print('\n'.join(file_ns))
save_folders = []
for ch_n in ch_ns:
curr_save_folder = os.path.join(data_folder, file_identifier, ch_n)
if not os.path.isdir(curr_save_folder):
os.makedirs(curr_save_folder)
save_folders.append(curr_save_folder)
curr_step = 0
for file_n in file_ns:
curr_mov = tf.imread(os.path.join(data_folder, file_n))
# reorient movie
curr_mov = ia.rigid_transform_cv2_2d(curr_mov[:, ::-1, :], rotation=135)
curr_frame_num = curr_mov.shape[0] / len(ch_ns)
if curr_frame_num % frames_per_step != 0:
raise ValueError(
'{}: total frame number is not divisible by frames per step.'.
format(file_n))
curr_mov_chs = []
for ch_i in range(len(ch_ns)):
curr_mov_chs.append(curr_mov[ch_i::len(ch_ns)])
steps = curr_frame_num // frames_per_step
for step_ind in range(steps):
curr_vasmap_ch = (curr_vasmap_ch * 255).astype(np.uint8)
curr_vasmap_ch = cv2.equalizeHist(curr_vasmap_ch).astype(
np.float32)
vasmaps[ch_n].append(curr_vasmap_ch)
for ch_n, ch_vasmap in vasmaps.items():
# save_vasmap = np.concatenate(ch_vasmap, axis=0)
# print(save_vasmap.shape)
# save_vasmap = ia.array_nor(np.mean(save_vasmap, axis=0))
# print(save_vasmap.shape)
save_vasmap = ia.array_nor(np.mean(ch_vasmap, axis=0))
if scope == 'scientifica':
save_vasmap_r = save_vasmap[::-1, :]
save_vasmap_r = ia.rigid_transform_cv2_2d(save_vasmap_r, rotation=135)
elif scope == 'sutter':
save_vasmap_r = save_vasmap.transpose()[::-1, :]
elif scope == 'deepscope':
save_vasmap_r = ia.rigid_transform_cv2(save_vasmap,
rotation=140)[:, ::-1]
else:
raise LookupError(
"Do not understand scope type. Should be 'sutter' or 'deepscope' or 'scientifica'."
)
tf.imsave('{}_{}.tif'.format(identifier, ch_n),
save_vasmap.astype(np.float32))
tf.imsave('{}_{}_rotated.tif'.format(identifier, ch_n),
save_vasmap_r.astype(np.float32))