What up we can find all empties, we best

main.py

@@ -3,11 +3,15 @@ import sys
 from collections import defaultdict
 from datetime import datetime
 
+import matplotlib.pyplot as plt
 import numpy as np
 
 import runner
 from util import load_classifier, PIECE, COLOR, POSITION, Board, Squares, PieceAndColor
+from sklearn.exceptions import DataConversionWarning
+import warnings
 
+warnings.filterwarnings(action='ignore', category=DataConversionWarning)
 np.set_printoptions(threshold=sys.maxsize)
 
 
@@ -37,7 +41,7 @@ def pred_test(position: POSITION, mystery_image=None, empty_bias=False):
     sift = cv2.xfeatures2d.SIFT_create()
     if mystery_image is None:
         mystery_image = cv2.imread("training_images/rook/white/rook_training_D4_2.png")
-    probs = classify(mystery_image, sift, empty_bias=empty_bias)
+    probs = identify_piece(mystery_image, sift, empty_bias=empty_bias)
     return probs
 
 
@@ -71,36 +75,36 @@ def test_entire_board() -> None:
 
 
 def predict(square: np.ndarray, position: POSITION) -> PIECE:
-    y, x = np.histogram(square.ravel(), bins=256, range=[0, 256])
+    y, x = np.histogram(square.ravel(), bins=32, range=[0, 256])
 
-    for color in COLOR:
+    left, right = x[:-1], x[1:]
-        empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{color}_square.pkl")
+    X = np.array([left, right]).T.flatten()
+    Y = np.array([y, y]).T.flatten()
+    area = sum(np.diff(x) * y)
+    plt.plot(X, Y)
+    plt.xlabel(f"{position}")
+    #plt.show()
+
+
+    #for color in COLOR:
+    empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{position.color}_square.pkl")
     prob = empty_classifier.predict_proba(np.array(y).reshape(1, -1))
-        print(f"{file}{rank}, {color}: {prob[0, 1]}")
+    print(f"{position}, {position.color}: {prob[0, 1]}")
-        if prob[0, 1] > 0.5:
+    if prob[0, 1] > 0.95:
+        print(f"{position} is empty")
         return PIECE.EMPTY
 
     return None
 
-
+def remove_most_empties(warped):
-if __name__ == '__main__':
-    board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
-    warped = runner.warp_board(board)
-
     empty = 0
-
-    files = "ABCDEFGH"
-    ranks = [1, 2, 3, 4, 5, 6, 7, 8]
-
-
     non_empties = []
-
     for position in POSITION:
         counter = 0
 
         src = runner.get_square(warped, position)
         width, height, _ = src.shape
-        src = src[width//25:, height//25:]
+        src = src[width // 25:, height // 25:]
         # src = src[:-width//200, :-height//200]
         segmentator = cv2.ximgproc.segmentation.createGraphSegmentation(sigma=0.8, k=150, min_size=700)
         segment = segmentator.processImage(src)
@@ -112,44 +116,32 @@ if __name__ == '__main__':
             masked.mask = mask != i
 
             y, x = np.where(segment == i)
-
+            pls.append(len(y))
             top, bottom, left, right = min(y), max(y), min(x), max(x)
 
             dst = masked.filled()[top: bottom + 1, left: right + 1]
-            lel = (bottom - top) * (right - left)
-            #print(f"this is lel: {lel} ")
-            #print(f"this is meh: {np.sum(mask[:,:,0])} ")
-            if position == POSITION.H7:
-                print("--"*20)
-                print("H7")
-                print(lel)
-                print(len(y))
-                print(np.max(segment))
-#                    print(lel)
-#                    print(np.sum(mask[:, :, 0]))
-                print("--"*20)
-
-
-            pls.append(len(y))
-            if len(y) < (164**2)*0.65:
-                counter += 1
-
             cv2.imwrite(f"segment_test/segment_{datetime.utcnow().timestamp()}_{position}.png", dst)
 
-        if np.max(segment) > 0 and not np.all([x < (164**2)*0.2 for x in pls]) and (np.max(segment) >= 3 or np.all([x < (164**2)*0.942 for x in pls])):
+        if np.max(segment) > 0 and not np.all([x < (164 ** 2) * 0.2 for x in pls]) and (
+                np.max(segment) >= 3 or np.all([x < (164 ** 2) * 0.942 for x in pls])):
             print(f"{position} is nonempty")
-            non_empties.append([f"{position}", src])
+            non_empties.append([position, src])
-            print(counter)
-            print(np.max(segment))
             empty += 1
-    print("++"*20)
-    print(counter)
-    print(64-empty)
 
-    for non_empty in non_empties:
+    print(64 - empty)
-        cv2.imshow(non_empty[0], non_empty[1])
+
-    cv2.waitKey(0)
+    return non_empties
-    exit()
+
+if __name__ == '__main__':
+
+
+    #board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
+    board = cv2.imread("whole_boards/boards_for_empty/board_1554288606.075646_rank_1.png")
+
+    warped = runner.warp_board(board)
+
+    non_empties = remove_most_empties(warped)
+
 
     #empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_white_square.pkl")
     #print(empty_classifier.predict_proba(np.array([0]*16).reshape(1, -1))[0, 1])
@@ -157,21 +149,33 @@ if __name__ == '__main__':
     #exit()
 
 
-    board = cv2.imread("whole_boards/board_102_1554110461.608167_.png")
-    warped = runner.warp_board(board)
-
-    files = "ABCDEFGH"
-    ranks = [1, 2, 3, 4, 5, 6, 7, 8]
-
     counter = 0
+    completely_non_empties = []
+    for position, square in non_empties:
+        #predict(square, position)
 
-    for file in files:
+        #y, x = np.histogram(square.ravel(), bins=32, range=[0, 256])
-        for rank in ranks:
+        #left, right = x[:-1], x[1:]
-            square_img = runner.get_square(warped, file, rank)
+        #X = np.array([left, right]).T.flatten()
-            if predict(square_img, file, rank) == 'empty':
+        #Y = np.array([y, y]).T.flatten()
+        #plt.plot(X, Y)
+        #plt.xlabel(f"{position}")
+        #plt.show()
+
+
+
+        if predict(square,position) == PIECE.EMPTY:
                 counter += 1
+        else:
+            completely_non_empties.append([position, square])
+
+
+
 
     print(counter)
+    for position, square in completely_non_empties:
+        cv2.imshow(f"{position}", square)
+    cv2.waitKey(0)
     exit()
 
 

runner.py

@@ -82,7 +82,7 @@ def train_empty_or_piece_hist() -> None:
         for piece in (PIECE.EMPTY, PIECE.ROOK, PIECE.KNIGHT):
             for filename in glob.glob(os.path.join("training_images", f"{piece}", f"{square_color}_square", "*.png")):
                 img = cv2.imread(filename)
-                y, x = np.histogram(img.ravel(), bins=256, range=[0, 256])
+                y, x = np.histogram(img.ravel(), bins=32, range=[0, 256])
                 X.append(y)
                 Y.append(piece == PIECE.EMPTY)
 
@@ -176,9 +176,10 @@ def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray:
     square_size = size // 8
     padding = 0
 
-    x1 = side + (square_size * position.file)
+    x1 = side + (square_size * (position.file - 1))
     x2 = x1 + square_size
     y1 = max(0, side + (square_size * (8 - position.rank)) - padding)  # 8 - rank because chessboard is from 8 to 1
+
     y2 = min(width, y1 + square_size + padding)
 
     square = warped_board[y1:y2, x1:x2]