def find_pieces(Im, board_corners, w, sq1_is_black=True, p=20):
"""
Given an (unwarped) 3-channel color image, finds the pieces.
Required arguments:
Im -- A 3-channels RGB image
board_corners -- a list of board_corners from find_board
w -- the classifier trained and returned by train_detector
Keyword arguments:
sq1_is_black -- Boolean value, true if the top left square in
board_corners is black. Use find_board.is_first_square_black()
to determine this if unknown.
Default: True
p -- an exponential factor which describes how much the board's perspective
should be taken into account when classifying squares as pieces/non-pieces.
Default: 20
Return value: An 8x8 numpy array, where the (i,j)-th entry is:
1 if the square contains a white piece
-1 if the square contains a black piece
0 if the square contains no piece
"""
# align the image
Im_aligned, H = find_board.align_image(Im, board_corners)
a1 = sqrt(sum((array(board_corners[0]) - array(board_corners[8]))**2))
a2 = sqrt(sum((array(board_corners[72]) - array(board_corners[80]))**2))
b1 = sqrt(sum((array(board_corners[8]) - array(board_corners[80]))**2))
b2 = sqrt(sum((array(board_corners[0]) - array(board_corners[72]))**2))
perspective = (b1/b2)**p, 1, (b1/b2)**p * (a1/a2)**p, (a1/a2)**p
classification = _identify_occupied_squares(Im_aligned, w, perspective = perspective)
pieces = sign(_identify_piece_color(Im_aligned, classification, sq1_is_black))
return pieces
def train_detector(frames, piece_locations, board_corners, p=20):
"""
Given a list of frames and known piece locations, trains a piece detector.
Required arguments:
frames -- a python list of numpy arrays, where each array is a color image
piece_locations -- a python list of lists of tuples describing the position
of pieces on the board. The tuple (i,j) describes that there is a piece
in the ith row, jth column, where the 0th row/column is the square
whose upper left corner is first in board_corners.
For a frame where the pieces are in the
default starting locations, this is just:
[[(i,j) for in range(8) for j in range(8)]]
board_corners -- a list of board_corners as generated by find_board
Keyword arguments:
p -- An exponential factor which describes how much the board's perspective
should be taken into account when classifying squares as pieces/non-pieces.
Default: 20
Return value: train_detector returns a sklearn object describing a trained linear SVM.
"""
# shape an array for features and a list for classes
features = zeros((0,N_FEATURES))
classes = []
# compute the perspective descriptors
a1 = sqrt(sum((array(board_corners[0]) - array(board_corners[8]))**2))
a2 = sqrt(sum((array(board_corners[72]) - array(board_corners[80]))**2))
b1 = sqrt(sum((array(board_corners[8]) - array(board_corners[80]))**2))
b2 = sqrt(sum((array(board_corners[0]) - array(board_corners[72]))**2))
perspective = (b1/b2)**p, 1, (b1/b2)**p * (a1/a2)**p, (a1/a2)**p
# for every frame, get the image patches, compute features, and add to the training data
for k,f in enumerate(frames):
squares = [(i,j) for i in range(8) for j in range(8)]
for (i,j) in squares:
f_warp = find_board.align_image(f, board_corners)[0]
# get the patch
W = find_board.get_patch(f_warp, (i,j))
# now compute some features
current_features = _make_features(W,perspective)
# add to training data
features = vstack((features, current_features))
# determine the class of this patch and add to training data
if (i,j) in piece_locations[k]:
classes.append(1)
else:
classes.append(-1)
# now we have our features and classes, and so we train up a linear least squares model
w = svm.SVC()
w.fit(features, classes)
return w
def test():
print "Training a classifier..."
# structures to hold frames and labels
frames = []
piece_locations = []
# video to train on
vid = cv2.VideoCapture('./data/full_game_Ruy_Lopez.avi')
# capture a frame and align it
Im = vid.read()[1]
board_corners = find_board.find_board(Im)
frames.append(Im)
# these are not the correct locations, but roll with it...
white_pieces = [(i,j) for i in range(8) for j in range(2)]
black_pieces = [(i,j) for i in range(8) for j in range(6,8)]
"""
# now selectively correct the pieces
white_pieces.remove((6,0))
white_pieces.remove((5,1))
white_pieces.append((5,2))
white_pieces.append((5,3))
black_pieces.remove((1,7))
black_pieces.remove((3,6))
black_pieces.append((2,5))
black_pieces.append((3,3))
"""
piece_locations.append(white_pieces+black_pieces)
w = train_detector(frames, piece_locations, board_corners)
# now test it
# video to test on
vid2 = cv2.VideoCapture('./data/full_game_Ruy_Lopez.avi')
for frame in range(2400,100000,200):
print frame
vid2.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frame)
# capture a frame and align it
Im2 = vid2.read()[1]
Im2_warp = find_board.align_image(Im2, board_corners)[0]
Im2_circ = Im2_warp.copy()
pieces = find_pieces(Im2, board_corners, w=w)
white_pieces = zip(*nonzero(pieces == 1))
black_pieces = zip(*nonzero(pieces == -1))
print pieces
for piece in white_pieces:
cv2.circle(Im2_circ, ((piece[1]+1)*50 + 15, 15 + (1 + piece[0])*50), 6, (0,0,200))
for piece in black_pieces:
cv2.circle(Im2_circ, ((piece[1]+1)*50 + 15, 15 + (1 + piece[0])*50), 6, (0,2000,0))
cv2.namedWindow('test',0)
cv2.imshow('test', Im2_circ)
cv2.waitKey(0)