def filter_frame_cl_batch(self, images):
if not self.filter_set_cl:
_update_filter_bank_cl(self.filter_bank_to_device)
self.filter_set_cl = True
assert(self.filter_width == 3)
result = _filter_frame_cl_3_batch(
images,
self.filter_bank_size,
self.nchannels, self.optype)
def filter_frame_cl(self, framenum):
if not self.filter_set_cl:
_update_filter_bank_cl(self.filter_bank_to_device)
self.filter_set_cl = True
frame = cvmat2array(self.origin.get_frame(framenum))
if len(frame.shape) == 2: #if this is a single channel image, fake the last channel
frame = np.reshape(frame,(frame.shape[0], frame.shape[1], 1))
#interleave the channels
frame_ilv = np.empty((frame.shape[0], frame.shape[1] * frame.shape[2]), dtype='float32')
for k in range(self.nchannels):
frame_ilv[:,k::self.nchannels] = frame[:,:,k]
frame_dm = DeviceMatrixCL(frame_ilv)
if self.filter_width == 3:
fn = _filter_frame_cl_3
elif self.filter_width == 5:
fn = _filter_frame_cl_5
elif self.filter_width == 7:
fn = _filter_frame_cl_7
result = fn(frame_dm,
self.filter_bank_size,
self.nchannels,
self.optype)
result = result.mat()
result[:, :self.apron_y,:] = -1
result[:,-self.apron_y:,:] = -1
result[:,:, :self.apron_x] = -1
result[:,:,-self.apron_x:] = -1
return result
