What up we can find all empties, we best
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main.py
124
main.py
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@ -3,11 +3,15 @@ import sys
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from collections import defaultdict
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from datetime import datetime
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import matplotlib.pyplot as plt
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import numpy as np
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import runner
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from util import load_classifier, PIECE, COLOR, POSITION, Board, Squares, PieceAndColor
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from sklearn.exceptions import DataConversionWarning
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import warnings
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warnings.filterwarnings(action='ignore', category=DataConversionWarning)
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np.set_printoptions(threshold=sys.maxsize)
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@ -37,7 +41,7 @@ def pred_test(position: POSITION, mystery_image=None, empty_bias=False):
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sift = cv2.xfeatures2d.SIFT_create()
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if mystery_image is None:
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mystery_image = cv2.imread("training_images/rook/white/rook_training_D4_2.png")
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probs = classify(mystery_image, sift, empty_bias=empty_bias)
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probs = identify_piece(mystery_image, sift, empty_bias=empty_bias)
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return probs
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@ -71,36 +75,36 @@ def test_entire_board() -> None:
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def predict(square: np.ndarray, position: POSITION) -> PIECE:
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y, x = np.histogram(square.ravel(), bins=256, range=[0, 256])
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y, x = np.histogram(square.ravel(), bins=32, range=[0, 256])
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for color in COLOR:
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empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{color}_square.pkl")
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prob = empty_classifier.predict_proba(np.array(y).reshape(1, -1))
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print(f"{file}{rank}, {color}: {prob[0, 1]}")
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if prob[0, 1] > 0.5:
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return PIECE.EMPTY
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left, right = x[:-1], x[1:]
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X = np.array([left, right]).T.flatten()
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Y = np.array([y, y]).T.flatten()
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area = sum(np.diff(x) * y)
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plt.plot(X, Y)
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plt.xlabel(f"{position}")
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#plt.show()
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#for color in COLOR:
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empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{position.color}_square.pkl")
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prob = empty_classifier.predict_proba(np.array(y).reshape(1, -1))
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print(f"{position}, {position.color}: {prob[0, 1]}")
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if prob[0, 1] > 0.95:
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print(f"{position} is empty")
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return PIECE.EMPTY
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return None
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if __name__ == '__main__':
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board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
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warped = runner.warp_board(board)
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def remove_most_empties(warped):
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empty = 0
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files = "ABCDEFGH"
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ranks = [1, 2, 3, 4, 5, 6, 7, 8]
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non_empties = []
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for position in POSITION:
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counter = 0
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src = runner.get_square(warped, position)
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width, height, _ = src.shape
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src = src[width//25:, height//25:]
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src = src[width // 25:, height // 25:]
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# src = src[:-width//200, :-height//200]
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segmentator = cv2.ximgproc.segmentation.createGraphSegmentation(sigma=0.8, k=150, min_size=700)
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segment = segmentator.processImage(src)
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@ -112,44 +116,32 @@ if __name__ == '__main__':
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masked.mask = mask != i
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y, x = np.where(segment == i)
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pls.append(len(y))
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top, bottom, left, right = min(y), max(y), min(x), max(x)
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dst = masked.filled()[top: bottom + 1, left: right + 1]
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lel = (bottom - top) * (right - left)
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#print(f"this is lel: {lel} ")
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#print(f"this is meh: {np.sum(mask[:,:,0])} ")
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if position == POSITION.H7:
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print("--"*20)
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print("H7")
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print(lel)
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print(len(y))
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print(np.max(segment))
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# print(lel)
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# print(np.sum(mask[:, :, 0]))
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print("--"*20)
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pls.append(len(y))
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if len(y) < (164**2)*0.65:
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counter += 1
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cv2.imwrite(f"segment_test/segment_{datetime.utcnow().timestamp()}_{position}.png", dst)
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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])):
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if np.max(segment) > 0 and not np.all([x < (164 ** 2) * 0.2 for x in pls]) and (
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np.max(segment) >= 3 or np.all([x < (164 ** 2) * 0.942 for x in pls])):
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print(f"{position} is nonempty")
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non_empties.append([f"{position}", src])
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print(counter)
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print(np.max(segment))
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non_empties.append([position, src])
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empty += 1
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print("++"*20)
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print(counter)
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print(64-empty)
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for non_empty in non_empties:
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cv2.imshow(non_empty[0], non_empty[1])
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cv2.waitKey(0)
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exit()
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print(64 - empty)
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return non_empties
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if __name__ == '__main__':
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#board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
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board = cv2.imread("whole_boards/boards_for_empty/board_1554288606.075646_rank_1.png")
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warped = runner.warp_board(board)
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non_empties = remove_most_empties(warped)
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#empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_white_square.pkl")
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#print(empty_classifier.predict_proba(np.array([0]*16).reshape(1, -1))[0, 1])
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@ -157,21 +149,33 @@ if __name__ == '__main__':
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#exit()
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board = cv2.imread("whole_boards/board_102_1554110461.608167_.png")
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warped = runner.warp_board(board)
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files = "ABCDEFGH"
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ranks = [1, 2, 3, 4, 5, 6, 7, 8]
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counter = 0
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completely_non_empties = []
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for position, square in non_empties:
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#predict(square, position)
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for file in files:
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for rank in ranks:
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square_img = runner.get_square(warped, file, rank)
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if predict(square_img, file, rank) == 'empty':
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#y, x = np.histogram(square.ravel(), bins=32, range=[0, 256])
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#left, right = x[:-1], x[1:]
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#X = np.array([left, right]).T.flatten()
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#Y = np.array([y, y]).T.flatten()
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#plt.plot(X, Y)
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#plt.xlabel(f"{position}")
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#plt.show()
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if predict(square,position) == PIECE.EMPTY:
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counter += 1
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else:
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completely_non_empties.append([position, square])
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print(counter)
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for position, square in completely_non_empties:
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cv2.imshow(f"{position}", square)
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cv2.waitKey(0)
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exit()
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@ -82,7 +82,7 @@ def train_empty_or_piece_hist() -> None:
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for piece in (PIECE.EMPTY, PIECE.ROOK, PIECE.KNIGHT):
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for filename in glob.glob(os.path.join("training_images", f"{piece}", f"{square_color}_square", "*.png")):
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img = cv2.imread(filename)
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y, x = np.histogram(img.ravel(), bins=256, range=[0, 256])
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y, x = np.histogram(img.ravel(), bins=32, range=[0, 256])
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X.append(y)
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Y.append(piece == PIECE.EMPTY)
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@ -176,9 +176,10 @@ def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray:
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square_size = size // 8
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padding = 0
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x1 = side + (square_size * position.file)
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x1 = side + (square_size * (position.file - 1))
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x2 = x1 + square_size
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y1 = max(0, side + (square_size * (8 - position.rank)) - padding) # 8 - rank because chessboard is from 8 to 1
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y2 = min(width, y1 + square_size + padding)
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square = warped_board[y1:y2, x1:x2]
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