fix: cv2.Mat typing annotations

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

@@ -12,6 +12,7 @@ from ....ocr import (
     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 *
@@ -67,10 +68,10 @@ class ChieriBotV4Ocr:
     def factor(self, factor: float):
         self.__rois.factor = factor
 
-    def set_factor(self, img: cv2.Mat):
+    def set_factor(self, img: Mat):
         self.factor = img.shape[0] / 4400
 
-    def ocr_component_rating_class(self, component_bgr: cv2.Mat) -> int:
+    def ocr_component_rating_class(self, component_bgr: Mat) -> int:
         rating_class_rect = construct_int_xywh_rect(
             self.rois.component_rois.rating_class_rect
         )
@@ -87,7 +88,7 @@ class ChieriBotV4Ocr:
         else:
             return max(enumerate(rating_class_results), key=lambda i: i[1])[0] + 1
 
-    def ocr_component_song_id(self, component_bgr: cv2.Mat):
+    def ocr_component_song_id(self, component_bgr: Mat):
         jacket_rect = construct_int_xywh_rect(
             self.rois.component_rois.jacket_rect, floor
         )
@@ -96,7 +97,7 @@ class ChieriBotV4Ocr:
         )
         return self.phash_db.lookup_jacket(jacket_roi)[0]
 
-    def ocr_component_score_knn(self, component_bgr: cv2.Mat) -> int:
+    def ocr_component_score_knn(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(
@@ -118,7 +119,7 @@ class ChieriBotV4Ocr:
             score_roi = cv2.fillPoly(score_roi, [contour], 0)
         return ocr_digits_by_contour_knn(score_roi, self.score_knn)
 
-    def find_pfl_rects(self, component_pfl_processed: cv2.Mat) -> List[List[int]]:
+    def find_pfl_rects(self, component_pfl_processed: Mat) -> List[List[int]]:
         # sourcery skip: inline-immediately-returned-variable
         pfl_roi_find = cv2.morphologyEx(
             component_pfl_processed,
@@ -144,7 +145,7 @@ class ChieriBotV4Ocr:
         ]
         return pfl_rects_adjusted
 
-    def preprocess_component_pfl(self, component_bgr: cv2.Mat) -> cv2.Mat:
+    def preprocess_component_pfl(self, component_bgr: Mat) -> Mat:
         pfl_rect = construct_int_xywh_rect(self.rois.component_rois.pfl_rect)
         pfl_roi = crop_xywh(component_bgr, pfl_rect)
         pfl_roi_hsv = cv2.cvtColor(pfl_roi, cv2.COLOR_BGR2HSV)
@@ -184,7 +185,7 @@ class ChieriBotV4Ocr:
         return result_eroded if len(self.find_pfl_rects(result_eroded)) == 3 else result
 
     def ocr_component_pfl(
-        self, component_bgr: cv2.Mat
+        self, component_bgr: Mat
     ) -> Tuple[Optional[int], Optional[int], Optional[int]]:
         try:
             pfl_roi = self.preprocess_component_pfl(component_bgr)
@@ -215,7 +216,7 @@ class ChieriBotV4Ocr:
         except Exception:
             return (None, None, None)
 
-    def ocr_component(self, component_bgr: cv2.Mat) -> B30OcrResultItem:
+    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)
         song_id = self.ocr_component_song_id(component_bgr)
@@ -234,7 +235,7 @@ class ChieriBotV4Ocr:
             date=None,
         )
 
-    def ocr(self, img_bgr: cv2.Mat) -> List[B30OcrResultItem]:
+    def ocr(self, img_bgr: Mat) -> List[B30OcrResultItem]:
         self.set_factor(img_bgr)
         return [
             self.ocr_component(component_bgr)

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

@@ -1,9 +1,7 @@
 from typing import List, Optional
 
-import cv2
-
 from ....crop import crop_xywh
-from ....types import XYWHRect
+from ....types import Mat, XYWHRect
 from ....utils import apply_factor, construct_int_xywh_rect
 
 
@@ -110,7 +108,7 @@ class ChieriBotV4Rois:
     def b33_vertical_gap(self):
         return apply_factor(121, self.factor)
 
-    def components(self, img_bgr: cv2.Mat) -> List[cv2.Mat]:
+    def components(self, img_bgr: Mat) -> List[Mat]:
         first_rect = XYWHRect(x=self.left, y=self.top, w=self.width, h=self.height)
         results = []
 

src/arcaea_offline_ocr/crop.py

@@ -4,24 +4,26 @@ from typing import Tuple
 import cv2
 import numpy as np
 
+from .types import Mat
+
 __all__ = ["crop_xywh", "CropBlackEdges"]
 
 
-def crop_xywh(mat: cv2.Mat, rect: Tuple[int, int, int, int]):
+def crop_xywh(mat: Mat, rect: Tuple[int, int, int, int]):
     x, y, w, h = rect
     return mat[y : y + h, x : x + w]
 
 
 class CropBlackEdges:
     @staticmethod
-    def is_black_edge(__img_gray_slice: cv2.Mat, black_pixel: int, ratio: float = 0.6):
+    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: cv2.Mat, black_threshold: int = 25):
+    def get_crop_rect(cls, img_gray: Mat, black_threshold: int = 25):
         height, width = img_gray.shape[:2]
         left = 0
         right = width
@@ -58,7 +60,7 @@ class CropBlackEdges:
 
     @classmethod
     def crop(
-        cls, img: cv2.Mat, convert_flag: cv2.COLOR_BGR2GRAY, black_threshold: int = 25
+        cls, img: Mat, convert_flag: cv2.COLOR_BGR2GRAY, black_threshold: int = 25
-    ) -> cv2.Mat:
+    ) -> Mat:
         rect = cls.get_crop_rect(cv2.cvtColor(img, convert_flag), black_threshold)
         return crop_xywh(img, rect)

src/arcaea_offline_ocr/device/ocr.py

@@ -10,6 +10,7 @@ from ..ocr import (
     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
@@ -28,7 +29,7 @@ class DeviceOcr:
         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
         )
@@ -105,7 +106,7 @@ class DeviceOcr:
         return self.lookup_song_id()[0]
 
     @staticmethod
-    def preprocess_char_icon(img_gray: cv2.Mat):
+    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]

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

@@ -1,11 +1,10 @@
-import cv2
-
 from ....crop import crop_xywh
+from ....types import Mat
 from ..definition.common import DeviceRois
 
 
 class DeviceRoisExtractor:
-    def __init__(self, img: cv2.Mat, rois: DeviceRois):
+    def __init__(self, img: Mat, rois: DeviceRois):
         self.img = img
         self.sizes = rois
 

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

@@ -1,6 +1,7 @@
 import cv2
 import numpy as np
 
+from ....types import Mat
 from .common import DeviceRoisMasker
 
 
@@ -40,7 +41,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
     PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
 
     @classmethod
-    def gray(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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)
@@ -49,19 +50,19 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
         return cv2.inRange(img_bgr, cls.GRAY_BGR_MIN, cls.GRAY_BGR_MAX)
 
     @classmethod
-    def pure(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def pure(cls, roi_bgr: Mat) -> Mat:
         return cls.gray(roi_bgr)
 
     @classmethod
-    def far(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def far(cls, roi_bgr: Mat) -> Mat:
         return cls.gray(roi_bgr)
 
     @classmethod
-    def lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def lost(cls, roi_bgr: Mat) -> Mat:
         return cls.gray(roi_bgr)
 
     @classmethod
-    def score(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def score(cls, roi_bgr: Mat) -> Mat:
         return cv2.inRange(
             cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
             cls.WHITE_HSV_MIN,
@@ -69,35 +70,35 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def rating_class_pst(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
         return cls.gray(roi_bgr)
 
     @classmethod
-    def clear_status_track_lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -105,7 +106,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_track_complete(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -113,7 +114,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_full_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -121,7 +122,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_pure_memory(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -164,25 +165,25 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
     PURE_MEMORY_HSV_MAX = np.array([110, 200, 175], np.uint8)
 
     @classmethod
-    def pfl(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    def pure(cls, roi_bgr: Mat) -> Mat:
         return cls.pfl(roi_bgr)
 
     @classmethod
-    def far(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def far(cls, roi_bgr: Mat) -> Mat:
         return cls.pfl(roi_bgr)
 
     @classmethod
-    def lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def lost(cls, roi_bgr: Mat) -> Mat:
         return cls.pfl(roi_bgr)
 
     @classmethod
-    def score(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    def score(cls, roi_bgr: Mat) -> Mat:
         return cv2.inRange(
             cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
             cls.WHITE_HSV_MIN,
@@ -190,31 +191,31 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def rating_class_pst(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    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: cv2.Mat) -> cv2.Mat:
+    def max_recall(cls, roi_bgr: Mat) -> Mat:
         return cv2.inRange(
             cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV),
             cls.MAX_RECALL_HSV_MIN,
@@ -222,7 +223,7 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_track_lost(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -230,7 +231,7 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_track_complete(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -238,7 +239,7 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_full_recall(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,
@@ -246,7 +247,7 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
         )
 
     @classmethod
-    def clear_status_pure_memory(cls, roi_bgr: cv2.Mat) -> cv2.Mat:
+    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,

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

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

src/arcaea_offline_ocr/ocr.py

@@ -5,6 +5,7 @@ import cv2
 import numpy as np
 
 from .crop import crop_xywh
+from .types import Mat
 
 __all__ = [
     "FixRects",
@@ -67,7 +68,7 @@ class FixRects:
 
     @staticmethod
     def split_connected(
-        img_masked: cv2.Mat,
+        img_masked: Mat,
         rects: Sequence[Tuple[int, int, int, int]],
         rect_wh_ratio: float = 1.05,
         width_range_ratio: float = 0.1,
@@ -117,7 +118,7 @@ class FixRects:
         return return_rects
 
 
-def resize_fill_square(img: cv2.Mat, target: int = 20):
+def resize_fill_square(img: Mat, target: int = 20):
     h, w = img.shape[:2]
     if h > w:
         new_h = target
@@ -156,7 +157,7 @@ def ocr_digit_samples_knn(__samples, knn_model: cv2.ml.KNearest, k: int = 4):
     return int(result_str) if result_str else 0
 
 
-def ocr_digits_by_contour_get_samples(__roi_gray: cv2.Mat, size: int):
+def ocr_digits_by_contour_get_samples(__roi_gray: Mat, size: int):
     roi = __roi_gray.copy()
     contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
     rects = [cv2.boundingRect(c) for c in contours]
@@ -169,7 +170,7 @@ def ocr_digits_by_contour_get_samples(__roi_gray: cv2.Mat, size: int):
 
 
 def ocr_digits_by_contour_knn(
-    __roi_gray: cv2.Mat,
+    __roi_gray: Mat,
     knn_model: cv2.ml.KNearest,
     *,
     k=4,

src/arcaea_offline_ocr/phash_db.py

@@ -4,9 +4,11 @@ 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[cv2.Mat, np.ndarray], int, int) -> np.ndarray
+    # type: (Union[Mat, np.ndarray], int, int) -> np.ndarray
     """
     Perceptual Hash computation.
 
@@ -76,7 +78,7 @@ class ImagePhashDatabase:
                     self.jacket_ids.append(id)
                     self.jacket_hashes.append(hash)
 
-    def calculate_phash(self, img_gray: cv2.Mat):
+    def calculate_phash(self, img_gray: Mat):
         return phash_opencv(
             img_gray, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor
         )
@@ -89,11 +91,11 @@ class ImagePhashDatabase:
         ]
         return sorted(xor_results, key=lambda r: r[1])[:limit]
 
-    def lookup_image(self, img_gray: cv2.Mat):
+    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: cv2.Mat, *, limit: int = 5):
+    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))
@@ -101,10 +103,10 @@ class ImagePhashDatabase:
         ]
         return sorted(xor_results, key=lambda r: r[1])[:limit]
 
-    def lookup_jacket(self, img_gray: cv2.Mat):
+    def lookup_jacket(self, img_gray: Mat):
         return self.lookup_jackets(img_gray)[0]
 
-    def lookup_partner_icons(self, img_gray: cv2.Mat, *, limit: int = 5):
+    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))
@@ -112,5 +114,5 @@ class ImagePhashDatabase:
         ]
         return sorted(xor_results, key=lambda r: r[1])[:limit]
 
-    def lookup_partner_icon(self, img_gray: cv2.Mat):
+    def lookup_partner_icon(self, img_gray: Mat):
         return self.lookup_partner_icons(img_gray)[0]

src/arcaea_offline_ocr/types.py

@@ -1,6 +1,10 @@
 from collections.abc import Iterable
 from typing import NamedTuple, Tuple, Union
 
+import numpy as np
+
+Mat = np.ndarray
+
 
 class XYWHRect(NamedTuple):
     x: int