chore!: add recognize parts

src/arcaea_offline_ocr/ocr.py

@@ -88,7 +88,11 @@ def filter_digit_results(
 
         if possible_results:
             # Sort the results based on "max_val" in descending order and select the top result
-            result = sorted(possible_results, key=lambda d: d["max_val"], reverse=True)[0]
+            result = sorted(
+                possible_results,
+                key=lambda d: d["max_val"],
+                reverse=True,
+            )[0]
             result_digit = result["digit"]  # Get the digit value from the result
             result.pop("digit", None)  # Remove the digit key from the result
 

src/arcaea_offline_ocr/recognize.py

@@ -9,16 +9,42 @@ from .mask import *
 from .ocr import *
 
 
-def process_digit_ocr_img(img_hsv, mask=Callable[[Mat], Mat]):
+def process_digits_ocr_img(img_hsv_cropped: Mat, mask=Callable[[Mat], Mat]):
-    img_hsv = mask(img_hsv)
+    img_hsv_cropped = mask(img_hsv_cropped)
-    img_hsv = GaussianBlur(img_hsv, (3, 3), 0)
+    img_hsv_cropped = GaussianBlur(img_hsv_cropped, (3, 3), 0)
-    return img_hsv
+    return img_hsv_cropped
 
 
-def process_tesseract_ocr_img(img_hsv, mask=Callable[[Mat], Mat]):
+def process_tesseract_ocr_img(img_hsv_cropped: Mat, mask=Callable[[Mat], Mat]):
-    img_hsv = mask(img_hsv)
+    img_hsv_cropped = mask(img_hsv_cropped)
-    img_hsv = GaussianBlur(img_hsv, (1, 1), 0)
+    img_hsv_cropped = GaussianBlur(img_hsv_cropped, (1, 1), 0)
-    return img_hsv
+    return img_hsv_cropped
+
+
+def recognize_pure(img_hsv_cropped: Mat):
+    return ocr_pure(process_digits_ocr_img(img_hsv_cropped, mask=mask_gray))
+
+
+def recognize_far_lost(img_hsv_cropped: Mat):
+    return ocr_far_lost(process_digits_ocr_img(img_hsv_cropped, mask=mask_gray))
+
+
+def recognize_score(img_hsv_cropped: Mat):
+    return ocr_score(process_digits_ocr_img(img_hsv_cropped, mask=mask_white))
+
+
+def recognize_max_recall(img_hsv_cropped: Mat):
+    return ocr_max_recall(process_tesseract_ocr_img(img_hsv_cropped, mask=mask_gray))
+
+
+def recognize_rating_class(img_hsv_cropped: Mat):
+    return ocr_rating_class(
+        process_tesseract_ocr_img(img_hsv_cropped, mask=mask_rating_class)
+    )
+
+
+def recognize_title(img_hsv_cropped: Mat):
+    return ocr_title(process_tesseract_ocr_img(img_hsv_cropped, mask=mask_white))
 
 
 @dataclass(kw_only=True)
@@ -37,29 +63,25 @@ def recognize(img_filename: str, device: Device):
     img_hsv = cvtColor(img, COLOR_BGR2HSV)
 
     pure_roi = crop_to_pure(img_hsv, device)
-    pure = ocr_pure(process_digit_ocr_img(pure_roi, mask=mask_gray))
+    pure = recognize_pure(pure_roi)
 
     far_roi = crop_to_far(img_hsv, device)
-    far = ocr_far_lost(process_digit_ocr_img(far_roi, mask=mask_gray))
+    far = recognize_far_lost(far_roi)
 
     lost_roi = crop_to_lost(img_hsv, device)
-    lost = ocr_far_lost(process_digit_ocr_img(lost_roi, mask=mask_gray))
+    lost = recognize_far_lost(lost_roi)
 
     score_roi = crop_to_score(img_hsv, device)
-    score = ocr_score(process_digit_ocr_img(score_roi, mask=mask_white))
+    score = recognize_score(score_roi)
 
     max_recall_roi = crop_to_max_recall(img_hsv, device)
-    max_recall = ocr_max_recall(
+    max_recall = recognize_max_recall(max_recall_roi)
-        process_tesseract_ocr_img(max_recall_roi, mask=mask_gray)
-    )
 
     rating_class_roi = crop_to_rating_class(img_hsv, device)
-    rating_class = ocr_rating_class(
+    rating_class = recognize_rating_class(rating_class_roi)
-        process_tesseract_ocr_img(rating_class_roi, mask=mask_rating_class)
-    )
 
     title_roi = crop_to_title(img_hsv, device)
-    title = ocr_title(process_tesseract_ocr_img(title_roi, mask=mask_white))
+    title = recognize_title(title_roi)
 
     return RecognizeResult(
         pure=pure,