def train(screenshot):
# for filename in os.listdir(DATA_FOLDER):
# if filename.endswith('data'):
# os.remove(os.path.join(DATA_FOLDER, filename))
if (os.path.exists(SAMPLES_FILE)):
samples = np.loadtxt(SAMPLES_FILE)
responses = [int(i) for i in np.loadtxt(RESPONSES_FILE)]
else:
samples = np.empty((0, imgUtils.FEATURE_VECTOR_SIZE))
responses = []
grid = imgUtils.cropBoard2Player(screenshot)
index = 0
for square, rowIndex, colIndex in imgUtils.parseSquaresFromBoard(grid):
# cv2.imshow('%d, %d' % (colIndex, rowIndex), square)
# # Enter the key corresponding to the board enums
# key = cv2.waitKey(0)
# if key == 27: break
# val = int(chr(key))
# printVal(val)
# cv2.destroyAllWindows()
val = TRAIN1_DATA[index]
index += 1
if val == board.CLEAR or val == board.EMPTY or val == board.DEAD:
continue
# Add to training data
feature_vec = imgUtils.squareFeatureVector(square)
responses.append(val)
samples = np.append(samples, feature_vec, 0)
np.savetxt(SAMPLES_FILE, samples)
np.savetxt(RESPONSES_FILE, responses)
def train(screenshot):
# for filename in os.listdir(DATA_FOLDER):
# if filename.endswith('data'):
# os.remove(os.path.join(DATA_FOLDER, filename))
if (os.path.exists(SAMPLES_FILE)):
samples = np.loadtxt(SAMPLES_FILE)
responses = [int(i) for i in np.loadtxt(RESPONSES_FILE)]
else:
samples = np.empty((0, imgUtils.FEATURE_VECTOR_SIZE))
responses = []
grid = imgUtils.cropBoard2Player(screenshot)
index = 0
for square, rowIndex, colIndex in imgUtils.parseSquaresFromBoard(grid):
# cv2.imshow('%d, %d' % (colIndex, rowIndex), square)
# # Enter the key corresponding to the board enums
# key = cv2.waitKey(0)
# if key == 27: break
# val = int(chr(key))
# printVal(val)
# cv2.destroyAllWindows()
val = TRAIN1_DATA[index]
index += 1
if val == board.CLEAR or val == board.EMPTY or val == board.DEAD: continue
# Add to training data
feature_vec = imgUtils.squareFeatureVector(square)
responses.append(val)
samples = np.append(samples, feature_vec, 0)
np.savetxt(SAMPLES_FILE, samples)
np.savetxt(RESPONSES_FILE, responses)
def run(brain):
print '[emulator.py] Running game...'
for i in xrange(100):
for _ in range(12):
controller.D_UP()
for _ in range(6):
controller.D_LEFT()
screenshot = screen.takeScreenshot()
if screenshot == None:
time.sleep(MOVE_WAIT)
continue
if imgUtils.gameHasEnded(screenshot):
break
# grid = imgUtils.cropBoard2Player(screenshot)
# grid = imgUtils.filterBackground(grid)
grid = imgUtils.cropBoard1Player(screenshot)
grid = imgUtils.filterBackground1Player(grid)
# Generate board model.
inputRows = [[-1] * board.WIDTH for _ in range(board.HEIGHT)]
for square, rowIndex, colIndex in imgUtils.parseSquaresFromBoard(grid):
val, score = imgUtils.getSquareClassification(
square, rowIndex, colIndex)
inputRows[rowIndex][colIndex] = Piece(val)
# Get next action and perform.
b = board.Board(inputRows=inputRows)
print '---BOARD---'
b.printBoard()
moves = brain.getNextMoves(b)
print '---MOVES---'
print moves
print ''
cursor_pos = performMoves(moves, (0, 0))
# End loop.
print '[emulator.py] Done running game.'
def run(brain):
print '[emulator.py] Running game...'
for i in xrange(100):
for _ in range(12): controller.D_UP()
for _ in range(6): controller.D_LEFT()
screenshot = screen.takeScreenshot()
if screenshot == None:
time.sleep(MOVE_WAIT)
continue
if imgUtils.gameHasEnded(screenshot):
break
# grid = imgUtils.cropBoard2Player(screenshot)
# grid = imgUtils.filterBackground(grid)
grid = imgUtils.cropBoard1Player(screenshot)
grid = imgUtils.filterBackground1Player(grid)
# Generate board model.
inputRows = [[-1] * board.WIDTH for _ in range(board.HEIGHT)]
for square, rowIndex, colIndex in imgUtils.parseSquaresFromBoard(grid):
val, score = imgUtils.getSquareClassification(square, rowIndex, colIndex)
inputRows[rowIndex][colIndex] = Piece(val)
# Get next action and perform.
b = board.Board(inputRows=inputRows)
print '---BOARD---'
b.printBoard()
moves = brain.getNextMoves(b)
print '---MOVES---'
print moves
print ''
cursor_pos = performMoves(moves, (0, 0))
# End loop.
print '[emulator.py] Done running game.'
