Repository: karayaman/Play-online-chess-with-real-chess-board Branch: main Commit: d62da44c11c8 Files: 23 Total size: 10.3 MB Directory structure: gitextract_antnu9xm/ ├── LICENSE ├── README.md ├── board_basics.py ├── board_calibration.py ├── board_calibration_machine_learning.py ├── chessboard_detection.py ├── classifier.py ├── cnn_color.onnx ├── cnn_piece.onnx ├── commentator.py ├── diagnostic.py ├── game.py ├── gui.py ├── helper.py ├── internet_game.py ├── languages.py ├── lichess_commentator.py ├── lichess_game.py ├── main.py ├── requirements.txt ├── speech.py ├── videocapture.py └── yolo_corner.onnx ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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But first, please read . ================================================ FILE: README.md ================================================ # Play online chess with a real chess board Program that enables you to play online chess using real chess boards. Using computer vision it will detect the moves you make on a chess board. After that, if it's your turn to move in the online game, it will make the necessary clicks to make the move. ## Setup 1. Turn off all the animations and extra features to keep chess board of online game as simple as possible. You can skip this step if you enter your Lichess API Access Token. 2. Take screenshots of the chess board of an online game at starting position, one for when you play white and one for when you play black and save them as "white.JPG" and "black.JPG" similar to the images included in the source code. You can skip this step if you enable "Find chess board of online game without template images." option or enter your Lichess API Access Token. 3. Enable auto-promotion to queen from settings of online game. You can skip this step if you enter your Lichess API Access Token. 4. Place your webcam near to your chessboard so that all of the squares and pieces can be clearly seen by it. 5. Select a board calibration mode and follow its instructions. ## Board Calibration(The board is empty.) 1. Remove all pieces from your chess board. 2. Click the "Board Calibration" button. 3. Check that corners of your chess board are correctly detected by "board_calibration.py" and press key "q" to save detected chess board corners. You don't need to manually select chess board corners; it should be automatically detected by the program. The square covered by points (0,0), (0,1),(1,0) and (1,1) should be a8. You can rotate the image by pressing the key "r" to adjust that. Example chess board detection result: ![](https://github.com/karayaman/Play-online-chess-with-real-chess-board/blob/main/chessboard_detection_result.jpg?raw=true) ## Board Calibration(Pieces are in their starting positions.) 1. Place the pieces in their starting positions. 2. Click the "Board Calibration" button. 3. Please ensure your chess board is correctly positioned and detected. Guiding lines will be drawn to mark the board's edges: - The line near the white pieces will be blue. - The line near the black pieces will be green. - Press any key to exit once you've confirmed the board setup. ## Board Calibration(Just before the game starts.) 1. Click the "Start Game" button. The software will calibrate the board just before it begins move recognition. ## Usage 1. Place pieces of chess board to their starting position. 2. Start the online game. 3. Click the "Start Game" button. 4. Switch to the online game so that program detects chess board of online game. You have 5 seconds to complete this step. You can skip this step if you enter your Lichess API Access Token. 5. Wait until the program says "game started". 6. Make your move if it's your turn , otherwise make your opponent's move. 8. Notice that the program actually makes your move on the internet game if it's your turn. Otherwise, wait until the program says starting and ending squares of the opponent's move. To save clock time, you may choose not to wait, but this is not recommended. 9. Go to step 6. ## GUI You need to run the GUI to do the steps in Setup, Usage and Diagnostic sections. Also, you can enter your Lichess API Access Token via Connection→Lichess (You need to enable "Play games with the board API" while generating the token). ![](https://github.com/karayaman/Play-online-chess-with-real-chess-board/blob/main/gui.jpg?raw=true) ## Diagnostic You need to click the "Diagnostic" button to run the diagnostic process. It will show your chessboard in a perspective-transformed form, exactly as the software sees it. Additionally, it will mark white pieces with a blue circle and black pieces with a green circle, allowing you to verify if the software can detect the pieces on the chess board. ![](https://github.com/karayaman/Play-online-chess-with-real-chess-board/blob/main/diagnostic.jpg?raw=true) ## Video In this section you can find video content related to the software. [Play online chess with real chess board and web camera | NO DGT BOARD!](https://www.youtube.com/watch?v=LX-4czb3xi0&lc=Ugxo6cXY0cR2TArDpuZ4AaABAg) ## Frequently Asked Questions ### What is the program doing? How does it work? It tracks your chess board via a webcam. You should place it on top of your chess board. Make sure there is enough light in the environment and all squares are clearly visible. When you make a move on your chess board, it understands the move you made and transfers it to the chess GUI by simulating mouse clicks (It clicks the starting and ending squares of your move). This way, using your chess board, you can play chess in any chess program, either websites like lichess.org, chess.com, or desktop programs like Fritz, Chessmaster etc. ### Placing a webcam on top of the chess board sounds difficult. Can I put my laptop aside with the webcam on the laptop display? Yes, you can do that with a small chess board. However, placing a webcam on top of the chess board is recommended. Personally, while using the program I am putting my laptop aside and it gives out moves via chess gui and shows clocks. Instead of using the laptop's webcam, I disable it and use my old android phone's camera as a webcam using an app called DroidCam. I place my phone somewhere high enough (a bookshelf, for instance) so that all of the squares and pieces can be clearly seen by it. ### How well does it work? Using this software I am able to make up to 100 moves in 15+10 rapid online game without getting any errors. ### I am getting error message "Move registration failed. Please redo your move." What is the problem? The program asked you to redo your move because it understood that you had made a move. However, it failed to figure out which move you made. This can happen if your board calibration is incorrect or the color of your pieces are very similar to the color of your squares. If the latter is the case, you will get this error message when playing white piece to light square or black piece to dark square. ### Why does it take forever to detect corners of the chess board? It should detect corners of the chess board almost immediately. Please do not spend any time waiting for it to detect corners of the chess board. If it can't detect corners of the chess board almost immediately, this means that it can't see your chess board well from that position/angle. Placing your webcam somewhere a bit higher or lower might solve the issue. ## Required libraries - opencv-python - python-chess - pyautogui - mss - numpy - pyttsx3 - scikit-image - pygrabber - berserk ================================================ FILE: board_basics.py ================================================ import sys from skimage.metrics import structural_similarity import chess import pickle import os class Board_basics: def __init__(self, side_view_compensation, rotation_count): self.d = [side_view_compensation, (0, 0)] self.rotation_count = rotation_count self.SSIM_THRESHOLD = 0.8 self.SSIM_THRESHOLD_LIGHT_WHITE = 1.0 self.SSIM_THRESHOLD_LIGHT_BLACK = 1.0 self.SSIM_THRESHOLD_DARK_WHITE = 1.0 self.SSIM_THRESHOLD_DARK_BLACK = 1.0 self.ssim_table = [[self.SSIM_THRESHOLD_DARK_BLACK, self.SSIM_THRESHOLD_DARK_WHITE], [self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_LIGHT_WHITE]] self.save_file = "ssim.bin" def initialize_ssim(self, frame): light_white = [] dark_white = [] light_empty = [] dark_empty = [] light_black = [] dark_black = [] for row in range(8): for column in range(8): square_name = self.convert_row_column_to_square_name(row, column) if square_name[1] == "2": if self.is_light(square_name): light_white.append(self.get_square_image(row, column, frame)) else: dark_white.append(self.get_square_image(row, column, frame)) elif square_name[1] == "4": if self.is_light(square_name): light_empty.append(self.get_square_image(row, column, frame)) else: dark_empty.append(self.get_square_image(row, column, frame)) elif square_name[1] == "7": if self.is_light(square_name): light_black.append(self.get_square_image(row, column, frame)) else: dark_black.append(self.get_square_image(row, column, frame)) ssim_light_white = max(structural_similarity(empty, piece, channel_axis=-1) for piece, empty in zip(light_white, light_empty)) ssim_light_black = max(structural_similarity(empty, piece, channel_axis=-1) for piece, empty in zip(light_black, light_empty)) ssim_dark_white = max(structural_similarity(empty, piece, channel_axis=-1) for piece, empty in zip(dark_white, dark_empty)) ssim_dark_black = max(structural_similarity(empty, piece, channel_axis=-1) for piece, empty in zip(dark_black, dark_empty)) self.SSIM_THRESHOLD_LIGHT_WHITE = min(self.SSIM_THRESHOLD_LIGHT_WHITE, ssim_light_white + 0.2) self.SSIM_THRESHOLD_LIGHT_BLACK = min(self.SSIM_THRESHOLD_LIGHT_BLACK, ssim_light_black + 0.2) self.SSIM_THRESHOLD_DARK_WHITE = min(self.SSIM_THRESHOLD_DARK_WHITE, ssim_dark_white + 0.2) self.SSIM_THRESHOLD_DARK_BLACK = min(self.SSIM_THRESHOLD_DARK_BLACK, ssim_dark_black + 0.2) self.SSIM_THRESHOLD = max( [self.SSIM_THRESHOLD, self.SSIM_THRESHOLD_LIGHT_WHITE, self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_DARK_WHITE, self.SSIM_THRESHOLD_DARK_BLACK]) print(self.SSIM_THRESHOLD_LIGHT_WHITE, self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_DARK_WHITE, self.SSIM_THRESHOLD_DARK_BLACK) self.ssim_table = [[self.SSIM_THRESHOLD_DARK_BLACK, self.SSIM_THRESHOLD_DARK_WHITE], [self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_LIGHT_WHITE]] outfile = open(self.save_file, 'wb') pickle.dump((self.SSIM_THRESHOLD_LIGHT_WHITE, self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_DARK_WHITE, self.SSIM_THRESHOLD_DARK_BLACK, self.SSIM_THRESHOLD), outfile) outfile.close() def load_ssim(self): if os.path.exists(self.save_file): infile = open(self.save_file, 'rb') (self.SSIM_THRESHOLD_LIGHT_WHITE, self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_DARK_WHITE, self.SSIM_THRESHOLD_DARK_BLACK, self.SSIM_THRESHOLD) = pickle.load(infile) infile.close() print(self.SSIM_THRESHOLD_LIGHT_WHITE, self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_DARK_WHITE, self.SSIM_THRESHOLD_DARK_BLACK) self.ssim_table = [[self.SSIM_THRESHOLD_DARK_BLACK, self.SSIM_THRESHOLD_DARK_WHITE], [self.SSIM_THRESHOLD_LIGHT_BLACK, self.SSIM_THRESHOLD_LIGHT_WHITE]] else: print("You need to play at least 1 game before starting a game from position.") sys.exit(0) def update_ssim(self, previous_frame, next_frame, move, is_capture, color): from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) for row in range(8): for column in range(8): square_name = self.convert_row_column_to_square_name(row, column) if square_name not in [from_square, to_square]: continue previous_square = self.get_square_image(row, column, previous_frame) next_square = self.get_square_image(row, column, next_frame) ssim = structural_similarity(next_square, previous_square, channel_axis=-1) ssim = ssim + 0.1 if ssim > self.SSIM_THRESHOLD: self.SSIM_THRESHOLD = ssim print("new threshold is " + str(ssim)) is_light = int(self.is_light(square_name)) if (square_name == from_square) or (not is_capture): if ssim > self.ssim_table[is_light][color]: self.ssim_table[is_light][color] = ssim print((is_light, color, ssim)) def get_square_image(self, row, column, board_img): height, width = board_img.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square = board_img[minY:maxY, minX:maxX] return square def convert_row_column_to_square_name(self, row, column): if self.rotation_count == 0: number = repr(8 - row) letter = str(chr(97 + column)) elif self.rotation_count == 1: number = repr(8 - column) letter = str(chr(97 + (7 - row))) elif self.rotation_count == 2: number = repr(row + 1) letter = str(chr(97 + (7 - column))) elif self.rotation_count == 3: number = repr(column + 1) letter = str(chr(97 + row)) return letter + number def square_region(self, row, column): region = set() for d_row, d_column in self.d: n_row = row + d_row n_column = column + d_column if not (0 <= n_row < 8): continue if not (0 <= n_column < 8): continue region.add((n_row, column)) return region def is_light(self, square_name): if square_name[0] in "aceg": if square_name[1] in "1357": return False else: return True else: if square_name[1] in "1357": return True else: return False def get_potential_moves(self, fgmask, previous_frame, next_frame, chessboard): board = [[self.get_square_image(row, column, fgmask).mean() for column in range(8)] for row in range(8)] previous_board = [[self.get_square_image(row, column, previous_frame) for column in range(8)] for row in range(8)] next_board = [[self.get_square_image(row, column, next_frame) for column in range(8)] for row in range(8)] potential_squares = [] for row in range(8): for column in range(8): score = board[row][column] if score < 10.0: continue ssim = structural_similarity(next_board[row][column], previous_board[row][column], channel_axis=-1) square_name = self.convert_row_column_to_square_name(row, column) print(ssim, square_name) if ssim > self.SSIM_THRESHOLD: continue square = chess.parse_square(square_name) piece = chessboard.piece_at(square) if piece and piece.color == chessboard.turn: is_light = int(self.is_light(square_name)) color = int(piece.color) if ssim > self.ssim_table[is_light][color]: continue potential_squares.append((score, row, column, ssim)) potential_squares.sort(reverse=True) potential_squares_castling = [] for i in range(min(6, len(potential_squares))): score, row, column, ssim = potential_squares[i] potential_square = (score, self.convert_row_column_to_square_name(row, column)) potential_squares_castling.append(potential_square) potential_squares = potential_squares[:4] potential_moves = [] for start_square_score, start_row, start_column, start_ssim in potential_squares: start_square_name = self.convert_row_column_to_square_name(start_row, start_column) start_square = chess.parse_square(start_square_name) start_piece = chessboard.piece_at(start_square) if start_piece: if start_piece.color != chessboard.turn: continue else: continue start_region = self.square_region(start_row, start_column) for arrival_square_score, arrival_row, arrival_column, arrival_ssim in potential_squares: if (start_row, start_column) == (arrival_row, arrival_column): continue arrival_square_name = self.convert_row_column_to_square_name(arrival_row, arrival_column) arrival_square = chess.parse_square(arrival_square_name) arrival_piece = chessboard.piece_at(arrival_square) if arrival_piece: if arrival_piece.color == chessboard.turn: continue else: is_light = int(self.is_light(arrival_square_name)) color = int(start_piece.color) if arrival_ssim > self.ssim_table[is_light][color]: continue arrival_region = self.square_region(arrival_row, arrival_column) region = start_region.union(arrival_region) total_square_score = sum( board[row][column] for row, column in region) + start_square_score + arrival_square_score potential_moves.append( (total_square_score, start_square_name, arrival_square_name)) potential_moves.sort(reverse=True) return potential_squares_castling, potential_moves ================================================ FILE: board_calibration.py ================================================ import cv2 import platform from math import inf import pickle from board_calibration_machine_learning import detect_board from helper import rotateMatrix, perspective_transform, edge_detection, euclidean_distance import numpy as np import sys from tkinter import messagebox import tkinter as tk filename = 'constants.bin' corner_model = cv2.dnn.readNetFromONNX("yolo_corner.onnx") piece_model = cv2.dnn.readNetFromONNX("cnn_piece.onnx") color_model = cv2.dnn.readNetFromONNX("cnn_color.onnx") webcam_width = None webcam_height = None fps = None is_machine_learning = False show_info = False cap_index = 0 cap_api = cv2.CAP_ANY platform_name = platform.system() for argument in sys.argv: if argument == "show-info": show_info = True elif argument.startswith("cap="): cap_index = int("".join(c for c in argument if c.isdigit())) if platform_name == "Darwin": cap_api = cv2.CAP_AVFOUNDATION elif platform_name == "Linux": cap_api = cv2.CAP_V4L2 else: cap_api = cv2.CAP_DSHOW elif argument == "ml": is_machine_learning = True elif argument.startswith("width="): webcam_width = int(argument[len("width="):]) elif argument.startswith("height="): webcam_height = int(argument[len("height="):]) elif argument.startswith("fps="): fps = int(argument[len("fps="):]) if show_info: root = tk.Tk() root.withdraw() messagebox.showinfo("Board Calibration", 'Board calibration will start. It should detect corners of the chess board almost immediately. If it does not, you should press key "q" to stop board calibration and change webcam/board position.') def mark_corners(frame, augmented_corners, rotation_count): height, width = frame.shape[:2] if rotation_count == 1: frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE) elif rotation_count == 2: frame = cv2.rotate(frame, cv2.ROTATE_180) elif rotation_count == 3: frame = cv2.rotate(frame, cv2.ROTATE_90_COUNTERCLOCKWISE) for i in range(len(augmented_corners)): for j in range(len(augmented_corners[i])): if rotation_count == 0: index = str(i) + "," + str(j) corner = augmented_corners[i][j] elif rotation_count == 1: index = str(j) + "," + str(8 - i) corner = (height - augmented_corners[i][j][1], augmented_corners[i][j][0]) elif rotation_count == 2: index = str(8 - i) + "," + str(8 - j) corner = (width - augmented_corners[i][j][0], height - augmented_corners[i][j][1]) elif rotation_count == 3: index = str(8 - j) + "," + str(i) corner = (augmented_corners[i][j][1], width - augmented_corners[i][j][0]) corner = (int(corner[0]), int(corner[1])) frame = cv2.putText(frame, index, corner, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1, cv2.LINE_AA) return frame cap = cv2.VideoCapture(cap_index, cap_api) if webcam_width is not None: cap.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width) if webcam_height is not None: cap.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height) if fps is not None: cap.set(cv2.CAP_PROP_FPS, fps) if not cap.isOpened(): print("Couldn't open your webcam. Please check your webcam connection.") sys.exit(0) board_dimensions = (7, 7) for _ in range(10): ret, frame = cap.read() if ret == False: print("Error reading frame. Please check your webcam connection.") continue while True: ret, frame = cap.read() if ret == False: print("Error reading frame. Please check your webcam connection.") continue if is_machine_learning: result = detect_board(frame, corner_model, piece_model, color_model) if result: pts1, side_view_compensation, rotation_count = result outfile = open(filename, 'wb') pickle.dump([is_machine_learning, [pts1, side_view_compensation, rotation_count]], outfile) outfile.close() if show_info: if platform_name == "Darwin": root = tk.Tk() root.withdraw() messagebox.showinfo( "Chess Board Detected", "Please ensure your chess board is correctly positioned and detected. " "Guiding lines will be drawn to mark the board's edges:\n" "- The line near the white pieces will be blue.\n" "- The line near the black pieces will be green.\n\n" "Press any key to exit once you've confirmed the board setup." ) root.destroy() cv2.imshow('frame', frame) cv2.waitKey(0) cap.release() cv2.destroyAllWindows() sys.exit(0) else: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) retval, corners = cv2.findChessboardCorners(gray, patternSize=board_dimensions) if retval: if show_info: if platform_name == "Darwin": root = tk.Tk() root.withdraw() messagebox.showinfo("Chess Board Detected", 'Please check that corners of your chess board are correctly detected. The square covered by points (0,0), (0,1),(1,0) and (1,1) should be a8. You can rotate the image by pressing key "r" to adjust that. Press key "q" to save detected chess board corners and finish board calibration.') root.destroy() if corners[0][0][0] > corners[-1][0][0]: # corners returned in reverse order corners = corners[::-1] minX, maxX, minY, maxY = inf, -inf, inf, -inf augmented_corners = [] row = [] for i in range(6): corner1 = corners[i] corner2 = corners[i + 8] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) for i in range(4, 7): corner1 = corners[i] corner2 = corners[i + 6] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) augmented_corners.append(row) for i in range(7): row = [] corner1 = corners[i * 7] corner2 = corners[i * 7 + 1] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) for corner in corners[i * 7:(i + 1) * 7]: x = corner[0][0] y = corner[0][1] row.append((x, y)) corner1 = corners[i * 7 + 6] corner2 = corners[i * 7 + 5] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) augmented_corners.append(row) row = [] for i in range(6): corner1 = corners[42 + i] corner2 = corners[42 + i - 6] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) for i in range(4, 7): corner1 = corners[42 + i] corner2 = corners[42 + i - 8] x = corner1[0][0] + (corner1[0][0] - corner2[0][0]) y = corner1[0][1] + (corner1[0][1] - corner2[0][1]) row.append((x, y)) augmented_corners.append(row) while augmented_corners[0][0][0] > augmented_corners[8][8][0] or augmented_corners[0][0][1] > \ augmented_corners[8][8][1]: rotateMatrix(augmented_corners) pts1 = np.float32([list(augmented_corners[0][0]), list(augmented_corners[8][0]), list(augmented_corners[0][8]), list(augmented_corners[8][8])]) empty_board = perspective_transform(frame, pts1) edges = edge_detection(empty_board) # cv2.imshow("edge", edges) # cv2.waitKey(0) kernel = np.ones((7, 7), np.uint8) edges = cv2.dilate(edges, kernel, iterations=1) roi_mask = cv2.bitwise_not(edges) # cv2.imshow("edge", edges) # cv2.waitKey(0) # cv2.imshow("roi", roi_mask) # cv2.waitKey(0) roi_mask[:7, :] = 0 roi_mask[:, :7] = 0 roi_mask[-7:, :] = 0 roi_mask[:, -7:] = 0 # cv2.imshow("roi", roi_mask) # cv2.waitKey(0) # cv2.imwrite("empty_board.jpg", empty_board) rotation_count = 0 while True: cv2.imshow('frame', mark_corners(frame.copy(), augmented_corners, rotation_count)) response = cv2.waitKey(0) if response & 0xFF == ord('r'): rotation_count += 1 rotation_count %= 4 elif response & 0xFF == ord('q'): break break cv2.imshow('frame', frame) if cv2.waitKey(3) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() first_row = euclidean_distance(augmented_corners[1][1], augmented_corners[1][7]) last_row = euclidean_distance(augmented_corners[7][1], augmented_corners[7][7]) first_column = euclidean_distance(augmented_corners[1][1], augmented_corners[7][1]) last_column = euclidean_distance(augmented_corners[1][7], augmented_corners[7][7]) if abs(first_row - last_row) >= abs(first_column - last_column): if first_row >= last_row: side_view_compensation = (1, 0) else: side_view_compensation = (-1, 0) else: if first_column >= last_column: side_view_compensation = (0, -1) else: side_view_compensation = (0, 1) print("Side view compensation" + str(side_view_compensation)) print("Rotation count " + str(rotation_count)) outfile = open(filename, 'wb') pickle.dump([is_machine_learning, [augmented_corners, side_view_compensation, rotation_count, roi_mask]], outfile) outfile.close() ================================================ FILE: board_calibration_machine_learning.py ================================================ import numpy as np import cv2 from helper import euclidean_distance, perspective_transform, predict def detect_board(original_image, corner_model, piece_model, color_model): [height, width, _] = original_image.shape length = max((height, width)) image = np.zeros((length, length, 3), np.uint8) image[0:height, 0:width] = original_image scale = length / 640 blob = cv2.dnn.blobFromImage(image, scalefactor=1 / 255, size=(640, 640), swapRB=True) corner_model.setInput(blob) outputs = corner_model.forward() outputs = np.array([cv2.transpose(outputs[0])]) rows = outputs.shape[1] boxes = [] scores = [] class_ids = [] for i in range(rows): classes_scores = outputs[0][i][4:] (minScore, maxScore, minClassLoc, (x, maxClassIndex)) = cv2.minMaxLoc(classes_scores) if maxScore >= 0.25: box = [ outputs[0][i][0] - (0.5 * outputs[0][i][2]), outputs[0][i][1] - (0.5 * outputs[0][i][3]), outputs[0][i][2], outputs[0][i][3]] boxes.append(box) scores.append(maxScore) class_ids.append(maxClassIndex) result_boxes = cv2.dnn.NMSBoxes(boxes, scores, 0.25, 0.45, 0.5) detections = [] for i in range(len(result_boxes)): index = result_boxes[i] box = boxes[index] detection = { 'confidence': scores[index], 'box': box, } detections.append(detection) if len(detections) < 4: return detections.sort(key=lambda detection: detection['confidence'], reverse=True) detections = detections[:4] middle_points = [] for detection in detections: box = detection['box'] x, y, w, h = box middle_x = (x + (w / 2)) * scale middle_y = (y + (h / 2)) * scale middle_points.append([middle_x, middle_y]) minX = min(point[0] for point in middle_points) minY = min(point[1] for point in middle_points) maxX = max(point[0] for point in middle_points) maxY = max(point[1] for point in middle_points) top_left = min(middle_points, key=lambda point: euclidean_distance(point, [minX, minY])) top_right = min(middle_points, key=lambda point: euclidean_distance(point, [maxX, minY])) bottom_left = min(middle_points, key=lambda point: euclidean_distance(point, [minX, maxY])) bottom_right = min(middle_points, key=lambda point: euclidean_distance(point, [maxX, maxY])) first_row = euclidean_distance(top_left, top_right) last_row = euclidean_distance(bottom_left, bottom_right) first_column = euclidean_distance(top_left, bottom_left) last_column = euclidean_distance(top_right, bottom_right) if abs(first_row - last_row) >= abs(first_column - last_column): if first_row >= last_row: side_view_compensation = (1, 0) else: side_view_compensation = (-1, 0) else: if first_column >= last_column: side_view_compensation = (0, -1) else: side_view_compensation = (0, 1) pts1 = np.float32([top_left, bottom_left, top_right, bottom_right]) board_image = perspective_transform(original_image, pts1) squares_to_check_for_rotation_count = [ [(0, i) for i in range(7)], [(i, 0) for i in range(7)], [(7, i) for i in range(7)], [(i, 7) for i in range(7)], ] rotation_count = 0 score = 0 for i in range(len(squares_to_check_for_rotation_count)): current_score = 0 for row, column in squares_to_check_for_rotation_count[i]: height, width = board_image.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square_image = board_image[minY:maxY, minX:maxX] is_piece = predict(square_image, piece_model) if is_piece: is_white = predict(square_image, color_model) if not is_white: current_score += 1 if current_score > score: score = current_score rotation_count = i green_color = (0, 255, 0) blue_color = (255, 0, 0) red_color = (0, 0, 255) top_left, top_right, bottom_left, bottom_right = [(int(point[0]), int(point[1])) for point in (top_left, top_right, bottom_left, bottom_right)] if rotation_count == 0: cv2.line(original_image, top_left, top_right, green_color, 5) cv2.line(original_image, top_right, bottom_right, red_color, 5) cv2.line(original_image, bottom_left, bottom_right, blue_color, 5) cv2.line(original_image, top_left, bottom_left, red_color, 5) elif rotation_count == 1: cv2.line(original_image, top_left, top_right, red_color, 5) cv2.line(original_image, top_right, bottom_right, blue_color, 5) cv2.line(original_image, bottom_left, bottom_right, red_color, 5) cv2.line(original_image, top_left, bottom_left, green_color, 5) elif rotation_count == 2: cv2.line(original_image, top_left, top_right, blue_color, 5) cv2.line(original_image, top_right, bottom_right, red_color, 5) cv2.line(original_image, bottom_left, bottom_right, green_color, 5) cv2.line(original_image, top_left, bottom_left, red_color, 5) elif rotation_count == 3: cv2.line(original_image, top_left, top_right, red_color, 5) cv2.line(original_image, top_right, bottom_right, green_color, 5) cv2.line(original_image, bottom_left, bottom_right, red_color, 5) cv2.line(original_image, top_left, bottom_left, blue_color, 5) print("Side view compensation" + str(side_view_compensation)) print("Rotation count " + str(rotation_count)) return pts1, side_view_compensation, rotation_count ================================================ FILE: chessboard_detection.py ================================================ import sys import numpy as np import cv2 import pyautogui import mss from statistics import median class Board_position: def __init__(self, minX, minY, maxX, maxY): self.minX = minX self.minY = minY self.maxX = maxX self.maxY = maxY def find_chessboard(): screenshot_shape = np.array(pyautogui.screenshot()).shape monitor = {'top': 0, 'left': 0, 'width': screenshot_shape[1], 'height': screenshot_shape[0]} sct = mss.mss() large_image = np.array(np.array(sct.grab(monitor))) large_image = cv2.cvtColor(large_image, cv2.COLOR_BGR2RGB) method = cv2.TM_SQDIFF_NORMED white_image = cv2.imread("white.JPG") black_image = cv2.imread("black.JPG") result_white = cv2.matchTemplate(white_image, large_image, method) result_black = cv2.matchTemplate(black_image, large_image, method) we_are_white = True result = result_white small_image = white_image if cv2.minMaxLoc(result_black)[0] < cv2.minMaxLoc(result_white)[0]: # If black is more accurate: result = result_black we_are_white = False small_image = black_image minimum_value, maximum_value, minimum_location, maximum_location = cv2.minMaxLoc(result) minX, minY = minimum_location maxX = minX + small_image.shape[1] maxY = minY + small_image.shape[0] position = Board_position(minX, minY, maxX, maxY) return position, we_are_white def auto_find_chessboard(): screenshot_shape = np.array(pyautogui.screenshot()).shape monitor = {'top': 0, 'left': 0, 'width': screenshot_shape[1], 'height': screenshot_shape[0]} sct = mss.mss() img = np.array(np.array(sct.grab(monitor))) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) is_found, current_chessboard_image, minX, minY, maxX, maxY, test_image = find_chessboard_from_image(img) if not is_found: sys.exit(0) position = Board_position(minX, minY, maxX, maxY) return position, is_white_on_bottom(current_chessboard_image) def is_white_on_bottom(current_chessboard_image): m1 = get_square_image(0, 0, current_chessboard_image).mean() m2 = get_square_image(7, 7, current_chessboard_image).mean() if m1 < m2: return True else: return False def get_square_image(row, column, board_img): height, width = board_img.shape minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * width / 8) maxY = int((row + 1) * width / 8) square = board_img[minY:maxY, minX:maxX] square_without_borders = square[3:-3, 3:-3] return square_without_borders def prepare(lines, kernel_close, kernel_open): ret, lines = cv2.threshold(lines, 30, 255, cv2.THRESH_BINARY) lines = cv2.morphologyEx(lines, cv2.MORPH_CLOSE, kernel_close) lines = cv2.morphologyEx(lines, cv2.MORPH_OPEN, kernel_open) return lines def prepare_vertical(lines): kernel_close = np.ones((3, 1), np.uint8) kernel_open = np.ones((50, 1), np.uint8) return prepare(lines, kernel_close, kernel_open) def prepare_horizontal(lines): kernel_close = np.ones((1, 3), np.uint8) kernel_open = np.ones((1, 50), np.uint8) return prepare(lines, kernel_close, kernel_open) def find_chessboard_from_image(img): image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) kernelH = np.array([[-1, 1]]) kernelV = np.array([[-1], [1]]) vertical_lines = np.absolute(cv2.filter2D(image.astype('float'), -1, kernelH)) image_vertical = prepare_vertical(vertical_lines) horizontal_lines = np.absolute(cv2.filter2D(image.astype('float'), -1, kernelV)) image_horizontal = prepare_horizontal(horizontal_lines) vertical_lines = cv2.HoughLinesP(image_vertical.astype(np.uint8), 1, np.pi / 180, 100, minLineLength=100, maxLineGap=10) horizontal_lines = cv2.HoughLinesP(image_horizontal.astype(np.uint8), 1, np.pi / 180, 100, minLineLength=100, maxLineGap=10) v_count = [0 for _ in range(len(vertical_lines))] h_count = [0 for _ in range(len(horizontal_lines))] for i, line in enumerate(vertical_lines): x1, y1, x2, y2 = line[0] for j, other_line in enumerate(horizontal_lines): x3, y3, x4, y4 = other_line[0] if ((x3 <= x1 <= x4) or (x4 <= x1 <= x3)) and ((y2 <= y3 <= y1) or (y1 <= y3 <= y2)): v_count[i] += 1 h_count[j] += 1 v_board = [] h_board = [] for i, line in enumerate(vertical_lines): if v_count[i] <= 6: continue v_board.append(line) for i, line in enumerate(horizontal_lines): if h_count[i] <= 6: continue h_board.append(line) if v_board and h_board: y_min = int(median(min(v[0][1], v[0][3]) for v in v_board)) y_max = int(median(max(v[0][1], v[0][3]) for v in v_board)) x_min = int(median(min(h[0][0], h[0][2]) for h in h_board)) x_max = int(median(max(h[0][0], h[0][2]) for h in h_board)) if abs((x_max - x_min) - (y_max - y_min)) > 3: print("Board is not square.") return False, image, 0, 0, 0, 0, image board = image[y_min:y_max, x_min:x_max] dim = (800, 800) resized_board = cv2.resize(board, dim, interpolation=cv2.INTER_AREA) # cv2.imwrite("board.jpg", resized_board) return True, resized_board, int(x_min), int(y_min), int(x_max), int(y_max), resized_board else: print("Chess board of online game could not be found.") return False, image, 0, 0, 0, 0, image ================================================ FILE: classifier.py ================================================ import numpy as np import cv2 from math import pi # https://github.com/youyexie/Chess-Piece-Recognition-using-Oriented-Chamfer-Matching-with-a-Comparison-to-CNN class Classifier: def __init__(self, game_state): self.dim = (480, 480) self.img = cv2.resize(game_state.previous_chessboard_image, self.dim, interpolation=cv2.INTER_AREA) self.img_x, self.img_y = self.unit_gradients(self.img) self.edges = cv2.Canny(self.img, 100, 200) self.inverted_edges = cv2.bitwise_not(self.edges) self.dist = cv2.distanceTransform(self.inverted_edges, cv2.DIST_L2, 3) self.dist_board = [[self.get_square_image(row, column, self.dist) for column in range(8)] for row in range(8)] self.edge_board = [[self.get_square_image(row, column, self.edges) for column in range(8)] for row in range(8)] self.gradient_x = [[self.get_square_image(row, column, self.img_x) for column in range(8)] for row in range(8)] self.gradient_y = [[self.get_square_image(row, column, self.img_y) for column in range(8)] for row in range(8)] def intensity(x): return self.edge_board[x[0]][x[1]].mean() pawn_templates = [max([(1, i) for i in range(8)], key=intensity), max([(6, i) for i in range(8)], key=intensity)] self.templates = [pawn_templates] + [[(0, i), (7, i)] for i in range(5)] if intensity((0, 6)) > intensity((0, 1)): self.templates[2][0] = (0, 6) if intensity((7, 6)) > intensity((7, 1)): self.templates[2][1] = (7, 6) self.piece_symbol = [".", "p", "r", "n", "b", "q", "k"] if game_state.we_play_white == False: self.piece_symbol[-1], self.piece_symbol[-2] = self.piece_symbol[-2], self.piece_symbol[-1] def classify(self, img): img = cv2.resize(img, self.dim, interpolation=cv2.INTER_AREA) img_x, img_y = self.unit_gradients(img) edges = cv2.Canny(img, 100, 200) inverted_edges = cv2.bitwise_not(edges) dist = cv2.distanceTransform(inverted_edges, cv2.DIST_L2, 3) dist_board = [[self.get_square_image(row, column, dist) for column in range(8)] for row in range(8)] gradient_x = [[self.get_square_image(row, column, img_x) for column in range(8)] for row in range(8)] gradient_y = [[self.get_square_image(row, column, img_y) for column in range(8)] for row in range(8)] result = [] for row in range(8): row_result = [] for col in range(8): d = dist_board[row][col] template_scores = [] for piece in self.templates: piece_scores = [] for tr, tc in piece: t = self.edge_board[tr][tc] e = t / 255.0 e_c = e.sum() r_d = np.multiply(d, e).sum() / e_c dp = np.multiply(self.gradient_x[tr][tc], gradient_x[row][col]) + np.multiply( self.gradient_y[tr][tc], gradient_y[row][col]) dp = np.abs(dp) dp[dp > 1.0] = 1.0 angle_difference = np.arccos(dp) r_o = np.multiply(angle_difference, e).sum() / (e_c * (pi / 2)) piece_scores.append(r_d * 0.5 + r_o * 0.5) template_scores.append(min(piece_scores)) min_score = float("inf") min_index = -1 for i in range(len(template_scores)): if min_score > template_scores[i]: min_score = template_scores[i] min_index = i if min_score < 2.0: row_result.append(self.piece_symbol[min_index + 1]) else: row_result.append(self.piece_symbol[0]) result.append(row_result) return result def unit_gradients(self, gray): sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=5) sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=5) mag, direction = cv2.cartToPolar(sobelx, sobely) mag[mag == 0] = 1 unit_x = sobelx / mag unit_y = sobely / mag return unit_x, unit_y def get_square_image(self, row, column, board_img): height, width = board_img.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square = board_img[minY:maxY, minX:maxX] square_without_borders = square[3:-3, 3:-3] return square_without_borders ================================================ FILE: commentator.py ================================================ from threading import Thread import chess import mss import numpy as np import cv2 import time from classifier import Classifier class Commentator_thread(Thread): def __init__(self, *args, **kwargs): super(Commentator_thread, self).__init__(*args, **kwargs) self.speech_thread = None self.game_state = Game_state() self.comment_me = None self.comment_opponent = None self.language = None self.classifier = None def run(self): self.game_state.sct = mss.mss() resized_chessboard = self.game_state.get_chessboard() self.game_state.previous_chessboard_image = resized_chessboard self.game_state.classifier = Classifier(self.game_state) while not self.game_state.board.is_game_over(): is_my_turn = (self.game_state.we_play_white) == (self.game_state.board.turn == chess.WHITE) found_move, move = self.game_state.register_move_if_needed() if found_move and ((self.comment_me and is_my_turn) or (self.comment_opponent and (not is_my_turn))): self.speech_thread.put_text(self.language.comment(self.game_state.board, move)) class Game_state: def __init__(self): self.game_thread = None self.we_play_white = None self.previous_chessboard_image = None self.board = chess.Board() self.board_position_on_screen = None self.sct = mss.mss() self.classifier = None self.registered_moves = [] self.resign_or_draw = False self.variant = 'standard' def get_chessboard(self): position = self.board_position_on_screen monitor = {'top': 0, 'left': 0, 'width': position.maxX + 10, 'height': position.maxY + 10} img = np.array(np.array(self.sct.grab(monitor))) image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) dim = (800, 800) resizedChessBoard = cv2.resize(image[position.minY:position.maxY, position.minX:position.maxX], dim, interpolation=cv2.INTER_AREA) return resizedChessBoard def get_square_image(self, row, column, board_img): height, width = board_img.shape minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * width / 8) maxY = int((row + 1) * width / 8) square = board_img[minY:maxY, minX:maxX] square_without_borders = square[6:-6, 6:-6] return square_without_borders def can_image_correspond_to_chessboard(self, move, result): self.board.push(move) squares = chess.SquareSet(chess.BB_ALL) for square in squares: row = chess.square_rank(square) column = chess.square_file(square) piece = self.board.piece_at(square) shouldBeEmpty = (piece == None) if self.we_play_white == True: rowOnImage = 7 - row columnOnImage = column else: rowOnImage = row columnOnImage = 7 - column isEmpty = result[rowOnImage][columnOnImage] == '.' if isEmpty != shouldBeEmpty: self.board.pop() # print("Problem with : ", self.board.uci(move), " the square ", # self.convert_row_column_to_square_name(row, column), "should ", # 'be empty' if shouldBeEmpty else 'contain a piece') return False if piece and (piece.symbol().lower() != result[rowOnImage][columnOnImage]): self.board.pop() # print(piece.symbol(), result[rowOnImage][columnOnImage], # self.convert_row_column_to_square_name(rowOnImage, columnOnImage)) return False self.board.pop() return True def find_premove(self, result): start_squares = [] squares = chess.SquareSet(chess.BB_ALL) for square in squares: row = chess.square_rank(square) column = chess.square_file(square) piece = self.board.piece_at(square) if self.we_play_white == True: rowOnImage = 7 - row columnOnImage = column else: rowOnImage = row columnOnImage = 7 - column isEmpty = result[rowOnImage][columnOnImage] == '.' if piece and isEmpty: start_squares.append(square) return squares def get_valid_move(self, potential_starts, potential_arrivals, current_chessboard_image): result = self.classifier.classify(current_chessboard_image) valid_move_string = "" for start in potential_starts: if valid_move_string: break for arrival in potential_arrivals: if valid_move_string: break if start == arrival: continue uci_move = start + arrival try: move = chess.Move.from_uci(uci_move) except: continue if move in self.board.legal_moves: if self.can_image_correspond_to_chessboard(move, result): valid_move_string = uci_move else: r, c = self.convert_square_name_to_row_column(arrival) if result[r][c] not in ["q", "r", "b", "n"]: continue uci_move_promoted = uci_move + result[r][c] promoted_move = chess.Move.from_uci(uci_move_promoted) if promoted_move in self.board.legal_moves: if self.can_image_correspond_to_chessboard(promoted_move, result): valid_move_string = uci_move_promoted # Detect castling king side with white if ("e1" in potential_starts) and ("h1" in potential_starts) and ("f1" in potential_arrivals) and ( "g1" in potential_arrivals) and (chess.Move.from_uci("e1g1") in self.board.legal_moves): if (self.board.peek() != chess.Move.from_uci("e1g1")) and \ self.can_image_correspond_to_chessboard(chess.Move.from_uci("e1g1"), result): valid_move_string = "e1g1" # Detect castling queen side with white if ("e1" in potential_starts) and ("a1" in potential_starts) and ("c1" in potential_arrivals) and ( "d1" in potential_arrivals) and (chess.Move.from_uci("e1c1") in self.board.legal_moves): if (self.board.peek() != chess.Move.from_uci("e1c1")) and \ self.can_image_correspond_to_chessboard(chess.Move.from_uci("e1c1"), result): valid_move_string = "e1c1" # Detect castling king side with black if ("e8" in potential_starts) and ("h8" in potential_starts) and ("f8" in potential_arrivals) and ( "g8" in potential_arrivals) and (chess.Move.from_uci("e8g8") in self.board.legal_moves): if (self.board.peek() != chess.Move.from_uci("e8g8")) and self.can_image_correspond_to_chessboard( chess.Move.from_uci("e8g8"), result): valid_move_string = "e8g8" # Detect castling queen side with black if ("e8" in potential_starts) and ("a8" in potential_starts) and ("c8" in potential_arrivals) and ( "d8" in potential_arrivals) and (chess.Move.from_uci("e8c8") in self.board.legal_moves): if (self.board.peek() != chess.Move.from_uci("e8c8")) and self.can_image_correspond_to_chessboard( chess.Move.from_uci("e8c8"), result): valid_move_string = "e8c8" if not valid_move_string: # Search for premove premove_starts = self.find_premove(result) for start_square in premove_starts: for move in self.board.legal_moves: if move.from_square == start_square: if self.can_image_correspond_to_chessboard(move, result): return move.uci() return valid_move_string def has_square_image_changed(self, old_square, new_square): diff = cv2.absdiff(old_square, new_square) if diff.mean() > 8: return True else: return False def convert_row_column_to_square_name(self, row, column): if self.we_play_white: number = repr(8 - row) letter = str(chr(97 + column)) return letter + number else: number = repr(row + 1) letter = str(chr(97 + (7 - column))) return letter + number def convert_square_name_to_row_column(self, square_name): for row in range(8): for column in range(8): this_square_name = self.convert_row_column_to_square_name(row, column) if this_square_name == square_name: return row, column return 0, 0 def get_potential_moves(self, old_image, new_image): potential_starts = [] potential_arrivals = [] for row in range(8): for column in range(8): old_square = self.get_square_image(row, column, old_image) new_square = self.get_square_image(row, column, new_image) if self.has_square_image_changed(old_square, new_square): square_name = self.convert_row_column_to_square_name(row, column) potential_starts.append(square_name) potential_arrivals.append(square_name) return potential_starts, potential_arrivals def register_move_if_needed(self): new_board = self.get_chessboard() potential_starts, potential_arrivals = self.get_potential_moves(self.previous_chessboard_image, new_board) valid_move_string1 = self.get_valid_move(potential_starts, potential_arrivals, new_board) if len(valid_move_string1) > 0: time.sleep(0.1) # Check that we were not in the middle of a move animation new_board = self.get_chessboard() potential_starts, potential_arrivals = self.get_potential_moves(self.previous_chessboard_image, new_board) valid_move_string2 = self.get_valid_move(potential_starts, potential_arrivals, new_board) if valid_move_string2 != valid_move_string1: return False, "The move has changed" valid_move_UCI = chess.Move.from_uci(valid_move_string1) self.register_move(valid_move_UCI, new_board) return True, valid_move_UCI elif potential_starts: # Fix for premove if len(self.registered_moves) < len(self.game_thread.played_moves): valid_move_UCI = self.game_thread.played_moves[len(self.registered_moves)] self.register_move(valid_move_UCI, self.previous_chessboard_image) return True, valid_move_UCI return False, "No move found" def register_move(self, move, board_image): if move in self.board.legal_moves: self.board.push(move) self.previous_chessboard_image = board_image self.registered_moves.append(move) # cv2.imwrite("registered.jpg", board_image) return True else: return False ================================================ FILE: diagnostic.py ================================================ import cv2 import numpy as np import pickle from board_calibration_machine_learning import detect_board from helper import perspective_transform, predict import platform import sys import tkinter as tk from tkinter import messagebox webcam_width = None webcam_height = None fps = None calibrate = False cap_index = 0 cap_api = cv2.CAP_ANY platform_name = platform.system() for argument in sys.argv: if argument.startswith("cap="): cap_index = int("".join(c for c in argument if c.isdigit())) if platform_name == "Darwin": cap_api = cv2.CAP_AVFOUNDATION elif platform_name == "Linux": cap_api = cv2.CAP_V4L2 else: cap_api = cv2.CAP_DSHOW elif argument == "calibrate": calibrate = True elif argument.startswith("width="): webcam_width = int(argument[len("width="):]) elif argument.startswith("height="): webcam_height = int(argument[len("height="):]) elif argument.startswith("fps="): fps = int(argument[len("fps="):]) corner_model = cv2.dnn.readNetFromONNX("yolo_corner.onnx") piece_model = cv2.dnn.readNetFromONNX("cnn_piece.onnx") color_model = cv2.dnn.readNetFromONNX("cnn_color.onnx") cap = cv2.VideoCapture(cap_index, cap_api) if webcam_width is not None: cap.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width) if webcam_height is not None: cap.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height) if fps is not None: cap.set(cv2.CAP_PROP_FPS, fps) if not cap.isOpened(): print("Couldn't open your webcam. Please check your webcam connection.") sys.exit(0) for _ in range(10): ret, frame = cap.read() if calibrate: is_detected = False for _ in range(100): ret, frame = cap.read() if not ret: print("Error reading frame. Please check your webcam connection.") continue result = detect_board(frame, corner_model, piece_model, color_model) if result: pts1, side_view_compensation, rotation_count = result is_detected = True break if not is_detected: print("Could not detect the chess board.") cap.release() sys.exit(0) else: filename = 'constants.bin' infile = open(filename, 'rb') calibration_data = pickle.load(infile) infile.close() if calibration_data[0]: pts1, side_view_compensation, rotation_count = calibration_data[1] else: corners, side_view_compensation, rotation_count, roi_mask = calibration_data[1] pts1 = np.float32([list(corners[0][0]), list(corners[8][0]), list(corners[0][8]), list(corners[8][8])]) def process(image): for row in range(8): for column in range(8): height, width = image.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square_image = image[minY:maxY, minX:maxX] is_piece = predict(square_image, piece_model) if is_piece: centerX = int((minX + maxX) / 2) centerY = int((minY + maxY) / 2) radius = 10 is_white = predict(square_image, color_model) if is_white: cv2.circle(image, (centerX, centerY), radius, (255, 0, 0), 2) else: cv2.circle(image, (centerX, centerY), radius, (0, 255, 0), 2) return image root = tk.Tk() root.withdraw() messagebox.showinfo("Diagnostic", "The diagnostic process will start. It will mark white pieces with a blue circle and black pieces with a green circle. Press the 'q' key to exit.") while True: ret, frame = cap.read() if not ret: print("Error reading frame. Please check your webcam connection.") continue frame = perspective_transform(frame, pts1) processed_frame = process(frame.copy()) cv2.imshow('Diagnostic', np.hstack((processed_frame, frame))) if cv2.waitKey(1000) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ================================================ FILE: game.py ================================================ import time import chess import cv2 import numpy as np import pickle import os import sys from helper import detect_state, get_square_image, predict from internet_game import Internet_game from lichess_game import Lichess_game from commentator import Commentator_thread from lichess_commentator import Lichess_commentator class Game: def __init__(self, board_basics, speech_thread, use_template, make_opponent, start_delay, comment_me, comment_opponent, drag_drop, language, token, roi_mask): if token: self.internet_game = Lichess_game(token) else: self.internet_game = Internet_game(use_template, start_delay, drag_drop) self.make_opponent = make_opponent self.board_basics = board_basics self.speech_thread = speech_thread self.executed_moves = [] self.played_moves = [] self.board = chess.Board() self.comment_me = comment_me self.comment_opponent = comment_opponent self.language = language self.roi_mask = roi_mask self.hog = cv2.HOGDescriptor((64, 64), (16, 16), (8, 8), (8, 8), 9) self.knn = cv2.ml.KNearest_create() self.features = None self.labels = None self.save_file = 'hog.bin' self.piece_model = cv2.dnn.readNetFromONNX("cnn_piece.onnx") self.color_model = cv2.dnn.readNetFromONNX("cnn_color.onnx") if token: commentator_thread = Lichess_commentator() commentator_thread.daemon = True commentator_thread.stream = self.internet_game.client.board.stream_game_state(self.internet_game.game_id) commentator_thread.speech_thread = self.speech_thread commentator_thread.game_state.we_play_white = self.internet_game.we_play_white commentator_thread.game_state.game = self commentator_thread.comment_me = self.comment_me commentator_thread.comment_opponent = self.comment_opponent commentator_thread.language = self.language self.commentator = commentator_thread else: commentator_thread = Commentator_thread() commentator_thread.daemon = True commentator_thread.speech_thread = self.speech_thread commentator_thread.game_state.game_thread = self commentator_thread.game_state.we_play_white = self.internet_game.we_play_white commentator_thread.game_state.board_position_on_screen = self.internet_game.position commentator_thread.comment_me = self.comment_me commentator_thread.comment_opponent = self.comment_opponent commentator_thread.language = self.language self.commentator = commentator_thread def initialize_hog(self, frame): frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) pieces = [] squares = [] for row in range(8): for column in range(8): square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) square_image = get_square_image(row, column, frame) square_image = cv2.resize(square_image, (64, 64)) if piece: pieces.append(square_image) else: squares.append(square_image) pieces_hog = [self.hog.compute(piece) for piece in pieces] squares_hog = [self.hog.compute(square) for square in squares] labels_pieces = np.ones((len(pieces_hog), 1), np.int32) labels_squares = np.zeros((len(squares_hog), 1), np.int32) pieces_hog = np.array(pieces_hog) squares_hog = np.array(squares_hog) features = np.float32(np.concatenate((pieces_hog, squares_hog), axis=0)) labels = np.concatenate((labels_pieces, labels_squares), axis=0) self.knn.train(features, cv2.ml.ROW_SAMPLE, labels) self.features = features self.labels = labels outfile = open(self.save_file, 'wb') pickle.dump([features, labels], outfile) outfile.close() def detect_state_cnn(self, chessboard_image): state = [] for row in range(8): row_state = [] for column in range(8): height, width = chessboard_image.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square_image = chessboard_image[minY:maxY, minX:maxX] is_piece = predict(square_image, self.piece_model) if is_piece: is_white = predict(square_image, self.color_model) if is_white: row_state.append('w') else: row_state.append('b') else: row_state.append('.') state.append(row_state) return state def check_state_cnn(self, result): for row in range(8): for column in range(8): square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) expected_state = '.' if piece: if piece.color == chess.WHITE: expected_state = 'w' else: expected_state = 'b' if result[row][column] != expected_state: return False return True def get_valid_2_move_cnn(self, frame): board_result = self.detect_state_cnn(frame) move_to_register = self.get_move_to_register() if move_to_register: self.board.push(move_to_register) for move in self.board.legal_moves: if move.promotion and move.promotion != chess.QUEEN: continue self.board.push(move) if self.check_state_cnn(board_result): self.board.pop() valid_move_string = move_to_register.uci() self.speech_thread.put_text(valid_move_string[:4]) self.played_moves.append(move_to_register) self.board.pop() self.executed_moves.append(self.board.san(move_to_register)) self.board.push(move_to_register) if self.internet_game: self.internet_game.is_our_turn = not self.internet_game.is_our_turn print(f"First move is {valid_move_string}") return True, move.uci() else: self.board.pop() self.board.pop() return False, "" def get_valid_move_cnn(self, frame): board_result = self.detect_state_cnn(frame) move_to_register = self.get_move_to_register() if move_to_register: self.board.push(move_to_register) if self.check_state_cnn(board_result): self.board.pop() return True, move_to_register.uci() else: self.board.pop() return False, "" else: for move in self.board.legal_moves: if move.promotion and move.promotion != chess.QUEEN: continue self.board.push(move) if self.check_state_cnn(board_result): self.board.pop() return True, move.uci() else: self.board.pop() return False, "" def load_hog(self): if os.path.exists(self.save_file): infile = open(self.save_file, 'rb') self.features, self.labels = pickle.load(infile) infile.close() self.knn.train(self.features, cv2.ml.ROW_SAMPLE, self.labels) else: print("You need to play at least 1 game before starting a game from position.") sys.exit(0) def detect_state_hog(self, chessboard_image): chessboard_image = cv2.cvtColor(chessboard_image, cv2.COLOR_BGR2GRAY) chessboard = [[get_square_image(row, column, chessboard_image) for column in range(8)] for row in range(8)] board_hog = [[self.hog.compute(cv2.resize(chessboard[row][column], (64, 64))) for column in range(8)] for row in range(8)] knn_result = [] for row in range(8): knn_row = [] for column in range(8): ret, result, neighbours, dist = self.knn.findNearest(np.array([board_hog[row][column]]), k=3) knn_row.append(result[0][0]) knn_result.append(knn_row) board_state = [[knn_result[row][column] > 0.5 for column in range(8)] for row in range(8)] return board_state def get_valid_move_hog(self, fgmask, frame): board = [[self.board_basics.get_square_image(row, column, fgmask).mean() for column in range(8)] for row in range(8)] potential_squares = [] square_scores = {} for row in range(8): for column in range(8): score = board[row][column] if score < 10.0: continue square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) potential_squares.append(square) square_scores[square] = score move_to_register = self.get_move_to_register() potential_moves = [] board_result = self.detect_state_hog(frame) if move_to_register: if (move_to_register.from_square in potential_squares) and ( move_to_register.to_square in potential_squares): self.board.push(move_to_register) if self.check_state_hog(board_result): self.board.pop() return True, move_to_register.uci() else: self.board.pop() return False, "" else: for move in self.board.legal_moves: if (move.from_square in potential_squares) and (move.to_square in potential_squares): if move.promotion and move.promotion != chess.QUEEN: continue self.board.push(move) if self.check_state_hog(board_result): self.board.pop() total_score = square_scores[move.from_square] + square_scores[move.to_square] potential_moves.append((total_score, move.uci())) else: self.board.pop() if potential_moves: return True, max(potential_moves)[1] else: return False, "" def get_move_to_register(self): if self.commentator: if len(self.executed_moves) < len(self.commentator.game_state.registered_moves): return self.commentator.game_state.registered_moves[len(self.executed_moves)] else: return None else: return None def is_light_change(self, frame): state = False if self.roi_mask is not None: result = detect_state(frame, self.board_basics.d[0], self.roi_mask) result_hog = self.detect_state_hog(frame) state = self.check_state_for_light(result, result_hog) if state: print("Light change") return True else: result_cnn = self.detect_state_cnn(frame) state_cnn = self.check_state_cnn(result_cnn) if state_cnn: print("Light change cnn") return state_cnn def check_state_hog(self, result): for row in range(8): for column in range(8): square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) if piece and (not result[row][column]): print("Expected piece at " + square_name) return False if (not piece) and (result[row][column]): print("Expected empty at " + square_name) return False return True def check_state_for_move(self, result): for row in range(8): for column in range(8): square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) if piece and (True not in result[row][column]): print("Expected piece at " + square_name) return False if (not piece) and (False not in result[row][column]): print("Expected empty at " + square_name) return False return True def check_state_for_light(self, result, result_hog): for row in range(8): for column in range(8): if len(result[row][column]) > 1: result[row][column] = [result_hog[row][column]] square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) if piece and (False in result[row][column]): print(square_name) return False if (not piece) and (True in result[row][column]): print(square_name) return False return True def get_valid_move_canny(self, fgmask, frame): if self.roi_mask is None: return False, "" board = [[self.board_basics.get_square_image(row, column, fgmask).mean() for column in range(8)] for row in range(8)] potential_squares = [] square_scores = {} for row in range(8): for column in range(8): score = board[row][column] if score < 10.0: continue square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) potential_squares.append(square) square_scores[square] = score move_to_register = self.get_move_to_register() potential_moves = [] board_result = detect_state(frame, self.board_basics.d[0], self.roi_mask) if move_to_register: if (move_to_register.from_square in potential_squares) and ( move_to_register.to_square in potential_squares): self.board.push(move_to_register) if self.check_state_for_move(board_result): self.board.pop() return True, move_to_register.uci() else: self.board.pop() return False, "" else: for move in self.board.legal_moves: if (move.from_square in potential_squares) and (move.to_square in potential_squares): if move.promotion and move.promotion != chess.QUEEN: continue self.board.push(move) if self.check_state_for_move(board_result): self.board.pop() total_score = square_scores[move.from_square] + square_scores[move.to_square] potential_moves.append((total_score, move.uci())) else: self.board.pop() if potential_moves: return True, max(potential_moves)[1] else: return False, "" def register_move(self, fgmask, previous_frame, next_frame): success, valid_move_string = self.get_valid_2_move_cnn(next_frame) if not success: success, valid_move_string = self.get_valid_move_cnn(next_frame) if not success: potential_squares, potential_moves = self.board_basics.get_potential_moves(fgmask, previous_frame, next_frame, self.board) success, valid_move_string = self.get_valid_move(potential_squares, potential_moves) if not success: success, valid_move_string = self.get_valid_move_canny(fgmask, next_frame) if not success: success, valid_move_string = self.get_valid_move_hog(fgmask, next_frame) if not success: self.speech_thread.put_text(self.language.move_failed) print(self.board.fen()) return False else: print("Valid move string 5:" + valid_move_string) else: print("Valid move string 4:" + valid_move_string) else: print("Valid move string 3:" + valid_move_string) else: print("Valid move string 2:" + valid_move_string) else: print("Valid move string 1:" + valid_move_string) valid_move_UCI = chess.Move.from_uci(valid_move_string) print("Move has been registered") if self.internet_game.is_our_turn or self.make_opponent: self.internet_game.move(valid_move_UCI) self.played_moves.append(valid_move_UCI) while self.commentator: time.sleep(0.1) move_to_register = self.get_move_to_register() if move_to_register: valid_move_UCI = move_to_register break else: self.speech_thread.put_text(valid_move_string[:4]) self.played_moves.append(valid_move_UCI) self.executed_moves.append(self.board.san(valid_move_UCI)) is_capture = self.board.is_capture(valid_move_UCI) color = int(self.board.turn) self.board.push(valid_move_UCI) self.internet_game.is_our_turn = not self.internet_game.is_our_turn self.learn(next_frame) self.board_basics.update_ssim(previous_frame, next_frame, valid_move_UCI, is_capture, color) return True def learn(self, frame): result = self.detect_state_hog(frame) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) new_pieces = [] new_squares = [] for row in range(8): for column in range(8): square_name = self.board_basics.convert_row_column_to_square_name(row, column) square = chess.parse_square(square_name) piece = self.board.piece_at(square) if piece and (not result[row][column]): print("Learning piece at " + square_name) piece_hog = self.hog.compute(cv2.resize(get_square_image(row, column, frame), (64, 64))) new_pieces.append(piece_hog) if (not piece) and (result[row][column]): print("Learning empty at " + square_name) square_hog = self.hog.compute(cv2.resize(get_square_image(row, column, frame), (64, 64))) new_squares.append(square_hog) labels_pieces = np.ones((len(new_pieces), 1), np.int32) labels_squares = np.zeros((len(new_squares), 1), np.int32) if new_pieces: new_pieces = np.array(new_pieces) self.features = np.float32(np.concatenate((self.features, new_pieces), axis=0)) self.labels = np.concatenate((self.labels, labels_pieces), axis=0) if new_squares: new_squares = np.array(new_squares) self.features = np.float32(np.concatenate((self.features, new_squares), axis=0)) self.labels = np.concatenate((self.labels, labels_squares), axis=0) self.features = self.features[:100] self.labels = self.labels[:100] self.knn = cv2.ml.KNearest_create() self.knn.train(self.features, cv2.ml.ROW_SAMPLE, self.labels) def get_valid_move(self, potential_squares, potential_moves): print("Potential squares") print(potential_squares) print("Potential moves") print(potential_moves) move_to_register = self.get_move_to_register() valid_move_string = "" for score, start, arrival in potential_moves: if valid_move_string: break if move_to_register: if chess.square_name(move_to_register.from_square) != start: continue if chess.square_name(move_to_register.to_square) != arrival: continue uci_move = start + arrival try: move = chess.Move.from_uci(uci_move) except Exception as e: print(e) continue if move in self.board.legal_moves: valid_move_string = uci_move else: if move_to_register: uci_move_promoted = move_to_register.uci() else: uci_move_promoted = uci_move + 'q' promoted_move = chess.Move.from_uci(uci_move_promoted) if promoted_move in self.board.legal_moves: valid_move_string = uci_move_promoted # print("There has been a promotion") potential_squares = [square[1] for square in potential_squares] print(potential_squares) # Detect castling king side with white if ("e1" in potential_squares) and ("h1" in potential_squares) and ("f1" in potential_squares) and ( "g1" in potential_squares) and (chess.Move.from_uci("e1g1") in self.board.legal_moves): valid_move_string = "e1g1" # Detect castling queen side with white if ("e1" in potential_squares) and ("a1" in potential_squares) and ("c1" in potential_squares) and ( "d1" in potential_squares) and (chess.Move.from_uci("e1c1") in self.board.legal_moves): valid_move_string = "e1c1" # Detect castling king side with black if ("e8" in potential_squares) and ("h8" in potential_squares) and ("f8" in potential_squares) and ( "g8" in potential_squares) and (chess.Move.from_uci("e8g8") in self.board.legal_moves): valid_move_string = "e8g8" # Detect castling queen side with black if ("e8" in potential_squares) and ("a8" in potential_squares) and ("c8" in potential_squares) and ( "d8" in potential_squares) and (chess.Move.from_uci("e8c8") in self.board.legal_moves): valid_move_string = "e8c8" if move_to_register and (move_to_register.uci() != valid_move_string): return False, valid_move_string if valid_move_string: return True, valid_move_string else: return False, valid_move_string ================================================ FILE: gui.py ================================================ import tkinter as tk from tkinter.simpledialog import askstring from tkinter import messagebox import subprocess import sys from threading import Thread import pickle import os running_process = None token = "" def lichess(): global token new_token = askstring("Lichess API Access Token", "Please enter your Lichess API Access Token below.", initialvalue=token) if new_token is None: pass else: token = new_token def on_closing(): if running_process: if running_process.poll() is None: running_process.terminate() save_settings() window.destroy() def log_process(process, finish_message): global button_frame button_stop = tk.Button(button_frame, text="Stop", command=stop_process) button_stop.grid(row=0, column=0, columnspan=3, sticky="ew") while True: output = process.stdout.readline() if output: logs_text.insert(tk.END, output.decode()) if process.poll() is not None: logs_text.insert(tk.END, finish_message) break global start, board start = tk.Button(button_frame, text="Start Game", command=start_game) start.grid(row=0, column=0) board = tk.Button(button_frame, text="Board Calibration", command=board_calibration) board.grid(row=0, column=1) diagnostic_button = tk.Button(button_frame, text="Diagnostic", command=diagnostic) diagnostic_button.grid(row=0, column=2) if promotion_menu.cget("state") == "normal": promotion.set(PROMOTION_OPTIONS[0]) promotion_menu.configure(state="disabled") def stop_process(ignore=None): if running_process: if running_process.poll() is None: running_process.terminate() def diagnostic(ignore=None): arguments = [sys.executable, "diagnostic.py"] # arguments = ["diagnostic.exe"] # working_directory = sys.argv[0][:-3] # arguments = [working_directory+"diagnostic"] selected_camera = camera.get() if selected_camera != OPTIONS[0]: cap_index = OPTIONS.index(selected_camera) - 1 arguments.append("cap=" + str(cap_index)) selected_resolution = resolution.get() if selected_resolution != RESOLUTION_OPTIONS[0]: width, height = selected_resolution.split(" x ") arguments.append(f"width={width}") arguments.append(f"height={height}") selected_fps = fps.get() if selected_fps != FPS_OPTIONS[0]: arguments.append(f"fps={selected_fps}") if calibration_mode.get() == CALIBRATION_OPTIONS[-1]: arguments.append("calibrate") process = subprocess.Popen(arguments, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) # startupinfo = subprocess.STARTUPINFO() # startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, # stderr=subprocess.STDOUT, stdin=subprocess.PIPE, startupinfo=startupinfo) # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, # stderr=subprocess.STDOUT, cwd=working_directory) global running_process running_process = process log_thread = Thread(target=log_process, args=(process, "Diagnostic finished.\n")) log_thread.daemon = True log_thread.start() def board_calibration(ignore=None): if calibration_mode.get() == CALIBRATION_OPTIONS[-1]: messagebox.showinfo( "Board Calibration Not Required", "Calibration is not necessary for this mode. " "You can proceed directly without calibration." ) return arguments = [sys.executable, "board_calibration.py", "show-info"] # arguments = ["board_calibration.exe", "show-info"] # working_directory = sys.argv[0][:-3] # arguments = [working_directory+"board_calibration", "show-info"] selected_camera = camera.get() if selected_camera != OPTIONS[0]: cap_index = OPTIONS.index(selected_camera) - 1 arguments.append("cap=" + str(cap_index)) selected_resolution = resolution.get() if selected_resolution != RESOLUTION_OPTIONS[0]: width, height = selected_resolution.split(" x ") arguments.append(f"width={width}") arguments.append(f"height={height}") selected_fps = fps.get() if selected_fps != FPS_OPTIONS[0]: arguments.append(f"fps={selected_fps}") if calibration_mode.get() == CALIBRATION_OPTIONS[1]: arguments.append("ml") process = subprocess.Popen(arguments, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) # startupinfo = subprocess.STARTUPINFO() # startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, # stderr=subprocess.STDOUT, stdin=subprocess.PIPE, startupinfo=startupinfo) # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, # stderr=subprocess.STDOUT, cwd=working_directory) global running_process running_process = process log_thread = Thread(target=log_process, args=(process, "Board calibration finished.\n")) log_thread.daemon = True log_thread.start() def start_game(ignore=None): arguments = [sys.executable, "main.py"] # arguments = ["main.exe"] # working_directory = sys.argv[0][:-3] # arguments = [working_directory+"main"] if no_template.get(): arguments.append("no-template") if make_opponent.get(): arguments.append("make-opponent") if comment_me.get(): arguments.append("comment-me") if comment_opponent.get(): arguments.append("comment-opponent") if drag_drop.get(): arguments.append("drag") global token if token: arguments.append("token=" + token) promotion_menu.configure(state="normal") promotion.set(PROMOTION_OPTIONS[0]) arguments.append("delay=" + str(values.index(default_value.get()))) selected_camera = camera.get() if selected_camera != OPTIONS[0]: cap_index = OPTIONS.index(selected_camera) - 1 arguments.append("cap=" + str(cap_index)) selected_resolution = resolution.get() if selected_resolution != RESOLUTION_OPTIONS[0]: width, height = selected_resolution.split(" x ") arguments.append(f"width={width}") arguments.append(f"height={height}") selected_fps = fps.get() if selected_fps != FPS_OPTIONS[0]: arguments.append(f"fps={selected_fps}") selected_voice = voice.get() if selected_voice != VOICE_OPTIONS[0]: voice_index = VOICE_OPTIONS.index(selected_voice) - 1 arguments.append("voice=" + str(voice_index)) language = "English" languages = ["English", "German", "Russian", "Turkish", "Italian", "French"] codes = ["en_", "de_", "ru_", "tr_", "it_", "fr_"] for l, c in zip(languages, codes): if (l in selected_voice) or (l.lower() in selected_voice) or (c in selected_voice): language = l break arguments.append("lang=" + language) if calibration_mode.get() == CALIBRATION_OPTIONS[-1]: arguments.append("calibrate") process = subprocess.Popen(arguments, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) # startupinfo = subprocess.STARTUPINFO() # startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, # stderr=subprocess.STDOUT, stdin=subprocess.PIPE, startupinfo=startupinfo) # process = subprocess.Popen(arguments, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, cwd=working_directory) global running_process running_process = process log_thread = Thread(target=log_process, args=(process, "Game finished.\n")) log_thread.daemon = True log_thread.start() window = tk.Tk() window.title("Play online chess with a real chess board by Alper Karayaman") menu_bar = tk.Menu(window) connection = tk.Menu(menu_bar, tearoff=False) connection.add_command(label="Lichess", command=lichess) menu_bar.add_cascade(label="Connection", menu=connection) window.config(menu=menu_bar) no_template = tk.IntVar() make_opponent = tk.IntVar() drag_drop = tk.IntVar() comment_me = tk.IntVar() comment_opponent = tk.IntVar() menu_frame = tk.Frame(window) menu_frame.grid(row=0, column=0, columnspan=2, sticky="W") camera = tk.StringVar() OPTIONS = ["Default"] try: import platform platform_name = platform.system() if platform_name == "Darwin": cmd = 'system_profiler SPCameraDataType | grep "^ [^ ]" | sed "s/ //" | sed "s/://"' result = subprocess.check_output(cmd, shell=True) result = result.decode() result = [r for r in result.split("\n") if r] OPTIONS.extend(result) elif platform_name == "Linux": cmd = 'for I in /sys/class/video4linux/*; do cat $I/name; done' result = subprocess.check_output(cmd, shell=True) result = result.decode() result = [r for r in result.split("\n") if r] OPTIONS.extend(result) else: from pygrabber.dshow_graph import FilterGraph OPTIONS.extend(FilterGraph().get_input_devices()) except: pass camera.set(OPTIONS[0]) label = tk.Label(menu_frame, text='Select Webcam:') label.grid(column=0, row=0, sticky=tk.W) menu = tk.OptionMenu(menu_frame, camera, *OPTIONS) menu.config(width=max(len(option) for option in OPTIONS), anchor="w") menu.grid(column=1, row=0, sticky=tk.W) resolution_frame = tk.Frame(window) resolution_frame.grid(row=1, column=0, columnspan=2, sticky="W") resolution = tk.StringVar() RESOLUTION_OPTIONS = ["Default", "640 x 480", "1280 x 720", "1920 x 1080", "2560 x 1440", "3840 x 2160"] resolution.set(RESOLUTION_OPTIONS[0]) resolution_label = tk.Label(resolution_frame, text='Select Webcam Resolution:') resolution_label.grid(column=0, row=0, sticky=tk.W) resolution_menu = tk.OptionMenu(resolution_frame, resolution, *RESOLUTION_OPTIONS) resolution_menu.config(width=max(len(option) for option in RESOLUTION_OPTIONS), anchor="w") resolution_menu.grid(column=1, row=0, sticky=tk.W) fps_frame = tk.Frame(window) fps_frame.grid(row=2, column=0, columnspan=2, sticky="W") fps = tk.StringVar() FPS_OPTIONS = ["Default", "15", "24", "30", "60", "120", "144", "240"] fps.set(FPS_OPTIONS[0]) fps_label = tk.Label(fps_frame, text='Select Webcam FPS:') fps_label.grid(column=0, row=0, sticky=tk.W) fps_menu = tk.OptionMenu(fps_frame, fps, *FPS_OPTIONS) fps_menu.config(width=max(len(option) for option in FPS_OPTIONS), anchor="w") fps_menu.grid(column=1, row=0, sticky=tk.W) calibration_frame = tk.Frame(window) calibration_frame.grid(row=3, column=0, columnspan=2, sticky="W") calibration_mode = tk.StringVar() CALIBRATION_OPTIONS = ["The board is empty.", "Pieces are in their starting positions.", "Just before the game starts."] calibration_mode.set(CALIBRATION_OPTIONS[0]) calibration_label = tk.Label(calibration_frame, text='Board Calibration Mode:') calibration_label.grid(column=0, row=0, sticky=tk.W) calibration_menu = tk.OptionMenu(calibration_frame, calibration_mode, *CALIBRATION_OPTIONS) calibration_menu.config(width=max(len(option) for option in CALIBRATION_OPTIONS), anchor="w") calibration_menu.grid(column=1, row=0, sticky=tk.W) voice_frame = tk.Frame(window) voice_frame.grid(row=4, column=0, columnspan=2, sticky="W") voice = tk.StringVar() VOICE_OPTIONS = ["Default"] try: import platform if platform.system() == "Darwin": result = subprocess.run(['say', '-v', '?'], stdout=subprocess.PIPE) output = result.stdout.decode('utf-8') for line in output.splitlines(): if line: voice_info = line.split() VOICE_OPTIONS.append(f'{voice_info[0]} {voice_info[1]}') else: import pyttsx3 engine = pyttsx3.init() for v in engine.getProperty('voices'): VOICE_OPTIONS.append(v.name) except: pass voice.set(VOICE_OPTIONS[0]) voice_label = tk.Label(voice_frame, text='Select Voice:') voice_label.grid(column=0, row=0, sticky=tk.W) voice_menu = tk.OptionMenu(voice_frame, voice, *VOICE_OPTIONS) voice_menu.config(width=max(len(option) for option in VOICE_OPTIONS), anchor="w") voice_menu.grid(column=1, row=0, sticky=tk.W) def save_promotion(*args): outfile = open("promotion.bin", 'wb') pickle.dump(promotion.get(), outfile) outfile.close() promotion_frame = tk.Frame(window) promotion_frame.grid(row=5, column=0, columnspan=2, sticky="W") promotion = tk.StringVar() promotion.trace("w", save_promotion) PROMOTION_OPTIONS = ["Queen", "Knight", "Rook", "Bishop"] promotion.set(PROMOTION_OPTIONS[0]) promotion_label = tk.Label(promotion_frame, text='Select Promotion Piece:') promotion_label.grid(column=0, row=0, sticky=tk.W) promotion_menu = tk.OptionMenu(promotion_frame, promotion, *PROMOTION_OPTIONS) promotion_menu.config(width=max(len(option) for option in PROMOTION_OPTIONS), anchor="w") promotion_menu.grid(column=1, row=0, sticky=tk.W) promotion_menu.configure(state="disabled") c = tk.Checkbutton(window, text="Find chess board of online game without template images.", variable=no_template) c.grid(row=6, column=0, sticky="W", columnspan=1) c1 = tk.Checkbutton(window, text="Make moves of opponent too.", variable=make_opponent) c1.grid(row=7, column=0, sticky="W", columnspan=1) c2 = tk.Checkbutton(window, text="Make moves by drag and drop.", variable=drag_drop) c2.grid(row=8, column=0, sticky="W", columnspan=1) c2 = tk.Checkbutton(window, text="Speak my moves.", variable=comment_me) c2.grid(row=9, column=0, sticky="W", columnspan=1) c3 = tk.Checkbutton(window, text="Speak opponent's moves.", variable=comment_opponent) c3.grid(row=10, column=0, sticky="W", columnspan=1) values = ["Do not delay game start.", "1 second delayed game start."] + [str(i) + " seconds delayed game start." for i in range(2, 6)] default_value = tk.StringVar() s = tk.Spinbox(window, values=values, textvariable=default_value, width=max(len(value) for value in values)) default_value.set(values[-1]) s.grid(row=11, column=0, sticky="W", columnspan=2) button_frame = tk.Frame(window) button_frame.grid(row=12, column=0, columnspan=2, sticky="W") start = tk.Button(button_frame, text="Start Game", command=start_game) start.grid(row=0, column=0) board = tk.Button(button_frame, text="Board Calibration", command=board_calibration) board.grid(row=0, column=1) diagnostic_button = tk.Button(button_frame, text="Diagnostic", command=diagnostic) diagnostic_button.grid(row=0, column=2) text_frame = tk.Frame(window) text_frame.grid(row=13, column=0) scroll_bar = tk.Scrollbar(text_frame) logs_text = tk.Text(text_frame, background='gray', yscrollcommand=scroll_bar.set) scroll_bar.config(command=logs_text.yview) scroll_bar.pack(side=tk.RIGHT, fill=tk.Y) logs_text.pack(side="left") fields = [no_template, make_opponent, comment_me, comment_opponent, calibration_mode, resolution, fps, drag_drop, default_value, camera, voice] save_file = 'gui.bin' def save_settings(): outfile = open(save_file, 'wb') pickle.dump([field.get() for field in fields] + [token], outfile) outfile.close() def load_settings(): if os.path.exists(save_file): infile = open(save_file, 'rb') variables = pickle.load(infile) infile.close() global token token = variables[-1] if variables[-2] in VOICE_OPTIONS: voice.set(variables[-2]) if variables[-3] in OPTIONS: camera.set(variables[-3]) for i in range(9): fields[i].set(variables[i]) load_settings() window.protocol("WM_DELETE_WINDOW", on_closing) window.mainloop() ================================================ FILE: helper.py ================================================ import cv2 import numpy as np from math import sqrt def euclidean_distance(first, second): return sqrt((first[0] - second[0]) ** 2 + (first[1] - second[1]) ** 2) def perspective_transform(image, pts1): dimension = 480 pts2 = np.float32([[0, 0], [0, dimension], [dimension, 0], [dimension, dimension]]) M = cv2.getPerspectiveTransform(pts1, pts2) dst = cv2.warpPerspective(image, M, (dimension, dimension)) return dst def rotateMatrix(matrix): size = len(matrix) for row in range(size // 2): for column in range(row, size - row - 1): temp = matrix[row][column] matrix[row][column] = matrix[column][size - 1 - row] matrix[column][size - 1 - row] = matrix[size - 1 - row][size - 1 - column] matrix[size - 1 - row][size - 1 - column] = matrix[size - 1 - column][row] matrix[size - 1 - column][row] = temp def auto_canny(image): sigma_upper = 0.2 sigma_lower = 0.8 median_intensity = np.median(image) lower = int(max(0, (1.0 - sigma_lower) * median_intensity)) upper = int(min(255, (1.0 + sigma_upper) * median_intensity)) edged = cv2.Canny(image, lower, upper) return edged def edge_detection(frame): kernel = np.ones((3, 3), np.uint8) clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) edges = [] for gray in cv2.split(frame): gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel) gray = clahe.apply(gray) gray = cv2.GaussianBlur(gray, (3, 3), 0) edge = auto_canny(gray) edges.append(edge) edges = cv2.bitwise_or(cv2.bitwise_or(edges[0], edges[1]), edges[2]) kernel2 = np.ones((3, 3), np.uint8) edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel2) return edges def get_square_image(row, column, board_img): height, width = board_img.shape[:2] minX = int(column * width / 8) maxX = int((column + 1) * width / 8) minY = int(row * height / 8) maxY = int((row + 1) * height / 8) square = board_img[minY:maxY, minX:maxX] square_without_borders = square[3:-3, 3:-3] return square_without_borders def contains_piece(square, view): height, width = square.shape[:2] if view == (0, -1): half = square[:, width // 2:] elif view == (0, 1): half = square[:, :width // 2] elif view == (1, 0): half = square[height // 2:, :] elif view == (-1, 0): half = square[:height // 2, :] if half.mean() < 1.0: return [False] elif square.mean() > 15.0: return [True] elif square.mean() > 6.0: return [True, False] else: if square.mean() > 2.0: print("empty " + str(square.mean())) return [False] def detect_state(frame, view, roi_mask): edges = edge_detection(frame) edges = cv2.bitwise_and(edges, roi_mask) # cv2.imwrite("edge.jpg", edges) board_image = [[get_square_image(row, column, edges) for column in range(8)] for row in range(8)] result = [[contains_piece(board_image[row][column], view) for column in range(8)] for row in range(8)] return result def predict(image, model): image = cv2.resize(image, (64, 64)) image = image.astype(np.float32) / 255.0 image = np.transpose(image, (2, 0, 1)) image = np.expand_dims(image, axis=0) # Make a forward pass through the network model.setInput(image) output = model.forward() # Get the predicted class label label = np.argmax(output) return label ================================================ FILE: internet_game.py ================================================ import chessboard_detection import pyautogui import time class Internet_game: def __init__(self, use_template, start_delay, drag_drop): self.drag_drop = drag_drop time.sleep(start_delay) if use_template: self.position, self.we_play_white = chessboard_detection.find_chessboard() else: self.position, self.we_play_white = chessboard_detection.auto_find_chessboard() self.is_our_turn = self.we_play_white def move(self, move): move_string = move.uci() origin_square = move_string[0:2] destination_square = move_string[2:4] centerXOrigin, centerYOrigin = self.get_square_center(origin_square) centerXDest, centerYDest = self.get_square_center(destination_square) if self.drag_drop: pyautogui.moveTo(centerXOrigin, centerYOrigin, 0.01) pyautogui.dragTo(centerXOrigin, centerYOrigin + 1, button='left', duration=0.01) pyautogui.dragTo(centerXDest, centerYDest, button='left', duration=0.3) else: pyautogui.click(centerXOrigin, centerYOrigin, duration=0.1) pyautogui.click(centerXDest, centerYDest, duration=0.1) print("Done playing move", origin_square, destination_square) return def get_square_center(self, square_name): row, column = self.convert_square_name_to_row_column(square_name, self.we_play_white) position = self.position centerX = int(position.minX + (column + 0.5) * (position.maxX - position.minX) / 8) centerY = int(position.minY + (row + 0.5) * (position.maxY - position.minY) / 8) return centerX, centerY def convert_square_name_to_row_column(self, square_name, is_white_on_bottom): for row in range(8): for column in range(8): this_square_name = self.convert_row_column_to_square_name(row, column, is_white_on_bottom) if this_square_name == square_name: return row, column return 0, 0 def convert_row_column_to_square_name(self, row, column, is_white_on_bottom): if is_white_on_bottom == True: number = repr(8 - row) letter = str(chr(97 + column)) return letter + number else: number = repr(row + 1) letter = str(chr(97 + (7 - column))) return letter + number ================================================ FILE: languages.py ================================================ import chess class English: def __init__(self): self.game_started = "Game started" self.move_failed = "Move registration failed. Please redo your move." def name(self, piece_type): if piece_type == chess.PAWN: return "pawn" elif piece_type == chess.KNIGHT: return "knight" elif piece_type == chess.BISHOP: return "bishop" elif piece_type == chess.ROOK: return "rook" elif piece_type == chess.QUEEN: return "queen" elif piece_type == chess.KING: return "king" def comment(self, board, move): check = "" if board.is_checkmate(): check = " checkmate" elif board.is_check(): check = " check" board.pop() if board.is_kingside_castling(move): board.push(move) return "castling short" + check if board.is_queenside_castling(move): board.push(move) return "castling long" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" comment += self.name(piece.piece_type) comment += " " + from_square comment += " takes" if is_capture else " to" comment += " " + to_square if promotion: comment += " promotion to " + self.name(promotion) comment += check return comment class German: def __init__(self): self.game_started = "Das Spiel hat gestartet." self.move_failed = "Der Zug ist ungültig, bitte wiederholen." def name(self, piece_type): if piece_type == chess.PAWN: return "Bauer" elif piece_type == chess.KNIGHT: return "Springer" elif piece_type == chess.BISHOP: return "Läufer" elif piece_type == chess.ROOK: return "Turm" elif piece_type == chess.QUEEN: return "Dame" elif piece_type == chess.KING: return "König" def comment(self, board, move): check = "" if board.is_checkmate(): check = " Schachmatt" elif board.is_check(): check = " Schach" board.pop() if board.is_kingside_castling(move): board.push(move) return "kurze Rochade" + check if board.is_queenside_castling(move): board.push(move) return "lange Rochade" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" comment += self.name(piece.piece_type) comment += " " + from_square comment += " schlägt" if is_capture else " nach" comment += " " + to_square if promotion: comment += " Umwandlung in " + self.name(promotion) comment += check return comment class Russian: def __init__(self): self.game_started = "игра началась" self.move_failed = "Ошибка регистрации хода. Пожалуйста, повторите свой ход" def name(self, piece_type): if piece_type == chess.PAWN: return "пешка" elif piece_type == chess.KNIGHT: return "конь" elif piece_type == chess.BISHOP: return "слон" elif piece_type == chess.ROOK: return "ладья" elif piece_type == chess.QUEEN: return "ферзь" elif piece_type == chess.KING: return "король" def comment(self, board, move): check = "" if board.is_checkmate(): check = " шах и мат" elif board.is_check(): check = " шах" board.pop() if board.is_kingside_castling(move): board.push(move) return "короткая рокировка" + check if board.is_queenside_castling(move): board.push(move) return "длинная рокировка" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" comment += self.name(piece.piece_type) comment += " " + from_square comment += " бьёт" if is_capture else "" comment += " " + to_square if promotion: comment += " превращение в " + self.name(promotion) comment += check return comment class Turkish: def __init__(self): self.game_started = "Oyun başladı." self.move_failed = "Hamle geçersiz. Lütfen hamlenizi yeniden yapın." def capture_suffix(self, to_square): if to_square[-1] in "158": return "i" elif to_square[-1] in "27": return "yi" elif to_square[-1] in "34": return "ü" else: # 6 return "yı" def from_suffix(self, from_square): if from_square[-1] in "1278": return "den" elif from_square[-1] in "345": return "ten" else: # 6 return "dan" def to_suffix(self, to_square): if to_square[-1] in "13458": return "e" elif to_square[-1] in "27": return "ye" else: # 6 return "ya" def comment(self, board, move): check = "" if board.is_checkmate(): check = " şahmat" elif board.is_check(): check = " şah" board.pop() if board.is_kingside_castling(move): board.push(move) return "kısa rok" + check if board.is_queenside_castling(move): board.push(move) return "uzun rok" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" if piece.piece_type == chess.PAWN: comment += "piyon" elif piece.piece_type == chess.KNIGHT: comment += "at" elif piece.piece_type == chess.BISHOP: comment += "fil" elif piece.piece_type == chess.ROOK: comment += "kale" elif piece.piece_type == chess.QUEEN: comment += "vezir" elif piece.piece_type == chess.KING: comment += "şah" comment += " " + from_square if is_capture: comment += " alır" comment += " " + to_square + "'" + self.capture_suffix(to_square) else: comment += "'" + self.from_suffix(from_square) + " " + to_square + "'" + self.to_suffix(to_square) if promotion: comment += " " if promotion == chess.KNIGHT: comment += "ata" elif promotion == chess.BISHOP: comment += "file" elif promotion == chess.ROOK: comment += "kaleye" elif promotion == chess.QUEEN: comment += "vezire" comment += " terfi" comment += check return comment class Italian: def __init__(self): self.game_started = "Gioco iniziato" self.move_failed = "Registrazione spostamento non riuscita. Per favore rifai la tua mossa." def name(self, piece_type): if piece_type == chess.PAWN: return "pedone" elif piece_type == chess.KNIGHT: return "cavallo" elif piece_type == chess.BISHOP: return "alfiere" elif piece_type == chess.ROOK: return "torre" elif piece_type == chess.QUEEN: return "regina" elif piece_type == chess.KING: return "re" def prefix_name(self, piece_type): if piece_type == chess.PAWN: return "il pedone" elif piece_type == chess.KNIGHT: return "il cavallo" elif piece_type == chess.BISHOP: return "l'alfiere" elif piece_type == chess.ROOK: return "la torre" elif piece_type == chess.QUEEN: return "la regina" elif piece_type == chess.KING: return "il re" def comment(self, board, move): check = "" if board.is_checkmate(): check = " scacco matto" elif board.is_check(): check = " scacco" board.pop() if board.is_kingside_castling(move): board.push(move) return "arrocco corto" + check if board.is_queenside_castling(move): board.push(move) return "arrocco lungo" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" if is_capture: comment += self.prefix_name(piece.piece_type) comment += " " + from_square comment += " cattura" comment += " " + to_square else: comment += self.name(piece.piece_type) comment += " da " + from_square comment += " a " + to_square if promotion: comment += " promuove a " + self.name(promotion) comment += check return comment class French: def __init__(self): self.game_started = "Partie démarrée" self.move_failed = "La reconnaissance a échoué. Veuillez réessayer." def name(self, piece_type): if piece_type == chess.PAWN: return "pion" elif piece_type == chess.KNIGHT: return "cavalier" elif piece_type == chess.BISHOP: return "fou" elif piece_type == chess.ROOK: return "tour" elif piece_type == chess.QUEEN: return "reine" elif piece_type == chess.KING: return "roi" def comment(self, board, move): check = "" if board.is_checkmate(): check = " échec et mat" elif board.is_check(): check = " échec" board.pop() if board.is_kingside_castling(move): board.push(move) return "petit roc" + check if board.is_queenside_castling(move): board.push(move) return "grand roc" + check piece = board.piece_at(move.from_square) from_square = chess.square_name(move.from_square) to_square = chess.square_name(move.to_square) promotion = move.promotion is_capture = board.is_capture(move) board.push(move) comment = "" comment += self.name(piece.piece_type) comment += " " + from_square comment += " prend" if is_capture else " vers" comment += " " + to_square if promotion: comment += " promu en " + self.name(promotion) comment += check return comment ================================================ FILE: lichess_commentator.py ================================================ from threading import Thread import chess class Lichess_commentator(Thread): def __init__(self, *args, **kwargs): super(Lichess_commentator, self).__init__(*args, **kwargs) self.stream = None self.speech_thread = None self.game_state = Game_state() self.comment_me = None self.comment_opponent = None self.language = None def run(self): while not self.game_state.board.is_game_over(): is_my_turn = (self.game_state.we_play_white) == (self.game_state.board.turn == chess.WHITE) found_move, move = self.game_state.register_move_if_needed(self.stream) if found_move and ((self.comment_me and is_my_turn) or (self.comment_opponent and (not is_my_turn))): self.speech_thread.put_text(self.language.comment(self.game_state.board, move)) class Game_state: def __init__(self): self.we_play_white = None self.board = chess.Board() self.registered_moves = [] self.resign_or_draw = False self.game = None self.variant = 'wait' def register_move_if_needed(self, stream): current_state = next(stream) if 'state' in current_state: if current_state['initialFen'] == 'startpos': self.variant = 'standard' else: self.variant = 'fromPosition' self.from_position(current_state['initialFen']) current_state = current_state['state'] if 'moves' in current_state: moves = current_state['moves'].split() if len(moves) > len(self.registered_moves): valid_move_string = moves[len(self.registered_moves)] valid_move_UCI = chess.Move.from_uci(valid_move_string) self.register_move(valid_move_UCI) return True, valid_move_UCI while len(moves) < len(self.registered_moves): self.unregister_move() if 'status' in current_state and current_state['status'] in ["resign", "draw"]: self.resign_or_draw = True return False, "No move found" def register_move(self, move): if move in self.board.legal_moves: self.board.push(move) self.registered_moves.append(move) return True else: return False def unregister_move(self): self.board.pop() self.registered_moves.pop() if len(self.registered_moves) < len(self.game.executed_moves): self.game.executed_moves.pop() self.game.played_moves.pop() self.game.board.pop() self.game.internet_game.is_our_turn = not self.game.internet_game.is_our_turn def from_position(self, fen): self.board = chess.Board(fen) self.game.board = chess.Board(fen) if self.board.turn == chess.BLACK: self.game.internet_game.is_our_turn = not self.game.internet_game.is_our_turn ================================================ FILE: lichess_game.py ================================================ import berserk import sys import os import chess import pickle class Lichess_game: def __init__(self, token): session = berserk.TokenSession(token) client = berserk.Client(session) games = client.games.get_ongoing() if len(games) == 0: print("No games found. Please create your game on Lichess.") sys.exit(0) if len(games) > 1: print("Multiple games found. Please make sure there is only one ongoing game on Lichess.") sys.exit(0) game = games[0] self.we_play_white = game['color'] == 'white' self.is_our_turn = self.we_play_white self.client = client self.game_id = game['gameId'] self.token = token self.save_file = "promotion.bin" self.promotion_pieces = { "Queen": chess.QUEEN, "Knight": chess.KNIGHT, "Rook": chess.ROOK, "Bishop": chess.BISHOP } def move(self, move): if move.promotion and os.path.exists(self.save_file): infile = open(self.save_file, 'rb') piece_name = pickle.load(infile) infile.close() move.promotion = self.promotion_pieces[piece_name] move_string = move.uci() try: self.client.board.make_move(self.game_id, move_string) except: session = berserk.TokenSession(self.token) self.client = berserk.Client(session) self.client.board.make_move(self.game_id, move_string) print("Reconnected to Lichess.") print("Done playing move " + move_string) ================================================ FILE: main.py ================================================ import time import cv2 import pickle import numpy as np import sys from collections import deque import platform from board_calibration_machine_learning import detect_board from game import Game from board_basics import Board_basics from helper import perspective_transform from speech import Speech_thread from videocapture import Video_capture_thread from languages import * webcam_width = None webcam_height = None fps = None use_template = True make_opponent = False drag_drop = False comment_me = False comment_opponent = False calibrate = False start_delay = 5 # seconds cap_index = 0 cap_api = cv2.CAP_ANY voice_index = 0 language = English() token = "" for argument in sys.argv: if argument == "no-template": use_template = False elif argument == "make-opponent": make_opponent = True elif argument == "comment-me": comment_me = True elif argument == "comment-opponent": comment_opponent = True elif argument.startswith("delay="): start_delay = int("".join(c for c in argument if c.isdigit())) elif argument == "drag": drag_drop = True elif argument.startswith("cap="): cap_index = int("".join(c for c in argument if c.isdigit())) platform_name = platform.system() if platform_name == "Darwin": cap_api = cv2.CAP_AVFOUNDATION elif platform_name == "Linux": cap_api = cv2.CAP_V4L2 else: cap_api = cv2.CAP_DSHOW elif argument.startswith("voice="): voice_index = int("".join(c for c in argument if c.isdigit())) elif argument.startswith("lang="): if "German" in argument: language = German() elif "Russian" in argument: language = Russian() elif "Turkish" in argument: language = Turkish() elif "Italian" in argument: language = Italian() elif "French" in argument: language = French() elif argument.startswith("token="): token = argument[len("token="):].strip() elif argument == "calibrate": calibrate = True elif argument.startswith("width="): webcam_width = int(argument[len("width="):]) elif argument.startswith("height="): webcam_height = int(argument[len("height="):]) elif argument.startswith("fps="): fps = int(argument[len("fps="):]) MOTION_START_THRESHOLD = 1.0 HISTORY = 100 MAX_MOVE_MEAN = 50 COUNTER_MAX_VALUE = 3 move_fgbg = cv2.createBackgroundSubtractorKNN() motion_fgbg = cv2.createBackgroundSubtractorKNN(history=HISTORY) video_capture_thread = Video_capture_thread() video_capture_thread.daemon = True video_capture_thread.capture = cv2.VideoCapture(cap_index, cap_api) if webcam_width is not None: video_capture_thread.capture.set(cv2.CAP_PROP_FRAME_WIDTH, webcam_width) if webcam_height is not None: video_capture_thread.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, webcam_height) if fps is not None: video_capture_thread.capture.set(cv2.CAP_PROP_FPS, fps) if calibrate: corner_model = cv2.dnn.readNetFromONNX("yolo_corner.onnx") piece_model = cv2.dnn.readNetFromONNX("cnn_piece.onnx") color_model = cv2.dnn.readNetFromONNX("cnn_color.onnx") for _ in range(10): ret, frame = video_capture_thread.capture.read() if ret == False: print("Error reading frame. Please check your webcam connection.") continue is_detected = False for _ in range(100): ret, frame = video_capture_thread.capture.read() if ret == False: print("Error reading frame. Please check your webcam connection.") continue result = detect_board(frame, corner_model, piece_model, color_model) if result: pts1, side_view_compensation, rotation_count = result roi_mask = None is_detected = True break if not is_detected: print("Could not detect the chess board.") video_capture_thread.capture.release() sys.exit(0) else: filename = 'constants.bin' infile = open(filename, 'rb') calibration_data = pickle.load(infile) infile.close() if calibration_data[0]: pts1, side_view_compensation, rotation_count = calibration_data[1] roi_mask = None else: corners, side_view_compensation, rotation_count, roi_mask = calibration_data[1] pts1 = np.float32([list(corners[0][0]), list(corners[8][0]), list(corners[0][8]), list(corners[8][8])]) video_capture_thread.start() board_basics = Board_basics(side_view_compensation, rotation_count) speech_thread = Speech_thread() speech_thread.daemon = True speech_thread.index = voice_index speech_thread.start() game = Game(board_basics, speech_thread, use_template, make_opponent, start_delay, comment_me, comment_opponent, drag_drop, language, token, roi_mask) def waitUntilMotionCompletes(): counter = 0 while counter < COUNTER_MAX_VALUE: frame = video_capture_thread.get_frame() frame = perspective_transform(frame, pts1) fgmask = motion_fgbg.apply(frame) ret, fgmask = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY) mean = fgmask.mean() if mean < MOTION_START_THRESHOLD: counter += 1 else: counter = 0 def stabilize_background_subtractors(): best_mean = float("inf") counter = 0 while counter < COUNTER_MAX_VALUE: frame = video_capture_thread.get_frame() frame = perspective_transform(frame, pts1) move_fgbg.apply(frame) fgmask = motion_fgbg.apply(frame, learningRate=0.1) ret, fgmask = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY) mean = fgmask.mean() if mean >= best_mean: counter += 1 else: best_mean = mean counter = 0 best_mean = float("inf") counter = 0 while counter < COUNTER_MAX_VALUE: frame = video_capture_thread.get_frame() frame = perspective_transform(frame, pts1) fgmask = move_fgbg.apply(frame, learningRate=0.1) ret, fgmask = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY) motion_fgbg.apply(frame) mean = fgmask.mean() if mean >= best_mean: counter += 1 else: best_mean = mean counter = 0 return frame previous_frame = stabilize_background_subtractors() previous_frame_queue = deque(maxlen=10) previous_frame_queue.append(previous_frame) speech_thread.put_text(language.game_started) game.commentator.start() while game.commentator.game_state.variant == 'wait': time.sleep(0.1) if game.commentator.game_state.variant == 'standard': board_basics.initialize_ssim(previous_frame) game.initialize_hog(previous_frame) else: board_basics.load_ssim() game.load_hog() while not game.board.is_game_over() and not game.commentator.game_state.resign_or_draw: sys.stdout.flush() frame = video_capture_thread.get_frame() frame = perspective_transform(frame, pts1) fgmask = motion_fgbg.apply(frame) ret, fgmask = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY) kernel = np.ones((11, 11), np.uint8) fgmask = cv2.erode(fgmask, kernel, iterations=1) mean = fgmask.mean() if mean > MOTION_START_THRESHOLD: # cv2.imwrite("motion.jpg", fgmask) waitUntilMotionCompletes() frame = video_capture_thread.get_frame() frame = perspective_transform(frame, pts1) fgmask = move_fgbg.apply(frame, learningRate=0.0) if fgmask.mean() >= 10.0: ret, fgmask = cv2.threshold(fgmask, 250, 255, cv2.THRESH_BINARY) # print("Move mean " + str(fgmask.mean())) if fgmask.mean() >= MAX_MOVE_MEAN: fgmask = np.zeros(fgmask.shape, dtype=np.uint8) motion_fgbg.apply(frame) move_fgbg.apply(frame, learningRate=1.0) last_frame = stabilize_background_subtractors() previous_frame = previous_frame_queue[0] if (game.is_light_change(last_frame) == False) and game.register_move(fgmask, previous_frame, last_frame): pass # cv2.imwrite(game.executed_moves[-1] + " frame.jpg", last_frame) # cv2.imwrite(game.executed_moves[-1] + " mask.jpg", fgmask) # cv2.imwrite(game.executed_moves[-1] + " background.jpg", previous_frame) else: pass # import uuid # id = str(uuid.uuid1()) # cv2.imwrite(id+"frame_fail.jpg", last_frame) # cv2.imwrite(id+"mask_fail.jpg", fgmask) # cv2.imwrite(id+"background_fail.jpg", previous_frame) previous_frame_queue = deque(maxlen=10) previous_frame_queue.append(last_frame) else: move_fgbg.apply(frame) previous_frame_queue.append(frame) cv2.destroyAllWindows() time.sleep(2) ================================================ FILE: requirements.txt ================================================ opencv-python python-chess pyautogui mss numpy pyttsx3 scikit-image pygrabber berserk ================================================ FILE: speech.py ================================================ from threading import Thread from queue import Queue import platform import os import subprocess class Speech_thread(Thread): def __init__(self, *args, **kwargs): super(Speech_thread, self).__init__(*args, **kwargs) self.queue = Queue() self.index = None def run(self): if platform.system() == "Darwin": result = subprocess.run(['say', '-v', '?'], stdout=subprocess.PIPE) output = result.stdout.decode('utf-8') voices = [] for line in output.splitlines(): if line: voices.append(line.split()[0]) name = voices[self.index] while True: text = self.queue.get() os.system(f'say -v {name} "{text}"') else: import pyttsx3 while True: engine = pyttsx3.init() voices = engine.getProperty('voices') voice = voices[self.index] engine.setProperty('voice', voice.id) text = self.queue.get() engine.say(text) engine.runAndWait() def put_text(self, text): self.queue.put(text) ================================================ FILE: videocapture.py ================================================ from threading import Thread from queue import Queue class Video_capture_thread(Thread): def __init__(self, *args, **kwargs): super(Video_capture_thread, self).__init__(*args, **kwargs) self.queue = Queue() self.capture = None def run(self): while True: ret, frame = self.capture.read() if ret == False: continue self.queue.put(frame) def get_frame(self): return self.queue.get() ================================================ FILE: yolo_corner.onnx ================================================ [File too large to display: 10.1 MB]