impr: digit preprocess

src/arcaea_offline_ocr/device/v2/ocr.py

@@ -1,9 +1,19 @@
+import math
+from functools import lru_cache
+from typing import Sequence
+
 import cv2
 import numpy as np
 
 from ...crop import crop_xywh
 from ...mask import mask_byd, mask_ftr, mask_gray, mask_prs, mask_pst, mask_white
-from ...ocr import ocr_digits_by_contour_knn
+from ...ocr import (
+    FixRects,
+    ocr_digit_samples_knn,
+    ocr_digits_by_contour_knn,
+    preprocess_hog,
+    resize_fill_square,
+)
 from ...sift_db import SIFTDatabase
 from ...types import Mat, cv2_ml_KNearest
 from ..shared import DeviceOcrResult
@@ -37,10 +47,42 @@ class DeviceV2Ocr:
     def sift_db(self, value: SIFTDatabase):
         self.__sift_db = value
 
-    def _base_ocr_digits(self, roi_masked: Mat):
+    @lru_cache
-        return ocr_digits_by_contour_knn(
+    def _get_digit_widths(self, num_list: Sequence[int], factor: float):
-            find_digits_preprocess(roi_masked), self.knn_model
+        widths = set()
+        for n in num_list:
+            lower = math.floor(n * factor)
+            upper = math.ceil(n * factor)
+            widths.update(range(lower, upper + 1))
+        return widths
+
+    def _base_ocr_pfl(self, roi_masked: Mat, factor: float = 1.0):
+        contours, _ = cv2.findContours(
+            roi_masked, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
         )
+        filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
+        rects = [cv2.boundingRect(c) for c in filtered_contours]
+        rects = FixRects.connect_broken(rects, roi_masked.shape[1], roi_masked.shape[0])
+        rect_contour_map = dict(zip(rects, filtered_contours))
+
+        filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
+        filtered_rects = FixRects.split_connected(roi_masked, filtered_rects)
+        filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
+
+        roi_ocr = roi_masked.copy()
+        filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
+        for contour in contours:
+            if tuple(contour.flatten()) in filtered_contours_flattened:
+                continue
+            roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
+        digit_rois = [
+            resize_fill_square(crop_xywh(roi_ocr, r), 20)
+            for r in sorted(filtered_rects, key=lambda r: r[0])
+        ]
+        # [cv2.imshow(f"r{i}", r) for i, r in enumerate(digit_rois)]
+        # cv2.waitKey(0)
+        samples = preprocess_hog(digit_rois)
+        return ocr_digit_samples_knn(samples, self.knn_model)
 
     def ocr_song_id(self, rois: DeviceV2Rois):
         cover = cv2.cvtColor(rois.cover, cv2.COLOR_BGR2GRAY)
@@ -54,19 +96,24 @@ class DeviceV2Ocr:
     def ocr_score(self, rois: DeviceV2Rois):
         roi = cv2.cvtColor(rois.score, cv2.COLOR_BGR2HSV)
         roi = mask_white(roi)
-        return self._base_ocr_digits(roi)
+        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:
+                roi = cv2.fillPoly(roi, [contour], [0])
+        return ocr_digits_by_contour_knn(roi, self.knn_model)
 
     def ocr_pure(self, rois: DeviceV2Rois):
         roi = mask_gray(rois.pure)
-        return self._base_ocr_digits(roi)
+        return self._base_ocr_pfl(roi, rois.sizes.factor)
 
     def ocr_far(self, rois: DeviceV2Rois):
         roi = mask_gray(rois.far)
-        return self._base_ocr_digits(roi)
+        return self._base_ocr_pfl(roi, rois.sizes.factor)
 
     def ocr_lost(self, rois: DeviceV2Rois):
         roi = mask_gray(rois.lost)
-        return self._base_ocr_digits(roi)
+        return self._base_ocr_pfl(roi, rois.sizes.factor)
 
     def ocr_max_recall(self, rois: DeviceV2Rois):
         roi = mask_gray(rois.max_recall_rating_class)
@@ -78,7 +125,7 @@ class DeviceV2Ocr:
             [cv2.boundingRect(c) for c in contours], key=lambda r: r[0], reverse=True
         )
         max_recall_roi = crop_xywh(roi, rects[0])
-        return self._base_ocr_digits(max_recall_roi)
+        return ocr_digits_by_contour_knn(max_recall_roi, self.knn_model)
 
     def ocr(self, rois: DeviceV2Rois):
         song_id = self.ocr_song_id(rois)