channels = ['DAPI', 'GCaMP', 'mRuby', 'NeuN']
downsample_rate = 0.1
curr_folder = os.path.realpath(os.path.dirname(__file__))
os.chdir(curr_folder)
fns = [f for f in os.listdir(curr_folder) if f[-4:] == '.btf']
fns.sort()
print('\n'.join(fns))
for fn in fns:
print('\nprocessing {} ...'.format(fn))
big_img = tf.imread(fn)
fname = os.path.splitext(fn)[0]
print('shape: {}'.format(big_img.shape))
print('dtype: {}'.format(big_img.dtype))
# comb_img = []
for chi, chn in enumerate(channels):
print('\tchannel: {}'.format(chn))
down_img_ch = ia.rigid_transform_cv2(big_img[chi],
zoom=downsample_rate).astype(
np.uint16)[::-1, :]
tf.imsave('thumbnail_{}_{:02d}_{}.tif'.format(fname, chi, chn),
down_img_ch)
# comb_img.append(down_img_ch)
# comb_img = np.array(comb_img)
# tf.imsave('{}_downsampled.tif'.format(fname), comb_img)
big_region = (np.array(thumbnail_region) / thumbnail_d_rate).astype(np.uint64)
print('region in big image: {}'.format(big_region))
thumbnail_fns = [
f for f in os.listdir(curr_folder) if base_name in f and f[-4:] == '.tif'
]
ch_lst = []
for chn in channels:
curr_chi = [int(f.split('_')[-2]) for f in thumbnail_fns if chn in f]
if len(curr_chi) != 1:
raise LookupError
ch_lst.append(curr_chi[0])
print('channel index list: {}'.format(ch_lst))
big_img = tf.imread(base_name + '.btf')
print('reading the big image: {}.btf ...'.format(base_name))
section_img = []
for ch_i in ch_lst:
curr_img = big_img[ch_i][::-1, :][big_region[0]:big_region[1],
big_region[2]:big_region[3]]
curr_img = ia.rigid_transform_cv2(curr_img, zoom=d_rate).astype(np.uint16)
section_img.append(curr_img)
section_img = np.array(section_img)
print('saving {} ...'.format(save_name))
tf.imsave(save_name, section_img)
print('planes:')
print('\n'.join(plane_ns))
for plane_n in plane_ns:
print('\nprocessing plane: {}'.format(plane_n))
save_folder = os.path.join(curr_folder, plane_n)
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
for ch_n in ch_ns:
print('\n\tprocessing channel: {}'.format(ch_n))
plane_folder = os.path.join(data_folder, plane_n, ch_n, 'corrected')
# f_ns = [f for f in os.listdir(plane_folder) if f[-14:] == '_corrected.tif']
f_ns= [f for f in os.listdir(plane_folder) if f[-4:] == '.tif' and identifier in f]
f_ns.sort()
print('\t\t'+'\n\t\t'.join(f_ns) + '\n')
mov_d = []
for f_n in f_ns:
print('\t\tprocessing {} ...'.format(f_n))
curr_mov = tf.imread(os.path.join(plane_folder, f_n))
curr_mov_d = ia.rigid_transform_cv2(img=curr_mov, zoom=(1. / xy_downsample_rate))
curr_mov_d = ia.z_downsample(curr_mov_d, downSampleRate=t_downsample_rate, is_verbose=False)
mov_d.append(curr_mov_d)
mov_d = np.concatenate(mov_d, axis=0)
save_n = '{}_{}_{}_downsampled.tif'.format(os.path.split(data_folder)[1], plane_n, ch_n)
tf.imsave(os.path.join(save_folder, save_n), mov_d)
import os
import numpy as np
import tifffile as tf
import NeuroAnalysisTools.MotionCorrection as mc
import NeuroAnalysisTools.core.ImageAnalysis as ia
fn = 'zstack_green_aligned.tif'
scope = 'sutter' # 'sutter' or 'deepscope' or 'scientifica'
curr_folder = os.path.dirname(os.path.realpath(__file__))
os.chdir(curr_folder)
stack = tf.imread(fn)
if scope == 'sutter':
stack_r = stack.transpose((0, 2, 1))[:, ::-1, :]
elif scope == 'deepscope':
h_new = int(stack.shape[1] * np.sqrt(2))
w_new = int(stack.shape[2] * np.sqrt(2))
stack_r = ia.rigid_transform_cv2(stack, rotation=140, outputShape=(h_new, w_new))[:, :, ::-1]
elif scope == 'scientifica':
h_new = int(stack.shape[1] * np.sqrt(2))
w_new = int(stack.shape[2] * np.sqrt(2))
stack_r = ia.rigid_transform_cv2(stack[:,::-1,:], rotation=135, outputShape=(h_new, w_new))
else:
raise LookupError("Do not understand scope type. Should be 'sutter' or 'deepscope' or 'scientifica'.")
tf.imsave(os.path.splitext(fn)[0] + '_rotated.tif', stack_r.astype(stack.dtype))