feat: support for ocr digits using cv2.ml.KNearest model

src/arcaea_offline_ocr/ocr.py

@@ -1,19 +1,8 @@
 import re
 from typing import Dict, List
 
-from cv2 import (
-    CHAIN_APPROX_SIMPLE,
-    RETR_EXTERNAL,
-    TM_CCOEFF_NORMED,
-    boundingRect,
-    findContours,
-    imshow,
-    matchTemplate,
-    minMaxLoc,
-    rectangle,
-    resize,
-    waitKey,
-)
+import cv2
+import numpy as np
 from imutils import grab_contours
 from imutils import resize as imutils_resize
 from pytesseract import image_to_string
@@ -24,7 +13,7 @@ from .template import (
     load_builtin_digit_template,
     matchTemplateMultiple,
 )
-from .types import Mat
+from .types import Mat, cv2_ml_KNearest
 
 __all__ = [
     "group_numbers",
@@ -155,6 +144,18 @@ def ocr_digits(
     return int(joined_str) if joined_str else None
 
 
+def ocr_digits_knn_model(img_gray: Mat, knn_model: cv2_ml_KNearest):
+    if img_gray.shape[:2] != (20, 20):
+        img = cv2.resize(img_gray, [20, 20])
+    else:
+        img = img_gray.copy()
+
+    img = img.astype(np.float32)
+    img = img.reshape([1, -1])
+    retval, _, _, _ = knn_model.findNearest(img, 10)
+    return int(retval)
+
+
 def ocr_pure(img_masked: Mat):
     template = load_builtin_digit_template("default")
     return ocr_digits(
@@ -173,9 +174,11 @@ def ocr_score(img_cropped: Mat):
     templates = load_builtin_digit_template("default").regular
     templates_dict = dict(enumerate(templates[:10]))
 
-    cnts = findContours(img_cropped.copy(), RETR_EXTERNAL, CHAIN_APPROX_SIMPLE)
+    cnts = cv2.findContours(
+        img_cropped.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+    )
     cnts = grab_contours(cnts)
-    rects = [boundingRect(cnt) for cnt in cnts]
+    rects = [cv2.boundingRect(cnt) for cnt in cnts]
     rects = sorted(rects, key=lambda r: r[0])
 
     # debug
@@ -190,9 +193,9 @@ def ocr_score(img_cropped: Mat):
 
         digit_results: Dict[int, float] = {}
         for digit, template in templates_dict.items():
-            template = resize(template, roi.shape[::-1])
-            template_result = matchTemplate(roi, template, TM_CCOEFF_NORMED)
-            min_val, max_val, min_loc, max_loc = minMaxLoc(template_result)
+            template = cv2.resize(template, roi.shape[::-1])
+            template_result = cv2.matchTemplate(roi, template, cv2.TM_CCOEFF_NORMED)
+            min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(template_result)
             digit_results[digit] = max_val
 
         digit_results = {k: v for k, v in digit_results.items() if v > 0.5}

src/arcaea_offline_ocr/types.py

@@ -1,5 +1,5 @@
 from collections.abc import Iterable
-from typing import NamedTuple, Tuple, Union
+from typing import Any, NamedTuple, Protocol, Tuple, Union
 
 import numpy as np
 
@@ -24,3 +24,19 @@ 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"""
+        ...