def _updateSquareData(self, i, j):
x0 = i * self._squareWidth
x1 = x0 + self._squareWidth
y0 = j * self._squareWidth
y1 = y0 + self._squareWidth
# Find the two dominant colors in the square.
self._clusterer.fit(
self._board[y0:y1, x0:x1].reshape(
self._squareArea, 3))
# Find the proportion of the square's area that is
# occupied by the less dominant color.
freq = numpy.mean(self._clusterer.labels_)
if freq > 0.5:
freq = 1.0 - freq
# Find the distance between the dominant colors.
dist = ColorUtils.normColorDist(
self._clusterer.cluster_centers_[0],
self._clusterer.cluster_centers_[1])
self._squareFreqs[j, i] = freq
self._squareDists[j, i] = dist
def _updateSquareData(self, i, j):
x0 = i * self._squareWidth
x1 = x0 + self._squareWidth
y0 = j * self._squareWidth
y1 = y0 + self._squareWidth
# Find the two dominant colors in the square.
self._clusterer.fit(self._board[y0:y1,
x0:x1].reshape(self._squareArea, 3))
# Find the proportion of the square's area that is
# occupied by the less dominant color.
freq = numpy.mean(self._clusterer.labels_)
if freq > 0.5:
freq = 1.0 - freq
# Find the distance between the dominant colors.
dist = ColorUtils.normColorDist(self._clusterer.cluster_centers_[0],
self._clusterer.cluster_centers_[1])
self._squareFreqs[j, i] = freq
self._squareDists[j, i] = dist
