# Construct the mean shift object from it, including a composite kernel...
ms = MeanShift()
ms.set_data(data, 'df')
ms.set_kernel('composite(2:gaussian,2:fisher(32.0))')
ms.set_spatial('kd_tree')
ms.set_scale(numpy.array([10.0,5.0,1.0,1.0]))
ms.merge_range = 0.05
# Print out information in a convoluted way to test some convoluted features!..
ms2 = MeanShift()
ms2.copy_kernel(ms)
print 'Kernel:', ms2.get_kernel()
del ms2
# For our first trick visualise the data set...
img = numpy.zeros((size, size, 3), dtype=numpy.float32)
for sample in data:
s_x = (size-1) * sample[1] / scale
s_y = (size-1) * sample[0] / scale
e_x = (size-1) * (sample[1] + angle_len * sample[3]) / scale
e_y = (size-1) * (sample[0] + angle_len * sample[2]) / scale
for i in xrange(angle_step):
(-1, 1)), numpy.sin(direction).reshape((-1, 1))),
axis=1)
data.append(block)
data = numpy.concatenate(data, axis=0)
# Construct the mean shift object from it, including a composite kernel...
ms = MeanShift()
ms.set_data(data, 'df')
ms.set_kernel('composite(2:gaussian,2:fisher(32.0))')
ms.set_spatial('kd_tree')
ms.set_scale(numpy.array([10.0, 5.0, 1.0, 1.0]))
ms.merge_range = 0.05
# Print out information in a convoluted way to test some convoluted features!..
ms2 = MeanShift()
ms2.copy_kernel(ms)
print 'Kernel:', ms2.get_kernel()
del ms2
# For our first trick visualise the data set...
img = numpy.zeros((size, size, 3), dtype=numpy.float32)
for sample in data:
s_x = (size - 1) * sample[1] / scale
s_y = (size - 1) * sample[0] / scale
e_x = (size - 1) * (sample[1] + angle_len * sample[3]) / scale
e_y = (size - 1) * (sample[0] + angle_len * sample[2]) / scale
for i in xrange(angle_step):
t = float(i) / (angle_step - 1)
t_x = int(t * s_x + (1 - t) * e_x)
