def dump_subvol(self,picking_result):
from aitom.classify.deep.unsupervised.autoencoder.autoencoder_util import peaks_to_subvolumes
subvols_loc = os.path.join(self.dump_path,"demo_single_particle_subvolumes.pickle")
a = io_file.read_mrc_data(self.path)
d = peaks_to_subvolumes(im_vol_util.cub_img(a)['vt'], picking_result, 32)
io_file.pickle_dump(d, subvols_loc)
print("Save subvolumes .pickle file to:", subvols_loc)
def particle_picking(mrc_header):
sigma1 = max(int(7 / voxel_spacing_in_nm),
2) # In general, 7 is optimal sigma1 val in nm according to the paper and sigma1 should at least be 2
print('sigma1=%d' % sigma1)
# For particular tomogram, larger sigma1 value may have better results. Use IMOD to display selected peaks and determine best sigma1.
# For 'aitom_demo_cellular_tomogram.mrc', sigma1 is 5 rather than 3 for better performance(in this tomogram, 7nm corresponds to 3.84 pixels)
# print(mrc_header['MRC']['xlen'], mrc_header['MRC']['nx'], voxel_spacing_in_nm, sigma1)
partition_op = {'nonoverlap_width': sigma1 * 20, 'overlap_width': sigma1 * 10, 'save_vg': False}
result = picking(path, s1=sigma1, s2=sigma1 * 1.1, t=3, find_maxima=False, partition_op=partition_op,
multiprocessing_process_num=10, pick_num=1000)
print("DoG done, %d particles picked" % len(result))
pprint(result[:5])
# (Optional) Save subvolumes of peaks for autoencoder input
dump_subvols = True
if dump_subvols: # use later for autoencoder
subvols_loc = "demo_single_particle_subvolumes.pickle"
from aitom.classify.deep.unsupervised.autoencoder.autoencoder_util import peaks_to_subvolumes
a = io_file.read_mrc_data(path)
d = peaks_to_subvolumes(im_vol_util.cub_img(a)['vt'], result, 32)
io_file.pickle_dump(d, subvols_loc)
print("Save subvolumes .pickle file to:", subvols_loc)
def main():
# Download from: https://cmu.box.com/s/9hn3qqtqmivauus3kgtasg5uzlj53wxp
path = '/ldap_shared/home/v_zhenxi_zhu/data/aitom_demo_single_particle_tomogram.mrc'
# Also, we can crop and only use part of the mrc image instead of binning for tasks requiring higher resolution
# crop_path = 'cropped.mrc'
# crop_mrc(path, crop_path)
mrc_header = io_file.read_mrc_header(path)
voxel_spacing_in_nm = mrc_header['MRC']['xlen'] / mrc_header['MRC'][
'nx'] / 10
sigma1 = max(
int(7 / voxel_spacing_in_nm), 2
) # In general, 7 is optimal sigma1 val in nm according to the paper and sigma1 should at least be 2
print('sigma1=%d' % sigma1)
# For particular tomogram, larger sigma1 value may have better results. Use IMOD to display selected peaks and determine best sigma1.
# For 'aitom_demo_cellular_tomogram.mrc', sigma1 is 5 rather than 3 for better performance(in this tomogram, 7nm corresponds to 3.84 pixels)
# print(mrc_header['MRC']['xlen'], mrc_header['MRC']['nx'], voxel_spacing_in_nm, sigma1)
partition_op = {
'nonoverlap_width': sigma1 * 20,
'overlap_width': sigma1 * 10,
'save_vg': False
}
result = picking(path,
s1=sigma1,
s2=sigma1 * 1.1,
t=3,
find_maxima=False,
partition_op=partition_op,
multiprocessing_process_num=10,
pick_num=1000)
print("DoG done, %d particles picked" % len(result))
pprint(result[:5])
# (Optional) Save subvolumes of peaks for autoencoder input
dump_subvols = True
if dump_subvols: # use later for autoencoder
subvols_loc = "demo_single_particle_subvolumes.pickle"
from aitom.classify.deep.unsupervised.autoencoder.autoencoder_util import peaks_to_subvolumes
a = io_file.read_mrc_data(path)
d = peaks_to_subvolumes(im_vol_util.cub_img(a)['vt'], result, 32)
io_file.pickle_dump(d, subvols_loc)
print("Save subvolumes .pickle file to:", subvols_loc)
# Display selected peaks using imod/3dmod (http://bio3d.colorado.edu/imod/)
'''
#Optional: smooth original image
a = io_file.read_mrc_data(path)
path =path[:-5]+'_smoothed'+path[-4:]
temp = im_vol_util.cub_img(a)
s1 = sigma1
s2=sigma1*1.1
vg = dog_smooth(temp['vt'], s1,s2)
#vg = smooth(temp['vt'], s1)
TIM.write_data(vg,path)
'''
json_data = [] # generate file for 3dmod
for i in range(len(result)):
loc_np = result[i]['x']
loc = []
for j in range(len(loc_np)):
loc.append(loc_np[j].tolist())
json_data.append({'peak': {'loc': loc}})
with open('data_json_file.json', 'w') as f:
json.dump(json_data, f)
dj = json_data
x = N.zeros((len(dj), 3))
for i, d in enumerate(dj):
x[i, :] = N.array(d['peak']['loc'])
l = generate_lines(x_full=x, rad=sigma1)
display_map_with_lines(l=l, map_file=path)