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
================================================
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
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:
## 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.
<img src="https://github.com/karayaman/Play-online-chess-with-real-chess-board/raw/main/board_detection_result.jpg" style="zoom:67%;" />
## 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).
## 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.
## 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]
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
SYMBOL INDEX (137 symbols across 17 files)
FILE: board_basics.py
class Board_basics (line 9) | class Board_basics:
method __init__ (line 10) | def __init__(self, side_view_compensation, rotation_count):
method initialize_ssim (line 22) | def initialize_ssim(self, frame):
method load_ssim (line 76) | def load_ssim(self):
method update_ssim (line 90) | def update_ssim(self, previous_frame, next_frame, move, is_capture, co...
method get_square_image (line 111) | def get_square_image(self, row, column,
method convert_row_column_to_square_name (line 121) | def convert_row_column_to_square_name(self, row, column):
method square_region (line 136) | def square_region(self, row, column):
method is_light (line 148) | def is_light(self, square_name):
method get_potential_moves (line 160) | def get_potential_moves(self, fgmask, previous_frame, next_frame, ches...
FILE: board_calibration.py
function mark_corners (line 53) | def mark_corners(frame, augmented_corners, rotation_count):
FILE: board_calibration_machine_learning.py
function detect_board (line 7) | def detect_board(original_image, corner_model, piece_model, color_model):
FILE: chessboard_detection.py
class Board_position (line 10) | class Board_position:
method __init__ (line 11) | def __init__(self, minX, minY, maxX, maxY):
function find_chessboard (line 18) | def find_chessboard():
function auto_find_chessboard (line 46) | def auto_find_chessboard():
function is_white_on_bottom (line 59) | def is_white_on_bottom(current_chessboard_image):
function get_square_image (line 68) | def get_square_image(row, column, board_img):
function prepare (line 79) | def prepare(lines, kernel_close, kernel_open):
function prepare_vertical (line 86) | def prepare_vertical(lines):
function prepare_horizontal (line 92) | def prepare_horizontal(lines):
function find_chessboard_from_image (line 98) | def find_chessboard_from_image(img):
FILE: classifier.py
class Classifier (line 7) | class Classifier:
method __init__ (line 8) | def __init__(self, game_state):
method classify (line 39) | def classify(self, img):
method unit_gradients (line 88) | def unit_gradients(self, gray):
method get_square_image (line 97) | def get_square_image(self, row, column,
FILE: commentator.py
class Commentator_thread (line 10) | class Commentator_thread(Thread):
method __init__ (line 12) | def __init__(self, *args, **kwargs):
method run (line 21) | def run(self):
class Game_state (line 34) | class Game_state:
method __init__ (line 36) | def __init__(self):
method get_chessboard (line 48) | def get_chessboard(self):
method get_square_image (line 58) | def get_square_image(self, row, column,
method can_image_correspond_to_chessboard (line 69) | def can_image_correspond_to_chessboard(self, move, result):
method find_premove (line 100) | def find_premove(self, result):
method get_valid_move (line 120) | def get_valid_move(self, potential_starts, potential_arrivals, current...
method has_square_image_changed (line 190) | def has_square_image_changed(self, old_square,
method convert_row_column_to_square_name (line 198) | def convert_row_column_to_square_name(self, row, column):
method convert_square_name_to_row_column (line 208) | def convert_square_name_to_row_column(self, square_name):
method get_potential_moves (line 216) | def get_potential_moves(self, old_image, new_image):
method register_move_if_needed (line 229) | def register_move_if_needed(self):
method register_move (line 253) | def register_move(self, move, board_image):
FILE: diagnostic.py
function process (line 88) | def process(image):
FILE: game.py
class Game (line 16) | class Game:
method __init__ (line 17) | def __init__(self, board_basics, speech_thread, use_template, make_opp...
method initialize_hog (line 64) | def initialize_hog(self, frame):
method detect_state_cnn (line 95) | def detect_state_cnn(self, chessboard_image):
method check_state_cnn (line 118) | def check_state_cnn(self, result):
method get_valid_2_move_cnn (line 135) | def get_valid_2_move_cnn(self, frame):
method get_valid_move_cnn (line 164) | def get_valid_move_cnn(self, frame):
method load_hog (line 189) | def load_hog(self):
method detect_state_hog (line 199) | def detect_state_hog(self, chessboard_image):
method get_valid_move_hog (line 220) | def get_valid_move_hog(self, fgmask, frame):
method get_move_to_register (line 266) | def get_move_to_register(self):
method is_light_change (line 275) | def is_light_change(self, frame):
method check_state_hog (line 291) | def check_state_hog(self, result):
method check_state_for_move (line 305) | def check_state_for_move(self, result):
method check_state_for_light (line 319) | def check_state_for_light(self, result, result_hog):
method get_valid_move_canny (line 335) | def get_valid_move_canny(self, fgmask, frame):
method register_move (line 383) | def register_move(self, fgmask, previous_frame, next_frame):
method learn (line 440) | def learn(self, frame):
method get_valid_move (line 475) | def get_valid_move(self, potential_squares, potential_moves):
FILE: gui.py
function lichess (line 15) | def lichess():
function on_closing (line 25) | def on_closing():
function log_process (line 33) | def log_process(process, finish_message):
function stop_process (line 56) | def stop_process(ignore=None):
function diagnostic (line 62) | def diagnostic(ignore=None):
function board_calibration (line 96) | def board_calibration(ignore=None):
function start_game (line 138) | def start_game(ignore=None):
function save_promotion (line 316) | def save_promotion(*args):
function save_settings (line 377) | def save_settings():
function load_settings (line 383) | def load_settings():
FILE: helper.py
function euclidean_distance (line 6) | def euclidean_distance(first, second):
function perspective_transform (line 10) | def perspective_transform(image, pts1):
function rotateMatrix (line 18) | def rotateMatrix(matrix):
function auto_canny (line 29) | def auto_canny(image):
function edge_detection (line 39) | def edge_detection(frame):
function get_square_image (line 54) | def get_square_image(row, column,
function contains_piece (line 66) | def contains_piece(square, view):
function detect_state (line 88) | def detect_state(frame, view, roi_mask):
function predict (line 100) | def predict(image, model):
FILE: internet_game.py
class Internet_game (line 6) | class Internet_game:
method __init__ (line 7) | def __init__(self, use_template, start_delay, drag_drop):
method move (line 16) | def move(self, move):
method get_square_center (line 37) | def get_square_center(self, square_name):
method convert_square_name_to_row_column (line 44) | def convert_square_name_to_row_column(self, square_name, is_white_on_b...
method convert_row_column_to_square_name (line 52) | def convert_row_column_to_square_name(self, row, column, is_white_on_b...
FILE: languages.py
class English (line 4) | class English:
method __init__ (line 5) | def __init__(self):
method name (line 9) | def name(self, piece_type):
method comment (line 23) | def comment(self, board, move):
class German (line 56) | class German:
method __init__ (line 57) | def __init__(self):
method name (line 61) | def name(self, piece_type):
method comment (line 75) | def comment(self, board, move):
class Russian (line 108) | class Russian:
method __init__ (line 109) | def __init__(self):
method name (line 113) | def name(self, piece_type):
method comment (line 127) | def comment(self, board, move):
class Turkish (line 160) | class Turkish:
method __init__ (line 161) | def __init__(self):
method capture_suffix (line 165) | def capture_suffix(self, to_square):
method from_suffix (line 175) | def from_suffix(self, from_square):
method to_suffix (line 183) | def to_suffix(self, to_square):
method comment (line 191) | def comment(self, board, move):
class Italian (line 248) | class Italian:
method __init__ (line 249) | def __init__(self):
method name (line 253) | def name(self, piece_type):
method prefix_name (line 267) | def prefix_name(self, piece_type):
method comment (line 281) | def comment(self, board, move):
class French (line 318) | class French:
method __init__ (line 319) | def __init__(self):
method name (line 323) | def name(self, piece_type):
method comment (line 337) | def comment(self, board, move):
FILE: lichess_commentator.py
class Lichess_commentator (line 5) | class Lichess_commentator(Thread):
method __init__ (line 7) | def __init__(self, *args, **kwargs):
method run (line 16) | def run(self):
class Game_state (line 24) | class Game_state:
method __init__ (line 26) | def __init__(self):
method register_move_if_needed (line 34) | def register_move_if_needed(self, stream):
method register_move (line 56) | def register_move(self, move):
method unregister_move (line 64) | def unregister_move(self):
method from_position (line 73) | def from_position(self, fen):
FILE: lichess_game.py
class Lichess_game (line 8) | class Lichess_game:
method __init__ (line 9) | def __init__(self, token):
method move (line 33) | def move(self, move):
FILE: main.py
function waitUntilMotionCompletes (line 141) | def waitUntilMotionCompletes():
function stabilize_background_subtractors (line 155) | def stabilize_background_subtractors():
FILE: speech.py
class Speech_thread (line 8) | class Speech_thread(Thread):
method __init__ (line 10) | def __init__(self, *args, **kwargs):
method run (line 15) | def run(self):
method put_text (line 38) | def put_text(self, text):
FILE: videocapture.py
class Video_capture_thread (line 5) | class Video_capture_thread(Thread):
method __init__ (line 7) | def __init__(self, *args, **kwargs):
method run (line 12) | def run(self):
method get_frame (line 19) | def get_frame(self):
Condensed preview — 23 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (182K chars).
[
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 6844,
"preview": "# Play online chess with a real chess board\nProgram that enables you to play online chess using real chess boards. Usin"
},
{
"path": "board_basics.py",
"chars": 11723,
"preview": "import sys\r\n\r\nfrom skimage.metrics import structural_similarity\r\nimport chess\r\nimport pickle\r\nimport os\r\n\r\n\r\nclass Board"
},
{
"path": "board_calibration.py",
"chars": 10857,
"preview": "import cv2\r\nimport platform\r\nfrom math import inf\r\nimport pickle\r\n\r\nfrom board_calibration_machine_learning import detec"
},
{
"path": "board_calibration_machine_learning.py",
"chars": 6083,
"preview": "import numpy as np\r\nimport cv2\r\n\r\nfrom helper import euclidean_distance, perspective_transform, predict\r\n\r\n\r\ndef detect_"
},
{
"path": "chessboard_detection.py",
"chars": 5777,
"preview": "import sys\r\n\r\nimport numpy as np\r\nimport cv2\r\nimport pyautogui\r\nimport mss\r\nfrom statistics import median\r\n\r\n\r\nclass Boa"
},
{
"path": "classifier.py",
"chars": 4944,
"preview": "import numpy as np\r\nimport cv2\r\nfrom math import pi\r\n\r\n\r\n# https://github.com/youyexie/Chess-Piece-Recognition-using-Ori"
},
{
"path": "commentator.py",
"chars": 11911,
"preview": "from threading import Thread\r\nimport chess\r\nimport mss\r\nimport numpy as np\r\nimport cv2\r\nimport time\r\nfrom classifier imp"
},
{
"path": "diagnostic.py",
"chars": 4296,
"preview": "import cv2\r\nimport numpy as np\r\nimport pickle\r\n\r\nfrom board_calibration_machine_learning import detect_board\r\nfrom helpe"
},
{
"path": "game.py",
"chars": 24401,
"preview": "import time\r\n\r\nimport chess\r\nimport cv2\r\nimport numpy as np\r\nimport pickle\r\nimport os\r\nimport sys\r\nfrom helper import de"
},
{
"path": "gui.py",
"chars": 16331,
"preview": "import tkinter as tk\r\nfrom tkinter.simpledialog import askstring\r\nfrom tkinter import messagebox\r\nimport subprocess\r\nimp"
},
{
"path": "helper.py",
"chars": 3703,
"preview": "import cv2\r\nimport numpy as np\r\nfrom math import sqrt\r\n\r\n\r\ndef euclidean_distance(first, second):\r\n return sqrt((firs"
},
{
"path": "internet_game.py",
"chars": 2497,
"preview": "import chessboard_detection\r\nimport pyautogui\r\nimport time\r\n\r\n\r\nclass Internet_game:\r\n def __init__(self, use_templat"
},
{
"path": "languages.py",
"chars": 11911,
"preview": "import chess\r\n\r\n\r\nclass English:\r\n def __init__(self):\r\n self.game_started = \"Game started\"\r\n self.move"
},
{
"path": "lichess_commentator.py",
"chars": 3065,
"preview": "from threading import Thread\r\nimport chess\r\n\r\n\r\nclass Lichess_commentator(Thread):\r\n\r\n def __init__(self, *args, **kw"
},
{
"path": "lichess_game.py",
"chars": 1680,
"preview": "import berserk\r\nimport sys\r\nimport os\r\nimport chess\r\nimport pickle\r\n\r\n\r\nclass Lichess_game:\r\n def __init__(self, toke"
},
{
"path": "main.py",
"chars": 9152,
"preview": "import time\r\nimport cv2\r\nimport pickle\r\nimport numpy as np\r\nimport sys\r\nfrom collections import deque\r\nimport platform\r\n"
},
{
"path": "requirements.txt",
"chars": 93,
"preview": "opencv-python\r\npython-chess\r\npyautogui\r\nmss\r\nnumpy\r\npyttsx3\r\nscikit-image\r\npygrabber\r\nberserk"
},
{
"path": "speech.py",
"chars": 1243,
"preview": "from threading import Thread\r\nfrom queue import Queue\r\nimport platform\r\nimport os\r\nimport subprocess\r\n\r\n\r\nclass Speech_t"
},
{
"path": "videocapture.py",
"chars": 509,
"preview": "from threading import Thread\r\nfrom queue import Queue\r\n\r\n\r\nclass Video_capture_thread(Thread):\r\n\r\n def __init__(self,"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the karayaman/Play-online-chess-with-real-chess-board GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 23 files (10.3 MB), approximately 39.0k tokens, and a symbol index with 137 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.