# Source code for perception.boardClass

```import cv2
import numpy as np

[docs]class Board:
"""
Holds all the Square instances and the BWE matrix.
"""
def __init__(self, squares, BWEmatrix = [], leah = 'noob coder'):
# Squares
self.squares = squares
# BWE Matrix
self.BWEmatrix = BWEmatrix
# Noob
self.leah = leah

[docs]    def draw(self,image):
"""
Draws the board and classifies the squares (draws the square state on the image).
"""
for square in self.squares:
square.draw(image)
square.classify(image)

[docs]    def assignBWE(self):
"""
Assigns initial setup states to squares and initialises the BWE matrix.
"""

for i in range(8):
self.squares[8*i + 0].state = 'W'
self.squares[8*i + 1].state = 'W'
self.squares[8*i + 2].state = 'E'
self.squares[8*i + 3].state = 'E'
self.squares[8*i + 4].state = 'E'
self.squares[8*i + 5].state = 'E'
self.squares[8*i + 6].state = 'B'
self.squares[8*i + 7].state = 'B'

for square in self.squares:
self.BWEmatrix.append(square.state)

return self.BWEmatrix

[docs]    def updateBWE(self, matches, current):
"""
Updates the BWE by looking at the two squares that have changed and determining which one is now empty. This
relies on calculated the distance in RGB space provided by the classify function. The one with a lower distance
to the colour of its empty square must now be empty and its old state can be assigned to the other square that
has changed.
"""

# Calculates distances to empty colors of squares
distance_one = matches.classify(current)
distance_two = matches.classify(current)

if distance_one < distance_two:
# Store old state
old = matches.state
# Assign new state
matches.state = 'E'
self.BWEmatrix[matches.index] = matches.state
# Replace state of other square with the previous one of the currently white one
matches.state = old
self.BWEmatrix[matches.index] = matches.state

else:
# Store old state
old = matches.state
# Assign new state
matches.state = 'E'
self.BWEmatrix[matches.index] = matches.state
# Replace state of other square with the previous one of the currently white one
matches.state = old
self.BWEmatrix[matches.index] = matches.state

[docs]    def getBWE(self):
"""
Converts BWE from list of strings to a rotated numpy array
"""
bwe = np.zeros((8,8),dtype=np.int8)

counter = 0

for i in range(8):
for j in range(8):
if self.BWEmatrix[counter] == 'E':
tmp = 0
elif self.BWEmatrix[counter] == 'W':
tmp = 1
elif self.BWEmatrix[counter] == 'B':
tmp = 2
bwe[i][j] = tmp
counter += 1

# Rotation in return statement
return np.rot90(bwe,k=1)

[docs]    def whichSquares(self, points):
"""
Returns the squares which a list of points lie within. This function is needed to filter out changes in the
images that are compared which have nothing to do with the game, e.g. an arm.
"""
matches = []
for square in self.squares:
for point in points:
dist = cv2.pointPolygonTest(square.contours,point,False)
if dist >= 0:
matches.append(square)

return matches
```