = __str__
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def __eq__(self, other):
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return str(self) == str(other)
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def __hash__(self):
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return hash(self.name)
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def __bool__(self):
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return True
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@property
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def is_keyword_matched(self) -> bool:
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return self.matched_keyword is not None
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class Ocr:
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# Merge results with box distance <= thres
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merge_thres_x = 0
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merge_thres_y = 0
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def __init__(self, button: ButtonWrapper, lang=None, name=None):
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"""
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Args:
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button:
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lang: If None, use in-game language
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name: If None, use button.name
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"""
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if lang is None:
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lang = server.lang
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if name is None:
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name = button.name
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self.button: ButtonWrapper = button
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self.lang: str = lang
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self.name: str = name
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@cached_property
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def model(self) -> TextSystem:
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return OCR_MODEL.get_by_lang(self.lang)
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def pre_process(self, image):
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"""
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Args:
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image (np.ndarray): Shape (height, width, channel)
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Returns:
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np.ndarray: Shape (width, height)
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"""
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return image
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def after_process(self, result):
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"""
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Args:
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result (str): '第二行'
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Returns:
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str:
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"""
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if result.startswith('UID'):
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result = 'UID'
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return result
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def format_result(self, result):
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"""
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Will be overriden.
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"""
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return result
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def _log_change(self, attr, func, before):
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after = func(before)
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if after != before:
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logger.attr(f'{self.name} {attr}', f'{before} -> {after}')
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return after
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def ocr_single_line(self, image, direct_ocr=False):
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# pre process
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start_time = time.time()
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if not direct_ocr:
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image = crop(image, self.button.area)
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image = self.pre_process(image)
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# ocr
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result, _ = self.model.ocr_single_line(image)
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# after proces
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result = self._log_change('after', self.after_process, result)
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result = self._log_change('format', self.format_result, result)
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logger.attr(name='%s %ss' % (self.name, float2str(time.time() - start_time)),
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text=str(result))
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return result
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def ocr_multi_lines(self, image_list):
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# pre process
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start_time = time.time()
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image_list = [self.pre_process(image) for image in image_list]
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# ocr
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result_list = self.model.ocr_lines(image_list)
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result_list = [(result, score) for result, score in result_list]
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# after process
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result_list = [(self.after_process(result), score) for result, score in result_list]
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result_list = [(self.format_result(result), score) for result, score in result_list]
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logger.attr(name="%s %ss" % (self.name, float2str(time.time() - start_time)),
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text=str([result for result, _ in result_list]))
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return result_list
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def filter_detected(self, result: BoxedResult) -> bool:
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"""
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Return False to drop result.
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"""
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return True
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def detect_and_ocr(self, image, direct_ocr=False) -> list[BoxedResult]:
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"""
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Args:
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image:
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direct_ocr: True to ignore `button` attribute and feed the image to OCR model without cropping.
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Returns:
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"""
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# pre process
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start_time = time.time()
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if not direct_ocr:
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image = crop(image, self.button.area)
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image = self.pre_process(image)
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# ocr
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results: list[BoxedResult] = self.model.detect_and_ocr(image)
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# after proces
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for result in results:
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if not direct_ocr:
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result.box += self.button.area[:2]
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result.box = tuple(corner2area(result.box))
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results = [result for result in results if self.filter_detected(result)]
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results = merge_buttons(results, thres_x=self.merge_thres_x, thres_y=self.merge_thres_y)
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for result in results:
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result.ocr_text = self.after_process(result.ocr_text)
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logger.attr(name='%s %ss' % (self.name, float2str(time.time() - start_time)),
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text=str([result.ocr_text for result in results]))
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return results
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def _match_result(
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self,
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result: str,
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keyword_classes,
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lang: str = None,
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ignore_punctuation=True,
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ignore_digit=True):
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"""
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Args:
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result (str):
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keyword_classes: A list of `Keyword` class or classes inherited `Keyword`
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Returns:
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If matched, return `Keyword` object or objects inherited `Keyword`
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If not match, return None
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"""
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if not isinstance(keyword_classes, list):
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keyword_classes = [keyword_classes]
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# Digits will be considered as the index of keyword
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if ignore_digit:
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if result.isdigit():
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return None
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# Try in current lang
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for keyword_class in keyword_classes:
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try:
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matched = keyword_class.find(
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result,
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lang=lang,
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ignore_punctuation=ignore_punctuation
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)
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return matched
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except ScriptError:
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continue
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return None
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def matched_single_line(
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self,
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image,
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keyword_classes,
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lang: str = None,
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ignore_punctuation=True
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) -> OcrResultButton:
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"""
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Args:
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image: Image to detect
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keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
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lang:
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ignore_punctuation:
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Returns:
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OcrResultButton: Or None if it didn't matched known keywords.
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"""
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result = self.ocr_single_line(image)
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result = self._match_result(
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result,
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keyword_classes=keyword_classes,
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lang=lang,
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ignore_punctuation=ignore_punctuation,
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)
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logger.attr(name=f'{self.name} matched',
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text=result)
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return result
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def matched_multi_lines(
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self,
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image_list,
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keyword_classes,
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lang: str = None,
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ignore_punctuation=True
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) -> list[OcrResultButton]:
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"""
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Args:
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image_list:
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keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
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lang:
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ignore_punctuation:
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Returns:
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List of matched OcrResultButton.
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OCR result which didn't matched known keywords will be dropped.
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"""
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results = self.ocr_multi_lines(image_list)
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results = [self._match_result(
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result,
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keyword_classes=keyword_classes,
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lang=lang,
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ignore_punctuation=ignore_punctuation,
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) for result in results]
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results = [result for result in results if result.is_keyword_matched]
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logger.attr(name=f'{self.name} matched',
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text=results)
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return results
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def _product_button(
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self,
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boxed_result: BoxedResult,
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keyword_classes,
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lang: str = None,
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ignore_punctuation=True,
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ignore_digit=True
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) -> OcrResultButton:
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if not isinstance(keyword_classes, list):
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keyword_classes = [keyword_classes]
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matched_keyword = self._match_result(
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boxed_result.ocr_text,
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keyword_classes=keyword_classes,
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lang=lang,
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ignore_punctuation=ignore_punctuation,
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ignore_digit=ignore_digit,
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)
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button = OcrResultButton(boxed_result, matched_keyword)
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return button
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def matched_ocr(self, image, keyword_classes, direct_ocr=False) -> list[OcrResultButton]:
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"""
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Args:
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image: Screenshot
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keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
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direct_ocr: True to ignore `button` attribute and feed the image to OCR model without cropping.
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Returns:
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List of matched OcrResultButton.
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OCR result which didn't matched known keywords will be dropped.
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"""
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results = self.detect_and_ocr(image, direct_ocr=direct_ocr)
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results = [self._product_button(result, keyword_classes) for result in results]
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results = [result for result in results if result.is_keyword_matched]
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logger.attr(name=f'{self.name} matched',
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text=results)
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return results
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class Digit(Ocr):
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def __init__(self, button: ButtonWrapper, lang='en', name=None):
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super().__init__(button, lang=lang, name=name)
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def format_result(self, result) -> int:
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"""
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Returns:
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int:
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"""
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result = super().after_process(result)
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logger.attr(name=self.name, text=str(result))
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res = re.search(r'(\d+)', result)
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if res:
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return int(res.group(1))
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else:
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logger.warning(f'No digit found in {result}')
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return 0
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class DigitCounter(Ocr):
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def __init__(self, button: ButtonWrapper, lang='en', name=None):
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super().__init__(button, lang=lang, name=name)
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def format_result(self, result) -> tuple[int, int, int]:
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"""
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Do OCR on a counter, such as `14/15`, and returns 14, 1, 15
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Returns:
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int:
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"""
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result = super().after_process(result)
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logger.attr(name=self.name, text=str(result))
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res = re.search(r'(\d+)/(\d+)', result)
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if res:
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groups = [int(s) for s in res.groups()]
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current, total = int(groups[0]), int(groups[1])
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# current = min(current, total)
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return current, total - current, total
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else:
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logger.warning(f'No digit counter found in {result}')
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return 0, 0, 0
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class Duration(Ocr):
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@classmethod
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def timedelta_regex(cls, lang):
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regex_str = {
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'cn': r'^(?P<prefix>.*?)'
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r'((?P<days>\d{1,2})\s*天\s*)?'
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r'((?P<hours>\d{1,2})\s*小时\s*)?'
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r'((?P<minutes>\d{1,2})\s*分钟\s*)?'
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r'((?P<seconds>\d{1,2})\s*秒)?'
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r'(?P<suffix>[^天时钟秒]*?)$',
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'en': r'^(?P<prefix>.*?)'
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r'((?P<days>\d{1,2})\s*d\s*)?'
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r'((?P<hours>\d{1,2})\s*h\s*)?'
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r'((?P<minutes>\d{1,2})\s*m\s*)?'
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r'((?P<seconds>\d{1,2})\s*s)?'
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r'(?P<suffix>[^dhms]*?)$'
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}[lang]
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return re.compile(regex_str)
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def after_process(self, result):
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result = super().after_process(result)
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result = result.strip('.,。，')
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result = result.replace('Oh', '0h').replace('oh', '0h')
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return result
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def format_result(self, result: str) -> timedelta:
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"""
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Do OCR on a duration, such as `18d 2h 13m 30s`, `2h`, `13m 30s`, `9s`
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Returns:
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timedelta:
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"""
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matched = self.timedelta_regex(self.lang).search(result)
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if not matched:
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return timedelta()
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days = self._sanitize_number(matched.group('days'))
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hours = self._sanitize_number(matched.group('hours'))
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minutes = self._sanitize_number(matched.group('minutes'))
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seconds = self._sanitize_number(matched.group('seconds'))
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return timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds)
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@staticmethod
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def _sanitize_number(number) -> int:
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if number is None:
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return 0
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return int(number)
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class OcrWhiteLetterOnComplexBackground(Ocr):
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def pre_process(self, image):
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image = extract_white_letters(image, threshold=255)
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image = cv2.merge([image, image, image])
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return image
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def detect_and_ocr(self, *args, **kwargs):
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# Try hard to lower TextSystem.box_thresh
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backup = self.model.text_detector.box_thresh
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self.model.text_detector.box_thresh = 0.2
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result = super().detect_and_ocr(*args, **kwargs)
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self.model.text_detector.box_thresh = backup
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return result
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