Made image detection work

shenzhen_solitaire/card_detection/board_parser.py

@@ -4,34 +4,49 @@ import numpy as np
 from .configuration import Configuration
 from ..board import Board
 from . import card_finder
+import cv2
+from typing import Iterable, Any, List
+import itertools
 
 
 def parse_board(image: np.ndarray, conf: Configuration) -> Board:
     """Parse a screenshot of the game, using a given configuration"""
+    fake_adjustments = conf.field_adjustment
+    fake_adjustments.x -= 5
+    fake_adjustments.y -= 5
+    fake_adjustments.h += 10
+    fake_adjustments.w += 10
+    row_count = 13
+    column_count = 8
+
+    def grouper(iterable: Iterable[Any], groupsize: int, fillvalue: Any = None) -> Iterable[Any]:
+        "Collect data into fixed-length chunks or blocks"
+        args = [iter(iterable)] * groupsize
+        return itertools.zip_longest(*args, fillvalue=fillvalue)
+
     squares = card_finder.get_field_squares(
-        image, conf.field_adjustment, count_x=13, count_y=8)
-    squares = [card_finder.simplify(square)[0] for square in squares]
-    square_rows = [squares[13 * i:13 * (i + 1)] for i in range(8)]
-    empty_square = np.full(
-        shape=(conf.field_adjustment.w,
-               conf.field_adjustment.h),
-        fill_value=card_finder.GREYSCALE_COLOR[card_finder.Cardcolor.Background],
-        dtype=np.uint8)
-    assert empty_square.shape == squares[0].shape
-    result: Board = Board()
-    for row_id, square_row in enumerate(square_rows):
-        for square in square_row:
-            fitting_square, _ = card_finder.find_square(
-                square, [empty_square] + [x[0] for x in conf.catalogue])
-            if np.array_equal(fitting_square, empty_square):
-                print("empty")
-                break
-            for cat_square, cardtype in conf.catalogue:
-                if np.array_equal(fitting_square, cat_square):
-                    print(cardtype)
-                    result.field[row_id].append(cardtype)
-                    break
-            else:
-                print("did not find image")
+        image, conf.field_adjustment, count_x=row_count, count_y=column_count
+    )
+    grouped_squares = grouper(squares, row_count)
+    result = Board()
+    for group_index, square_group in enumerate(grouped_squares):
+        group_field = []
+        for index, square in enumerate(square_group):
+            best_val = None
+            best_name = None
+            for template, name in conf.catalogue:
+                res = cv2.matchTemplate(square, template, cv2.TM_CCOEFF_NORMED)
+                min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+                if best_val is None or max_val > best_val:
+                    best_val = max_val
+                    best_name = name
+            assert best_name is not None
+            group_field.append(best_name)
+
+            # print(f"\t{best_val}: {best_name}")
+            # cv2.imshow("Catalogue", cv2.resize(square, (500, 500)))
+            # cv2.waitKey()
+
+        result.field[group_index] = group_field
 
     return result