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refactor!: OCR text result provider

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283375
2025-06-21 21:49:34 +08:00
parent 3ebb058cdf
commit abfd37dbef
7 changed files with 142 additions and 112 deletions
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1
src/arcaea_offline_ocr/__init__.py
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@ -1,4 +1,3 @@
from .crop import *
from .device import *
from .ocr import *
from .utils import *

55
src/arcaea_offline_ocr/b30/chieri/v4/ocr.py
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@ -4,12 +4,6 @@ import cv2
import numpy as np
from ....crop import crop_xywh
from ....ocr import (
FixRects,
ocr_digits_by_contour_knn,
preprocess_hog,
resize_fill_square,
)
from ....phash_db import ImagePhashDatabase
from ....types import Mat
from ...shared import B30OcrResultItem
@ -28,36 +22,21 @@ from .colors import (
PURE_BG_MIN_HSV,
)
from .rois import ChieriBotV4Rois
from ....providers.knn import OcrKNearestTextProvider
class ChieriBotV4Ocr:
def __init__(
self,
score_knn: cv2.ml.KNearest,
pfl_knn: cv2.ml.KNearest,
score_knn_provider: OcrKNearestTextProvider,
pfl_knn_provider: OcrKNearestTextProvider,
phash_db: ImagePhashDatabase,
factor: float = 1.0,
):
self.__score_knn = score_knn
self.__pfl_knn = pfl_knn
self.__phash_db = phash_db
self.__rois = ChieriBotV4Rois(factor)
@property
def score_knn(self):
return self.__score_knn
@score_knn.setter
def score_knn(self, knn_digits_model: cv2.ml.KNearest):
self.__score_knn = knn_digits_model
@property
def pfl_knn(self):
return self.__pfl_knn
@pfl_knn.setter
def pfl_knn(self, knn_digits_model: cv2.ml.KNearest):
self.__pfl_knn = knn_digits_model
self.pfl_knn_provider = pfl_knn_provider
self.score_knn_provider = score_knn_provider
@property
def phash_db(self):
@ -125,7 +104,9 @@ class ChieriBotV4Ocr:
if rect[3] > score_roi.shape[0] * 0.5:
continue
score_roi = cv2.fillPoly(score_roi, [contour], 0)
return ocr_digits_by_contour_knn(score_roi, self.score_knn)
ocr_result = self.score_knn_provider.result(score_roi)
return int(ocr_result) if ocr_result else 0
def find_pfl_rects(
self, component_pfl_processed: Mat
@ -203,25 +184,9 @@ class ChieriBotV4Ocr:
pure_far_lost = []
for pfl_roi_rect in pfl_rects:
roi = crop_xywh(pfl_roi, pfl_roi_rect)
digit_contours, _ = cv2.findContours(
roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
)
digit_rects = [cv2.boundingRect(c) for c in digit_contours]
digit_rects = FixRects.connect_broken(
digit_rects, roi.shape[1], roi.shape[0]
)
digit_rects = FixRects.split_connected(roi, digit_rects)
digit_rects = sorted(digit_rects, key=lambda r: r[0])
digits = []
for digit_rect in digit_rects:
digit = crop_xywh(roi, digit_rect)
digit = resize_fill_square(digit, 20)
digits.append(digit)
samples = preprocess_hog(digits)
result = self.pfl_knn_provider.result(roi)
pure_far_lost.append(int(result) if result else None)
_, results, _, _ = self.pfl_knn.findNearest(samples, 4)
results = [str(int(i)) for i in results.ravel()]
pure_far_lost.append(int("".join(results)))
return tuple(pure_far_lost)
except Exception:
return (None, None, None)

6
src/arcaea_offline_ocr/device/common.py
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@ -5,10 +5,10 @@ from typing import Optional
@dataclass
class DeviceOcrResult:
rating_class: int
pure: int
far: int
lost: int
score: int
pure: Optional[int] = None
far: Optional[int] = None
lost: Optional[int] = None
max_recall: Optional[int] = None
song_id: Optional[str] = None
song_id_possibility: Optional[float] = None

54
src/arcaea_offline_ocr/device/ocr.py
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@ -1,15 +1,8 @@
import cv2
import numpy as np
from ..crop import crop_xywh
from ..ocr import (
FixRects,
ocr_digit_samples_knn,
ocr_digits_by_contour_knn,
preprocess_hog,
resize_fill_square,
)
from ..phash_db import ImagePhashDatabase
from ..providers.knn import OcrKNearestTextProvider
from ..types import Mat
from .common import DeviceOcrResult
from .rois.extractor import DeviceRoisExtractor
@ -21,38 +14,37 @@ class DeviceOcr:
self,
extractor: DeviceRoisExtractor,
masker: DeviceRoisMasker,
knn_model: cv2.ml.KNearest,
knn_provider: OcrKNearestTextProvider,
phash_db: ImagePhashDatabase,
):
self.extractor = extractor
self.masker = masker
self.knn_model = knn_model
self.knn_provider = knn_provider
self.phash_db = phash_db
def pfl(self, roi_gray: Mat, factor: float = 1.25):
contours, _ = cv2.findContours(
roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
)
filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
rects = [cv2.boundingRect(c) for c in filtered_contours]
rects = FixRects.connect_broken(rects, roi_gray.shape[1], roi_gray.shape[0])
def contour_filter(cnt):
return cv2.contourArea(cnt) >= 5 * factor
filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
filtered_rects = FixRects.split_connected(roi_gray, filtered_rects)
filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
contours = self.knn_provider.contours(roi_gray)
contours_filtered = self.knn_provider.contours(
roi_gray, contours_filter=contour_filter
)
roi_ocr = roi_gray.copy()
filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
contours_filtered_flattened = {tuple(c.flatten()) for c in contours_filtered}
for contour in contours:
if tuple(contour.flatten()) in filtered_contours_flattened:
if tuple(contour.flatten()) in contours_filtered_flattened:
continue
roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
digit_rois = [
resize_fill_square(crop_xywh(roi_ocr, r), 20) for r in filtered_rects
]
samples = preprocess_hog(digit_rois)
return ocr_digit_samples_knn(samples, self.knn_model)
ocr_result = self.knn_provider.result(
roi_ocr,
contours_filter=lambda cnt: cv2.contourArea(cnt) >= 5 * factor,
rects_filter=lambda rect: rect[2] >= 5 * factor and rect[3] >= 6 * factor,
)
return int(ocr_result) if ocr_result else 0
def pure(self):
return self.pfl(self.masker.pure(self.extractor.pure))
@ -65,13 +57,14 @@ class DeviceOcr:
def score(self):
roi = self.masker.score(self.extractor.score)
contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = self.knn_provider.contours(roi)
for contour in contours:
if (
cv2.boundingRect(contour)[3] < roi.shape[0] * 0.6
): # h < score_component_h * 0.6
roi = cv2.fillPoly(roi, [contour], [0])
return ocr_digits_by_contour_knn(roi, self.knn_model)
ocr_result = self.knn_provider.result(roi)
return int(ocr_result) if ocr_result else 0
def rating_class(self):
roi = self.extractor.rating_class
@ -85,9 +78,10 @@ class DeviceOcr:
return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
def max_recall(self):
return ocr_digits_by_contour_knn(
self.masker.max_recall(self.extractor.max_recall), self.knn_model
ocr_result = self.knn_provider.result(
self.masker.max_recall(self.extractor.max_recall)
)
return int(ocr_result) if ocr_result else None
def clear_status(self):
roi = self.extractor.clear_status

0
src/arcaea_offline_ocr/providers/__init__.py Normal file
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12
src/arcaea_offline_ocr/providers/base.py Normal file
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@ -0,0 +1,12 @@
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, Optional
if TYPE_CHECKING:
from ..types import Mat
class OcrTextProvider(ABC):
@abstractmethod
def result_raw(self, img: "Mat", /, *args, **kwargs) -> Any: ...
@abstractmethod
def result(self, img: "Mat", /, *args, **kwargs) -> Optional[str]: ...

124
src/arcaea_offline_ocr/ocr.py → src/arcaea_offline_ocr/providers/knn.py
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@ -1,18 +1,19 @@
import logging
import math
from typing import Optional, Sequence, Tuple
from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple
import cv2
import numpy as np
from .crop import crop_xywh
from .types import Mat
from ..crop import crop_xywh
from .base import OcrTextProvider
__all__ = [
"FixRects",
"preprocess_hog",
"ocr_digits_by_contour_get_samples",
"ocr_digits_by_contour_knn",
]
if TYPE_CHECKING:
from cv2.ml import KNearest
from ..types import Mat
logger = logging.getLogger(__name__)
class FixRects:
@ -68,7 +69,7 @@ class FixRects:
@staticmethod
def split_connected(
img_masked: Mat,
img_masked: "Mat",
rects: Sequence[Tuple[int, int, int, int]],
rect_wh_ratio: float = 1.05,
width_range_ratio: float = 0.1,
@ -118,7 +119,7 @@ class FixRects:
return return_rects
def resize_fill_square(img: Mat, target: int = 20):
def resize_fill_square(img: "Mat", target: int = 20):
h, w = img.shape[:2]
if h > w:
new_h = target
@ -152,29 +153,88 @@ def preprocess_hog(digit_rois):
def ocr_digit_samples_knn(__samples, knn_model: cv2.ml.KNearest, k: int = 4):
_, results, _, _ = knn_model.findNearest(__samples, k)
result_list = [int(r) for r in results.ravel()]
result_str = "".join(str(r) for r in result_list if r > -1)
return int(result_str) if result_str else 0
return [int(r) for r in results.ravel()]
def ocr_digits_by_contour_get_samples(__roi_gray: Mat, size: int):
roi = __roi_gray.copy()
contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
rects = [cv2.boundingRect(c) for c in contours]
rects = FixRects.connect_broken(rects, roi.shape[1], roi.shape[0])
rects = FixRects.split_connected(roi, rects)
rects = sorted(rects, key=lambda r: r[0])
# digit_rois = [cv2.resize(crop_xywh(roi, rect), size) for rect in rects]
digit_rois = [resize_fill_square(crop_xywh(roi, rect), size) for rect in rects]
return preprocess_hog(digit_rois)
class OcrKNearestTextProvider(OcrTextProvider):
_ContourFilter = Callable[["Mat"], bool]
_RectsFilter = Callable[[Sequence[int]], bool]
def __init__(self, model: "KNearest"):
self.model = model
def ocr_digits_by_contour_knn(
__roi_gray: Mat,
knn_model: cv2.ml.KNearest,
def contours(
self, img: "Mat", /, *, contours_filter: Optional[_ContourFilter] = None
):
cnts, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if contours_filter:
cnts = list(filter(contours_filter, cnts))
return cnts
def result_raw(
self,
img: "Mat",
/,
*,
k=4,
size: int = 20,
) -> int:
samples = ocr_digits_by_contour_get_samples(__roi_gray, size)
return ocr_digit_samples_knn(samples, knn_model, k)
fix_rects: bool = True,
contours_filter: Optional[_ContourFilter] = None,
rects_filter: Optional[_RectsFilter] = None,
):
"""
:param img: grayscaled roi
"""
try:
cnts, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours_filter:
cnts = list(filter(contours_filter, cnts))
rects = [cv2.boundingRect(cnt) for cnt in cnts]
if fix_rects and rects_filter:
rects = FixRects.connect_broken(rects, img.shape[1], img.shape[0]) # type: ignore
rects = list(filter(rects_filter, rects))
rects = FixRects.split_connected(img, rects)
elif fix_rects:
rects = FixRects.connect_broken(rects, img.shape[1], img.shape[0]) # type: ignore
rects = FixRects.split_connected(img, rects)
elif rects_filter:
rects = list(filter(rects_filter, rects))
rects = sorted(rects, key=lambda r: r[0])
digits = []
for rect in rects:
digit = crop_xywh(img, rect)
digit = resize_fill_square(digit, 20)
digits.append(digit)
samples = preprocess_hog(digits)
return ocr_digit_samples_knn(samples, self.model)
except Exception:
logger.exception("Error occurred during KNearest OCR")
return None
def result(
self,
img: "Mat",
/,
*,
fix_rects: bool = True,
contours_filter: Optional[_ContourFilter] = None,
rects_filter: Optional[_RectsFilter] = None,
):
"""
:param img: grayscaled roi
"""
raw = self.result_raw(
img,
fix_rects=fix_rects,
contours_filter=contours_filter,
rects_filter=rects_filter,
)
return (
"".join(["".join(str(r) for r in raw if r > -1)])
if raw is not None
else None
)