chore: partial numpy & cv2 import to full import

src/arcaea_offline_ocr/crop.py

@@ -1,7 +1,7 @@
 from math import floor
 from typing import Tuple
 
-from numpy import all, array, count_nonzero
+import numpy as np
 
 from .types import Mat
 
@@ -15,14 +15,16 @@ def crop_xywh(mat: Mat, rect: Tuple[int, int, int, int]):
 
 def is_black_edge(list_of_pixels: Mat, black_pixel=None):
     if black_pixel is None:
-        black_pixel = array([0, 0, 0], list_of_pixels.dtype)
+        black_pixel = np.array([0, 0, 0], list_of_pixels.dtype)
     pixels = list_of_pixels.reshape([-1, 3])
-    return count_nonzero(all(pixels < black_pixel, axis=1)) > floor(len(pixels) * 0.6)
+    return np.count_nonzero(all(pixels < black_pixel, axis=1)) > floor(
+        len(pixels) * 0.6
+    )
 
 
 def crop_black_edges(screenshot: Mat):
     cropped = screenshot.copy()
-    black_pixel = array([50, 50, 50], screenshot.dtype)
+    black_pixel = np.array([50, 50, 50], screenshot.dtype)
     height, width = screenshot.shape[:2]
     left = 0
     right = width

src/arcaea_offline_ocr/device/v1/crop.py

@@ -1,7 +1,7 @@
 from math import floor
 from typing import Any, Tuple
 
-from numpy import all, array, count_nonzero
+import numpy as np
 
 from ...types import Mat
 from .definition import Device
@@ -59,6 +59,8 @@ def crop_to_title(screenshot: Mat, device: Device):
 
 def is_black_edge(list_of_pixels: Mat, black_pixel=None):
     if black_pixel is None:
-        black_pixel = array([0, 0, 0], list_of_pixels.dtype)
+        black_pixel = np.array([0, 0, 0], list_of_pixels.dtype)
     pixels = list_of_pixels.reshape([-1, 3])
-    return count_nonzero(all(pixels < black_pixel, axis=1)) > floor(len(pixels) * 0.6)
+    return np.count_nonzero(all(pixels < black_pixel, axis=1)) > floor(
+        len(pixels) * 0.6
+    )

src/arcaea_offline_ocr/device/v2/shared.py

@@ -1,4 +1,4 @@
-from cv2 import MORPH_CROSS, MORPH_ELLIPSE, MORPH_RECT, getStructuringElement
+from cv2 import MORPH_RECT, getStructuringElement
 
 PFL_DENOISE_KERNEL = getStructuringElement(MORPH_RECT, [2, 2])
 PFL_ERODE_KERNEL = getStructuringElement(MORPH_RECT, [3, 3])

src/arcaea_offline_ocr/mask.py

@@ -1,5 +1,5 @@
 import cv2
-from numpy import array, max, min, uint8
+import numpy as np
 
 from .types import Mat
 
@@ -25,33 +25,33 @@ __all__ = [
     "mask_rating_class",
 ]
 
-GRAY_MIN_HSV = array([0, 0, 70], uint8)
-GRAY_MAX_HSV = array([0, 0, 200], uint8)
+GRAY_MIN_HSV = np.array([0, 0, 70], np.uint8)
+GRAY_MAX_HSV = np.array([0, 0, 200], np.uint8)
 
-GRAY_MIN_BGR = array([50] * 3, uint8)
-GRAY_MAX_BGR = array([160] * 3, uint8)
+GRAY_MIN_BGR = np.array([50] * 3, np.uint8)
+GRAY_MAX_BGR = np.array([160] * 3, np.uint8)
 
-WHITE_MIN_HSV = array([0, 0, 240], uint8)
-WHITE_MAX_HSV = array([179, 10, 255], uint8)
+WHITE_MIN_HSV = np.array([0, 0, 240], np.uint8)
+WHITE_MAX_HSV = np.array([179, 10, 255], np.uint8)
 
-PST_MIN_HSV = array([100, 50, 80], uint8)
-PST_MAX_HSV = array([100, 255, 255], uint8)
+PST_MIN_HSV = np.array([100, 50, 80], np.uint8)
+PST_MAX_HSV = np.array([100, 255, 255], np.uint8)
 
-PRS_MIN_HSV = array([43, 40, 75], uint8)
-PRS_MAX_HSV = array([50, 155, 190], uint8)
+PRS_MIN_HSV = np.array([43, 40, 75], np.uint8)
+PRS_MAX_HSV = np.array([50, 155, 190], np.uint8)
 
-FTR_MIN_HSV = array([149, 30, 0], uint8)
-FTR_MAX_HSV = array([155, 181, 150], uint8)
+FTR_MIN_HSV = np.array([149, 30, 0], np.uint8)
+FTR_MAX_HSV = np.array([155, 181, 150], np.uint8)
 
-BYD_MIN_HSV = array([170, 50, 50], uint8)
-BYD_MAX_HSV = array([179, 210, 198], 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 = max(__img_bgr, axis=2) - min(__img_bgr, axis=2) <= 5
     img_bgr = __img_bgr.copy()
-    img_bgr[~bgr_value_equal_mask] = array([0, 0, 0], __img_bgr.dtype)
+    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)

src/arcaea_offline_ocr/recognize.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 from typing import Callable, Optional
 
-from cv2 import COLOR_BGR2HSV, GaussianBlur, cvtColor, imread
+import cv2
 
 from .crop import *
 
@@ -29,13 +29,13 @@ __all__ = [
 
 def process_digits_ocr_img(img_hsv_cropped: Mat, mask=Callable[[Mat], Mat]):
     img_hsv_cropped = mask(img_hsv_cropped)
-    img_hsv_cropped = GaussianBlur(img_hsv_cropped, (3, 3), 0)
+    img_hsv_cropped = cv2.GaussianBlur(img_hsv_cropped, (3, 3), 0)
     return img_hsv_cropped
 
 
 def process_tesseract_ocr_img(img_hsv_cropped: Mat, mask=Callable[[Mat], Mat]):
     img_hsv_cropped = mask(img_hsv_cropped)
-    img_hsv_cropped = GaussianBlur(img_hsv_cropped, (1, 1), 0)
+    img_hsv_cropped = cv2.GaussianBlur(img_hsv_cropped, (1, 1), 0)
     return img_hsv_cropped
 
 
@@ -78,7 +78,7 @@ class RecognizeResult:
 
 def recognize(img_filename: str, device: Device):
     img = imread_unicode(img_filename)
-    img_hsv = cvtColor(img, COLOR_BGR2HSV)
+    img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
 
     pure_roi = crop_to_pure(img_hsv, device)
     pure = recognize_pure(pure_roi)

src/arcaea_offline_ocr/template.py

@@ -2,30 +2,8 @@ import pickle
 from base64 import b64decode
 from typing import Any, Dict, List, Literal, Tuple, TypedDict, Union
 
-from cv2 import (
-    CHAIN_APPROX_SIMPLE,
-    COLOR_BGR2GRAY,
-    COLOR_GRAY2BGR,
-    FONT_HERSHEY_SIMPLEX,
-    IMREAD_GRAYSCALE,
-    RETR_EXTERNAL,
-    THRESH_BINARY_INV,
-    TM_CCOEFF_NORMED,
-    boundingRect,
-    cvtColor,
-    destroyAllWindows,
-    findContours,
-    imdecode,
-    imread,
-    imshow,
-    matchTemplate,
-    minMaxLoc,
-    putText,
-    rectangle,
-    threshold,
-    waitKey,
-)
-from numpy import ndarray
+import cv2
+import numpy as np
 
 from ._builtin_templates import (
     DEFAULT_ITALIC,
@@ -60,7 +38,7 @@ class DigitTemplate:
         return (
             isinstance(item, (list, tuple))
             and len(item) == 11
-            and all(isinstance(i, ndarray) for i in item)
+            and all(isinstance(i, np.ndarray) for i in item)
         )
 
     def __init__(self, regular, italic, regular_eroded, italic_eroded):
@@ -109,8 +87,8 @@ class MatchTemplateMultipleResult(TypedDict):
 def matchTemplateMultiple(
     src: Mat, template: Mat, threshold: float = 0.1
 ) -> List[MatchTemplateMultipleResult]:
-    template_result = matchTemplate(src, template, TM_CCOEFF_NORMED)
-    min_val, max_val, min_loc, max_loc = minMaxLoc(template_result)
+    template_result = cv2.matchTemplate(src, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(template_result)
     template_h, template_w = template.shape[:2]
     results = []
 
@@ -123,7 +101,7 @@ def matchTemplateMultiple(
     # CC BY-SA 4.0
     prev_min_val, prev_max_val, prev_min_loc, prev_max_loc = None, None, None, None
     while max_val > threshold:
-        min_val, max_val, min_loc, max_loc = minMaxLoc(template_result)
+        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(template_result)
 
         # Prevent infinite loop. If those 4 values are the same as previous ones, break the loop.
         if (

src/arcaea_offline_ocr/utils.py

@@ -1,9 +1,8 @@
 from collections.abc import Iterable
 from typing import Callable, Tuple, TypeVar, Union, overload
 
-from cv2 import IMREAD_UNCHANGED, imdecode
-from numpy import fromfile as np_fromfile
-from numpy import uint8
+import cv2
+import numpy as np
 
 from .types import Mat, XYWHRect
 
@@ -13,7 +12,7 @@ __all__ = ["imread_unicode"]
 def imread_unicode(filepath: str) -> Mat:
     # https://stackoverflow.com/a/57872297/16484891
     # CC BY-SA 4.0
-    return imdecode(np_fromfile(filepath, dtype=uint8), IMREAD_UNCHANGED)
+    return cv2.imdecode(np.fromfile(filepath, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
 
 
 def construct_int_xywh_rect(