mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-12-12 07:29:03 +00:00
480 lines
15 KiB
Python
480 lines
15 KiB
Python
import time
|
|
from datetime import timedelta
|
|
|
|
import numpy as np
|
|
from pponnxcr.predict_system import BoxedResult
|
|
|
|
import module.config.server as server
|
|
from module.base.button import ButtonWrapper
|
|
from module.base.decorator import cached_property
|
|
from module.base.utils import *
|
|
from module.exception import ScriptError
|
|
from module.logger import logger
|
|
from module.ocr.models import OCR_MODEL, TextSystem
|
|
from module.ocr.utils import merge_buttons
|
|
|
|
|
|
class OcrResultButton:
|
|
def __init__(self, boxed_result: BoxedResult, matched_keyword):
|
|
"""
|
|
Args:
|
|
boxed_result: BoxedResult from ppocr-onnx
|
|
matched_keyword: Keyword object or None
|
|
"""
|
|
self.area = boxed_result.box
|
|
self.search = area_pad(self.area, pad=-20)
|
|
# self.color =
|
|
self.button = boxed_result.box
|
|
|
|
if matched_keyword is not None:
|
|
self.matched_keyword = matched_keyword
|
|
self.name = str(matched_keyword)
|
|
else:
|
|
self.matched_keyword = None
|
|
self.name = boxed_result.ocr_text
|
|
|
|
self.text = boxed_result.ocr_text
|
|
self.score = boxed_result.score
|
|
|
|
def __str__(self):
|
|
return self.name
|
|
|
|
__repr__ = __str__
|
|
|
|
def __eq__(self, other):
|
|
return str(self) == str(other)
|
|
|
|
def __hash__(self):
|
|
return hash(self.name)
|
|
|
|
def __bool__(self):
|
|
return True
|
|
|
|
@property
|
|
def is_keyword_matched(self) -> bool:
|
|
return self.matched_keyword is not None
|
|
|
|
|
|
class Ocr:
|
|
# Merge results with box distance <= thres
|
|
merge_thres_x = 0
|
|
merge_thres_y = 0
|
|
|
|
def __init__(self, button: ButtonWrapper, lang=None, name=None):
|
|
"""
|
|
Args:
|
|
button:
|
|
lang: If None, use in-game language
|
|
name: If None, use button.name
|
|
"""
|
|
if lang is None:
|
|
lang = server.lang
|
|
if name is None:
|
|
name = button.name
|
|
|
|
self.button: ButtonWrapper = button
|
|
self.lang: str = lang
|
|
self.name: str = name
|
|
|
|
@cached_property
|
|
def model(self) -> TextSystem:
|
|
return OCR_MODEL.get_by_lang(self.lang)
|
|
|
|
def pre_process(self, image):
|
|
"""
|
|
Args:
|
|
image (np.ndarray): Shape (height, width, channel)
|
|
|
|
Returns:
|
|
np.ndarray: Shape (width, height)
|
|
"""
|
|
return image
|
|
|
|
def after_process(self, result):
|
|
"""
|
|
Args:
|
|
result (str): '第二行'
|
|
|
|
Returns:
|
|
str:
|
|
"""
|
|
if result.startswith('UID'):
|
|
result = 'UID'
|
|
return result
|
|
|
|
def format_result(self, result):
|
|
"""
|
|
Will be overriden.
|
|
"""
|
|
return result
|
|
|
|
def _log_change(self, attr, func, before):
|
|
after = func(before)
|
|
if after != before:
|
|
logger.attr(f'{self.name} {attr}', f'{before} -> {after}')
|
|
return after
|
|
|
|
def ocr_single_line(self, image, direct_ocr=False):
|
|
# pre process
|
|
start_time = time.time()
|
|
if not direct_ocr:
|
|
image = crop(image, self.button.area)
|
|
image = self.pre_process(image)
|
|
# ocr
|
|
result, _ = self.model.ocr_single_line(image)
|
|
# after proces
|
|
result = self._log_change('after', self.after_process, result)
|
|
result = self._log_change('format', self.format_result, result)
|
|
logger.attr(name='%s %ss' % (self.name, float2str(time.time() - start_time)),
|
|
text=str(result))
|
|
return result
|
|
|
|
def ocr_multi_lines(self, image_list):
|
|
# pre process
|
|
start_time = time.time()
|
|
image_list = [self.pre_process(image) for image in image_list]
|
|
# ocr
|
|
result_list = self.model.ocr_lines(image_list)
|
|
result_list = [(result, score) for result, score in result_list]
|
|
# after process
|
|
result_list = [(self.after_process(result), score) for result, score in result_list]
|
|
result_list = [(self.format_result(result), score) for result, score in result_list]
|
|
logger.attr(name="%s %ss" % (self.name, float2str(time.time() - start_time)),
|
|
text=str([result for result, _ in result_list]))
|
|
return result_list
|
|
|
|
def filter_detected(self, result: BoxedResult) -> bool:
|
|
"""
|
|
Return False to drop result.
|
|
"""
|
|
return True
|
|
|
|
def detect_and_ocr(self, image, direct_ocr=False) -> list[BoxedResult]:
|
|
"""
|
|
Args:
|
|
image:
|
|
direct_ocr: True to ignore `button` attribute and feed the image to OCR model without cropping.
|
|
|
|
Returns:
|
|
|
|
"""
|
|
# pre process
|
|
start_time = time.time()
|
|
if not direct_ocr:
|
|
image = crop(image, self.button.area)
|
|
image = self.pre_process(image)
|
|
# ocr
|
|
results: list[BoxedResult] = self.model.detect_and_ocr(image)
|
|
# after proces
|
|
for result in results:
|
|
if not direct_ocr:
|
|
result.box += self.button.area[:2]
|
|
result.box = tuple(corner2area(result.box))
|
|
|
|
results = [result for result in results if self.filter_detected(result)]
|
|
results = merge_buttons(results, thres_x=self.merge_thres_x, thres_y=self.merge_thres_y)
|
|
for result in results:
|
|
result.ocr_text = self.after_process(result.ocr_text)
|
|
|
|
logger.attr(name='%s %ss' % (self.name, float2str(time.time() - start_time)),
|
|
text=str([result.ocr_text for result in results]))
|
|
return results
|
|
|
|
def _match_result(
|
|
self,
|
|
result: str,
|
|
keyword_classes,
|
|
lang: str = None,
|
|
ignore_punctuation=True,
|
|
ignore_digit=True):
|
|
"""
|
|
Args:
|
|
result (str):
|
|
keyword_classes: A list of `Keyword` class or classes inherited `Keyword`
|
|
|
|
Returns:
|
|
If matched, return `Keyword` object or objects inherited `Keyword`
|
|
If not match, return None
|
|
"""
|
|
if not isinstance(keyword_classes, list):
|
|
keyword_classes = [keyword_classes]
|
|
|
|
# Digits will be considered as the index of keyword
|
|
if ignore_digit:
|
|
if result.isdigit():
|
|
return None
|
|
|
|
# Try in current lang
|
|
for keyword_class in keyword_classes:
|
|
try:
|
|
matched = keyword_class.find(
|
|
result,
|
|
lang=lang,
|
|
ignore_punctuation=ignore_punctuation
|
|
)
|
|
return matched
|
|
except ScriptError:
|
|
continue
|
|
|
|
return None
|
|
|
|
def matched_single_line(
|
|
self,
|
|
image,
|
|
keyword_classes,
|
|
lang: str = None,
|
|
ignore_punctuation=True
|
|
) -> OcrResultButton:
|
|
"""
|
|
Args:
|
|
image: Image to detect
|
|
keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
|
|
lang:
|
|
ignore_punctuation:
|
|
|
|
Returns:
|
|
OcrResultButton: Or None if it didn't matched known keywords.
|
|
"""
|
|
result = self.ocr_single_line(image)
|
|
|
|
result = self._match_result(
|
|
result,
|
|
keyword_classes=keyword_classes,
|
|
lang=lang,
|
|
ignore_punctuation=ignore_punctuation,
|
|
)
|
|
|
|
logger.attr(name=f'{self.name} matched',
|
|
text=result)
|
|
return result
|
|
|
|
def matched_multi_lines(
|
|
self,
|
|
image_list,
|
|
keyword_classes,
|
|
lang: str = None,
|
|
ignore_punctuation=True
|
|
) -> list[OcrResultButton]:
|
|
"""
|
|
Args:
|
|
image_list:
|
|
keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
|
|
lang:
|
|
ignore_punctuation:
|
|
|
|
Returns:
|
|
List of matched OcrResultButton.
|
|
OCR result which didn't matched known keywords will be dropped.
|
|
"""
|
|
results = self.ocr_multi_lines(image_list)
|
|
|
|
results = [self._match_result(
|
|
result,
|
|
keyword_classes=keyword_classes,
|
|
lang=lang,
|
|
ignore_punctuation=ignore_punctuation,
|
|
) for result in results]
|
|
results = [result for result in results if result.is_keyword_matched]
|
|
|
|
logger.attr(name=f'{self.name} matched',
|
|
text=results)
|
|
return results
|
|
|
|
def _product_button(
|
|
self,
|
|
boxed_result: BoxedResult,
|
|
keyword_classes,
|
|
lang: str = None,
|
|
ignore_punctuation=True,
|
|
ignore_digit=True
|
|
) -> OcrResultButton:
|
|
if not isinstance(keyword_classes, list):
|
|
keyword_classes = [keyword_classes]
|
|
|
|
matched_keyword = self._match_result(
|
|
boxed_result.ocr_text,
|
|
keyword_classes=keyword_classes,
|
|
lang=lang,
|
|
ignore_punctuation=ignore_punctuation,
|
|
ignore_digit=ignore_digit,
|
|
)
|
|
button = OcrResultButton(boxed_result, matched_keyword)
|
|
return button
|
|
|
|
def matched_ocr(self, image, keyword_classes, direct_ocr=False) -> list[OcrResultButton]:
|
|
"""
|
|
Args:
|
|
image: Screenshot
|
|
keyword_classes: `Keyword` class or classes inherited `Keyword`, or a list of them.
|
|
direct_ocr: True to ignore `button` attribute and feed the image to OCR model without cropping.
|
|
|
|
Returns:
|
|
List of matched OcrResultButton.
|
|
OCR result which didn't matched known keywords will be dropped.
|
|
"""
|
|
results = self.detect_and_ocr(image, direct_ocr=direct_ocr)
|
|
|
|
results = [self._product_button(result, keyword_classes) for result in results]
|
|
results = [result for result in results if result.is_keyword_matched]
|
|
|
|
logger.attr(name=f'{self.name} matched',
|
|
text=results)
|
|
return results
|
|
|
|
|
|
class Digit(Ocr):
|
|
def __init__(self, button: ButtonWrapper, lang=None, name=None):
|
|
super().__init__(button, lang=lang, name=name)
|
|
|
|
def format_result(self, result) -> int:
|
|
"""
|
|
Returns:
|
|
int:
|
|
"""
|
|
result = super().after_process(result)
|
|
logger.attr(name=self.name, text=str(result))
|
|
|
|
res = re.search(r'(\d+)', result)
|
|
if res:
|
|
return int(res.group(1))
|
|
else:
|
|
logger.warning(f'No digit found in {result}')
|
|
return 0
|
|
|
|
|
|
class DigitCounter(Ocr):
|
|
def __init__(self, button: ButtonWrapper, lang=None, name=None):
|
|
super().__init__(button, lang=lang, name=name)
|
|
|
|
@classmethod
|
|
def is_format_matched(cls, result) -> bool:
|
|
return '/' in result
|
|
|
|
def format_result(self, result) -> tuple[int, int, int]:
|
|
"""
|
|
Do OCR on a counter, such as `14/15`, and returns 14, 1, 15
|
|
|
|
Returns:
|
|
int, int, int: current, remain, total
|
|
"""
|
|
result = super().after_process(result)
|
|
logger.attr(name=self.name, text=str(result))
|
|
|
|
res = re.search(r'(\d+)\s*/\s*(\d+)', result)
|
|
if res:
|
|
groups = [int(s) for s in res.groups()]
|
|
current, total = int(groups[0]), int(groups[1])
|
|
# current = min(current, total)
|
|
return current, total - current, total
|
|
else:
|
|
logger.warning(f'No digit counter found in {result}')
|
|
return 0, 0, 0
|
|
|
|
|
|
class Duration(Ocr):
|
|
@classmethod
|
|
def timedelta_regex(cls, lang):
|
|
regex_str = {
|
|
'cn': r'^(?P<prefix>.*?)'
|
|
r'((?P<days>\d{1,2})\s*天\s*)?'
|
|
r'((?P<hours>\d{1,2})\s*小时\s*)?'
|
|
r'((?P<minutes>\d{1,2})\s*分钟\s*)?'
|
|
r'((?P<seconds>\d{1,2})\s*秒)?'
|
|
r'(?P<suffix>[^天时钟秒]*?)$',
|
|
'en': r'^(?P<prefix>.*?)'
|
|
r'((?P<days>\d{1,2})\s*d\s*)?'
|
|
r'((?P<hours>\d{1,2})\s*h\s*)?'
|
|
r'((?P<minutes>\d{1,2})\s*m\s*)?'
|
|
r'((?P<seconds>\d{1,2})\s*s)?'
|
|
r'(?P<suffix>[^dhms]*?)$'
|
|
}[lang]
|
|
return re.compile(regex_str)
|
|
|
|
def after_process(self, result):
|
|
result = super().after_process(result)
|
|
result = result.strip('.,。，')
|
|
result = result.replace('Oh', '0h').replace('oh', '0h')
|
|
return result
|
|
|
|
def format_result(self, result: str) -> timedelta:
|
|
"""
|
|
Do OCR on a duration, such as `18d 2h 13m 30s`, `2h`, `13m 30s`, `9s`
|
|
|
|
Returns:
|
|
timedelta:
|
|
"""
|
|
matched = self.timedelta_regex(self.lang).search(result)
|
|
if not matched:
|
|
return timedelta()
|
|
days = self._sanitize_number(matched.group('days'))
|
|
hours = self._sanitize_number(matched.group('hours'))
|
|
minutes = self._sanitize_number(matched.group('minutes'))
|
|
seconds = self._sanitize_number(matched.group('seconds'))
|
|
return timedelta(days=days, hours=hours, minutes=minutes, seconds=seconds)
|
|
|
|
@staticmethod
|
|
def _sanitize_number(number) -> int:
|
|
if number is None:
|
|
return 0
|
|
return int(number)
|
|
|
|
|
|
class OcrWhiteLetterOnComplexBackground(Ocr):
|
|
white_preprocess = True
|
|
# 0.6 by default, 0.2 for lower
|
|
box_thresh = 0.2
|
|
# (x, y) Enlarge detected boxes to `min_boxes`
|
|
# So standalone digits can be better detected
|
|
# Note that min_box should be 4px larger than the actual letter
|
|
min_box = None
|
|
|
|
def pre_process(self, image):
|
|
if self.white_preprocess:
|
|
image = extract_white_letters(image, threshold=255)
|
|
image = cv2.merge([image, image, image])
|
|
return image
|
|
|
|
@staticmethod
|
|
def enlarge_box(box, min_box):
|
|
area = corner2area(box)
|
|
center = (int(x) for x in area_center(area))
|
|
size_x, size_y = area_size(area)
|
|
min_x, min_y = min_box
|
|
if size_x < min_x or size_y < min_y:
|
|
size_x = max(size_x, min_x) // 2
|
|
size_y = max(size_y, min_y) // 2
|
|
area = area_offset((-size_x, -size_y, size_x, size_y), center)
|
|
box = area2corner(area)
|
|
box = np.array([box[0], box[1], box[3], box[2]]).astype(np.float32)
|
|
return box
|
|
else:
|
|
return box
|
|
|
|
def enlarge_boxes(self, boxes):
|
|
if self.min_box is None:
|
|
return boxes
|
|
|
|
boxes = [self.enlarge_box(box, self.min_box) for box in boxes]
|
|
boxes = np.array(boxes)
|
|
return boxes
|
|
|
|
def detect_and_ocr(self, *args, **kwargs):
|
|
# Try hard to lower TextSystem.box_thresh
|
|
backup = self.model.text_detector.box_thresh
|
|
self.model.text_detector.box_thresh = 0.2
|
|
# Patch TextDetector
|
|
text_detector = self.model.text_detector
|
|
|
|
def text_detector_with_min_box(*args, **kwargs):
|
|
dt_boxes, elapse = text_detector(*args, **kwargs)
|
|
dt_boxes = self.enlarge_boxes(dt_boxes)
|
|
return dt_boxes, elapse
|
|
|
|
self.model.text_detector = text_detector_with_min_box
|
|
try:
|
|
result = super().detect_and_ocr(*args, **kwargs)
|
|
finally:
|
|
self.model.text_detector.box_thresh = backup
|
|
self.model.text_detector = text_detector
|
|
return result
|