def main(net_output_filename, image_filename, output_filename): print "Importing data..." net_output = emio.znn_img_read(net_output_filename) image = emio.znn_img_read(image_filename) print "Cropping channel data..." #cropping the channel data to the 3d shape of the affinity graph cropped_image = crop(image, net_output.shape[-3:]) image_outname = 'channel_{}'.format(path.basename(output_filename)) print "Writing network output file..." write_affinity_file(net_output, output_filename) print "Writing image file..." write_channel_file(cropped_image, image_outname)
def main(filename, fov_x, fov_y, fov_z, outname): print "Reading data..." original_data = emio.znn_img_read(filename) assert len(original_data.shape) == 3 buffer_sizes = np.array((fov_z, fov_y, fov_x)) / 2 print "Buffering..." result = mirror_data(original_data, buffer_sizes) print "Saving..." emio.znn_img_save(result, outname)
def load_data(output_fname): if 'h5' not in output_fname: vol = emio.znn_img_read(output_fname) if len(vol.shape) > 3: if vol.shape[0] > 2: #multiclass output vol = vol[0,:,:,:] # vol = np.argmax(vol, axis=0) else: #binary output vol = vol[0,:,:,:] else: f = h5py.File(output_fname) vol = f['/main'] return vol
def load_data(output_fname): if 'h5' not in output_fname: vol = emio.znn_img_read(output_fname) if len(vol.shape) > 3: if vol.shape[0] > 2: #multiclass output vol = vol[0, :, :, :] # vol = np.argmax(vol, axis=0) else: #binary output vol = vol[0, :, :, :] else: f = h5py.File(output_fname) vol = f['/main'] return vol