chore: v0.0.99

clean up legacy code

.github/workflows/build-and-draft-release.yml

@@ -0,0 +1,48 @@
+name: "Build and draft a release"
+
+on:
+  workflow_dispatch:
+  push:
+    tags:
+      - "v[0-9]+.[0-9]+.[0-9]+"
+
+permissions:
+  contents: write
+  discussions: write
+
+jobs:
+  build-and-draft-release:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Set up Python environment
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      - name: Build package
+        run: |
+          pip install build
+          python -m build
+
+      - name: Remove `v` in tag name
+        uses: mad9000/actions-find-and-replace-string@5
+        id: tagNameReplaced
+        with:
+          source: ${{ github.ref_name }}
+          find: "v"
+          replace: ""
+
+      - name: Draft a release
+        uses: softprops/action-gh-release@v2
+        with:
+          discussion_category_name: New releases
+          draft: true
+          generate_release_notes: true
+          files: |
+            dist/arcaea_offline_ocr-${{ steps.tagNameReplaced.outputs.value }}*.whl
+            dist/arcaea-offline-ocr-${{ steps.tagNameReplaced.outputs.value }}.tar.gz

.gitignore

@@ -1,6 +1,8 @@
 __debug*
 .vscode/
 
+*venv*/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.pre-commit-config.yaml

@@ -8,9 +8,7 @@ repos:
     rev: 23.1.0
     hooks:
       - id: black
-        exclude: _builtin_templates.py
   - repo: https://github.com/PyCQA/isort
     rev: 5.12.0
     hooks:
       - id: isort
-        exclude: _builtin_templates.py

LICENSE

@@ -0,0 +1,675 @@
+# GNU GENERAL PUBLIC LICENSE
+
+Version 3, 29 June 2007
+
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+<https://fsf.org/>
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+will be similar in spirit to the present version, but may differ in
+detail to address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program
+specifies that a certain numbered version of the GNU General Public
+License "or any later version" applies to it, you have the option of
+following the terms and conditions either of that numbered version or
+of any later version published by the Free Software Foundation. If the
+Program does not specify a version number of the GNU General Public
+License, you may choose any version ever published by the Free
+Software Foundation.
+
+If the Program specifies that a proxy can decide which future versions
+of the GNU General Public License can be used, that proxy's public
+statement of acceptance of a version permanently authorizes you to
+choose that version for the Program.
+
+Later license versions may give you additional or different
+permissions. However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+### 15. Disclaimer of Warranty.
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT
+WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND
+PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
+DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR
+CORRECTION.
+
+### 16. Limitation of Liability.
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR
+CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
+INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES
+ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT
+NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR
+LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM
+TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER
+PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+### 17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+END OF TERMS AND CONDITIONS
+
+## How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these
+terms.
+
+To do so, attach the following notices to the program. It is safest to
+attach them to the start of each source file to most effectively state
+the exclusion of warranty; and each file should have at least the
+"copyright" line and a pointer to where the full notice is found.
+
+        <one line to give the program's name and a brief idea of what it does.>
+        Copyright (C) <year>  <name of author>
+
+        This program is free software: you can redistribute it and/or modify
+        it under the terms of the GNU General Public License as published by
+        the Free Software Foundation, either version 3 of the License, or
+        (at your option) any later version.
+
+        This program is distributed in the hope that it will be useful,
+        but WITHOUT ANY WARRANTY; without even the implied warranty of
+        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+        GNU General Public License for more details.
+
+        You should have received a copy of the GNU General Public License
+        along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper
+mail.
+
+If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+        <program>  Copyright (C) <year>  <name of author>
+        This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+        This is free software, and you are welcome to redistribute it
+        under certain conditions; type `show c' for details.
+
+The hypothetical commands \`show w' and \`show c' should show the
+appropriate parts of the General Public License. Of course, your
+program's commands might be different; for a GUI interface, you would
+use an "about box".
+
+You should also get your employer (if you work as a programmer) or
+school, if any, to sign a "copyright disclaimer" for the program, if
+necessary. For more information on this, and how to apply and follow
+the GNU GPL, see <https://www.gnu.org/licenses/>.
+
+The GNU General Public License does not permit incorporating your
+program into proprietary programs. If your program is a subroutine
+library, you may consider it more useful to permit linking proprietary
+applications with the library. If this is what you want to do, use the
+GNU Lesser General Public License instead of this License. But first,
+please read <https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -3,26 +3,49 @@
 ## Example
 
 ```py
-from arcaea_offline_ocr.device.v2.rois import DeviceV2AutoRois
-from arcaea_offline_ocr.device.v2.ocr import DeviceV2Ocr
-from arcaea_offline_ocr.sift_db import SIFTDatabase
-from arcaea_offline_ocr.utils import imread_unicode
 import cv2
+from arcaea_offline_ocr.device.ocr import DeviceOcr
+from arcaea_offline_ocr.device.rois.definition import DeviceRoisAutoT2
+from arcaea_offline_ocr.device.rois.extractor import DeviceRoisExtractor
+from arcaea_offline_ocr.device.rois.masker import DeviceRoisMaskerAutoT2
+from arcaea_offline_ocr.phash_db import ImagePhashDatabase
 
-knn_model = cv2.ml.KNearest_load(r'/path/to/knn/model')
-sift_db = SIFTDatabase(r'/path/to/sift/database.db')
+img_path = "/path/to/opencv/supported/image/formats.jpg"
+img = cv2.imread(img_path, cv2.IMREAD_COLOR)
 
-rois = DeviceV2AutoRois(imread_unicode(r'/path/to/your/screenshot.jpg'))  # any format that opencv-python supports
-ocr = DeviceV2Ocr(knn_model, sift_db)
-result = ocr.ocr(rois)
-print(result)
+rois = DeviceRoisAutoT2(img.shape[1], img.shape[0])
+extractor = DeviceRoisExtractor(img, rois)
+masker = DeviceRoisMaskerAutoT2()
+
+knn_model = cv2.ml.KNearest.load("/path/to/trained/knn/model.dat")
+phash_db = ImagePhashDatabase("/path/to/image/phash/database.db")
+
+ocr = DeviceOcr(extractor, masker, knn_model, phash_db)
+print(ocr.ocr())
 ```
 
 ```sh
 $ python example.py
-DeviceOcrResult(rating_class=2, pure=1371, far=62, lost=34, score=9558078, max_recall=330, song_id='abstrusedilemma', title=None, clear_type=None)
+DeviceOcrResult(rating_class=2, pure=1135, far=11, lost=0, score=9953016, max_recall=1146, song_id='ringedgenesis', song_id_possibility=0.953125, clear_status=2, partner_id='45', partner_id_possibility=0.8046875)
 ```
 
+## License
+
+This file is part of arcaea-offline-ocr, as called "This program" below.
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
 ## Credits
 
-[283375/image-sift-database](https://github.com/283375/image-sift-database)
+[283375/image-phash-database](https://github.com/283375/image-phash-database)

pyproject.toml

@@ -4,20 +4,20 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "arcaea-offline-ocr"
-version = "0.1.0"
+version = "0.0.99"
 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",
 ]
 
 [project.urls]
-"Homepage" = "https://github.com/283375/arcaea-offline-ocr"
-"Bug Tracker" = "https://github.com/283375/arcaea-offline-ocr/issues"
+"Homepage" = "https://github.com/ArcaeaOffline/core-ocr"
+"Bug Tracker" = "https://github.com/ArcaeaOffline/core-ocr/issues"
 
 [tool.isort]
 profile = "black"
@@ -25,3 +25,14 @@ src_paths = ["src/arcaea_offline_ocr"]
 
 [tool.pyright]
 ignore = ["**/__debug*.*"]
+
+[tool.pylint.main]
+# extension-pkg-allow-list = ["cv2"]
+generated-members = ["cv2.*"]
+
+[tool.pylint.logging]
+disable = [
+  "missing-module-docstring",
+  "missing-class-docstring",
+  "missing-function-docstring"
+]

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/__init__.py

@@ -1,5 +1,4 @@
 from .crop import *
 from .device import *
-from .mask import *
 from .ocr import *
 from .utils import *

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

@@ -5,9 +5,14 @@ import cv2
 import numpy as np
 
 from ....crop import crop_xywh
-from ....ocr import FixRects, ocr_digits_by_contour_knn, preprocess_hog
-from ....sift_db import SIFTDatabase
-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 *
@@ -17,14 +22,14 @@ from .rois import ChieriBotV4Rois
 class ChieriBotV4Ocr:
     def __init__(
         self,
-        score_knn: cv2_ml_KNearest,
-        pfl_knn: cv2_ml_KNearest,
-        sift_db: SIFTDatabase,
+        score_knn: cv2.ml.KNearest,
+        pfl_knn: cv2.ml.KNearest,
+        phash_db: ImagePhashDatabase,
         factor: Optional[float] = 1.0,
     ):
         self.__score_knn = score_knn
         self.__pfl_knn = pfl_knn
-        self.__sift_db = sift_db
+        self.__phash_db = phash_db
         self.__rois = ChieriBotV4Rois(factor)
 
     @property
@@ -32,7 +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
@@ -40,16 +45,16 @@ 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
-    def sift_db(self):
-        return self.__sift_db
+    def phash_db(self):
+        return self.__phash_db
 
-    @sift_db.setter
-    def sift_db(self, sift_db: SIFTDatabase):
-        self.__sift_db = sift_db
+    @phash_db.setter
+    def phash_db(self, phash_db: ImagePhashDatabase):
+        self.__phash_db = phash_db
 
     @property
     def rois(self):
@@ -83,14 +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
@@ -98,20 +95,7 @@ class ChieriBotV4Ocr:
         jacket_roi = cv2.cvtColor(
             crop_xywh(component_bgr, jacket_rect), cv2.COLOR_BGR2GRAY
         )
-        return self.sift_db.lookup_img(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
@@ -221,7 +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)
 
@@ -232,15 +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/b30/shared.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Optional, Union
+from typing import Optional
 
 import attrs
 

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

@@ -0,0 +1,18 @@
+from typing import Optional
+
+import attrs
+
+
+@attrs.define
+class DeviceOcrResult:
+    rating_class: int
+    pure: int
+    far: int
+    lost: int
+    score: int
+    max_recall: Optional[int] = None
+    song_id: 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

@@ -0,0 +1,162 @@
+import cv2
+import numpy as np
+
+from ..crop import crop_xywh
+from ..ocr import (
+    FixRects,
+    ocr_digit_samples_knn,
+    ocr_digits_by_contour_knn,
+    preprocess_hog,
+    resize_fill_square,
+)
+from ..phash_db import ImagePhashDatabase
+from ..types import Mat
+from .common import DeviceOcrResult
+from .rois.extractor import DeviceRoisExtractor
+from .rois.masker import DeviceRoisMasker
+
+
+class DeviceOcr:
+    def __init__(
+        self,
+        extractor: DeviceRoisExtractor,
+        masker: DeviceRoisMasker,
+        knn_model: cv2.ml.KNearest,
+        phash_db: ImagePhashDatabase,
+    ):
+        self.extractor = extractor
+        self.masker = masker
+        self.knn_model = knn_model
+        self.phash_db = phash_db
+
+    def pfl(self, roi_gray: Mat, factor: float = 1.25):
+        contours, _ = cv2.findContours(
+            roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+        )
+        filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
+        rects = [cv2.boundingRect(c) for c in filtered_contours]
+        rects = FixRects.connect_broken(rects, roi_gray.shape[1], roi_gray.shape[0])
+
+        filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
+        filtered_rects = FixRects.split_connected(roi_gray, filtered_rects)
+        filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
+
+        roi_ocr = roi_gray.copy()
+        filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
+        for contour in contours:
+            if tuple(contour.flatten()) in filtered_contours_flattened:
+                continue
+            roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
+        digit_rois = [
+            resize_fill_square(crop_xywh(roi_ocr, r), 20) for r in filtered_rects
+        ]
+
+        samples = preprocess_hog(digit_rois)
+        return ocr_digit_samples_knn(samples, self.knn_model)
+
+    def pure(self):
+        return self.pfl(self.masker.pure(self.extractor.pure))
+
+    def far(self):
+        return self.pfl(self.masker.far(self.extractor.far))
+
+    def lost(self):
+        return self.pfl(self.masker.lost(self.extractor.lost))
+
+    def score(self):
+        roi = self.masker.score(self.extractor.score)
+        contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+        for contour in contours:
+            if (
+                cv2.boundingRect(contour)[3] < roi.shape[0] * 0.6
+            ):  # h < score_component_h * 0.6
+                roi = cv2.fillPoly(roi, [contour], [0])
+        return ocr_digits_by_contour_knn(roi, self.knn_model)
+
+    def rating_class(self):
+        roi = self.extractor.rating_class
+        results = [
+            self.masker.rating_class_pst(roi),
+            self.masker.rating_class_prs(roi),
+            self.masker.rating_class_ftr(roi),
+            self.masker.rating_class_byd(roi),
+            self.masker.rating_class_etr(roi),
+        ]
+        return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
+
+    def max_recall(self):
+        return ocr_digits_by_contour_knn(
+            self.masker.max_recall(self.extractor.max_recall), self.knn_model
+        )
+
+    def clear_status(self):
+        roi = self.extractor.clear_status
+        results = [
+            self.masker.clear_status_track_lost(roi),
+            self.masker.clear_status_track_complete(roi),
+            self.masker.clear_status_full_recall(roi),
+            self.masker.clear_status_pure_memory(roi),
+        ]
+        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.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/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 * 0.4,
+            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 * 0.4,
+            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

@@ -0,0 +1,15 @@
+from typing import Tuple
+
+Rect = Tuple[int, int, int, int]
+
+
+class DeviceRois:
+    pure: Rect
+    far: Rect
+    lost: Rect
+    score: Rect
+    rating_class: Rect
+    max_recall: Rect
+    jacket: Rect
+    clear_status: Rect
+    partner_icon: 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

@@ -0,0 +1,48 @@
+from ....crop import crop_xywh
+from ....types import Mat
+from ..definition.common import DeviceRois
+
+
+class DeviceRoisExtractor:
+    def __init__(self, img: Mat, rois: DeviceRois):
+        self.img = img
+        self.sizes = rois
+
+    def __construct_int_rect(self, rect):
+        return tuple(round(r) for r in rect)
+
+    @property
+    def pure(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.pure))
+
+    @property
+    def far(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.far))
+
+    @property
+    def lost(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.lost))
+
+    @property
+    def score(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.score))
+
+    @property
+    def jacket(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.jacket))
+
+    @property
+    def rating_class(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.rating_class))
+
+    @property
+    def max_recall(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.max_recall))
+
+    @property
+    def clear_status(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.clear_status))
+
+    @property
+    def partner_icon(self):
+        return crop_xywh(self.img, self.__construct_int_rect(self.sizes.partner_icon))

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 +1,231 @@
+import cv2
+import numpy as np
+
+from ....types import Mat
+from .common import DeviceRoisMasker
+
+
+class DeviceRoisMaskerAuto(DeviceRoisMasker):
+    # pylint: disable=abstract-method
+
+    @staticmethod
+    def mask_bgr_in_hsv(roi_bgr: Mat, hsv_lower: Mat, hsv_upper: Mat):
+        return cv2.inRange(
+            cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV), hsv_lower, hsv_upper
+        )
+
+
+class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
+    GRAY_BGR_MIN = np.array([50] * 3, np.uint8)
+    GRAY_BGR_MAX = np.array([160] * 3, np.uint8)
+
+    SCORE_HSV_MIN = np.array([0, 0, 180], np.uint8)
+    SCORE_HSV_MAX = np.array([179, 255, 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)
+
+    ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
+    ETR_HSV_MAX = np.array([140, 145, 180], 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 cls.mask_bgr_in_hsv(roi_bgr, cls.SCORE_HSV_MIN, cls.SCORE_HSV_MAX)
+
+    @classmethod
+    def rating_class_pst(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.PST_HSV_MIN, cls.PST_HSV_MAX)
+
+    @classmethod
+    def rating_class_prs(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.PRS_HSV_MIN, cls.PRS_HSV_MAX)
+
+    @classmethod
+    def rating_class_ftr(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.FTR_HSV_MIN, cls.FTR_HSV_MAX)
+
+    @classmethod
+    def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
+
+    @classmethod
+    def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_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 cls.mask_bgr_in_hsv(
+            roi_bgr, cls.TRACK_LOST_HSV_MIN, cls.TRACK_LOST_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_track_complete(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.TRACK_COMPLETE_HSV_MIN, cls.TRACK_COMPLETE_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_full_recall(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.FULL_RECALL_HSV_MIN, cls.FULL_RECALL_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_pure_memory(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, 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, 40, 255], np.uint8)
+
+    SCORE_HSV_MIN = np.array([0, 0, 180], np.uint8)
+    SCORE_HSV_MAX = np.array([179, 255, 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)
+
+    ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
+    ETR_HSV_MAX = np.array([140, 145, 180], 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 cls.mask_bgr_in_hsv(roi_bgr, 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 cls.mask_bgr_in_hsv(roi_bgr, cls.SCORE_HSV_MIN, cls.SCORE_HSV_MAX)
+
+    @classmethod
+    def rating_class_pst(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.PST_HSV_MIN, cls.PST_HSV_MAX)
+
+    @classmethod
+    def rating_class_prs(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.PRS_HSV_MIN, cls.PRS_HSV_MAX)
+
+    @classmethod
+    def rating_class_ftr(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.FTR_HSV_MIN, cls.FTR_HSV_MAX)
+
+    @classmethod
+    def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
+
+    @classmethod
+    def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_HSV_MAX)
+
+    @classmethod
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.MAX_RECALL_HSV_MIN, cls.MAX_RECALL_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_track_lost(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.TRACK_LOST_HSV_MIN, cls.TRACK_LOST_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_track_complete(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.TRACK_COMPLETE_HSV_MIN, cls.TRACK_COMPLETE_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_full_recall(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.FULL_RECALL_HSV_MIN, cls.FULL_RECALL_HSV_MAX
+        )
+
+    @classmethod
+    def clear_status_pure_memory(cls, roi_bgr: Mat) -> Mat:
+        return cls.mask_bgr_in_hsv(
+            roi_bgr, cls.PURE_MEMORY_HSV_MIN, cls.PURE_MEMORY_HSV_MAX
+        )

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

@@ -0,0 +1,59 @@
+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 rating_class_etr(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/device/shared.py

@@ -1,16 +0,0 @@
-from typing import Optional
-
-import attrs
-
-
-@attrs.define
-class DeviceOcrResult:
-    rating_class: int
-    pure: int
-    far: int
-    lost: int
-    score: int
-    max_recall: int
-    song_id: Optional[str] = None
-    title: Optional[str] = None
-    clear_type: Optional[str] = None

src/arcaea_offline_ocr/device/v1/crop.py

@@ -1,53 +0,0 @@
-from typing import Tuple
-
-from ...types import Mat
-from .definition import DeviceV1
-
-__all__ = [
-    "crop_img",
-    "crop_from_device_attr",
-    "crop_to_pure",
-    "crop_to_far",
-    "crop_to_lost",
-    "crop_to_max_recall",
-    "crop_to_rating_class",
-    "crop_to_score",
-    "crop_to_title",
-]
-
-
-def crop_img(img: Mat, *, top: int, left: int, bottom: int, right: int):
-    return img[top:bottom, left:right]
-
-
-def crop_from_device_attr(img: Mat, rect: Tuple[int, int, int, int]):
-    x, y, w, h = rect
-    return crop_img(img, top=y, left=x, bottom=y + h, right=x + w)
-
-
-def crop_to_pure(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.pure)
-
-
-def crop_to_far(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.far)
-
-
-def crop_to_lost(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.lost)
-
-
-def crop_to_max_recall(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.max_recall)
-
-
-def crop_to_rating_class(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.rating_class)
-
-
-def crop_to_score(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.score)
-
-
-def crop_to_title(screenshot: Mat, device: DeviceV1):
-    return crop_from_device_attr(screenshot, device.title)

src/arcaea_offline_ocr/device/v1/definition.py

@@ -1,37 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Dict, Tuple
-
-__all__ = ["DeviceV1"]
-
-
-@dataclass(kw_only=True)
-class DeviceV1:
-    version: int
-    uuid: str
-    name: str
-    pure: Tuple[int, int, int, int]
-    far: Tuple[int, int, int, int]
-    lost: Tuple[int, int, int, int]
-    max_recall: Tuple[int, int, int, int]
-    rating_class: Tuple[int, int, int, int]
-    score: Tuple[int, int, int, int]
-    title: Tuple[int, int, int, int]
-
-    @classmethod
-    def from_json_object(cls, json_dict: Dict[str, Any]):
-        if json_dict["version"] == 1:
-            return cls(
-                version=1,
-                uuid=json_dict["uuid"],
-                name=json_dict["name"],
-                pure=json_dict["pure"],
-                far=json_dict["far"],
-                lost=json_dict["lost"],
-                max_recall=json_dict["max_recall"],
-                rating_class=json_dict["rating_class"],
-                score=json_dict["score"],
-                title=json_dict["title"],
-            )
-
-    def repr_info(self):
-        return f"Device(version={self.version}, uuid={repr(self.uuid)}, name={repr(self.name)})"

src/arcaea_offline_ocr/device/v1/ocr.py

@@ -1,86 +0,0 @@
-from typing import List
-
-import cv2
-
-from ...crop import crop_xywh
-from ...mask import mask_gray, mask_white
-from ...ocr import ocr_digits_by_contour_knn, ocr_rating_class
-from ...types import Mat, cv2_ml_KNearest
-from ..shared import DeviceOcrResult
-from .crop import *
-from .definition import DeviceV1
-
-
-class DeviceV1Ocr:
-    def __init__(self, device: DeviceV1, knn_model: cv2_ml_KNearest):
-        self.__device = device
-        self.__knn_model = knn_model
-
-    @property
-    def device(self):
-        return self.__device
-
-    @device.setter
-    def device(self, value):
-        self.__device = value
-
-    @property
-    def knn_model(self):
-        return self.__knn_model
-
-    @knn_model.setter
-    def knn_model(self, value):
-        self.__knn_model = value
-
-    def preprocess_score_roi(self, __roi_gray: Mat) -> List[Mat]:
-        roi_gray = __roi_gray.copy()
-        contours, _ = cv2.findContours(
-            roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-        )
-        for contour in contours:
-            rect = cv2.boundingRect(contour)
-            if rect[3] > roi_gray.shape[0] * 0.6:
-                continue
-            roi_gray = cv2.fillPoly(roi_gray, [contour], 0)
-        return roi_gray
-
-    def ocr(self, img_bgr: Mat):
-        rating_class_roi = crop_to_rating_class(img_bgr, self.device)
-        rating_class = ocr_rating_class(rating_class_roi)
-
-        pfl_mr_roi = [
-            crop_to_pure(img_bgr, self.device),
-            crop_to_far(img_bgr, self.device),
-            crop_to_lost(img_bgr, self.device),
-            crop_to_max_recall(img_bgr, self.device),
-        ]
-        pfl_mr_roi = [mask_gray(roi) for roi in pfl_mr_roi]
-
-        pure, far, lost = [
-            ocr_digits_by_contour_knn(roi, self.knn_model) for roi in pfl_mr_roi[:3]
-        ]
-
-        max_recall_contours, _ = cv2.findContours(
-            pfl_mr_roi[3], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-        )
-        max_recall_rects = [cv2.boundingRect(c) for c in max_recall_contours]
-        max_recall_rect = sorted(max_recall_rects, key=lambda r: r[0])[-1]
-        max_recall_roi = crop_xywh(img_bgr, max_recall_rect)
-        max_recall = ocr_digits_by_contour_knn(max_recall_roi, self.knn_model)
-
-        score_roi = crop_to_score(img_bgr, self.device)
-        score_roi = mask_white(score_roi)
-        score_roi = self.preprocess_score_roi(score_roi)
-        score = ocr_digits_by_contour_knn(score_roi, self.knn_model)
-
-        return DeviceOcrResult(
-            song_id=None,
-            title=None,
-            rating_class=rating_class,
-            pure=pure,
-            far=far,
-            lost=lost,
-            score=score,
-            max_recall=max_recall,
-            clear_type=None,
-        )

src/arcaea_offline_ocr/device/v2/__init__.py

@@ -1,4 +0,0 @@
-from .definition import DeviceV2
-from .ocr import DeviceV2Ocr
-from .rois import DeviceV2AutoRois, DeviceV2Rois
-from .shared import MAX_RECALL_CLOSE_KERNEL

src/arcaea_offline_ocr/device/v2/definition.py

@@ -1,26 +0,0 @@
-from typing import Iterable
-
-from attrs import define, field
-
-from ...types import XYWHRect
-
-
-def iterable_to_xywh_rect(__iter: Iterable) -> XYWHRect:
-    return XYWHRect(*__iter)
-
-
-@define(kw_only=True)
-class DeviceV2:
-    version = field(type=int)
-    uuid = field(type=str)
-    name = field(type=str)
-    crop_black_edges = field(type=bool)
-    factor = field(type=float)
-    pure = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
-    far = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
-    lost = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
-    score = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])
-    max_recall_rating_class = field(
-        converter=iterable_to_xywh_rect, default=[0, 0, 0, 0]
-    )
-    title = field(converter=iterable_to_xywh_rect, default=[0, 0, 0, 0])

src/arcaea_offline_ocr/device/v2/ocr.py

@@ -1,147 +0,0 @@
-import math
-from functools import lru_cache
-from typing import Sequence
-
-import cv2
-import numpy as np
-
-from ...crop import crop_xywh
-from ...mask import mask_byd, mask_ftr, mask_gray, mask_prs, mask_pst, mask_white
-from ...ocr import (
-    FixRects,
-    ocr_digit_samples_knn,
-    ocr_digits_by_contour_knn,
-    preprocess_hog,
-    resize_fill_square,
-)
-from ...sift_db import SIFTDatabase
-from ...types import Mat, cv2_ml_KNearest
-from ..shared import DeviceOcrResult
-from .preprocess import find_digits_preprocess
-from .rois import DeviceV2Rois
-from .shared import MAX_RECALL_CLOSE_KERNEL
-
-
-class DeviceV2Ocr:
-    def __init__(self, knn_model: cv2_ml_KNearest, sift_db: SIFTDatabase):
-        self.__knn_model = knn_model
-        self.__sift_db = sift_db
-
-    @property
-    def knn_model(self):
-        if not self.__knn_model:
-            raise ValueError("`knn_model` unset.")
-        return self.__knn_model
-
-    @knn_model.setter
-    def knn_model(self, value: cv2_ml_KNearest):
-        self.__knn_model = value
-
-    @property
-    def sift_db(self):
-        if not self.__sift_db:
-            raise ValueError("`sift_db` unset.")
-        return self.__sift_db
-
-    @sift_db.setter
-    def sift_db(self, value: SIFTDatabase):
-        self.__sift_db = value
-
-    @lru_cache
-    def _get_digit_widths(self, num_list: Sequence[int], factor: float):
-        widths = set()
-        for n in num_list:
-            lower = math.floor(n * factor)
-            upper = math.ceil(n * factor)
-            widths.update(range(lower, upper + 1))
-        return widths
-
-    def _base_ocr_pfl(self, roi_masked: Mat, factor: float = 1.0):
-        contours, _ = cv2.findContours(
-            roi_masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-        )
-        filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
-        rects = [cv2.boundingRect(c) for c in filtered_contours]
-        rects = FixRects.connect_broken(rects, roi_masked.shape[1], roi_masked.shape[0])
-        rect_contour_map = dict(zip(rects, filtered_contours))
-
-        filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
-        filtered_rects = FixRects.split_connected(roi_masked, filtered_rects)
-        filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
-
-        roi_ocr = roi_masked.copy()
-        filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
-        for contour in contours:
-            if tuple(contour.flatten()) in filtered_contours_flattened:
-                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])
-        ]
-        # [cv2.imshow(f"r{i}", r) for i, r in enumerate(digit_rois)]
-        # cv2.waitKey(0)
-        samples = preprocess_hog(digit_rois)
-        return ocr_digit_samples_knn(samples, self.knn_model)
-
-    def ocr_song_id(self, rois: DeviceV2Rois):
-        jacket = cv2.cvtColor(rois.jacket, cv2.COLOR_BGR2GRAY)
-        return self.sift_db.lookup_img(jacket)[0]
-
-    def ocr_rating_class(self, rois: DeviceV2Rois):
-        roi = cv2.cvtColor(rois.max_recall_rating_class, cv2.COLOR_BGR2HSV)
-        results = [mask_pst(roi), mask_prs(roi), mask_ftr(roi), mask_byd(roi)]
-        return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
-
-    def ocr_score(self, rois: DeviceV2Rois):
-        roi = cv2.cvtColor(rois.score, cv2.COLOR_BGR2HSV)
-        roi = mask_white(roi)
-        contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
-        for contour in contours:
-            x, y, w, h = cv2.boundingRect(contour)
-            if h < roi.shape[0] * 0.6:
-                roi = cv2.fillPoly(roi, [contour], [0])
-        return ocr_digits_by_contour_knn(roi, self.knn_model)
-
-    def ocr_pure(self, rois: DeviceV2Rois):
-        roi = mask_gray(rois.pure)
-        return self._base_ocr_pfl(roi, rois.sizes.factor)
-
-    def ocr_far(self, rois: DeviceV2Rois):
-        roi = mask_gray(rois.far)
-        return self._base_ocr_pfl(roi, rois.sizes.factor)
-
-    def ocr_lost(self, rois: DeviceV2Rois):
-        roi = mask_gray(rois.lost)
-        return self._base_ocr_pfl(roi, rois.sizes.factor)
-
-    def ocr_max_recall(self, rois: DeviceV2Rois):
-        roi = mask_gray(rois.max_recall_rating_class)
-        roi_closed = cv2.morphologyEx(roi, cv2.MORPH_CLOSE, MAX_RECALL_CLOSE_KERNEL)
-        contours, _ = cv2.findContours(
-            roi_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-        )
-        rects = sorted(
-            [cv2.boundingRect(c) for c in contours], key=lambda r: r[0], reverse=True
-        )
-        max_recall_roi = crop_xywh(roi, rects[0])
-        return ocr_digits_by_contour_knn(max_recall_roi, self.knn_model)
-
-    def ocr(self, rois: DeviceV2Rois):
-        song_id = self.ocr_song_id(rois)
-        rating_class = self.ocr_rating_class(rois)
-        score = self.ocr_score(rois)
-        pure = self.ocr_pure(rois)
-        far = self.ocr_far(rois)
-        lost = self.ocr_lost(rois)
-        max_recall = self.ocr_max_recall(rois)
-
-        return DeviceOcrResult(
-            rating_class=rating_class,
-            pure=pure,
-            far=far,
-            lost=lost,
-            score=score,
-            max_recall=max_recall,
-            song_id=song_id,
-        )

src/arcaea_offline_ocr/device/v2/preprocess.py

@@ -1,54 +0,0 @@
-import cv2
-
-from ...types import Mat
-from .shared import *
-
-
-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

src/arcaea_offline_ocr/device/v2/rois.py

@@ -1,367 +0,0 @@
-from typing import Tuple, Union
-
-from ...crop import crop_black_edges, crop_xywh
-from ...types import Mat, XYWHRect
-from .definition import DeviceV2
-
-
-def to_int(num: Union[int, float]) -> int:
-    return round(num)
-
-
-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 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)

src/arcaea_offline_ocr/device/v2/shared.py

@@ -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])

src/arcaea_offline_ocr/mask.py

@@ -1,95 +0,0 @@
-import cv2
-import numpy as np
-
-from .types import Mat
-
-__all__ = [
-    "GRAY_MIN_HSV",
-    "GRAY_MAX_HSV",
-    "WHITE_MIN_HSV",
-    "WHITE_MAX_HSV",
-    "PST_MIN_HSV",
-    "PST_MAX_HSV",
-    "PRS_MIN_HSV",
-    "PRS_MAX_HSV",
-    "FTR_MIN_HSV",
-    "FTR_MAX_HSV",
-    "BYD_MIN_HSV",
-    "BYD_MAX_HSV",
-    "mask_gray",
-    "mask_white",
-    "mask_pst",
-    "mask_prs",
-    "mask_ftr",
-    "mask_byd",
-    "mask_rating_class",
-]
-
-GRAY_MIN_HSV = np.array([0, 0, 70], np.uint8)
-GRAY_MAX_HSV = np.array([0, 0, 200], np.uint8)
-
-GRAY_MIN_BGR = np.array([50] * 3, np.uint8)
-GRAY_MAX_BGR = np.array([160] * 3, np.uint8)
-
-WHITE_MIN_HSV = np.array([0, 0, 240], np.uint8)
-WHITE_MAX_HSV = np.array([179, 10, 255], np.uint8)
-
-PST_MIN_HSV = np.array([100, 50, 80], np.uint8)
-PST_MAX_HSV = np.array([100, 255, 255], np.uint8)
-
-PRS_MIN_HSV = np.array([43, 40, 75], np.uint8)
-PRS_MAX_HSV = np.array([50, 155, 190], np.uint8)
-
-FTR_MIN_HSV = np.array([149, 30, 0], np.uint8)
-FTR_MAX_HSV = np.array([155, 181, 150], np.uint8)
-
-BYD_MIN_HSV = np.array([170, 50, 50], np.uint8)
-BYD_MAX_HSV = np.array([179, 210, 198], np.uint8)
-
-
-def mask_gray(__img_bgr: Mat):
-    # bgr_value_equal_mask = all(__img_bgr[:, 1:] == __img_bgr[:, :-1], axis=1)
-    bgr_value_equal_mask = np.max(__img_bgr, axis=2) - np.min(__img_bgr, axis=2) <= 5
-    img_bgr = __img_bgr.copy()
-    img_bgr[~bgr_value_equal_mask] = np.array([0, 0, 0], __img_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_MIN_BGR, GRAY_MAX_BGR)
-
-
-def mask_white(img_hsv: Mat):
-    mask = cv2.inRange(img_hsv, WHITE_MIN_HSV, WHITE_MAX_HSV)
-    mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
-    return mask
-
-
-def mask_pst(img_hsv: Mat):
-    mask = cv2.inRange(img_hsv, PST_MIN_HSV, PST_MAX_HSV)
-    mask = cv2.dilate(mask, (1, 1))
-    return mask
-
-
-def mask_prs(img_hsv: Mat):
-    mask = cv2.inRange(img_hsv, PRS_MIN_HSV, PRS_MAX_HSV)
-    mask = cv2.dilate(mask, (1, 1))
-    return mask
-
-
-def mask_ftr(img_hsv: Mat):
-    mask = cv2.inRange(img_hsv, FTR_MIN_HSV, FTR_MAX_HSV)
-    mask = cv2.dilate(mask, (1, 1))
-    return mask
-
-
-def mask_byd(img_hsv: Mat):
-    mask = cv2.inRange(img_hsv, BYD_MIN_HSV, BYD_MAX_HSV)
-    mask = cv2.dilate(mask, (2, 2))
-    return mask
-
-
-def mask_rating_class(img_hsv: Mat):
-    pst = mask_pst(img_hsv)
-    prs = mask_prs(img_hsv)
-    ftr = mask_ftr(img_hsv)
-    byd = mask_byd(img_hsv)
-    return cv2.bitwise_or(byd, cv2.bitwise_or(ftr, cv2.bitwise_or(pst, prs)))

src/arcaea_offline_ocr/ocr.py

@@ -1,14 +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 .mask import mask_byd, mask_ftr, mask_prs, mask_pst
-from .types import Mat, cv2_ml_KNearest
+from .types import Mat
 
 __all__ = [
     "FixRects",
@@ -39,7 +36,7 @@ class FixRects:
             if rect in consumed_rects:
                 continue
 
-            x, y, w, h = rect
+            x, _, w, h = rect
             # grab those small rects
             if not img_height * 0.1 <= h <= img_height * 0.6:
                 continue
@@ -49,7 +46,7 @@ class FixRects:
             for other_rect in rects:
                 if rect == other_rect:
                     continue
-                ox, oy, ow, oh = other_rect
+                ox, _, ow, _ = other_rect
                 if abs(x - ox) < tolerance and abs((x + w) - (ox + ow)) < tolerance:
                     group.append(other_rect)
 
@@ -65,8 +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
 
@@ -81,42 +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
@@ -145,33 +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)
@@ -192,20 +171,10 @@ 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,
 ) -> int:
     samples = ocr_digits_by_contour_get_samples(__roi_gray, size)
     return ocr_digit_samples_knn(samples, knn_model, k)
-
-
-def ocr_rating_class(roi_hsv: Mat):
-    mask_results = [
-        mask_pst(roi_hsv),
-        mask_prs(roi_hsv),
-        mask_ftr(roi_hsv),
-        mask_byd(roi_hsv),
-    ]
-    return max(enumerate(mask_results), key=lambda e: np.count_nonzero(e[1]))[0]

src/arcaea_offline_ocr/phash_db.py

@@ -0,0 +1,119 @@
+import sqlite3
+from typing import List, Union
+
+import cv2
+import numpy as np
+
+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:
+    return np.count_nonzero(
+        np.frombuffer(user_input, bool) ^ np.frombuffer(db_entry, bool)
+    )
+
+
+class ImagePhashDatabase:
+    def __init__(self, db_path: str):
+        with sqlite3.connect(db_path) as conn:
+            self.hash_size = int(
+                conn.execute(
+                    "SELECT value FROM properties WHERE key = 'hash_size'"
+                ).fetchone()[0]
+            )
+            self.highfreq_factor = int(
+                conn.execute(
+                    "SELECT value FROM properties WHERE key = 'highfreq_factor'"
+                ).fetchone()[0]
+            )
+            self.built_timestamp = int(
+                conn.execute(
+                    "SELECT value FROM properties WHERE key = 'built_timestamp'"
+                ).fetchone()[0]
+            )
+
+            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.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, 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/sift_db.py

@@ -1,110 +0,0 @@
-import io
-import sqlite3
-from gzip import GzipFile
-from typing import Tuple
-
-import cv2
-import numpy as np
-
-from .types import Mat
-
-
-class SIFTDatabase:
-    def __init__(self, db_path: str, load: bool = True):
-        self.__db_path = db_path
-        self.__tags = []
-        self.__descriptors = []
-        self.__size = None
-
-        self.__sift = cv2.SIFT_create()
-        self.__bf_matcher = cv2.BFMatcher()
-
-        if load:
-            self.load_db()
-
-    @property
-    def db_path(self):
-        return self.__db_path
-
-    @db_path.setter
-    def db_path(self, value):
-        self.__db_path = value
-
-    @property
-    def tags(self):
-        return self.__tags
-
-    @property
-    def descriptors(self):
-        return self.__descriptors
-
-    @property
-    def size(self):
-        return self.__size
-
-    @size.setter
-    def size(self, value: Tuple[int, int]):
-        self.__size = value
-
-    @property
-    def sift(self):
-        return self.__sift
-
-    @property
-    def bf_matcher(self):
-        return self.__bf_matcher
-
-    def load_db(self):
-        conn = sqlite3.connect(self.db_path)
-        with conn:
-            cursor = conn.cursor()
-
-            size_str = cursor.execute(
-                "SELECT value FROM properties WHERE id = 'size'"
-            ).fetchone()[0]
-            sizr_str_arr = size_str.split(", ")
-            self.size = tuple(int(s) for s in sizr_str_arr)
-            tag__descriptors_bytes = cursor.execute(
-                "SELECT tag, descriptors FROM sift"
-            ).fetchall()
-
-            gzipped = int(
-                cursor.execute(
-                    "SELECT value FROM properties WHERE id = 'gzip'"
-                ).fetchone()[0]
-            )
-            for tag, descriptor_bytes in tag__descriptors_bytes:
-                buffer = io.BytesIO(descriptor_bytes)
-                self.tags.append(tag)
-                if gzipped == 0:
-                    self.descriptors.append(np.load(buffer))
-                else:
-                    gzipped_buffer = GzipFile(None, "rb", fileobj=buffer)
-                    self.descriptors.append(np.load(gzipped_buffer))
-
-    def lookup_img(
-        self,
-        __img: Mat,
-        *,
-        sift=None,
-        bf=None,
-    ) -> Tuple[str, float]:
-        sift = sift or self.sift
-        bf = bf or self.bf_matcher
-
-        img = __img.copy()
-        if self.size is not None:
-            img = cv2.resize(img, self.size)
-        _, descriptors = sift.detectAndCompute(img, None)
-
-        good_results = []
-        for des in self.descriptors:
-            matches = bf.knnMatch(descriptors, des, k=2)
-            good = sum(m.distance < 0.75 * n.distance for m, n in matches)
-            good_results.append(good)
-        best_match_index = max(enumerate(good_results), key=lambda i: i[1])[0]
-
-        return (
-            self.tags[best_match_index],
-            good_results[best_match_index] / len(descriptors),
-        )

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)
@@ -44,27 +42,5 @@ def apply_factor(item: T, factor: float) -> T:
 def apply_factor(item, factor: float):
     if isinstance(item, (int, float)):
         return item * factor
-    elif isinstance(item, Iterable):
+    if 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