示例#1
0
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. This program is still experimental. Please consult the developers before using. "
	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=20, 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("--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)
		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)
				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")
					e.write_image(options.trainset_output,-1)
					e=s.process("xform",{"transform":tr})
					e.write_image(options.trainset_output,-1)
		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=EMData(neg_in,i)
				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)
					e=s.process("xform",{"transform":tr})
					e.write_image(options.trainset_output,-1)

		print "Shuffling particles..."
		### randomize
		n=EMUtil.get_image_count(options.trainset_output)
		idx=range(n/2)
		random.shuffle(idx)
		tmpfile="tmpfile_maketomotrainset.hdf"
		for i in idx:
			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)