├── .gitignore ├── LICENSE ├── README.md ├── arm.py ├── exceptions.py ├── images ├── board.png ├── camera.png ├── grayscale.png └── setup.jpg ├── main.py ├── positions.py └── vision.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | .vscode/ 3 | data/ 4 | build/ 5 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Raspberry Chess 项目报告 2 | 3 | ## 项目介绍 4 | 5 | Raspberry Chess是基于树莓派平台的自动下棋机器人。 6 | 整个项目有大约600行Python代码，利用机器视觉和开源象棋引擎，控制机械臂，从而完成与用户对弈的功能。 7 | 8 | 项目主要分为三个模块： 9 | 10 | * 视觉：识别棋盘和棋子的位置和类别 11 | * 行棋逻辑：分析用户走法，计算最佳走法 12 | * 机械臂控制：操作机械臂移动棋子 13 | 14 | 代码开源在[Github](https://github.com/YizhePKU/Raspberry-Chess)。 15 | 16 | 报告的第二部分是方案设计，例如在几个可能的方案中，我是如何做出选择的。 17 | 报告的第三部分是代码的详细文档，以及如何搭建这个项目的经验。 18 | 19 | 20 | ## 方案设计 21 | 22 | ### 器材的选用 23 | 我第一个考虑的问题是，如何让机械臂稳定地抓取和移动棋子？ 24 | 25 | 与中国象棋扁平的棋子不同，国际象棋的棋子大都是立体的，并且顶面并不平整。 26 | 我使用的机械臂（越疆魔术师）有两种抓取模式，钩爪和吸盘，但对于形状不规整的棋子来说，这两种模式都不能很好地抓取棋子。 27 | 此外，机械臂的另一个问题是臂展相对较短，伸缩幅度只有25cm左右。这就意味着棋盘不能太大。 28 | 这两个问题意味着，市面上常见的国际象棋棋盘不能满足项目的需要。 29 | 30 | 几个可能的解决方案： 31 | 32 | 1. 对常见的棋盘加以改造，例如在棋子顶端黏贴硬纸板，以方便吸盘吸取。但这并不能解决机械臂臂展不足的问题。 33 | 2. 定制棋盘，例如自己设计棋子和棋盘，然后3d打印。缺点是比较麻烦。 34 | 35 | 最终的解决方法比较幸运：我恰好在淘宝上发现了合适的棋盘（[商品链接][1]）。 36 | 这种棋盘的棋子是平面的，而且棋盘较小，很适合机械臂的操作。 37 | 38 | [1]: https://item.taobao.com/item.htm?spm=a1z09.2.0.0.69b52e8dbms51T&id=532708644440&_u=s2u449ftcb3f 39 | 40 | ### 棋盘位置的识别 41 | 对棋盘进行图像处理的第一步，就是找出棋盘四个角在图片中的位置，用透视变换把这四个角放到图片的四个角上去。 42 | 43 | OpenCV提供一个函数：`findChessboardCorners()`。 44 | 这个函数会识别一个黑白棋盘的内部格点。例如，一个8x8的棋盘会识别出7x7个内部格点。 45 | 对这些内部格点做一个线性外插，应该就能得到棋盘四个角对应的位置。 46 | 47 | 我在这个方案上花了两节课时间，试图让`findChessboardCorners()`能够工作。 48 | 但是这个函数也有不少问题。 49 | 50 | 1. `findChessboardCorners()`的设计初衷是用于摄像头的矫正，而不是识别一个实际的象棋棋盘。因此在棋盘上有棋子时，识别正确率会大大降低。 51 | 2. 运算量很大，在树莓派上运行速度很慢（调用一次需要1~2秒）。 52 | 3. 无法分辨哪个是棋盘的“左上角”，哪个是“右下角”等等。 53 | 54 | 最终我放弃了这个方案。 55 | 新方案是，在棋盘的四个角贴上四种颜色的贴纸，利用颜色识别四个角。 56 | 这个方案的优点是简单方便，运行速度快，准确率高。 57 | 但缺点是，四块贴纸的中心与棋盘的四个角并不是完全重合的，需要在代码里手动矫正（比较hacky，第三部分会提到）。 58 | 59 | ### 棋子类型的识别 60 | 有了棋盘的图片，下面只需要把图片切割成8x8的小方格，然后分别识别棋子的类型即可。 61 | 62 | 这里有个小技巧：我们其实只需要识别棋子的颜色（白子，黑子，空格），而不需要识别棋子的类型（车，象，王，后）。 63 | 这是因为，国际象棋的每一步走法都可以用（棋子的出发地，棋子的目的地）这个二元组合唯一地表示。 64 | 例如，如果我们知道一步走法的出发点是e2，目的地是e4，那么我们就能判断出，白方的兵从e2走到了e4。 65 | 我们将这个状态保存起来，下次再遇见e4的时候，我们就知道这里肯定是一个白兵，不需要通过图像处理的手段识别棋子类型了。 66 | 67 | 有一个例外，就是兵的升变。国际象棋的规则是，兵在走到底线后，可以变成（后，车，象，马）中的任何一个棋子。 68 | 实际的对弈中大部分棋手都会选择变后（因为后最强大），但也有极少数情况，变后不是最佳选择。 69 | 不过对我们来说，在出现兵升变的情况时，默认变后已经足够了。 70 | 71 | 识别棋子类型的算法在第三部分会详细解释。 72 | 73 | ### 用户着法的分析，最佳着法的获取 74 | 在发现棋盘有变动之后，我们就可以通过对比棋盘前后哪些方格有变化，分析出用户的着法。 75 | 之后，将着法历史转换为代数记谱法（例如 ["e2e4", "d7d5", "e4d5"]），传给开源象棋引擎Stockfish。Stockfish会计算出机械臂的下一步最佳着法。 76 | 我利用了`python-chess`的一些封装，简化调用Stockfish的过程。 77 | 78 | 此外，从`python-chess`和Stockfish获取的最佳着法只包含了简要的走子信息（例如“f3上的马吃e5的兵”）， 79 | 我们还需要将其转化为机械臂的实际操作流程（例如“先将e5上的棋子移出棋盘，再将f3上的棋子移到e5”） 80 | 81 | ### 机械臂的定位和校准 82 | 至此，最后一步就是让机械臂执行具体的操作指令（例如“将f3上的棋子移到e5”）。 83 | 84 | 如何定位呢？ 85 | 最理想的方法是反馈控制，即用摄像头拍摄的实时画面，对机械臂的位置进行微调。但这个方式实现起来太过麻烦。 86 | 因此我采用了开环控制，在棋局开始时，让用户将机械臂放置在棋盘中央，之后的定位都按照相对这个中央的位置进行移动。 87 | 显然，这个做法会导致误差累积，偶尔会有定位偏差导致吸盘没能抓起棋子。此时我会稍微移动一下棋盘，手动纠正误差。 88 | 如果要实现真正的反馈控制，那么图像处理那方面就会更加复杂了。 89 | 90 | 91 | ## 代码文档与搭建经验 92 | 93 | ## 搭建准备 94 | 需要的硬件： 95 | 96 | * 树莓派开发板 97 | * 越疆魔术师机械臂（配吸盘） 98 | * 摄像头 99 | * 用于固定摄像头的支架 100 | * 国际象棋棋盘 101 | 102 | 在树莓派上安装依赖： 103 | 104 | * OpenCV-python，版本3（树莓派不支持版本4） 105 | * `python3 -m pip install pydobot python-chess` 106 | * `apt-get install stockfish` 107 | 108 | 然后如图搭建。下面是几个注意事项： 109 | 110 | 1. 棋盘的左上角的贴纸应该是红色。“外黑内白”的棋子是白子，应该放在用户一侧。“外白内黑”的棋子是黑子，应该放在机械臂一侧。 111 | 2. 棋盘稍微垫高一点，因为机械臂的臂展与操作的高度有关。 112 | 3. 棋盘如果不是正常的开局（例如少了几个棋子）需要在代码中设定（main.py）。目前版本的代码对应的就是如图的棋盘布局。 113 | 4. 摄像头固定得越高越好，可以减少因为画面扭曲造成的失真。 114 | 5. 机械臂开机后，长按Key键五秒后松开，可以重置机械臂，有利于提高定位精度。 115 | 116 | 搭建范例 117 | 118 | ### 视觉模块：arm.py 119 | `SquareType`表示一个方格的类型。 120 | `SquareType.empty`表示这个方格上没有棋子。 121 | `SquareType.white`表示这个方格上有一个白子。 122 | `SquareType.black`表示这个方格上有一个黑子。 123 | 124 | 视觉模块提供了下面几个函数： 125 | 126 | * `find_corners(image)` 从摄像头拍摄的图片中提取棋盘四个角的坐标。 127 | * `transform(image, corners)` 对图片进行透视变换。 128 | * `detect_square_type(image)` 识别（已经切割好的）一块图片是黑子还是白子。 129 | * `get_position_from_image(image)` 上面三个函数的合并，从摄像头拍摄的图片中获取棋盘所有方格是黑子还是白子。 130 | 131 | `find_corners(image)` 利用`cv2.inRange()`和`cv2.find_contour()`识别棋盘上贴的四张彩色贴纸。 132 | 识别颜色时用到的`masks`对于光线和摄像头很敏感。如果不能正常识别色块，可能需要调整`masks`中对应的hsv色彩范围。 133 | 134 | 由于贴纸的中心与棋盘四个角并不完全重合，`adjusted_points`对点的位置做了一些相对调整。搭建时需要观察程序识别的结果，适当调整参数，保证透视变换完成的图片刚好只包括棋盘本身，不要包括棋盘外面的画面。 135 | 136 | `detect_square_type(image)`利用FloodFill算法判断一块图片是黑子还是白子。 137 | 算法如下： 138 | 139 | 1. 先裁剪图片，只保留中间部分，去除那些由切割图片不准确导致的其他方格的色素。 140 | 2. 选择合适的阈值，将图片转换为黑白。阈值的选取与场地光照有关。 141 | 3. 此时进入正题。图片的背景可能是黑方块，也可能是白方块。图片里可能有一个白子，一个黑子，或者什么都没有。一共六种情况。按照下面的算法做判断。 142 | 1. 检查图片是否是单色的（纯黑或纯白），如果是，那么返回结果“这里没有棋子”。 143 | 2. 对图片边缘FloodFill黑色。再次检查图片是否是单色，如果是，那么返回结果“这里有一个黑子”（对应的是黑子在白色背景上的情况）。 144 | 3. 对图片边缘FloodFill白色。再次检查图片是否是单色，如果是，那么返回结果“这里有一个白子”。否则返回结果“这里有一个黑子”（对应的是黑子在黑色背景上的情况） 145 | 146 | ### 行棋逻辑模块：positions.py 147 | `position`指的是一个8x8的`SquareType`数组，用来记录棋盘的情况。 148 | 149 | 行棋逻辑模块提供了下面几个函数： 150 | 151 | * `print_position(position)` 打印一个`position`，用于调试。 152 | * `get_position(board)` 从一个chess.Board对象中提取出`position`。 153 | * `get_move_from_diff(board, old_position, new_position)` 从前后两个`position`中分析出用户的走法。这个函数本来还应该处理兵升变的特殊情况（记谱法会有一些变化），但是这部分最终没时间实现了。 154 | 155 | 这些代码大部分涉及到国际象棋的具体规则，所以如果出问题的话...最好找个会下棋的程序员来看看。 156 | 157 | ### 机械臂控制模块：arm.py 158 | 机械臂控制模块提供了一个类，`class Arm`。 159 | 这个类提供了以下方法： 160 | 161 | * `__init__(self)` 初始化机械臂，打开串口通信。 162 | * `calibrate(self)` 将机械臂当前的位置设为参考位置。 163 | * `act(self, board, move)` 分析走法（是否是吃子，易位等特殊规则），将走法转化为指令序列并执行。 164 | 165 | 166 | 按照越疆魔术师的文档，机械臂面朝的方向为x轴正半轴，x轴逆时针旋转90度为y轴正半轴。 167 | 所有距离均以cm为单位。 168 | 169 | 棋盘每个正方形方格的边长为20cm。 170 | 171 | 如果机械臂的x-y轴与棋盘恰好一致，那么只需要一个参考位置就可以准确定位。实际操作的时候，机械臂的x轴偶尔会有一点点歪（不完全是机械臂面朝的方向）。这应该是机械臂的问题，重置一下应该会好。 172 | 173 | ### 运行项目：main.py 174 | 用`python3 main.py`运行项目。项目会做下面的事： 175 | 176 | 1. 初始化摄像头，机械臂，象棋引擎 177 | 2. 设定棋盘的初始局面。如果棋盘少一些棋子，则需要在这里设定。 178 | 3. 显示摄像头的拍摄画面，提示用户设定机械臂的参考位置。用户需要将机械臂的吸盘移动到棋盘最中间那个格点（交叉点），吸盘紧贴棋盘，然后按空格键完成设定。机械臂会自动归位。 179 | 4. 游戏开始。程序额外打开两个窗口，显示经过矫正的棋盘。代码会不断检查局面，如果发现局面变化，则尝试分析用户的着法。如果用户着法合理，那么调用象棋引擎计算最佳着法，并让机械臂执行着法。如果用户着法不合理，或者视觉模块分析出的结果不稳定（连续5帧画面分析出相同的结果认为是稳定）会在命令行打印相关日记。游戏结束时（将死/和棋）程序自动退出。 180 | 181 | 182 | ## 参考文献 183 | [Raspberry Turk](https://www.raspberryturk.com/)是国外一个自动下棋的机械臂的项目，给了我很多思路和启发。 184 | -------------------------------------------------------------------------------- /arm.py: -------------------------------------------------------------------------------- 1 | from serial.tools import list_ports 2 | 3 | from pydobot import Dobot 4 | 5 | 6 | class Arm: 7 | x0 = 0 8 | y0 = 0 9 | z0 = 0 10 | device = None 11 | 12 | def __init__(self): 13 | port = list_ports.comports()[0].device 14 | self.device = Dobot(port=port, verbose=False) 15 | 16 | # Put the arm back to waiting position. 17 | def reset(self): 18 | (x, y, z, r, j1, j2, j3, j4) = self.device.pose() 19 | # self.device.move_to(x, y, z + 10, r, wait=True) 20 | self.device.move_to(self.x0, self.y0, self.z0 + 130, 0, wait=True) 21 | 22 | # Set current pose as reference pose. 23 | def calibrate(self): 24 | (x, y, z, r, j1, j2, j3, j4) = self.device.pose() 25 | self.x0 = x 26 | self.y0 = y 27 | self.z0 = z 28 | 29 | # Execute a move according to the move and the board info. 30 | # The board should be in a state that **has NOT pushed the move**. 31 | def act(self, board, move): 32 | x1 = 7 - (move.from_square // 8) 33 | y1 = move.from_square % 8 34 | x2 = 7 - (move.to_square // 8) 35 | y2 = move.to_square % 8 36 | 37 | print(board) 38 | print("act:", x1, y1, x2, y2) 39 | 40 | if board.is_capture(move): 41 | self._move_piece(x2, y2, 4, -2) 42 | self._move_piece(x1, y1, x2, y2) 43 | elif board.is_kingside_castling(move): 44 | self._move_piece(x1, y1, x2, y2) 45 | self._move_piece(x1, 7, x2, 5) 46 | elif board.is_queenside_castling(move): 47 | self._move_piece(x1, y1, x2, y2) 48 | self._move_piece(x1, 0, x2, 3) 49 | else: 50 | self._move_piece(x1, y1, x2, y2) 51 | self.reset() 52 | 53 | # Move a piece from (x1, y1) to (x2, y2) 54 | # The center of the top left square is (0, 0) 55 | def _move_piece(self, x1, y1, x2, y2): 56 | dx = 20 57 | dy = 20 58 | dz = 15 59 | dz2 = -5 60 | self.device.move_to(self.x0, self.y0, self.z0 + dz, 0, wait=True) 61 | self.device.move_to( 62 | self.x0 + (x1 - 3.5) * dx, 63 | self.y0 + (y1 - 3.5) * dy, 64 | self.z0 + dz, 65 | 0, 66 | wait=True, 67 | ) 68 | self.device.move_to( 69 | self.x0 + (x1 - 3.5) * dx, 70 | self.y0 + (y1 - 3.5) * dy, 71 | self.z0 + dz2, 72 | 0, 73 | wait=True, 74 | ) 75 | self.device.suck(True) 76 | self.device.move_to( 77 | self.x0 + (x1 - 3.5) * dx, 78 | self.y0 + (y1 - 3.5) * dy, 79 | self.z0 + dz, 80 | 0, 81 | wait=True, 82 | ) 83 | 84 | self.device.move_to( 85 | self.x0 + (x2 - 3.5) * dx, 86 | self.y0 + (y2 - 3.5) * dy, 87 | self.z0 + dz, 88 | 0, 89 | wait=True, 90 | ) 91 | self.device.move_to( 92 | self.x0 + (x2 - 3.5) * dx, 93 | self.y0 + (y2 - 3.5) * dy, 94 | self.z0 + dz2, 95 | 0, 96 | wait=True, 97 | ) 98 | self.device.suck(False) 99 | self.device.move_to( 100 | self.x0 + (x2 - 3.5) * dx, 101 | self.y0 + (y2 - 3.5) * dy, 102 | self.z0 + dz, 103 | 0, 104 | wait=True, 105 | ) 106 | 107 | 108 | if __name__ == "__main__": 109 | arm = Arm() 110 | -------------------------------------------------------------------------------- /exceptions.py: -------------------------------------------------------------------------------- 1 | class RaspberryChessException(Exception): 2 | pass 3 | 4 | 5 | class ChessboardNotFoundError(RaspberryChessException): 6 | pass 7 | 8 | 9 | class IllegalMoveError(RaspberryChessException): 10 | pass 11 | -------------------------------------------------------------------------------- /images/board.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yizhepku/Raspberry-Chess/89b6ad54300f7fa9fc854c9f123f00cf2f04725a/images/board.png -------------------------------------------------------------------------------- /images/camera.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yizhepku/Raspberry-Chess/89b6ad54300f7fa9fc854c9f123f00cf2f04725a/images/camera.png -------------------------------------------------------------------------------- /images/grayscale.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yizhepku/Raspberry-Chess/89b6ad54300f7fa9fc854c9f123f00cf2f04725a/images/grayscale.png -------------------------------------------------------------------------------- /images/setup.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/yizhepku/Raspberry-Chess/89b6ad54300f7fa9fc854c9f123f00cf2f04725a/images/setup.jpg -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import chess 3 | import chess.engine 4 | 5 | from arm import Arm 6 | from exceptions import ChessboardNotFoundError, IllegalMoveError 7 | from positions import get_position, get_move_from_diff, print_position 8 | from vision import get_position_from_image, width, height 9 | 10 | 11 | # Setup USB camera 12 | camera = cv2.VideoCapture(0) 13 | camera.set(cv2.CAP_PROP_FRAME_WIDTH, width) 14 | camera.set(cv2.CAP_PROP_FRAME_HEIGHT, height) 15 | 16 | # Setup robotic arm 17 | arm = Arm() 18 | 19 | # Setup Stockfish engine 20 | engine = chess.engine.SimpleEngine.popen_uci("/usr/games/stockfish") 21 | 22 | 23 | # Returns a single frame of image captured from camera. 24 | def capture_image(): 25 | _, image = camera.read() 26 | return image 27 | 28 | 29 | def main(): 30 | # while True: 31 | # image = capture_image() 32 | # try: 33 | # position = get_position_from_image(image) 34 | # print_position(position) 35 | # except ChessboardNotFoundError: 36 | # pass 37 | # cv2.waitKey(1) 38 | 39 | board = chess.Board() 40 | current_position = get_position(board) 41 | stable_counter = 10 42 | 43 | # Test with an "almost" full chessboard 44 | board.remove_piece_at(chess.square(0, 0)) 45 | board.remove_piece_at(chess.square(1, 7)) 46 | board.remove_piece_at(chess.square(0, 7)) 47 | current_position = get_position(board) 48 | cached_position = None 49 | 50 | # Arm calibration 51 | while cv2.waitKey(100) != 32: 52 | image = capture_image() 53 | cv2.imshow("Camera view", image) 54 | print() 55 | print("-" * 80) 56 | print("First, adjust the chessboard so that it fits in the screen.") 57 | print("Then, move the arm to the center crosspoint of the board.") 58 | print("Make sure the arm touches the board.") 59 | print("Finally, press spacebar to complete calibration.") 60 | print("-" * 80) 61 | print() 62 | arm.calibrate() 63 | arm.reset() 64 | print("Calibration complete.") 65 | 66 | while not board.is_game_over(): 67 | image = capture_image() 68 | cv2.imshow("Camera view", image) 69 | 70 | try: 71 | position = get_position_from_image(image) 72 | 73 | # Todo: noise reduction 74 | if position == cached_position and position != current_position: 75 | if stable_counter > 0: 76 | stable_counter -= 1 77 | print(f"Counter: {stable_counter}") 78 | # print("Current position:") 79 | # print_position(current_position) 80 | # print() 81 | # print("Captured position:") 82 | # print_position(position) 83 | # print() 84 | continue 85 | 86 | player_move = get_move_from_diff(board, current_position, position) 87 | if player_move not in board.legal_moves: 88 | raise IllegalMoveError 89 | 90 | print("Player move: ", player_move) 91 | board.push(player_move) 92 | 93 | engine_move = engine.play(board, chess.engine.Limit(time=1)).move 94 | print("Engine move: ", engine_move) 95 | arm.act(board, engine_move) 96 | board.push(engine_move) 97 | 98 | current_position = get_position(board) 99 | 100 | else: 101 | cached_position = position 102 | stable_counter = 5 103 | 104 | except ChessboardNotFoundError: 105 | print("Chess board not found!") 106 | stable_counter = 15 107 | except IllegalMoveError: 108 | print("Illegal move!") 109 | print_position(cached_position) 110 | stable_counter = 15 111 | 112 | cv2.waitKey(1) 113 | 114 | 115 | if __name__ == "__main__": 116 | main() 117 | -------------------------------------------------------------------------------- /positions.py: -------------------------------------------------------------------------------- 1 | from collections import namedtuple 2 | 3 | import chess 4 | 5 | from exceptions import IllegalMoveError 6 | from vision import SquareType 7 | 8 | 9 | def print_position(position): 10 | for x in range(8): 11 | for y in range(8): 12 | print(position[x][y], sep="", end="") 13 | print() 14 | 15 | 16 | # Extracts position from a chess.Board. 17 | def get_position(board): 18 | mapping = { 19 | 'P': 1, # White Pawn 20 | 'p': -1, # Black Pawn 21 | 'N': 2, # White Knight 22 | 'n': -2, # Black Knight 23 | 'B': 3, # White Bishop 24 | 'b': -3, # Black Bishop 25 | 'R': 4, # White Rook 26 | 'r': -4, # Black Rook 27 | 'Q': 5, # White Queen 28 | 'q': -5, # Black Queen 29 | 'K': 6, # White King 30 | 'k': -6 # Black King 31 | } 32 | 33 | def convert_to_int(board): 34 | epd_string = board.epd() 35 | list_int = [] 36 | for i in epd_string: 37 | if i == " ": 38 | return list_int 39 | elif i != "/": 40 | if i in mapping: 41 | list_int.append(mapping[i]) 42 | else: 43 | for counter in range(0, int(i)): 44 | list_int.append(0) 45 | 46 | def type_of(x): 47 | if x > 0: 48 | return SquareType.white 49 | if x < 0: 50 | return SquareType.black 51 | else: 52 | return SquareType.empty 53 | 54 | lst = convert_to_int(board) 55 | return [[type_of(lst[i * 8 + j]) for j in range(8)] for i in range(8)] 56 | 57 | 58 | # Returns a chess.Move object by analysing change of positions. 59 | # [board] should be the board of the [new_position] 60 | def get_move_from_diff(board, old_position, new_position): 61 | # A SquarePair denotes the starting and ending point of a move. 62 | Square = namedtuple("Square", ["x", "y"]) 63 | SquarePair = namedtuple("SquarePair", ["start", "end"]) 64 | 65 | # Convert a move(point pair) to algebraic notation. 66 | def pair_to_algebraic(pair): 67 | def square_to_string(square): 68 | mapping = "abcdefgh" 69 | return mapping[square.y] + str(8 - square.x) 70 | 71 | return square_to_string(pair.start) + square_to_string(pair.end) 72 | 73 | # Returns a move(point pair) by analysing differences between two positions. 74 | def diff_position(old_position, new_position): 75 | diff = [ 76 | Square(x, y) 77 | for x in range(8) 78 | for y in range(8) 79 | if old_position[x][y] != new_position[x][y] 80 | ] 81 | 82 | # Number of differences between boards: 83 | # Castling has 4. 84 | # Capture en passant has 3. 85 | # Any other move, promotion, or normal capture has 2. 86 | if len(diff) == 4: 87 | # Castling 88 | king_points = [p for p in diff if p.y == 4] 89 | rook_points = [p for p in diff if p.y == 0 or p.y == 7] 90 | if len(king_points) != 1 or len(rook_points) != 1: 91 | raise IllegalMoveError 92 | 93 | p = king_points[0] 94 | q = rook_points[0] 95 | if q.y == 0: 96 | # Castle long 97 | return SquarePair(p, Square(p.x, 2)) 98 | elif q.y == 7: 99 | # Castle short 100 | return SquarePair(p, Square(p.x, 6)) 101 | else: 102 | raise Exception("") 103 | 104 | elif len(diff) == 3: 105 | # Capture en passant 106 | pawn_points = [ 107 | p for p in diff if new_position[p.x][p.y] != SquareType.empty 108 | ] 109 | if len(pawn_points) != 1: 110 | raise IllegalMoveError 111 | 112 | p = pawn_points[0] 113 | start_points = [ 114 | q 115 | for q in diff 116 | if q != p and old_position[q.x][q.y] == new_position[p.x][p.y] 117 | ] 118 | if len(start_points) != 1: 119 | raise IllegalMoveError 120 | 121 | return SquarePair(start_points[0], p) 122 | 123 | elif len(diff) == 2: 124 | # Normal move, capture, or promotion 125 | start_points = [ 126 | p for p in diff if new_position[p.x][p.y] == SquareType.empty 127 | ] 128 | if len(start_points) != 1: 129 | raise IllegalMoveError 130 | start = start_points[0] 131 | 132 | end_points = [p for p in diff if p != start] 133 | if len(end_points) != 1: 134 | raise IllegalMoveError 135 | end = end_points[0] 136 | return SquarePair(start, end) 137 | 138 | else: 139 | raise IllegalMoveError 140 | 141 | square_pair = diff_position(old_position, new_position) 142 | assert square_pair is not None 143 | 144 | s = pair_to_algebraic(square_pair) 145 | 146 | # Todo: deal with promotion. 147 | # Algerbraic notation requires that promotion is identified like "a7a8q". 148 | # Currently it is not supported. 149 | return chess.Move.from_uci(s) 150 | -------------------------------------------------------------------------------- /vision.py: -------------------------------------------------------------------------------- 1 | from enum import Enum 2 | from collections import namedtuple 3 | 4 | import cv2 5 | import numpy as np 6 | 7 | from exceptions import ChessboardNotFoundError 8 | 9 | 10 | # width, height = 1280, 720 11 | width, height = 640, 480 12 | 13 | Point = namedtuple("Point", ["x", "y"]) 14 | 15 | 16 | # Colors in BGR mode, used by opencv. 17 | class Colors(Enum): 18 | white = (255, 255, 255) 19 | black = (0, 0, 0) 20 | blue = (255, 0, 0) 21 | green = (0, 255, 0) 22 | red = (0, 0, 255) 23 | 24 | 25 | # The "type" of a piece is the color of the pattern, not the background. 26 | class SquareType(Enum): 27 | empty = 0 28 | white = 1 29 | black = 2 30 | 31 | def __str__(self): 32 | if self == SquareType.empty: 33 | return ' ' 34 | if self == SquareType.white: 35 | return 'o' 36 | if self == SquareType.black: 37 | return 'x' 38 | return None 39 | 40 | 41 | # Returns the four corners of the chessboard, or None if not found. 42 | # Red: Upper left 43 | # Green: Upper right 44 | # Blue: Bottom left 45 | # Pink: Bottom right 46 | # Todo: make it more robust when the chessboard is facing other directions 47 | def find_corners(image): 48 | # Draw a circle at [center] with [radius] 49 | def draw_circle(image, center, radius=1, color=Colors.white): 50 | cv2.circle(image, center, radius, color.value, 2) 51 | 52 | # Return a new image converted to black & white 53 | def threshold(image): 54 | blurred = cv2.blur(image, (10, 10)) 55 | ret, thresholded = cv2.threshold(blurred, 200, 255, cv2.THRESH_BINARY) 56 | return thresholded 57 | 58 | # Extract center point from largest contour in a black & white image 59 | def find_point(image): 60 | def center(contour): 61 | x, y, w, h = cv2.boundingRect(contour) 62 | return Point(x + w // 2, y + h // 2) 63 | 64 | _, contours, hierarchy = cv2.findContours( 65 | image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE, 66 | ) 67 | if len(contours) < 1: 68 | return None 69 | contour = max(contours, key=cv2.contourArea) 70 | return center(contour) 71 | 72 | hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) 73 | masks = [ 74 | # Red 75 | cv2.bitwise_or( 76 | cv2.inRange(hsv, (160, 60, 100), (180, 255, 255)), 77 | cv2.inRange(hsv, (0, 60, 100), (10, 255, 255)), 78 | ), 79 | # Green 80 | cv2.inRange(hsv, (60, 60, 100), (90, 255, 255)), 81 | # Blue 82 | cv2.inRange(hsv, (90, 100, 100), (120, 255, 255)), 83 | # Pink 84 | cv2.inRange(hsv, (130, 30, 100), (160, 255, 255)), 85 | ] 86 | points = [find_point(threshold(mask)) for mask in masks] 87 | 88 | if not all(points): 89 | return None 90 | 91 | x0, y0 = points[0] 92 | x1, y1 = points[1] 93 | x2, y2 = points[2] 94 | x3, y3 = points[3] 95 | 96 | adjusted_points = [ 97 | Point( 98 | int(x0 + (x0 - x1) * 0.00 - (x0 - x2) * 0.05), 99 | int(y0 + (y0 - y1) * 0.00 - (y0 - y2) * 0.05), 100 | ), 101 | Point( 102 | int(x1 + (x1 - x0) * 0.08 - (x1 - x3) * 0.05), 103 | int(y1 + (y1 - y0) * 0.08 - (y1 - y3) * 0.05), 104 | ), 105 | Point( 106 | int(x2 + (x2 - x3) * 0.02 - (x2 - x0) * 0.05), 107 | int(y2 + (y2 - y3) * 0.02 - (y2 - y0) * 0.05), 108 | ), 109 | Point( 110 | int(x3 + (x3 - x2) * 0.08 - (x3 - x1) * 0.05), 111 | int(y3 + (y3 - y2) * 0.08 - (y3 - y1) * 0.05), 112 | ), 113 | ] 114 | 115 | # for point in adjusted_points: 116 | # draw_circle(image, center=point, radius=3, color=Colors.white) 117 | 118 | # contours, _ = cv2.findContours( 119 | # threshold(masks[0]), 120 | # cv2.RETR_EXTERNAL, 121 | # cv2.CHAIN_APPROX_SIMPLE, 122 | # ) 123 | # if len(contours) > 0: 124 | # contour = max(contours, key=cv2.contourArea) 125 | # cv2.drawContours(image, contours, -1, -1, 1) 126 | 127 | return adjusted_points 128 | 129 | 130 | # Returns a new image after a perspective transform, 131 | # moving [corners] to the corners of the image. 132 | def transform(image, corners): 133 | src = np.array( 134 | [ 135 | (corners[0].x, corners[0].y), 136 | (corners[1].x, corners[1].y), 137 | (corners[2].x, corners[2].y), 138 | (corners[3].x, corners[3].y), 139 | ], np.float32) 140 | 141 | dst = np.array( 142 | [ 143 | (0, 0), 144 | (width, 0), 145 | (0, height), 146 | (width, height), 147 | ], np.float32) 148 | 149 | matrix = cv2.getPerspectiveTransform(src, dst) 150 | return cv2.warpPerspective(image, matrix, (width, height)) 151 | 152 | 153 | # Return the square type of the given of image(usually a crop of the origial). 154 | def detect_square_type(image): 155 | # Crop the edges away. 156 | h, w, _ = image.shape 157 | image = image[int(h * 0.15) : int(h * 0.85), int(w * 0.15) : int(w * 0.85)] 158 | 159 | # Convert the image to black & white 160 | image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 161 | _, image = cv2.threshold(image, 125, 255, cv2.THRESH_BINARY) 162 | 163 | # Algorithm: 164 | # First, check if the square is empty. 165 | # Then, floodfill black. 166 | # If the square is now empty, this must be a black piece. 167 | # Finally, floodfill white. 168 | # If the square is now empty, this must be a white piece. 169 | # Otherwise, this must be a black piece. 170 | 171 | def is_empty(image): 172 | percentage = cv2.countNonZero(image) / (image.size) 173 | # print(percentage) 174 | return percentage < 0.05 or percentage > 0.95 175 | 176 | def floodfill(image, color): 177 | h, w = image.shape 178 | mask = np.zeros((h + 2, w + 2), np.uint8) 179 | seeds = [ 180 | (0, 0), 181 | (w - 1, 0), 182 | (0, h - 1), 183 | (w - 1, h - 1), 184 | ] 185 | for seed in seeds: 186 | cv2.floodFill(image, mask, seed, color.value) 187 | 188 | if is_empty(image): 189 | return SquareType.empty 190 | 191 | floodfill(image, Colors.black) 192 | if is_empty(image): 193 | return SquareType.black 194 | 195 | floodfill(image, Colors.white) 196 | if is_empty(image): 197 | return SquareType.white 198 | else: 199 | return SquareType.black 200 | 201 | 202 | # Recognize the chessboard in the image, and returns the coresponding position. 203 | # A position is an 8x8 array of SquareType. 204 | def get_position_from_image(image): 205 | # Crop the image into 8x8 squares. 206 | # Returns the square on the jth row, ith column(zero based). 207 | def crop(image, i, j): 208 | w = int(width / 8) 209 | h = int(height / 8) 210 | return image[h * j : h * (j + 1), w * i : w * (i + 1)] 211 | 212 | corners = find_corners(image) 213 | if corners is None: 214 | raise ChessboardNotFoundError() 215 | 216 | chessboard_image = transform(image, corners) 217 | 218 | debug_image = cv2.cvtColor(chessboard_image, cv2.COLOR_BGR2GRAY) 219 | cv2.imshow("Chess board", debug_image) 220 | 221 | _, debug_image = cv2.threshold(debug_image, 120, 255, cv2.THRESH_BINARY) 222 | cv2.imshow("Gray scale", debug_image) 223 | 224 | return [[detect_square_type(crop(chessboard_image, i, j)) for i in range(8)] for j in range(8)] 225 | --------------------------------------------------------------------------------