chore: v0.0.95

pyproject.toml

@@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "arcaea-offline-ocr"
-version = "0.1.0"
+version = "0.0.95"
 authors = [{ name = "283375", email = "log_283375@163.com" }]
 description = "Extract your Arcaea play result from screenshot."
 readme = "README.md"
 requires-python = ">=3.8"
-dependencies = ["attrs==23.1.0", "numpy==1.25.2", "opencv-python==4.8.0.76"]
+dependencies = ["attrs==23.1.0", "numpy==1.26.1", "opencv-python==4.8.1.78"]
 classifiers = [
   "Development Status :: 3 - Alpha",
   "Programming Language :: Python :: 3",

requirements.txt

@@ -1,3 +1,3 @@
 attrs==23.1.0
-numpy==1.25.2
-opencv-python==4.8.0.76
+numpy==1.26.1
+opencv-python==4.8.1.78

src/arcaea_offline_ocr/b30/chieri/v4/ocr.py

@@ -3,12 +3,16 @@ from typing import List, Optional, Tuple
 
 import cv2
 import numpy as np
-from PIL import Image
 
 from ....crop import crop_xywh
-from ....ocr import FixRects, ocr_digits_by_contour_knn, preprocess_hog
-from ....phash_db import ImagePHashDatabase
-from ....types import Mat, cv2_ml_KNearest
+from ....ocr import (
+    FixRects,
+    ocr_digits_by_contour_knn,
+    preprocess_hog,
+    resize_fill_square,
+)
+from ....phash_db import ImagePhashDatabase
+from ....types import Mat
 from ....utils import construct_int_xywh_rect
 from ...shared import B30OcrResultItem
 from .colors import *
@@ -18,9 +22,9 @@ from .rois import ChieriBotV4Rois
 class ChieriBotV4Ocr:
     def __init__(
         self,
-        score_knn: cv2_ml_KNearest,
-        pfl_knn: cv2_ml_KNearest,
-        phash_db: ImagePHashDatabase,
+        score_knn: cv2.ml.KNearest,
+        pfl_knn: cv2.ml.KNearest,
+        phash_db: ImagePhashDatabase,
         factor: Optional[float] = 1.0,
     ):
         self.__score_knn = score_knn
@@ -33,7 +37,7 @@ class ChieriBotV4Ocr:
         return self.__score_knn
 
     @score_knn.setter
-    def score_knn(self, knn_digits_model: Mat):
+    def score_knn(self, knn_digits_model: cv2.ml.KNearest):
         self.__score_knn = knn_digits_model
 
     @property
@@ -41,7 +45,7 @@ class ChieriBotV4Ocr:
         return self.__pfl_knn
 
     @pfl_knn.setter
-    def pfl_knn(self, knn_digits_model: Mat):
+    def pfl_knn(self, knn_digits_model: cv2.ml.KNearest):
         self.__pfl_knn = knn_digits_model
 
     @property
@@ -49,7 +53,7 @@ class ChieriBotV4Ocr:
         return self.__phash_db
 
     @phash_db.setter
-    def phash_db(self, phash_db: ImagePHashDatabase):
+    def phash_db(self, phash_db: ImagePhashDatabase):
         self.__phash_db = phash_db
 
     @property
@@ -84,14 +88,6 @@ class ChieriBotV4Ocr:
         else:
             return max(enumerate(rating_class_results), key=lambda i: i[1])[0] + 1
 
-    # def ocr_component_title(self, component_bgr: Mat) -> str:
-    #     # sourcery skip: inline-immediately-returned-variable
-    #     title_rect = construct_int_xywh_rect(self.rois.component_rois.title_rect)
-    #     title_roi = crop_xywh(component_bgr, title_rect)
-    #     ocr_result = self.sift_db.ocr(title_roi, cls=False)
-    #     title = ocr_result[0][-1][1][0] if ocr_result and ocr_result[0] else ""
-    #     return title
-
     def ocr_component_song_id(self, component_bgr: Mat):
         jacket_rect = construct_int_xywh_rect(
             self.rois.component_rois.jacket_rect, floor
@@ -99,20 +95,7 @@ class ChieriBotV4Ocr:
         jacket_roi = cv2.cvtColor(
             crop_xywh(component_bgr, jacket_rect), cv2.COLOR_BGR2GRAY
         )
-        return self.phash_db.lookup_image(Image.fromarray(jacket_roi))[0]
-
-    # def ocr_component_score_paddle(self, component_bgr: Mat) -> int:
-    #     # sourcery skip: inline-immediately-returned-variable
-    #     score_rect = construct_int_xywh_rect(self.rois.component_rois.score_rect)
-    #     score_roi = cv2.cvtColor(
-    #         crop_xywh(component_bgr, score_rect), cv2.COLOR_BGR2GRAY
-    #     )
-    #     _, score_roi = cv2.threshold(
-    #         score_roi, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
-    #     )
-    #     score_str = self.sift_db.ocr(score_roi, cls=False)[0][-1][1][0]
-    #     score = int(score_str.replace("'", "").replace(" ", ""))
-    #     return score
+        return self.phash_db.lookup_jacket(jacket_roi)[0]
 
     def ocr_component_score_knn(self, component_bgr: Mat) -> int:
         # sourcery skip: inline-immediately-returned-variable
@@ -222,7 +205,7 @@ class ChieriBotV4Ocr:
                 digits = []
                 for digit_rect in digit_rects:
                     digit = crop_xywh(roi, digit_rect)
-                    digit = cv2.resize(digit, (20, 20))
+                    digit = resize_fill_square(digit, 20)
                     digits.append(digit)
                 samples = preprocess_hog(digits)
 
@@ -233,15 +216,6 @@ class ChieriBotV4Ocr:
         except Exception:
             return (None, None, None)
 
-    # def ocr_component_date(self, component_bgr: Mat):
-    #     date_rect = construct_int_xywh_rect(self.rois.component_rois.date_rect)
-    #     date_roi = cv2.cvtColor(crop_xywh(component_bgr, date_rect), cv2.COLOR_BGR2GRAY)
-    #     _, date_roi = cv2.threshold(
-    #         date_roi, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
-    #     )
-    #     date_str = self.sift_db.ocr(date_roi, cls=False)[0][-1][1][0]
-    #     return date_str
-
     def ocr_component(self, component_bgr: Mat) -> B30OcrResultItem:
         component_blur = cv2.GaussianBlur(component_bgr, (5, 5), 0)
         rating_class = self.ocr_component_rating_class(component_blur)

src/arcaea_offline_ocr/crop.py

@@ -1,11 +1,12 @@
-from math import floor
+import math
 from typing import Tuple
 
+import cv2
 import numpy as np
 
 from .types import Mat
 
-__all__ = ["crop_xywh", "crop_black_edges", "crop_black_edges_grayscale"]
+__all__ = ["crop_xywh", "CropBlackEdges"]
 
 
 def crop_xywh(mat: Mat, rect: Tuple[int, int, int, int]):
@@ -13,92 +14,53 @@ def crop_xywh(mat: Mat, rect: Tuple[int, int, int, int]):
     return mat[y : y + h, x : x + w]
 
 
-def is_black_edge(list_of_pixels: Mat, black_pixel: Mat, ratio: float = 0.6):
-    pixels = list_of_pixels.reshape([-1, 3])
-    return np.count_nonzero(np.all(pixels < black_pixel, axis=1)) > floor(
-        len(pixels) * ratio
-    )
+class CropBlackEdges:
+    @staticmethod
+    def is_black_edge(__img_gray_slice: Mat, black_pixel: int, ratio: float = 0.6):
+        pixels_compared = __img_gray_slice < black_pixel
+        return np.count_nonzero(pixels_compared) > math.floor(
+            __img_gray_slice.size * ratio
+        )
 
+    @classmethod
+    def get_crop_rect(cls, img_gray: Mat, black_threshold: int = 25):
+        height, width = img_gray.shape[:2]
+        left = 0
+        right = width
+        top = 0
+        bottom = height
 
-def crop_black_edges(img_bgr: Mat, black_threshold: int = 50):
-    cropped = img_bgr.copy()
-    black_pixel = np.array([black_threshold] * 3, img_bgr.dtype)
-    height, width = img_bgr.shape[:2]
-    left = 0
-    right = width
-    top = 0
-    bottom = height
+        for i in range(width):
+            column = img_gray[:, i]
+            if not cls.is_black_edge(column, black_threshold):
+                break
+            left += 1
 
-    for i in range(width):
-        column = cropped[:, i]
-        if not is_black_edge(column, black_pixel):
-            break
-        left += 1
+        for i in sorted(range(width), reverse=True):
+            column = img_gray[:, i]
+            if i <= left + 1 or not cls.is_black_edge(column, black_threshold):
+                break
+            right -= 1
 
-    for i in sorted(range(width), reverse=True):
-        column = cropped[:, i]
-        if i <= left + 1 or not is_black_edge(column, black_pixel):
-            break
-        right -= 1
+        for i in range(height):
+            row = img_gray[i]
+            if not cls.is_black_edge(row, black_threshold):
+                break
+            top += 1
 
-    for i in range(height):
-        row = cropped[i]
-        if not is_black_edge(row, black_pixel):
-            break
-        top += 1
+        for i in sorted(range(height), reverse=True):
+            row = img_gray[i]
+            if i <= top + 1 or not cls.is_black_edge(row, black_threshold):
+                break
+            bottom -= 1
 
-    for i in sorted(range(height), reverse=True):
-        row = cropped[i]
-        if i <= top + 1 or not is_black_edge(row, black_pixel):
-            break
-        bottom -= 1
+        assert right > left, "cropped width < 0"
+        assert bottom > top, "cropped height < 0"
+        return (left, top, right - left, bottom - top)
 
-    return cropped[top:bottom, left:right]
-
-
-def is_black_edge_grayscale(
-    gray_value_list: np.ndarray, black_threshold: int = 50, ratio: float = 0.6
-) -> bool:
-    return (
-        np.count_nonzero(gray_value_list < black_threshold)
-        > len(gray_value_list) * ratio
-    )
-
-
-def crop_black_edges_grayscale(
-    img_gray: Mat, black_threshold: int = 50
-) -> Tuple[int, int, int, int]:
-    """Returns cropped rect"""
-    height, width = img_gray.shape[:2]
-    left = 0
-    right = width
-    top = 0
-    bottom = height
-
-    for i in range(width):
-        column = img_gray[:, i]
-        if not is_black_edge_grayscale(column, black_threshold):
-            break
-        left += 1
-
-    for i in sorted(range(width), reverse=True):
-        column = img_gray[:, i]
-        if i <= left + 1 or not is_black_edge_grayscale(column, black_threshold):
-            break
-        right -= 1
-
-    for i in range(height):
-        row = img_gray[i]
-        if not is_black_edge_grayscale(row, black_threshold):
-            break
-        top += 1
-
-    for i in sorted(range(height), reverse=True):
-        row = img_gray[i]
-        if i <= top + 1 or not is_black_edge_grayscale(row, black_threshold):
-            break
-        bottom -= 1
-
-    assert right > left, "cropped width > 0"
-    assert bottom > top, "cropped height > 0"
-    return (left, top, right - left, bottom - top)
+    @classmethod
+    def crop(
+        cls, img: Mat, convert_flag: cv2.COLOR_BGR2GRAY, black_threshold: int = 25
+    ) -> Mat:
+        rect = cls.get_crop_rect(cv2.cvtColor(img, convert_flag), black_threshold)
+        return crop_xywh(img, rect)

src/arcaea_offline_ocr/device/__init__.py

@@ -0,0 +1,2 @@
+from .common import DeviceOcrResult
+from .ocr import DeviceOcr

src/arcaea_offline_ocr/device/common.py

@@ -10,7 +10,9 @@ class DeviceOcrResult:
     far: int
     lost: int
     score: int
-    max_recall: int
+    max_recall: Optional[int] = None
     song_id: Optional[str] = None
-    title: Optional[str] = None
-    clear_type: Optional[str] = None
+    song_id_possibility: Optional[float] = None
+    clear_status: Optional[int] = None
+    partner_id: Optional[str] = None
+    partner_id_possibility: Optional[float] = None

src/arcaea_offline_ocr/device/ocr.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-from PIL import Image
 
 from ..crop import crop_xywh
 from ..ocr import (
@@ -10,25 +9,27 @@ from ..ocr import (
     preprocess_hog,
     resize_fill_square,
 )
-from ..phash_db import ImagePHashDatabase
-from .roi.extractor import DeviceRoiExtractor
-from .roi.masker import DeviceRoiMasker
+from ..phash_db import ImagePhashDatabase
+from ..types import Mat
+from .common import DeviceOcrResult
+from .rois.extractor import DeviceRoisExtractor
+from .rois.masker import DeviceRoisMasker
 
 
 class DeviceOcr:
     def __init__(
         self,
-        extractor: DeviceRoiExtractor,
-        masker: DeviceRoiMasker,
+        extractor: DeviceRoisExtractor,
+        masker: DeviceRoisMasker,
         knn_model: cv2.ml.KNearest,
-        phash_db: ImagePHashDatabase,
+        phash_db: ImagePhashDatabase,
     ):
         self.extractor = extractor
         self.masker = masker
         self.knn_model = knn_model
         self.phash_db = phash_db
 
-    def pfl(self, roi_gray: cv2.Mat, factor: float = 1.25):
+    def pfl(self, roi_gray: Mat, factor: float = 1.25):
         contours, _ = cv2.findContours(
             roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
         )
@@ -47,8 +48,7 @@ class DeviceOcr:
                 continue
             roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
         digit_rois = [
-            resize_fill_square(crop_xywh(roi_ocr, r), 20)
-            for r in sorted(filtered_rects, key=lambda r: r[0])
+            resize_fill_square(crop_xywh(roi_ocr, r), 20) for r in filtered_rects
         ]
 
         samples = preprocess_hog(digit_rois)
@@ -97,5 +97,64 @@ class DeviceOcr:
         ]
         return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
 
+    def lookup_song_id(self):
+        return self.phash_db.lookup_jacket(
+            cv2.cvtColor(self.extractor.jacket, cv2.COLOR_BGR2GRAY)
+        )
+
     def song_id(self):
-        return self.phash_db.lookup_image(Image.fromarray(self.extractor.jacket))[0]
+        return self.lookup_song_id()[0]
+
+    @staticmethod
+    def preprocess_char_icon(img_gray: Mat):
+        h, w = img_gray.shape[:2]
+        img = cv2.copyMakeBorder(img_gray, w - h, 0, 0, 0, cv2.BORDER_REPLICATE)
+        h, w = img.shape[:2]
+        img = cv2.fillPoly(
+            img,
+            [
+                np.array([[0, 0], [round(w / 2), 0], [0, round(h / 2)]], np.int32),
+                np.array([[w, 0], [round(w / 2), 0], [w, round(h / 2)]], np.int32),
+                np.array([[0, h], [round(w / 2), h], [0, round(h / 2)]], np.int32),
+                np.array([[w, h], [round(w / 2), h], [w, round(h / 2)]], np.int32),
+            ],
+            (128),
+        )
+        return img
+
+    def lookup_partner_id(self):
+        return self.phash_db.lookup_partner_icon(
+            self.preprocess_char_icon(
+                cv2.cvtColor(self.extractor.partner_icon, cv2.COLOR_BGR2GRAY)
+            )
+        )
+
+    def partner_id(self):
+        return self.lookup_partner_id()[0]
+
+    def ocr(self) -> DeviceOcrResult:
+        rating_class = self.rating_class()
+        pure = self.pure()
+        far = self.far()
+        lost = self.lost()
+        score = self.score()
+        max_recall = self.max_recall()
+        clear_status = self.clear_status()
+
+        hash_len = self.phash_db.hash_size**2
+        song_id, song_id_distance = self.lookup_song_id()
+        partner_id, partner_id_distance = self.lookup_partner_id()
+
+        return DeviceOcrResult(
+            rating_class=rating_class,
+            pure=pure,
+            far=far,
+            lost=lost,
+            score=score,
+            max_recall=max_recall,
+            song_id=song_id,
+            song_id_possibility=1 - song_id_distance / hash_len,
+            clear_status=clear_status,
+            partner_id=partner_id,
+            partner_id_possibility=1 - partner_id_distance / hash_len,
+        )

src/arcaea_offline_ocr/device/roi/definitions/__init__.py

@@ -1 +0,0 @@
-from .auto import *

src/arcaea_offline_ocr/device/roi/definitions/auto/__init__.py

@@ -1,3 +0,0 @@
-from .common import DeviceAutoRoiSizes
-from .t1 import DeviceAutoRoiSizesT1
-from .t2 import DeviceAutoRoiSizesT2

src/arcaea_offline_ocr/device/roi/definitions/auto/common.py

@@ -1,7 +0,0 @@
-from ..common import DeviceRoiSizes
-
-
-class DeviceAutoRoiSizes(DeviceRoiSizes):
-    def __init__(self, w: int, h: int):
-        self.w = w
-        self.h = h

src/arcaea_offline_ocr/device/roi/definitions/auto/t1.py

@@ -1,123 +0,0 @@
-from .common import DeviceAutoRoiSizes
-
-
-class DeviceAutoRoiSizesT1(DeviceAutoRoiSizes):
-    @property
-    def factor(self):
-        return (
-            ((self.w / 16) * 9) / 720 if (self.w / self.h) < (16 / 9) else self.h / 720
-        )
-
-    @property
-    def w_mid(self):
-        return self.w / 2
-
-    @property
-    def h_mid(self):
-        return self.h / 2
-
-    @property
-    def top_bar(self):
-        return (0, 0, self.w, 50 * self.factor)
-
-    @property
-    def layout_area_h_mid(self):
-        return self.h / 2 + self.top_bar[3]
-
-    @property
-    def pfl_left_from_w_mid(self):
-        return 5 * self.factor
-
-    @property
-    def pfl_x(self):
-        return self.w_mid + self.pfl_left_from_w_mid
-
-    @property
-    def pfl_w(self):
-        return 76 * self.factor
-
-    @property
-    def pfl_h(self):
-        return 26 * self.factor
-
-    @property
-    def pure(self):
-        return (
-            self.pfl_x,
-            self.layout_area_h_mid + 110 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def far(self):
-        return (
-            self.pfl_x,
-            self.pure[1] + self.pure[3] + 12 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def lost(self):
-        return (
-            self.pfl_x,
-            self.far[1] + self.far[3] + 10 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def score(self):
-        w = 280 * self.factor
-        h = 45 * self.factor
-        return (
-            self.w_mid - w / 2,
-            self.layout_area_h_mid - 75 * self.factor - h,
-            w,
-            h,
-        )
-
-    @property
-    def rating_class(self):
-        return (
-            self.w_mid - 610 * self.factor,
-            self.layout_area_h_mid - 180 * self.factor,
-            265 * self.factor,
-            35 * self.factor,
-        )
-
-    @property
-    def max_recall(self):
-        return (
-            self.w_mid - 465 * self.factor,
-            self.layout_area_h_mid - 215 * self.factor,
-            150 * self.factor,
-            35 * self.factor,
-        )
-
-    @property
-    def jacket(self):
-        return (
-            self.w_mid - 610 * self.factor,
-            self.layout_area_h_mid - 143 * self.factor,
-            375 * self.factor,
-            375 * self.factor,
-        )
-
-    @property
-    def clear_status(self):
-        w = 550 * self.factor
-        h = 60 * self.factor
-        return (
-            self.w_mid - w / 2,
-            self.layout_area_h_mid - 155 * self.factor - h,
-            w,
-            h,
-        )
-
-    @property
-    def partner_icon(self):
-        w = 90 * self.factor
-        h = 75 * self.factor
-        return (self.w_mid - w / 2, 0, w, h)

src/arcaea_offline_ocr/device/roi/definitions/auto/t2.py

@@ -1,125 +0,0 @@
-from .common import DeviceAutoRoiSizes
-
-
-class DeviceAutoRoiSizesT2(DeviceAutoRoiSizes):
-    @property
-    def factor(self):
-        return (
-            ((self.w / 16) * 9) / 1080
-            if (self.w / self.h) < (16 / 9)
-            else self.h / 1080
-        )
-
-    @property
-    def w_mid(self):
-        return self.w / 2
-
-    @property
-    def h_mid(self):
-        return self.h / 2
-
-    @property
-    def top_bar(self):
-        return (0, 0, self.w, 75 * self.factor)
-
-    @property
-    def layout_area_h_mid(self):
-        return self.h / 2 + self.top_bar[3]
-
-    @property
-    def pfl_mid_from_w_mid(self):
-        return 60 * self.factor
-
-    @property
-    def pfl_x(self):
-        return self.w_mid + 10 * self.factor
-
-    @property
-    def pfl_w(self):
-        return 100 * self.factor
-
-    @property
-    def pfl_h(self):
-        return 24 * self.factor
-
-    @property
-    def pure(self):
-        return (
-            self.pfl_x,
-            self.layout_area_h_mid + 175 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def far(self):
-        return (
-            self.pfl_x,
-            self.pure[1] + self.pure[3] + 30 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def lost(self):
-        return (
-            self.pfl_x,
-            self.far[1] + self.far[3] + 35 * self.factor,
-            self.pfl_w,
-            self.pfl_h,
-        )
-
-    @property
-    def score(self):
-        w = 420 * self.factor
-        h = 70 * self.factor
-        return (
-            self.w_mid - w / 2,
-            self.layout_area_h_mid - 110 * self.factor - h,
-            w,
-            h,
-        )
-
-    @property
-    def rating_class(self):
-        return (
-            max(0, self.w_mid - 965 * self.factor),
-            self.layout_area_h_mid - 330 * self.factor,
-            350 * self.factor,
-            110 * self.factor,
-        )
-
-    @property
-    def max_recall(self):
-        return (
-            self.w_mid - 625 * self.factor,
-            self.layout_area_h_mid - 275 * self.factor,
-            150 * self.factor,
-            50 * self.factor,
-        )
-
-    @property
-    def jacket(self):
-        return (
-            self.w_mid - 915 * self.factor,
-            self.layout_area_h_mid - 215 * self.factor,
-            565 * self.factor,
-            565 * self.factor,
-        )
-
-    @property
-    def clear_status(self):
-        w = 825 * self.factor
-        h = 90 * self.factor
-        return (
-            self.w_mid - w / 2,
-            self.layout_area_h_mid - 235 * self.factor - h,
-            w,
-            h,
-        )
-
-    @property
-    def partner_icon(self):
-        w = 135 * self.factor
-        h = 110 * self.factor
-        return (self.w_mid - w / 2, 0, w, h)

src/arcaea_offline_ocr/device/roi/extractor/__init__.py

@@ -1 +0,0 @@
-from .common import DeviceRoiExtractor

src/arcaea_offline_ocr/device/roi/masker/__init__.py

@@ -1,2 +0,0 @@
-from .auto import *
-from .common import DeviceRoiMasker

src/arcaea_offline_ocr/device/roi/masker/auto/__init__.py

@@ -1,3 +0,0 @@
-from .common import DeviceAutoRoiMasker
-from .t1 import DeviceAutoRoiMaskerT1
-from .t2 import DeviceAutoRoiMaskerT2

src/arcaea_offline_ocr/device/roi/masker/auto/common.py

@@ -1,5 +0,0 @@
-from ..common import DeviceRoiMasker
-
-
-class DeviceAutoRoiMasker(DeviceRoiMasker):
-    ...

src/arcaea_offline_ocr/device/roi/masker/auto/t1.py

@@ -1,123 +0,0 @@
-import cv2
-import numpy as np
-
-from .common import DeviceAutoRoiMasker
-
-GRAY_BGR_MIN = np.array([50] * 3, np.uint8)
-GRAY_BGR_MAX = np.array([160] * 3, np.uint8)
-
-WHITE_HSV_MIN = np.array([0, 0, 240], np.uint8)
-WHITE_HSV_MAX = np.array([179, 10, 255], np.uint8)
-
-PST_HSV_MIN = np.array([100, 50, 80], np.uint8)
-PST_HSV_MAX = np.array([100, 255, 255], np.uint8)
-
-PRS_HSV_MIN = np.array([43, 40, 75], np.uint8)
-PRS_HSV_MAX = np.array([50, 155, 190], np.uint8)
-
-FTR_HSV_MIN = np.array([149, 30, 0], np.uint8)
-FTR_HSV_MAX = np.array([155, 181, 150], np.uint8)
-
-BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
-BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
-
-TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
-TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
-
-TRACK_COMPLETE_HSV_MIN = np.array([140, 0, 50], np.uint8)
-TRACK_COMPLETE_HSV_MAX = np.array([145, 50, 130], np.uint8)
-
-FULL_RECALL_HSV_MIN = np.array([140, 60, 80], np.uint8)
-FULL_RECALL_HSV_MAX = np.array([150, 130, 145], np.uint8)
-
-PURE_MEMORY_HSV_MIN = np.array([90, 70, 80], np.uint8)
-PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
-
-
-class DeviceAutoRoiMaskerT1(DeviceAutoRoiMasker):
-    @classmethod
-    def gray(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        bgr_value_equal_mask = np.max(roi_bgr, axis=2) - np.min(roi_bgr, axis=2) <= 5
-        img_bgr = roi_bgr.copy()
-        img_bgr[~bgr_value_equal_mask] = np.array([0, 0, 0], roi_bgr.dtype)
-        img_bgr = cv2.erode(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
-        img_bgr = cv2.dilate(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)))
-        return cv2.inRange(img_bgr, GRAY_BGR_MIN, GRAY_BGR_MAX)
-
-    @classmethod
-    def pure(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.gray(roi_bgr)
-
-    @classmethod
-    def far(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.gray(roi_bgr)
-
-    @classmethod
-    def lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.gray(roi_bgr)
-
-    @classmethod
-    def score(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), WHITE_HSV_MIN, WHITE_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_pst(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), PST_HSV_MIN, PST_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_prs(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), PRS_HSV_MIN, PRS_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_ftr(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), FTR_HSV_MIN, FTR_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_byd(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), BYD_HSV_MIN, BYD_HSV_MAX
-        )
-
-    @classmethod
-    def max_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.gray(roi_bgr)
-
-    @classmethod
-    def clear_status_track_lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            TRACK_LOST_HSV_MIN,
-            TRACK_LOST_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_track_complete(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            TRACK_COMPLETE_HSV_MIN,
-            TRACK_COMPLETE_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_full_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            FULL_RECALL_HSV_MIN,
-            FULL_RECALL_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_pure_memory(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            PURE_MEMORY_HSV_MIN,
-            PURE_MEMORY_HSV_MAX,
-        )

src/arcaea_offline_ocr/device/roi/masker/auto/t2.py

@@ -1,128 +0,0 @@
-import cv2
-import numpy as np
-
-from .common import DeviceAutoRoiMasker
-
-PFL_HSV_MIN = np.array([0, 0, 248], np.uint8)
-PFL_HSV_MAX = np.array([179, 10, 255], np.uint8)
-
-WHITE_HSV_MIN = np.array([0, 0, 240], np.uint8)
-WHITE_HSV_MAX = np.array([179, 10, 255], np.uint8)
-
-
-PST_HSV_MIN = np.array([100, 50, 80], np.uint8)
-PST_HSV_MAX = np.array([100, 255, 255], np.uint8)
-
-PRS_HSV_MIN = np.array([43, 40, 75], np.uint8)
-PRS_HSV_MAX = np.array([50, 155, 190], np.uint8)
-
-FTR_HSV_MIN = np.array([149, 30, 0], np.uint8)
-FTR_HSV_MAX = np.array([155, 181, 150], np.uint8)
-
-BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
-BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
-
-MAX_RECALL_HSV_MIN = np.array([125, 0, 0], np.uint8)
-MAX_RECALL_HSV_MAX = np.array([130, 100, 150], np.uint8)
-
-TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
-TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
-
-TRACK_COMPLETE_HSV_MIN = np.array([140, 0, 50], np.uint8)
-TRACK_COMPLETE_HSV_MAX = np.array([145, 50, 130], np.uint8)
-
-FULL_RECALL_HSV_MIN = np.array([140, 60, 80], np.uint8)
-FULL_RECALL_HSV_MAX = np.array([150, 130, 145], np.uint8)
-
-PURE_MEMORY_HSV_MIN = np.array([90, 70, 80], np.uint8)
-PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
-
-
-class DeviceAutoRoiMaskerT2(DeviceAutoRoiMasker):
-    @classmethod
-    def pfl(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), PFL_HSV_MIN, PFL_HSV_MAX
-        )
-
-    @classmethod
-    def pure(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.pfl(roi_bgr)
-
-    @classmethod
-    def far(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.pfl(roi_bgr)
-
-    @classmethod
-    def lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cls.pfl(roi_bgr)
-
-    @classmethod
-    def score(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), WHITE_HSV_MIN, WHITE_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_pst(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), PST_HSV_MIN, PST_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_prs(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), PRS_HSV_MIN, PRS_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_ftr(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), FTR_HSV_MIN, FTR_HSV_MAX
-        )
-
-    @classmethod
-    def rating_class_byd(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), BYD_HSV_MIN, BYD_HSV_MAX
-        )
-
-    @classmethod
-    def max_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            MAX_RECALL_HSV_MIN,
-            MAX_RECALL_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_track_lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            TRACK_LOST_HSV_MIN,
-            TRACK_LOST_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_track_complete(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            TRACK_COMPLETE_HSV_MIN,
-            TRACK_COMPLETE_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_full_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            FULL_RECALL_HSV_MIN,
-            FULL_RECALL_HSV_MAX,
-        )
-
-    @classmethod
-    def clear_status_pure_memory(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        return cv2.inRange(
-            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
-            PURE_MEMORY_HSV_MIN,
-            PURE_MEMORY_HSV_MAX,
-        )

src/arcaea_offline_ocr/device/roi/masker/common.py

@@ -1,55 +0,0 @@
-import cv2
-
-
-class DeviceRoiMasker:
-    @classmethod
-    def pure(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def far(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def score(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def rating_class_pst(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def rating_class_prs(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def rating_class_ftr(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def rating_class_byd(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def max_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def clear_status_track_lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def clear_status_track_complete(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def clear_status_full_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()
-
-    @classmethod
-    def clear_status_pure_memory(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
-        raise NotImplementedError()

src/arcaea_offline_ocr/device/rois/__init__.py

@@ -0,0 +1,3 @@
+from .definition import *
+from .extractor import *
+from .masker import *

src/arcaea_offline_ocr/device/rois/definition/__init__.py

@@ -0,0 +1,2 @@
+from .auto import *
+from .common import DeviceRois

src/arcaea_offline_ocr/device/rois/definition/auto.py

@@ -0,0 +1,255 @@
+from .common import DeviceRois
+
+__all__ = ["DeviceRoisAuto", "DeviceRoisAutoT1", "DeviceRoisAutoT2"]
+
+
+class DeviceRoisAuto(DeviceRois):
+    def __init__(self, w: int, h: int):
+        self.w = w
+        self.h = h
+
+
+class DeviceRoisAutoT1(DeviceRoisAuto):
+    @property
+    def factor(self):
+        return (
+            ((self.w / 16) * 9) / 720 if (self.w / self.h) < (16 / 9) else self.h / 720
+        )
+
+    @property
+    def w_mid(self):
+        return self.w / 2
+
+    @property
+    def h_mid(self):
+        return self.h / 2
+
+    @property
+    def top_bar(self):
+        return (0, 0, self.w, 50 * self.factor)
+
+    @property
+    def layout_area_h_mid(self):
+        return self.h / 2 + self.top_bar[3]
+
+    @property
+    def pfl_left_from_w_mid(self):
+        return 5 * self.factor
+
+    @property
+    def pfl_x(self):
+        return self.w_mid + self.pfl_left_from_w_mid
+
+    @property
+    def pfl_w(self):
+        return 76 * self.factor
+
+    @property
+    def pfl_h(self):
+        return 26 * self.factor
+
+    @property
+    def pure(self):
+        return (
+            self.pfl_x,
+            self.layout_area_h_mid + 110 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def far(self):
+        return (
+            self.pfl_x,
+            self.pure[1] + self.pure[3] + 12 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def lost(self):
+        return (
+            self.pfl_x,
+            self.far[1] + self.far[3] + 10 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def score(self):
+        w = 280 * self.factor
+        h = 45 * self.factor
+        return (
+            self.w_mid - w / 2,
+            self.layout_area_h_mid - 75 * self.factor - h,
+            w,
+            h,
+        )
+
+    @property
+    def rating_class(self):
+        return (
+            self.w_mid - 610 * self.factor,
+            self.layout_area_h_mid - 180 * self.factor,
+            265 * self.factor,
+            35 * self.factor,
+        )
+
+    @property
+    def max_recall(self):
+        return (
+            self.w_mid - 465 * self.factor,
+            self.layout_area_h_mid - 215 * self.factor,
+            150 * self.factor,
+            35 * self.factor,
+        )
+
+    @property
+    def jacket(self):
+        return (
+            self.w_mid - 610 * self.factor,
+            self.layout_area_h_mid - 143 * self.factor,
+            375 * self.factor,
+            375 * self.factor,
+        )
+
+    @property
+    def clear_status(self):
+        w = 550 * self.factor
+        h = 60 * self.factor
+        return (
+            self.w_mid - w / 2,
+            self.layout_area_h_mid - 155 * self.factor - h,
+            w,
+            h,
+        )
+
+    @property
+    def partner_icon(self):
+        w = 90 * self.factor
+        h = 75 * self.factor
+        return (self.w_mid - w / 2, 0, w, h)
+
+
+class DeviceRoisAutoT2(DeviceRoisAuto):
+    @property
+    def factor(self):
+        return (
+            ((self.w / 16) * 9) / 1080
+            if (self.w / self.h) < (16 / 9)
+            else self.h / 1080
+        )
+
+    @property
+    def w_mid(self):
+        return self.w / 2
+
+    @property
+    def h_mid(self):
+        return self.h / 2
+
+    @property
+    def top_bar(self):
+        return (0, 0, self.w, 75 * self.factor)
+
+    @property
+    def layout_area_h_mid(self):
+        return self.h / 2 + self.top_bar[3]
+
+    @property
+    def pfl_mid_from_w_mid(self):
+        return 60 * self.factor
+
+    @property
+    def pfl_x(self):
+        return self.w_mid + 10 * self.factor
+
+    @property
+    def pfl_w(self):
+        return 100 * self.factor
+
+    @property
+    def pfl_h(self):
+        return 24 * self.factor
+
+    @property
+    def pure(self):
+        return (
+            self.pfl_x,
+            self.layout_area_h_mid + 175 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def far(self):
+        return (
+            self.pfl_x,
+            self.pure[1] + self.pure[3] + 30 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def lost(self):
+        return (
+            self.pfl_x,
+            self.far[1] + self.far[3] + 35 * self.factor,
+            self.pfl_w,
+            self.pfl_h,
+        )
+
+    @property
+    def score(self):
+        w = 420 * self.factor
+        h = 70 * self.factor
+        return (
+            self.w_mid - w / 2,
+            self.layout_area_h_mid - 110 * self.factor - h,
+            w,
+            h,
+        )
+
+    @property
+    def rating_class(self):
+        return (
+            max(0, self.w_mid - 965 * self.factor),
+            self.layout_area_h_mid - 330 * self.factor,
+            350 * self.factor,
+            110 * self.factor,
+        )
+
+    @property
+    def max_recall(self):
+        return (
+            self.w_mid - 625 * self.factor,
+            self.layout_area_h_mid - 275 * self.factor,
+            150 * self.factor,
+            50 * self.factor,
+        )
+
+    @property
+    def jacket(self):
+        return (
+            self.w_mid - 915 * self.factor,
+            self.layout_area_h_mid - 215 * self.factor,
+            565 * self.factor,
+            565 * self.factor,
+        )
+
+    @property
+    def clear_status(self):
+        w = 825 * self.factor
+        h = 90 * self.factor
+        return (
+            self.w_mid - w / 2,
+            self.layout_area_h_mid - 235 * self.factor - h,
+            w,
+            h,
+        )
+
+    @property
+    def partner_icon(self):
+        w = 135 * self.factor
+        h = 110 * self.factor
+        return (self.w_mid - w / 2, 0, w, h)

src/arcaea_offline_ocr/device/rois/definition/common.py

@@ -3,7 +3,7 @@ from typing import Tuple
 Rect = Tuple[int, int, int, int]
 
 
-class DeviceRoiSizes:
+class DeviceRois:
     pure: Rect
     far: Rect
     lost: Rect

src/arcaea_offline_ocr/device/rois/extractor/__init__.py

@@ -0,0 +1 @@
+from .common import DeviceRoisExtractor

src/arcaea_offline_ocr/device/rois/extractor/common.py

@@ -1,13 +1,12 @@
-import cv2
-
 from ....crop import crop_xywh
-from ..definitions.common import DeviceRoiSizes
+from ....types import Mat
+from ..definition.common import DeviceRois
 
 
-class DeviceRoiExtractor:
-    def __init__(self, img: cv2.Mat, sizes: DeviceRoiSizes):
+class DeviceRoisExtractor:
+    def __init__(self, img: Mat, rois: DeviceRois):
         self.img = img
-        self.sizes = sizes
+        self.sizes = rois
 
     def __construct_int_rect(self, rect):
         return tuple(round(r) for r in rect)

src/arcaea_offline_ocr/device/rois/masker/__init__.py

@@ -0,0 +1,2 @@
+from .auto import *
+from .common import DeviceRoisMasker

src/arcaea_offline_ocr/device/rois/masker/auto.py

@@ -0,0 +1,255 @@
+import cv2
+import numpy as np
+
+from ....types import Mat
+from .common import DeviceRoisMasker
+
+
+class DeviceRoisMaskerAuto(DeviceRoisMasker):
+    ...
+
+
+class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
+    GRAY_BGR_MIN = np.array([50] * 3, np.uint8)
+    GRAY_BGR_MAX = np.array([160] * 3, np.uint8)
+
+    WHITE_HSV_MIN = np.array([0, 0, 240], np.uint8)
+    WHITE_HSV_MAX = np.array([179, 10, 255], np.uint8)
+
+    PST_HSV_MIN = np.array([100, 50, 80], np.uint8)
+    PST_HSV_MAX = np.array([100, 255, 255], np.uint8)
+
+    PRS_HSV_MIN = np.array([43, 40, 75], np.uint8)
+    PRS_HSV_MAX = np.array([50, 155, 190], np.uint8)
+
+    FTR_HSV_MIN = np.array([149, 30, 0], np.uint8)
+    FTR_HSV_MAX = np.array([155, 181, 150], np.uint8)
+
+    BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
+    BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
+
+    TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
+    TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
+
+    TRACK_COMPLETE_HSV_MIN = np.array([140, 0, 50], np.uint8)
+    TRACK_COMPLETE_HSV_MAX = np.array([145, 50, 130], np.uint8)
+
+    FULL_RECALL_HSV_MIN = np.array([140, 60, 80], np.uint8)
+    FULL_RECALL_HSV_MAX = np.array([150, 130, 145], np.uint8)
+
+    PURE_MEMORY_HSV_MIN = np.array([90, 70, 80], np.uint8)
+    PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
+
+    @classmethod
+    def gray(cls, roi_bgr: Mat) -> Mat:
+        bgr_value_equal_mask = np.max(roi_bgr, axis=2) - np.min(roi_bgr, axis=2) <= 5
+        img_bgr = roi_bgr.copy()
+        img_bgr[~bgr_value_equal_mask] = np.array([0, 0, 0], roi_bgr.dtype)
+        img_bgr = cv2.erode(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
+        img_bgr = cv2.dilate(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)))
+        return cv2.inRange(img_bgr, cls.GRAY_BGR_MIN, cls.GRAY_BGR_MAX)
+
+    @classmethod
+    def pure(cls, roi_bgr: Mat) -> Mat:
+        return cls.gray(roi_bgr)
+
+    @classmethod
+    def far(cls, roi_bgr: Mat) -> Mat:
+        return cls.gray(roi_bgr)
+
+    @classmethod
+    def lost(cls, roi_bgr: Mat) -> Mat:
+        return cls.gray(roi_bgr)
+
+    @classmethod
+    def score(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.WHITE_HSV_MIN,
+            cls.WHITE_HSV_MAX,
+        )
+
+    @classmethod
+    def rating_class_pst(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.PST_HSV_MIN, cls.PST_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_prs(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.PRS_HSV_MIN, cls.PRS_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_ftr(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.FTR_HSV_MIN, cls.FTR_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.BYD_HSV_MIN, cls.BYD_HSV_MAX
+        )
+
+    @classmethod
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
+        return cls.gray(roi_bgr)
+
+    @classmethod
+    def clear_status_track_lost(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.TRACK_LOST_HSV_MIN,
+            cls.TRACK_LOST_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_track_complete(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.TRACK_COMPLETE_HSV_MIN,
+            cls.TRACK_COMPLETE_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_full_recall(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.FULL_RECALL_HSV_MIN,
+            cls.FULL_RECALL_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_pure_memory(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.PURE_MEMORY_HSV_MIN,
+            cls.PURE_MEMORY_HSV_MAX,
+        )
+
+
+class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
+    PFL_HSV_MIN = np.array([0, 0, 248], np.uint8)
+    PFL_HSV_MAX = np.array([179, 10, 255], np.uint8)
+
+    WHITE_HSV_MIN = np.array([0, 0, 240], np.uint8)
+    WHITE_HSV_MAX = np.array([179, 10, 255], np.uint8)
+
+    PST_HSV_MIN = np.array([100, 50, 80], np.uint8)
+    PST_HSV_MAX = np.array([100, 255, 255], np.uint8)
+
+    PRS_HSV_MIN = np.array([43, 40, 75], np.uint8)
+    PRS_HSV_MAX = np.array([50, 155, 190], np.uint8)
+
+    FTR_HSV_MIN = np.array([149, 30, 0], np.uint8)
+    FTR_HSV_MAX = np.array([155, 181, 150], np.uint8)
+
+    BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
+    BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
+
+    MAX_RECALL_HSV_MIN = np.array([125, 0, 0], np.uint8)
+    MAX_RECALL_HSV_MAX = np.array([145, 100, 150], np.uint8)
+
+    TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
+    TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
+
+    TRACK_COMPLETE_HSV_MIN = np.array([140, 0, 50], np.uint8)
+    TRACK_COMPLETE_HSV_MAX = np.array([145, 50, 130], np.uint8)
+
+    FULL_RECALL_HSV_MIN = np.array([140, 60, 80], np.uint8)
+    FULL_RECALL_HSV_MAX = np.array([150, 130, 145], np.uint8)
+
+    PURE_MEMORY_HSV_MIN = np.array([90, 70, 80], np.uint8)
+    PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
+
+    @classmethod
+    def pfl(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.PFL_HSV_MIN, cls.PFL_HSV_MAX
+        )
+
+    @classmethod
+    def pure(cls, roi_bgr: Mat) -> Mat:
+        return cls.pfl(roi_bgr)
+
+    @classmethod
+    def far(cls, roi_bgr: Mat) -> Mat:
+        return cls.pfl(roi_bgr)
+
+    @classmethod
+    def lost(cls, roi_bgr: Mat) -> Mat:
+        return cls.pfl(roi_bgr)
+
+    @classmethod
+    def score(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.WHITE_HSV_MIN,
+            cls.WHITE_HSV_MAX,
+        )
+
+    @classmethod
+    def rating_class_pst(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.PST_HSV_MIN, cls.PST_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_prs(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.PRS_HSV_MIN, cls.PRS_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_ftr(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.FTR_HSV_MIN, cls.FTR_HSV_MAX
+        )
+
+    @classmethod
+    def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), cls.BYD_HSV_MIN, cls.BYD_HSV_MAX
+        )
+
+    @classmethod
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.MAX_RECALL_HSV_MIN,
+            cls.MAX_RECALL_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_track_lost(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.TRACK_LOST_HSV_MIN,
+            cls.TRACK_LOST_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_track_complete(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.TRACK_COMPLETE_HSV_MIN,
+            cls.TRACK_COMPLETE_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_full_recall(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.FULL_RECALL_HSV_MIN,
+            cls.FULL_RECALL_HSV_MAX,
+        )
+
+    @classmethod
+    def clear_status_pure_memory(cls, roi_bgr: Mat) -> Mat:
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
+            cls.PURE_MEMORY_HSV_MIN,
+            cls.PURE_MEMORY_HSV_MAX,
+        )

src/arcaea_offline_ocr/device/rois/masker/common.py

@@ -0,0 +1,55 @@
+from ....types import Mat
+
+
+class DeviceRoisMasker:
+    @classmethod
+    def pure(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def far(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def lost(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def score(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def rating_class_pst(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def rating_class_prs(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def rating_class_ftr(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def clear_status_track_lost(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def clear_status_track_complete(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def clear_status_full_recall(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()
+
+    @classmethod
+    def clear_status_pure_memory(cls, roi_bgr: Mat) -> Mat:
+        raise NotImplementedError()

src/arcaea_offline_ocr/ocr.py

@@ -1,13 +1,11 @@
 import math
-from copy import deepcopy
 from typing import Optional, Sequence, Tuple
 
 import cv2
 import numpy as np
-from numpy.linalg import norm
 
 from .crop import crop_xywh
-from .types import Mat, cv2_ml_KNearest
+from .types import Mat
 
 __all__ = [
     "FixRects",
@@ -64,8 +62,7 @@ class FixRects:
                 new_h = new_bottom - new_y
                 new_rects.append((new_x, new_y, new_w, new_h))
 
-        return_rects = deepcopy(rects)
-        return_rects = [r for r in return_rects if r not in consumed_rects]
+        return_rects = [r for r in rects if r not in consumed_rects]
         return_rects.extend(new_rects)
         return return_rects
 
@@ -80,42 +77,42 @@ class FixRects:
         new_rects = []
         for rect in rects:
             rx, ry, rw, rh = rect
-            if rw / rh > rect_wh_ratio:
-                # consider this is a connected contour
-                connected_rects.append(rect)
+            if rw / rh <= rect_wh_ratio:
+                continue
 
-                # find the thinnest part
-                border_ignore = round(rw * width_range_ratio)
-                img_cropped = crop_xywh(
-                    img_masked,
-                    (border_ignore, ry, rw - border_ignore, rh),
-                )
-                white_pixels = {}  # dict[x, white_pixel_number]
-                for i in range(img_cropped.shape[1]):
-                    col = img_cropped[:, i]
-                    white_pixels[rx + border_ignore + i] = np.count_nonzero(col > 200)
-                least_white_pixels = min(v for v in white_pixels.values() if v > 0)
-                x_values = [
-                    x
-                    for x, pixel in white_pixels.items()
-                    if pixel == least_white_pixels
-                ]
-                # select only middle values
-                x_mean = np.mean(x_values)
-                x_std = np.std(x_values)
-                x_values = [
-                    x
-                    for x in x_values
-                    if x_mean - x_std * 1.5 <= x <= x_mean + x_std * 1.5
-                ]
-                x_mid = round(np.median(x_values))
+            connected_rects.append(rect)
 
-                # split the rect
-                new_rects.extend(
-                    [(rx, ry, x_mid - rx, rh), (x_mid, ry, rx + rw - x_mid, rh)]
-                )
+            # find the thinnest part
+            border_ignore = round(rw * width_range_ratio)
+            img_cropped = crop_xywh(
+                img_masked,
+                (border_ignore, ry, rw - border_ignore, rh),
+            )
+            white_pixels = {}  # dict[x, white_pixel_number]
+            for i in range(img_cropped.shape[1]):
+                col = img_cropped[:, i]
+                white_pixels[rx + border_ignore + i] = np.count_nonzero(col > 200)
+
+            if all(v == 0 for v in white_pixels.values()):
+                return rects
+
+            least_white_pixels = min(v for v in white_pixels.values() if v > 0)
+            x_values = [
+                x for x, pixel in white_pixels.items() if pixel == least_white_pixels
+            ]
+            # select only middle values
+            x_mean = np.mean(x_values)
+            x_std = np.std(x_values)
+            x_values = [
+                x for x in x_values if x_mean - x_std * 1.5 <= x <= x_mean + x_std * 1.5
+            ]
+            x_mid = round(np.median(x_values))
+
+            # split the rect
+            new_rects.extend(
+                [(rx, ry, x_mid - rx, rh), (x_mid, ry, rx + rw - x_mid, rh)]
+            )
 
-        return_rects = deepcopy(rects)
         return_rects = [r for r in rects if r not in connected_rects]
         return_rects.extend(new_rects)
         return return_rects
@@ -144,33 +141,16 @@ def resize_fill_square(img: Mat, target: int = 20):
 
 
 def preprocess_hog(digit_rois):
-    # https://github.com/opencv/opencv/blob/f834736307c8328340aea48908484052170c9224/samples/python/digits.py
+    # https://learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial/
     samples = []
     for digit in digit_rois:
-        gx = cv2.Sobel(digit, cv2.CV_32F, 1, 0)
-        gy = cv2.Sobel(digit, cv2.CV_32F, 0, 1)
-        mag, ang = cv2.cartToPolar(gx, gy)
-        bin_n = 16
-        _bin = np.int32(bin_n * ang / (2 * np.pi))
-        bin_cells = _bin[:10, :10], _bin[10:, :10], _bin[:10, 10:], _bin[10:, 10:]
-        mag_cells = mag[:10, :10], mag[10:, :10], mag[:10, 10:], mag[10:, 10:]
-        hists = [
-            np.bincount(b.ravel(), m.ravel(), bin_n)
-            for b, m in zip(bin_cells, mag_cells)
-        ]
-        hist = np.hstack(hists)
-
-        # transform to Hellinger kernel
-        eps = 1e-7
-        hist /= hist.sum() + eps
-        hist = np.sqrt(hist)
-        hist /= norm(hist) + eps
-
+        hog = cv2.HOGDescriptor((20, 20), (10, 10), (5, 5), (10, 10), 9)
+        hist = hog.compute(digit)
         samples.append(hist)
     return np.float32(samples)
 
 
-def ocr_digit_samples_knn(__samples, knn_model: cv2_ml_KNearest, k: int = 4):
+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)
@@ -191,7 +171,7 @@ def ocr_digits_by_contour_get_samples(__roi_gray: Mat, size: int):
 
 def ocr_digits_by_contour_knn(
     __roi_gray: Mat,
-    knn_model: cv2_ml_KNearest,
+    knn_model: cv2.ml.KNearest,
     *,
     k=4,
     size: int = 20,

src/arcaea_offline_ocr/phash_db.py

@@ -1,8 +1,34 @@
 import sqlite3
+from typing import List, Union
 
-import imagehash
+import cv2
 import numpy as np
-from PIL import Image
+
+from .types import Mat
+
+
+def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
+    # type: (Union[Mat, np.ndarray], int, int) -> np.ndarray
+    """
+    Perceptual Hash computation.
+
+    Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
+
+    Adapted from `imagehash.phash`, pure opencv implementation
+
+    The result is slightly different from `imagehash.phash`.
+    """
+    if hash_size < 2:
+        raise ValueError("Hash size must be greater than or equal to 2")
+
+    img_size = hash_size * highfreq_factor
+    image = cv2.resize(img_gray, (img_size, img_size), interpolation=cv2.INTER_LANCZOS4)
+    image = np.float32(image)
+    dct = cv2.dct(image)
+    dctlowfreq = dct[:hash_size, :hash_size]
+    med = np.median(dctlowfreq)
+    diff = dctlowfreq > med
+    return diff
 
 
 def hamming_distance_sql_function(user_input, db_entry) -> int:
@@ -11,7 +37,7 @@ def hamming_distance_sql_function(user_input, db_entry) -> int:
     )
 
 
-class ImagePHashDatabase:
+class ImagePhashDatabase:
     def __init__(self, db_path: str):
         with sqlite3.connect(db_path) as conn:
             self.hash_size = int(
@@ -30,36 +56,63 @@ class ImagePHashDatabase:
                 ).fetchone()[0]
             )
 
-            # self.conn.create_function(
-            #     "HAMMING_DISTANCE",
-            #     2,
-            #     hamming_distance_sql_function,
-            #     deterministic=True,
-            # )
-
-            self.ids = [i[0] for i in conn.execute("SELECT id FROM hashes").fetchall()]
+            self.ids: List[str] = [
+                i[0] for i in conn.execute("SELECT id FROM hashes").fetchall()
+            ]
             self.hashes_byte = [
                 i[0] for i in conn.execute("SELECT hash FROM hashes").fetchall()
             ]
             self.hashes = [np.frombuffer(hb, bool) for hb in self.hashes_byte]
-            self.hashes_slice_size = round(len(self.hashes_byte[0]) * 0.25)
-            self.hashes_head = [h[: self.hashes_slice_size] for h in self.hashes]
-            self.hashes_tail = [h[-self.hashes_slice_size :] for h in self.hashes]
 
-    def lookup_hash(self, image_hash: imagehash.ImageHash, *, limit: int = 5):
-        image_hash = image_hash.hash.flatten()
-        # image_hash_head = image_hash[: self.hashes_slice_size]
-        # image_hash_tail = image_hash[-self.hashes_slice_size :]
-        # head_xor_results = [image_hash_head ^ h for h in self.hashes]
-        # tail_xor_results = [image_hash_head ^ h for h in self.hashes]
+            self.jacket_ids: List[str] = []
+            self.jacket_hashes = []
+            self.partner_icon_ids: List[str] = []
+            self.partner_icon_hashes = []
+
+            for id, hash in zip(self.ids, self.hashes):
+                id_splitted = id.split("||")
+                if len(id_splitted) > 1 and id_splitted[0] == "partner_icon":
+                    self.partner_icon_ids.append(id_splitted[1])
+                    self.partner_icon_hashes.append(hash)
+                else:
+                    self.jacket_ids.append(id)
+                    self.jacket_hashes.append(hash)
+
+    def calculate_phash(self, img_gray: Mat):
+        return phash_opencv(
+            img_gray, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor
+        )
+
+    def lookup_hash(self, image_hash: np.ndarray, *, limit: int = 5):
+        image_hash = image_hash.flatten()
         xor_results = [
             (id, np.count_nonzero(image_hash ^ h))
             for id, h in zip(self.ids, self.hashes)
         ]
         return sorted(xor_results, key=lambda r: r[1])[:limit]
 
-    def lookup_image(self, pil_image: Image.Image):
-        image_hash = imagehash.phash(
-            pil_image, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor
-        )
+    def lookup_image(self, img_gray: Mat):
+        image_hash = self.calculate_phash(img_gray)
         return self.lookup_hash(image_hash)[0]
+
+    def lookup_jackets(self, img_gray: Mat, *, limit: int = 5):
+        image_hash = self.calculate_phash(img_gray).flatten()
+        xor_results = [
+            (id, np.count_nonzero(image_hash ^ h))
+            for id, h in zip(self.jacket_ids, self.jacket_hashes)
+        ]
+        return sorted(xor_results, key=lambda r: r[1])[:limit]
+
+    def lookup_jacket(self, img_gray: Mat):
+        return self.lookup_jackets(img_gray)[0]
+
+    def lookup_partner_icons(self, img_gray: Mat, *, limit: int = 5):
+        image_hash = self.calculate_phash(img_gray).flatten()
+        xor_results = [
+            (id, np.count_nonzero(image_hash ^ h))
+            for id, h in zip(self.partner_icon_ids, self.partner_icon_hashes)
+        ]
+        return sorted(xor_results, key=lambda r: r[1])[:limit]
+
+    def lookup_partner_icon(self, img_gray: Mat):
+        return self.lookup_partner_icons(img_gray)[0]

src/arcaea_offline_ocr/types.py

@@ -1,10 +1,9 @@
 from collections.abc import Iterable
-from typing import Any, NamedTuple, Protocol, Tuple, Union
+from typing import NamedTuple, Tuple, Union
 
 import numpy as np
 
-# from pylance
-Mat = np.ndarray[int, np.dtype[np.generic]]
+Mat = np.ndarray
 
 
 class XYWHRect(NamedTuple):
@@ -24,19 +23,3 @@ class XYWHRect(NamedTuple):
             raise ValueError()
 
         return self.__class__(*[a - b for a, b in zip(self, other)])
-
-
-class cv2_ml_StatModel(Protocol):
-    def predict(self, samples: np.ndarray, results: np.ndarray, flags: int = 0):
-        ...
-
-    def train(self, samples: np.ndarray, layout: int, responses: np.ndarray):
-        ...
-
-
-class cv2_ml_KNearest(cv2_ml_StatModel, Protocol):
-    def findNearest(
-        self, samples: np.ndarray, k: int
-    ) -> Tuple[Any, np.ndarray, np.ndarray, np.ndarray]:
-        """cv.ml.KNearest.findNearest(samples, k[, results[, neighborResponses[, dist]]]) -> retval, results, neighborResponses, dist"""
-        ...

src/arcaea_offline_ocr/utils.py

@@ -1,17 +1,15 @@
-import io
 from collections.abc import Iterable
-from typing import Callable, Tuple, TypeVar, Union, overload
+from typing import Callable, TypeVar, Union, overload
 
 import cv2
 import numpy as np
-from PIL import Image, ImageCms
 
-from .types import Mat, XYWHRect
+from .types import XYWHRect
 
 __all__ = ["imread_unicode"]
 
 
-def imread_unicode(filepath: str, flags: int = cv2.IMREAD_UNCHANGED) -> Mat:
+def imread_unicode(filepath: str, flags: int = cv2.IMREAD_UNCHANGED):
     # https://stackoverflow.com/a/57872297/16484891
     # CC BY-SA 4.0
     return cv2.imdecode(np.fromfile(filepath, dtype=np.uint8), flags)
@@ -46,25 +44,3 @@ def apply_factor(item, factor: float):
         return item * factor
     elif isinstance(item, Iterable):
         return item.__class__([i * factor for i in item])
-
-
-def convert_to_srgb(pil_img: Image.Image):
-    """
-    Convert PIL image to sRGB color space (if possible)
-    and save the converted file.
-
-    https://stackoverflow.com/a/65667797/16484891
-
-    CC BY-SA 4.0
-    """
-    icc = pil_img.info.get("icc_profile", "")
-    icc_conv = ""
-
-    if icc:
-        io_handle = io.BytesIO(icc)  # virtual file
-        src_profile = ImageCms.ImageCmsProfile(io_handle)
-        dst_profile = ImageCms.createProfile("sRGB")
-        img_conv = ImageCms.profileToProfile(pil_img, src_profile, dst_profile)
-        icc_conv = img_conv.info.get("icc_profile", "")
-
-    return img_conv if icc != icc_conv else pil_img