feat: ETERNAL rating class support

pyproject.toml

@@ -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"
+]

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/device/ocr.py

@@ -67,8 +67,9 @@ class DeviceOcr:
         roi = self.masker.score(self.extractor.score)
         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:
+            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)
 
@@ -79,6 +80,7 @@ class DeviceOcr:
             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]
 

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

@@ -6,6 +6,8 @@ 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(
@@ -32,6 +34,9 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
     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)
 
@@ -85,6 +90,10 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
     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)
@@ -133,6 +142,9 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
     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)
 
@@ -184,6 +196,10 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
     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(

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

@@ -34,6 +34,10 @@ class DeviceRoisMasker:
     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()

src/arcaea_offline_ocr/ocr.py

@@ -36,7 +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
@@ -46,7 +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)
 

src/arcaea_offline_ocr/phash_db.py

@@ -12,7 +12,8 @@ def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
     """
     Perceptual Hash computation.
 
-    Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
+    Implementation follows
+    http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
 
     Adapted from `imagehash.phash`, pure opencv implementation
 
@@ -69,14 +70,14 @@ class ImagePhashDatabase:
             self.partner_icon_ids: List[str] = []
             self.partner_icon_hashes = []
 
-            for id, hash in zip(self.ids, self.hashes):
-                id_splitted = id.split("||")
+            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)
+                    self.partner_icon_hashes.append(_hash)
                 else:
-                    self.jacket_ids.append(id)
-                    self.jacket_hashes.append(hash)
+                    self.jacket_ids.append(_id)
+                    self.jacket_hashes.append(_hash)
 
     def calculate_phash(self, img_gray: Mat):
         return phash_opencv(

src/arcaea_offline_ocr/utils.py

@@ -42,5 +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])