def display(img,app,title="EMAN2 image"): if len(img)==1 : img=img[0] w=EMImageWidget(data=img,old=None,app=app) w.setWindowTitle(title) try: if file_exists(title): w.set_file_name(title) except: pass app.show_specific(w) try: w.optimally_resize() except: pass return w
def main():
usage = "Generate training set for tomogram segmentation. Please run this program from the GUI in e2projectmanager.py."
#print usage
parser = EMArgumentParser(usage=usage, version=EMANVERSION)
#parser.add_header(name="tmpheader", help='temp label', title="### This program is NOT avaliable yet... ###", row=0, col=0, rowspan=1, colspan=2, mode="box,seg,set")
#### boxing ####
parser.add_argument("--boxing",
action="store_true",
help="Boxing particles.",
default=False,
guitype='boolbox',
row=4,
col=0,
rowspan=1,
colspan=1,
mode='box[True]')
parser.add_pos_argument(
name="micrographs",
help="List the file to process with e2boxer here.",
default="",
guitype='filebox',
browser="EMRawDataTable(withmodal=True,startpath=\"rawtomograms\")",
row=1,
col=0,
rowspan=1,
colspan=3,
mode="box")
parser.add_argument("--boxsize",
"-B",
type=int,
help="Box size in pixels",
default=-1,
guitype='intbox',
row=3,
col=0,
rowspan=1,
colspan=3,
mode="box")
#### segment ####
#parser.add_header(name="instruction", help='instruction', title="### Mark the target features white ###", row=0, col=0, rowspan=1, colspan=2, mode="seg")
parser.add_argument("--segment",
action="store_true",
help="Segment particles.",
default=False,
guitype='boolbox',
row=4,
col=0,
rowspan=1,
colspan=1,
mode='seg[True]')
parser.add_pos_argument(name="particles",
help="Particle file.",
default="",
guitype='filebox',
browser="EMParticlesTable(withmodal=True)",
row=1,
col=0,
rowspan=1,
colspan=3,
mode="seg")
parser.add_argument(
"--output",
type=str,
help=
"output file name. Default is the input particle file name plus _seg.hdf",
default=None,
guitype='strbox',
row=3,
col=0,
rowspan=1,
colspan=3,
mode="seg")
#### build set ####
parser.add_argument("--buildset",
action="store_true",
help="Segment particles.",
default=False,
guitype='boolbox',
row=7,
col=0,
rowspan=1,
colspan=1,
mode='set[True]')
parser.add_argument("--particles_raw",
type=str,
help="Input raw particle file",
default=None,
guitype='filebox',
browser="EMParticlesTable(withmodal=True)",
row=1,
col=0,
rowspan=1,
colspan=3,
mode="set")
parser.add_argument("--particles_label",
type=str,
help="Input labels for particle file",
default=None,
guitype='filebox',
browser="EMParticlesTable(withmodal=True)",
row=2,
col=0,
rowspan=1,
colspan=3,
mode="set")
parser.add_argument("--boxes_negative",
type=str,
help="Input boxes of negative samples",
default=None,
guitype='filebox',
browser="EMParticlesTable(withmodal=True)",
row=3,
col=0,
rowspan=1,
colspan=3,
mode="set")
parser.add_argument(
"--ncopy",
type=int,
help=
"Number of copies for NEGATIVE samples. (number of copies of particles is calculated accordingly) ",
default=10,
guitype='intbox',
row=5,
col=0,
rowspan=1,
colspan=1,
mode="set")
parser.add_argument(
"--trainset_output",
type=str,
help=
"output file name of the training set.Default is the input particle file name plus _trainset.hdf",
default=None,
guitype='strbox',
row=4,
col=0,
rowspan=1,
colspan=3,
mode="set")
parser.add_argument("--zthick",
type=int,
help="Thickness in z ",
default=0,
guitype='intbox',
row=5,
col=1,
rowspan=1,
colspan=1,
mode="set")
parser.add_argument(
"--validset",
type=float,
help="Propotion of particles in validation set. Default is 0.2 ",
default=0.0,
guitype='floatbox',
row=7,
col=1,
rowspan=1,
colspan=1,
mode="set")
##################
parser.add_argument(
"--ppid",
type=int,
help="Set the PID of the parent process, used for cross platform PPID",
default=-1)
(options, args) = parser.parse_args()
logid = E2init(sys.argv)
#### boxing ###
if options.boxing:
db = js_open_dict(EMBOXERBASE_DB)
cache_box_size = True
if options.boxsize == -1:
cache_box_size = False
options.boxsize = db.setdefault("box_size", 64)
application = EMApp()
module = EMBoxerModule(args, options.boxsize)
module.show_interfaces()
application.execute(logid)
#### segment ###
if options.segment:
filename = args[0]
if options.output == None:
options.output = filename[:-4] + "_seg.hdf"
app = EMApp()
img = EMData.read_images(filename)
#print img[0]["mean"]
w = EMImageWidget(data=img, old=None, app=app, force_2d=True)
#w = EMWidgetFromFile(filename,application=app,force_2d=True)
w.setWindowTitle(base_name(filename))
w.show_inspector(1)
ins = w.get_inspector()
ins.mmtab.setCurrentIndex(5)
ins.dtpenv.setText('100')
w.set_mouse_mode(5)
app.show_specific(w)
app.exec_()
try:
os.remove(options.output)
except:
pass
for e in img:
e.process_inplace("threshold.belowtozero", {"minval": 99})
e.process_inplace("threshold.binary", {"value": 1})
e.write_image(options.output, -1)
#### build set ###
if options.buildset:
tomo_in = options.particles_raw
seg_in = options.particles_label
neg_in = options.boxes_negative
if tomo_in and neg_in and seg_in:
n_ptcl = EMUtil.get_image_count(tomo_in)
n_neg = EMUtil.get_image_count(neg_in)
if options.trainset_output == None:
options.trainset_output = tomo_in[:-4] + "_trainset.hdf"
p_copy = options.ncopy * n_neg / n_ptcl
else:
p_copy = options.ncopy
try:
os.remove(options.trainset_output)
except:
pass
print(
"making {} copies for particles, and {} copies for negative samples"
.format(p_copy, options.ncopy))
imgs = []
if tomo_in and seg_in:
n_ptcl = EMUtil.get_image_count(tomo_in)
for i in range(n_ptcl):
#t=EMData(tomo_in,i)
t = get_box(tomo_in, i, options.zthick)
if t == None: continue
s = EMData(seg_in, i)
for c in range(p_copy):
tr = Transform()
rd = random.random() * 360
tr.set_rotation({"type": "2d", "alpha": rd})
e = t.process("xform", {"transform": tr})
#e.process_inplace("normalize")
imgs.append(e)
#e.write_image(options.trainset_output,-1)
e = s.process("xform", {"transform": tr})
#e.write_image(options.trainset_output,-1)
imgs.append(e)
ngood = len(imgs)
if neg_in:
s = EMData(neg_in, 0)
s.to_zero()
n_neg = EMUtil.get_image_count(neg_in)
for i in range(n_neg):
t = get_box(neg_in, i, options.zthick)
if t == None: continue
for c in range(options.ncopy):
tr = Transform()
rd = random.random() * 360
tr.set_rotation({"type": "2d", "alpha": rd})
e = t.process("xform", {"transform": tr})
#e.process_inplace("normalize")
#e.write_image(options.trainset_output,-1)
imgs.append(e)
e = s.process("xform", {"transform": tr})
#e.write_image(options.trainset_output,-1)
imgs.append(e)
print("Shuffling particles...")
### randomize
n = len(imgs)
#n=EMUtil.get_image_count(options.trainset_output)
idx = range(int((ngood / 2) * (1 - options.validset))) + range(
(ngood / 2), int(n / 2 * (1 - options.validset)))
random.shuffle(idx)
for i in idx:
imgs[i * 2]["valid_set"] = 0
imgs[i * 2].write_image(options.trainset_output, -1)
imgs[i * 2 + 1]["valid_set"] = 0
imgs[i * 2 + 1].write_image(options.trainset_output, -1)
idx = range(int(
(ngood / 2) * (1 - options.validset)), ngood / 2) + range(
int(n / 2 * (1 - options.validset)), n / 2)
random.shuffle(idx)
for i in idx:
imgs[i * 2]["valid_set"] = 1
imgs[i * 2].write_image(options.trainset_output, -1)
imgs[i * 2 + 1]["valid_set"] = 1
imgs[i * 2 + 1].write_image(options.trainset_output, -1)
#e=EMData(options.trainset_output,i*2)
#e.process_inplace("normalize")
#e.write_image(tmpfile,-1)
#e=EMData(options.trainset_output,i*2+1)
#e.write_image(tmpfile,-1)
#shutil.move(tmpfile,options.trainset_output)
print("Generate a training set of {:d} samples.".format(n / 2))
print("Done")
E2end(logid)