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refactor: replace device structure
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@ -2,24 +2,24 @@ import cv2
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import numpy as np
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from PIL import Image
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from .crop import crop_xywh
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from .extractor import Extractor
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from .masker import Masker
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from .ocr import (
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from ..crop import crop_xywh
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from ..ocr import (
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FixRects,
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ocr_digit_samples_knn,
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ocr_digits_by_contour_knn,
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preprocess_hog,
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resize_fill_square,
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)
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from .phash_db import ImagePHashDatabase
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from ..phash_db import ImagePHashDatabase
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from .roi.extractor import DeviceRoiExtractor
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from .roi.masker import DeviceRoiMasker
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class DeviceOcr:
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def __init__(
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self,
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extractor: Extractor,
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masker: Masker,
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extractor: DeviceRoiExtractor,
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masker: DeviceRoiMasker,
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knn_model: cv2.ml.KNearest,
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phash_db: ImagePHashDatabase,
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):
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@ -1,2 +1 @@
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from .common import DeviceRoiExtractor
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from .sizes import *
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@ -1,7 +1,7 @@
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import cv2
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from ..crop import crop_xywh
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from .sizes.common import DeviceRoiSizes
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from ....crop import crop_xywh
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from ..definitions.common import DeviceRoiSizes
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class DeviceRoiExtractor:
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@ -1,53 +0,0 @@
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from typing import Tuple
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from ...types import Mat
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from .definition import DeviceV1
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__all__ = [
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"crop_img",
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"crop_from_device_attr",
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"crop_to_pure",
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"crop_to_far",
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"crop_to_lost",
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"crop_to_max_recall",
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"crop_to_rating_class",
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"crop_to_score",
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"crop_to_title",
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]
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def crop_img(img: Mat, *, top: int, left: int, bottom: int, right: int):
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return img[top:bottom, left:right]
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def crop_from_device_attr(img: Mat, rect: Tuple[int, int, int, int]):
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x, y, w, h = rect
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return crop_img(img, top=y, left=x, bottom=y + h, right=x + w)
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def crop_to_pure(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.pure)
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def crop_to_far(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.far)
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def crop_to_lost(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.lost)
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def crop_to_max_recall(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.max_recall)
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def crop_to_rating_class(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.rating_class)
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def crop_to_score(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.score)
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def crop_to_title(screenshot: Mat, device: DeviceV1):
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return crop_from_device_attr(screenshot, device.title)
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@ -1,37 +0,0 @@
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from dataclasses import dataclass
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from typing import Any, Dict, Tuple
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__all__ = ["DeviceV1"]
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@dataclass(kw_only=True)
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class DeviceV1:
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version: int
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uuid: str
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name: str
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pure: Tuple[int, int, int, int]
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far: Tuple[int, int, int, int]
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lost: Tuple[int, int, int, int]
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max_recall: Tuple[int, int, int, int]
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rating_class: Tuple[int, int, int, int]
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score: Tuple[int, int, int, int]
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title: Tuple[int, int, int, int]
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@classmethod
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def from_json_object(cls, json_dict: Dict[str, Any]):
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if json_dict["version"] == 1:
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return cls(
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version=1,
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uuid=json_dict["uuid"],
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name=json_dict["name"],
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pure=json_dict["pure"],
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far=json_dict["far"],
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lost=json_dict["lost"],
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max_recall=json_dict["max_recall"],
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rating_class=json_dict["rating_class"],
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score=json_dict["score"],
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title=json_dict["title"],
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)
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def repr_info(self):
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return f"Device(version={self.version}, uuid={repr(self.uuid)}, name={repr(self.name)})"
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@ -1,86 +0,0 @@
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from typing import List
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import cv2
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from ...crop import crop_xywh
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from ...mask import mask_gray, mask_white
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from ...ocr import ocr_digits_by_contour_knn, ocr_rating_class
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from ...types import Mat, cv2_ml_KNearest
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from ..shared import DeviceOcrResult
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from .crop import *
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from .definition import DeviceV1
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class DeviceV1Ocr:
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def __init__(self, device: DeviceV1, knn_model: cv2_ml_KNearest):
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self.__device = device
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self.__knn_model = knn_model
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@property
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def device(self):
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return self.__device
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@device.setter
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def device(self, value):
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self.__device = value
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@property
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def knn_model(self):
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return self.__knn_model
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@knn_model.setter
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def knn_model(self, value):
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self.__knn_model = value
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def preprocess_score_roi(self, __roi_gray: Mat) -> List[Mat]:
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roi_gray = __roi_gray.copy()
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contours, _ = cv2.findContours(
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roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
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)
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for contour in contours:
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rect = cv2.boundingRect(contour)
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if rect[3] > roi_gray.shape[0] * 0.6:
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continue
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roi_gray = cv2.fillPoly(roi_gray, [contour], 0)
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return roi_gray
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def ocr(self, img_bgr: Mat):
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rating_class_roi = crop_to_rating_class(img_bgr, self.device)
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rating_class = ocr_rating_class(rating_class_roi)
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pfl_mr_roi = [
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crop_to_pure(img_bgr, self.device),
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crop_to_far(img_bgr, self.device),
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crop_to_lost(img_bgr, self.device),
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crop_to_max_recall(img_bgr, self.device),
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]
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pfl_mr_roi = [mask_gray(roi) for roi in pfl_mr_roi]
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pure, far, lost = [
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ocr_digits_by_contour_knn(roi, self.knn_model) for roi in pfl_mr_roi[:3]
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]
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max_recall_contours, _ = cv2.findContours(
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pfl_mr_roi[3], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
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)
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max_recall_rects = [cv2.boundingRect(c) for c in max_recall_contours]
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max_recall_rect = sorted(max_recall_rects, key=lambda r: r[0])[-1]
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max_recall_roi = crop_xywh(img_bgr, max_recall_rect)
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max_recall = ocr_digits_by_contour_knn(max_recall_roi, self.knn_model)
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score_roi = crop_to_score(img_bgr, self.device)
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score_roi = mask_white(score_roi)
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score_roi = self.preprocess_score_roi(score_roi)
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score = ocr_digits_by_contour_knn(score_roi, self.knn_model)
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return DeviceOcrResult(
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song_id=None,
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title=None,
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rating_class=rating_class,
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pure=pure,
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far=far,
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lost=lost,
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score=score,
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max_recall=max_recall,
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clear_type=None,
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)
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@ -1,4 +0,0 @@
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from .definition import DeviceV2
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from .ocr import DeviceV2Ocr
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from .rois import DeviceV2AutoRois, DeviceV2Rois
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from .shared import MAX_RECALL_CLOSE_KERNEL
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@ -1,26 +0,0 @@
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from typing import Iterable
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from attrs import define, field
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from ...types import XYWHRect
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def iterable_to_xywh_rect(__iter: Iterable) -> XYWHRect:
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return XYWHRect(*__iter)
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@define(kw_only=True)
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class DeviceV2:
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version = field(type=int)
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uuid = field(type=str)
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name = field(type=str)
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crop_black_edges = field(type=bool)
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factor = field(type=float)
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pure = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
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far = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
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lost = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
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score = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
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max_recall_rating_class = field(
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converter=iterable_to_xywh_rect, default=[0, 0, 0, 0]
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)
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title = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
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@ -1,172 +0,0 @@
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import math
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from functools import lru_cache
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from typing import Sequence
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import cv2
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import numpy as np
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from PIL import Image
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from ...crop import crop_xywh
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from ...mask import (
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mask_byd,
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mask_ftr,
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mask_gray,
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mask_max_recall_purple,
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mask_pfl_white,
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mask_prs,
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mask_pst,
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mask_white,
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)
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from ...ocr import (
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FixRects,
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ocr_digit_samples_knn,
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ocr_digits_by_contour_knn,
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preprocess_hog,
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resize_fill_square,
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)
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from ...phash_db import ImagePHashDatabase
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from ...sift_db import SIFTDatabase
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from ...types import Mat, cv2_ml_KNearest
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from ..shared import DeviceOcrResult
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from .preprocess import find_digits_preprocess
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from .rois import DeviceV2Rois
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from .shared import MAX_RECALL_CLOSE_KERNEL
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from .sizes import SizesV2
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class DeviceV2Ocr:
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def __init__(self, knn_model: cv2_ml_KNearest, phash_db: ImagePHashDatabase):
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self.__knn_model = knn_model
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self.__phash_db = phash_db
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@property
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def knn_model(self):
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if not self.__knn_model:
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raise ValueError("`knn_model` unset.")
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return self.__knn_model
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@knn_model.setter
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def knn_model(self, value: cv2_ml_KNearest):
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self.__knn_model = value
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@property
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def phash_db(self):
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if not self.__phash_db:
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raise ValueError("`phash_db` unset.")
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return self.__phash_db
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@phash_db.setter
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def phash_db(self, value: SIFTDatabase):
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self.__phash_db = value
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@lru_cache
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def _get_digit_widths(self, num_list: Sequence[int], factor: float):
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widths = set()
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for n in num_list:
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lower = math.floor(n * factor)
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upper = math.ceil(n * factor)
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widths.update(range(lower, upper + 1))
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return widths
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def _base_ocr_pfl(self, roi_masked: Mat, factor: float = 1.0):
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contours, _ = cv2.findContours(
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roi_masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
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)
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filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
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rects = [cv2.boundingRect(c) for c in filtered_contours]
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rects = FixRects.connect_broken(rects, roi_masked.shape[1], roi_masked.shape[0])
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rect_contour_map = dict(zip(rects, filtered_contours))
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filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
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filtered_rects = FixRects.split_connected(roi_masked, filtered_rects)
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filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
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roi_ocr = roi_masked.copy()
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filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
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for contour in contours:
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if tuple(contour.flatten()) in filtered_contours_flattened:
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continue
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roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
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digit_rois = [
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resize_fill_square(crop_xywh(roi_ocr, r), 20)
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for r in sorted(filtered_rects, key=lambda r: r[0])
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]
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# [cv2.imshow(f"r{i}", r) for i, r in enumerate(digit_rois)]
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# cv2.waitKey(0)
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samples = preprocess_hog(digit_rois)
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return ocr_digit_samples_knn(samples, self.knn_model)
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def ocr_song_id(self, rois: DeviceV2Rois):
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jacket = cv2.cvtColor(rois.jacket, cv2.COLOR_BGR2GRAY)
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return self.phash_db.lookup_image(Image.fromarray(jacket))[0]
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def ocr_rating_class(self, rois: DeviceV2Rois):
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roi = cv2.cvtColor(rois.max_recall_rating_class, cv2.COLOR_BGR2HSV)
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results = [mask_pst(roi), mask_prs(roi), mask_ftr(roi), mask_byd(roi)]
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return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
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def ocr_score(self, rois: DeviceV2Rois):
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roi = cv2.cvtColor(rois.score, cv2.COLOR_BGR2HSV)
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roi = mask_white(roi)
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contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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if h < roi.shape[0] * 0.6:
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roi = cv2.fillPoly(roi, [contour], [0])
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return ocr_digits_by_contour_knn(roi, self.knn_model)
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def mask_pfl(self, pfl_roi: Mat, rois: DeviceV2Rois):
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return (
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mask_pfl_white(cv2.cvtColor(pfl_roi, cv2.COLOR_BGR2HSV))
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if isinstance(rois.sizes, SizesV2)
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else mask_gray(pfl_roi)
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)
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def ocr_pure(self, rois: DeviceV2Rois):
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roi = self.mask_pfl(rois.pure, rois)
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return self._base_ocr_pfl(roi, rois.sizes.factor)
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def ocr_far(self, rois: DeviceV2Rois):
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roi = self.mask_pfl(rois.far, rois)
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return self._base_ocr_pfl(roi, rois.sizes.factor)
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def ocr_lost(self, rois: DeviceV2Rois):
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roi = self.mask_pfl(rois.lost, rois)
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return self._base_ocr_pfl(roi, rois.sizes.factor)
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def ocr_max_recall(self, rois: DeviceV2Rois):
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roi = (
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mask_max_recall_purple(
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cv2.cvtColor(rois.max_recall_rating_class, cv2.COLOR_BGR2HSV)
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)
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if isinstance(rois.sizes, SizesV2)
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else mask_gray(rois.max_recall_rating_class)
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)
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roi_closed = cv2.morphologyEx(roi, cv2.MORPH_CLOSE, MAX_RECALL_CLOSE_KERNEL)
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contours, _ = cv2.findContours(
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roi_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
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)
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rects = sorted(
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[cv2.boundingRect(c) for c in contours], key=lambda r: r[0], reverse=True
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)
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max_recall_roi = crop_xywh(roi, rects[0])
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return ocr_digits_by_contour_knn(max_recall_roi, self.knn_model)
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def ocr(self, rois: DeviceV2Rois):
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song_id = self.ocr_song_id(rois)
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rating_class = self.ocr_rating_class(rois)
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score = self.ocr_score(rois)
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pure = self.ocr_pure(rois)
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far = self.ocr_far(rois)
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lost = self.ocr_lost(rois)
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max_recall = self.ocr_max_recall(rois)
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return DeviceOcrResult(
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rating_class=rating_class,
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pure=pure,
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far=far,
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lost=lost,
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score=score,
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max_recall=max_recall,
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song_id=song_id,
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)
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@ -1,54 +0,0 @@
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import cv2
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from ...types import Mat
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from .shared import *
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|
||||
|
||||
def find_digits_preprocess(__img_masked: Mat) -> Mat:
|
||||
img = __img_masked.copy()
|
||||
img_denoised = cv2.morphologyEx(img, cv2.MORPH_OPEN, PFL_DENOISE_KERNEL)
|
||||
# img_denoised = cv2.bitwise_and(img, img_denoised)
|
||||
|
||||
denoise_contours, _ = cv2.findContours(
|
||||
img_denoised, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
|
||||
)
|
||||
# cv2.drawContours(img_denoised, contours, -1, [128], 2)
|
||||
|
||||
# fill all contour.area < max(contour.area) * ratio with black pixels
|
||||
# for denoise purposes
|
||||
|
||||
# define threshold contour area
|
||||
# we assume the smallest digit "1", is 80% height of the image,
|
||||
# and at least 1.5 pixel wide, considering cv2.contourArea always
|
||||
# returns a smaller value than the actual contour area.
|
||||
max_contour_area = __img_masked.shape[0] * 0.8 * 1.5
|
||||
filtered_contours = list(
|
||||
filter(lambda c: cv2.contourArea(c) >= max_contour_area, denoise_contours)
|
||||
)
|
||||
|
||||
filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
|
||||
|
||||
for contour in denoise_contours:
|
||||
if tuple(contour.flatten()) not in filtered_contours_flattened:
|
||||
img_denoised = cv2.fillPoly(img_denoised, [contour], [0])
|
||||
|
||||
# old algorithm, finding the largest contour area
|
||||
## contour_area_tuples = [(contour, cv2.contourArea(contour)) for contour in contours]
|
||||
## contour_area_tuples = sorted(
|
||||
## contour_area_tuples, key=lambda item: item[1], reverse=True
|
||||
## )
|
||||
## max_contour_area = contour_area_tuples[0][1]
|
||||
## print(max_contour_area, [item[1] for item in contour_area_tuples])
|
||||
## contours_filter_end_index = len(contours)
|
||||
## for i, item in enumerate(contour_area_tuples):
|
||||
## contour, area = item
|
||||
## if area < max_contour_area * 0.15:
|
||||
## contours_filter_end_index = i
|
||||
## break
|
||||
## contours = [item[0] for item in contour_area_tuples]
|
||||
## for contour in contours[-contours_filter_end_index - 1:]:
|
||||
## img = cv2.fillPoly(img, [contour], [0])
|
||||
## img_denoised = cv2.fillPoly(img_denoised, [contour], [0])
|
||||
## contours = contours[:contours_filter_end_index]
|
||||
|
||||
return img_denoised
|
||||
@ -1,199 +0,0 @@
|
||||
from typing import Union
|
||||
|
||||
from ...crop import crop_black_edges, crop_xywh
|
||||
from ...types import Mat, XYWHRect
|
||||
from .definition import DeviceV2
|
||||
from .sizes import Sizes, SizesV1
|
||||
|
||||
|
||||
def to_int(num: Union[int, float]) -> int:
|
||||
return round(num)
|
||||
|
||||
|
||||
class DeviceV2Rois:
|
||||
def __init__(self, device: DeviceV2, img: Mat):
|
||||
self.device = device
|
||||
self.sizes = SizesV1(self.device.factor)
|
||||
self.__img = img
|
||||
|
||||
@staticmethod
|
||||
def construct_int_xywh_rect(x, y, w, h) -> XYWHRect:
|
||||
return XYWHRect(*[to_int(item) for item in [x, y, w, h]])
|
||||
|
||||
@property
|
||||
def img(self):
|
||||
return self.__img
|
||||
|
||||
@img.setter
|
||||
def img(self, img: Mat):
|
||||
self.__img = (
|
||||
crop_black_edges(img) if self.device.crop_black_edges else img.copy()
|
||||
)
|
||||
|
||||
@property
|
||||
def h(self):
|
||||
return self.img.shape[0]
|
||||
|
||||
@property
|
||||
def vmid(self):
|
||||
return self.h / 2
|
||||
|
||||
@property
|
||||
def w(self):
|
||||
return self.img.shape[1]
|
||||
|
||||
@property
|
||||
def hmid(self):
|
||||
return self.w / 2
|
||||
|
||||
@property
|
||||
def h_without_top_bar(self):
|
||||
"""img_height -= top_bar_height"""
|
||||
return self.h - self.sizes.TOP_BAR_HEIGHT
|
||||
|
||||
@property
|
||||
def h_without_top_bar_mid(self):
|
||||
return self.sizes.TOP_BAR_HEIGHT + self.h_without_top_bar / 2
|
||||
|
||||
@property
|
||||
def pfl_top(self):
|
||||
return self.h_without_top_bar_mid + self.sizes.PFL_TOP_FROM_VMID
|
||||
|
||||
@property
|
||||
def pfl_left(self):
|
||||
return self.hmid + self.sizes.PFL_LEFT_FROM_HMID
|
||||
|
||||
@property
|
||||
def pure_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=self.pfl_left,
|
||||
y=self.pfl_top,
|
||||
w=self.sizes.PFL_WIDTH,
|
||||
h=self.sizes.PFL_FONT_PX,
|
||||
)
|
||||
|
||||
@property
|
||||
def pure(self):
|
||||
return crop_xywh(self.img, self.pure_rect)
|
||||
|
||||
@property
|
||||
def far_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=self.pfl_left,
|
||||
y=self.pfl_top + self.sizes.PFL_FONT_PX + self.sizes.PURE_FAR_GAP,
|
||||
w=self.sizes.PFL_WIDTH,
|
||||
h=self.sizes.PFL_FONT_PX,
|
||||
)
|
||||
|
||||
@property
|
||||
def far(self):
|
||||
return crop_xywh(self.img, self.far_rect)
|
||||
|
||||
@property
|
||||
def lost_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=self.pfl_left,
|
||||
y=(
|
||||
self.pfl_top
|
||||
+ self.sizes.PFL_FONT_PX * 2
|
||||
+ self.sizes.PURE_FAR_GAP
|
||||
+ self.sizes.FAR_LOST_GAP
|
||||
),
|
||||
w=self.sizes.PFL_WIDTH,
|
||||
h=self.sizes.PFL_FONT_PX,
|
||||
)
|
||||
|
||||
@property
|
||||
def lost(self):
|
||||
return crop_xywh(self.img, self.lost_rect)
|
||||
|
||||
@property
|
||||
def score_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=self.hmid - (self.sizes.SCORE_WIDTH / 2),
|
||||
y=(
|
||||
self.h_without_top_bar_mid
|
||||
+ self.sizes.SCORE_BOTTOM_FROM_VMID
|
||||
- self.sizes.SCORE_FONT_PX
|
||||
),
|
||||
w=self.sizes.SCORE_WIDTH,
|
||||
h=self.sizes.SCORE_FONT_PX,
|
||||
)
|
||||
|
||||
@property
|
||||
def score(self):
|
||||
return crop_xywh(self.img, self.score_rect)
|
||||
|
||||
@property
|
||||
def max_recall_rating_class_rect(self):
|
||||
x = (
|
||||
self.hmid
|
||||
+ self.sizes.JACKET_RIGHT_FROM_HOR_MID
|
||||
- self.sizes.JACKET_WIDTH
|
||||
- 25 * self.sizes.factor
|
||||
)
|
||||
return self.construct_int_xywh_rect(
|
||||
x=x,
|
||||
y=(
|
||||
self.h_without_top_bar_mid
|
||||
- self.sizes.SCORE_PANEL[1] / 2
|
||||
- self.sizes.MR_RT_HEIGHT
|
||||
),
|
||||
w=self.sizes.MR_RT_WIDTH,
|
||||
h=self.sizes.MR_RT_HEIGHT,
|
||||
)
|
||||
|
||||
@property
|
||||
def max_recall_rating_class(self):
|
||||
return crop_xywh(self.img, self.max_recall_rating_class_rect)
|
||||
|
||||
@property
|
||||
def title_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=0,
|
||||
y=self.h_without_top_bar_mid
|
||||
+ self.sizes.TITLE_BOTTOM_FROM_VMID
|
||||
- self.sizes.TITLE_FONT_PX,
|
||||
w=self.hmid + self.sizes.TITLE_WIDTH_RIGHT,
|
||||
h=self.sizes.TITLE_FONT_PX,
|
||||
)
|
||||
|
||||
@property
|
||||
def title(self):
|
||||
return crop_xywh(self.img, self.title_rect)
|
||||
|
||||
@property
|
||||
def jacket_rect(self):
|
||||
return self.construct_int_xywh_rect(
|
||||
x=self.hmid
|
||||
+ self.sizes.JACKET_RIGHT_FROM_HOR_MID
|
||||
- self.sizes.JACKET_WIDTH,
|
||||
y=self.h_without_top_bar_mid - self.sizes.SCORE_PANEL[1] / 2,
|
||||
w=self.sizes.JACKET_WIDTH,
|
||||
h=self.sizes.JACKET_WIDTH,
|
||||
)
|
||||
|
||||
@property
|
||||
def jacket(self):
|
||||
return crop_xywh(self.img, self.jacket_rect)
|
||||
|
||||
|
||||
class DeviceV2AutoRois(DeviceV2Rois):
|
||||
@staticmethod
|
||||
def get_factor(width: int, height: int):
|
||||
ratio = width / height
|
||||
return ((width / 16) * 9) / 720 if ratio < (16 / 9) else height / 720
|
||||
|
||||
def __init__(self, img: Mat):
|
||||
factor = self.get_factor(img.shape[1], img.shape[0])
|
||||
self.sizes = SizesV1(factor)
|
||||
self.__img = None
|
||||
self.img = img
|
||||
|
||||
@property
|
||||
def img(self):
|
||||
return self.__img
|
||||
|
||||
@img.setter
|
||||
def img(self, img: Mat):
|
||||
self.__img = crop_black_edges(img)
|
||||
@ -1,9 +0,0 @@
|
||||
from cv2 import MORPH_RECT, getStructuringElement
|
||||
|
||||
PFL_DENOISE_KERNEL = getStructuringElement(MORPH_RECT, [2, 2])
|
||||
PFL_ERODE_KERNEL = getStructuringElement(MORPH_RECT, [3, 3])
|
||||
PFL_CLOSE_HORIZONTAL_KERNEL = getStructuringElement(MORPH_RECT, [10, 1])
|
||||
|
||||
MAX_RECALL_DENOISE_KERNEL = getStructuringElement(MORPH_RECT, [3, 3])
|
||||
MAX_RECALL_ERODE_KERNEL = getStructuringElement(MORPH_RECT, [2, 2])
|
||||
MAX_RECALL_CLOSE_KERNEL = getStructuringElement(MORPH_RECT, [20, 1])
|
||||
@ -1,254 +0,0 @@
|
||||
from typing import Tuple, Union
|
||||
|
||||
|
||||
def apply_factor(num: Union[int, float], factor: float):
|
||||
return num * factor
|
||||
|
||||
|
||||
class Sizes:
|
||||
def __init__(self, factor: float):
|
||||
raise NotImplementedError()
|
||||
|
||||
@property
|
||||
def TOP_BAR_HEIGHT(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def SCORE_PANEL(self) -> Tuple[int, int]:
|
||||
...
|
||||
|
||||
@property
|
||||
def PFL_TOP_FROM_VMID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def PFL_LEFT_FROM_HMID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def PFL_WIDTH(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def PFL_FONT_PX(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def PURE_FAR_GAP(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def FAR_LOST_GAP(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def SCORE_BOTTOM_FROM_VMID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def SCORE_FONT_PX(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def SCORE_WIDTH(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def JACKET_RIGHT_FROM_HOR_MID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def JACKET_WIDTH(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def MR_RT_RIGHT_FROM_HMID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def MR_RT_WIDTH(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def MR_RT_HEIGHT(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def TITLE_BOTTOM_FROM_VMID(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def TITLE_FONT_PX(self):
|
||||
...
|
||||
|
||||
@property
|
||||
def TITLE_WIDTH_RIGHT(self):
|
||||
...
|
||||
|
||||
|
||||
class SizesV1(Sizes):
|
||||
def __init__(self, factor: float):
|
||||
self.factor = factor
|
||||
|
||||
def apply_factor(self, num):
|
||||
return apply_factor(num, self.factor)
|
||||
|
||||
@property
|
||||
def TOP_BAR_HEIGHT(self):
|
||||
return self.apply_factor(50)
|
||||
|
||||
@property
|
||||
def SCORE_PANEL(self) -> Tuple[int, int]:
|
||||
return tuple(self.apply_factor(num) for num in [485, 239])
|
||||
|
||||
@property
|
||||
def PFL_TOP_FROM_VMID(self):
|
||||
return self.apply_factor(135)
|
||||
|
||||
@property
|
||||
def PFL_LEFT_FROM_HMID(self):
|
||||
return self.apply_factor(5)
|
||||
|
||||
@property
|
||||
def PFL_WIDTH(self):
|
||||
return self.apply_factor(76)
|
||||
|
||||
@property
|
||||
def PFL_FONT_PX(self):
|
||||
return self.apply_factor(26)
|
||||
|
||||
@property
|
||||
def PURE_FAR_GAP(self):
|
||||
return self.apply_factor(12)
|
||||
|
||||
@property
|
||||
def FAR_LOST_GAP(self):
|
||||
return self.apply_factor(10)
|
||||
|
||||
@property
|
||||
def SCORE_BOTTOM_FROM_VMID(self):
|
||||
return self.apply_factor(-50)
|
||||
|
||||
@property
|
||||
def SCORE_FONT_PX(self):
|
||||
return self.apply_factor(45)
|
||||
|
||||
@property
|
||||
def SCORE_WIDTH(self):
|
||||
return self.apply_factor(280)
|
||||
|
||||
@property
|
||||
def JACKET_RIGHT_FROM_HOR_MID(self):
|
||||
return self.apply_factor(-235)
|
||||
|
||||
@property
|
||||
def JACKET_WIDTH(self):
|
||||
return self.apply_factor(375)
|
||||
|
||||
@property
|
||||
def MR_RT_RIGHT_FROM_HMID(self):
|
||||
return self.apply_factor(-300)
|
||||
|
||||
@property
|
||||
def MR_RT_WIDTH(self):
|
||||
return self.apply_factor(275)
|
||||
|
||||
@property
|
||||
def MR_RT_HEIGHT(self):
|
||||
return self.apply_factor(75)
|
||||
|
||||
@property
|
||||
def TITLE_BOTTOM_FROM_VMID(self):
|
||||
return self.apply_factor(-265)
|
||||
|
||||
@property
|
||||
def TITLE_FONT_PX(self):
|
||||
return self.apply_factor(40)
|
||||
|
||||
@property
|
||||
def TITLE_WIDTH_RIGHT(self):
|
||||
return self.apply_factor(275)
|
||||
|
||||
|
||||
class SizesV2(Sizes):
|
||||
def __init__(self, factor: float):
|
||||
self.factor = factor
|
||||
|
||||
def apply_factor(self, num):
|
||||
return apply_factor(num, self.factor)
|
||||
|
||||
@property
|
||||
def TOP_BAR_HEIGHT(self):
|
||||
return self.apply_factor(50)
|
||||
|
||||
@property
|
||||
def SCORE_PANEL(self) -> Tuple[int, int]:
|
||||
return tuple(self.apply_factor(num) for num in [447, 233])
|
||||
|
||||
@property
|
||||
def PFL_TOP_FROM_VMID(self):
|
||||
return self.apply_factor(142)
|
||||
|
||||
@property
|
||||
def PFL_LEFT_FROM_HMID(self):
|
||||
return self.apply_factor(10)
|
||||
|
||||
@property
|
||||
def PFL_WIDTH(self):
|
||||
return self.apply_factor(60)
|
||||
|
||||
@property
|
||||
def PFL_FONT_PX(self):
|
||||
return self.apply_factor(16)
|
||||
|
||||
@property
|
||||
def PURE_FAR_GAP(self):
|
||||
return self.apply_factor(20)
|
||||
|
||||
@property
|
||||
def FAR_LOST_GAP(self):
|
||||
return self.apply_factor(23)
|
||||
|
||||
@property
|
||||
def SCORE_BOTTOM_FROM_VMID(self):
|
||||
return self.apply_factor(-50)
|
||||
|
||||
@property
|
||||
def SCORE_FONT_PX(self):
|
||||
return self.apply_factor(45)
|
||||
|
||||
@property
|
||||
def SCORE_WIDTH(self):
|
||||
return self.apply_factor(280)
|
||||
|
||||
@property
|
||||
def JACKET_RIGHT_FROM_HOR_MID(self):
|
||||
return self.apply_factor(-235)
|
||||
|
||||
@property
|
||||
def JACKET_WIDTH(self):
|
||||
return self.apply_factor(375)
|
||||
|
||||
@property
|
||||
def MR_RT_RIGHT_FROM_HMID(self):
|
||||
return self.apply_factor(-330)
|
||||
|
||||
@property
|
||||
def MR_RT_WIDTH(self):
|
||||
return self.apply_factor(330)
|
||||
|
||||
@property
|
||||
def MR_RT_HEIGHT(self):
|
||||
return self.apply_factor(75)
|
||||
|
||||
@property
|
||||
def TITLE_BOTTOM_FROM_VMID(self):
|
||||
return self.apply_factor(-265)
|
||||
|
||||
@property
|
||||
def TITLE_FONT_PX(self):
|
||||
return self.apply_factor(40)
|
||||
|
||||
@property
|
||||
def TITLE_WIDTH_RIGHT(self):
|
||||
return self.apply_factor(275)
|
||||
Loading…
x
Reference in New Issue
Block a user