def makedata(settype='train/', nframes=10):
# create directories and filenames
directory = os.path.expanduser(config['directory']) + settype
for dir in ('images', 'params'):
if not os.path.exists(os.path.join(directory, dir)):
os.makedirs(os.path.join(directory, dir))
shutil.copy2(configfile, directory)
filetxtname = os.path.join(directory, 'filenames.txt')
imgname = os.path.join(directory, 'images', 'image{:04d}.' + imgtype)
jsonname = os.path.join(directory, 'params', 'image{:04d}.json')
filetxt = open(filetxtname, 'w')
img_list = []
zlist = []
alist = []
nlist = []
for n in range(nframes): # for each frame ...
print(imgname.format(n))
sample = make_sample(config) # ... get params for particles
s = sample[0]
zlist.append(s.z_p)
alist.append(s.a_p)
nlist.append(s.n_p)
# ... calculate hologram
frame = np.random.normal(0, config['noise'], shape)
if len(sample) > 0:
holo.particle = sample
frame += holo.hologram().reshape(shape)
else:
frame += 1.
frame = np.clip(100 * frame, 0, 255).astype(np.uint8)
img_list.append(frame)
# ... and save the results
#do we need?
cv2.imwrite(imgname.format(n), frame)
with open(jsonname.format(n), 'w') as fp:
fp.write(format_json(sample, config))
filetxt.write(imgname.format(n) + '\n')
img_list = np.array(img_list).astype('float32')
img_list *= 1. / 255
zlist = np.array(zlist).astype('float32')
zlist = rescale(zmin, zmax, zlist)
alist = np.array(alist).astype('float32')
alist = rescale(amin, amax, alist)
nlist = np.array(nlist).astype('float32')
nlist = rescale(nmin, nmax, nlist)
params_list = [zlist, alist, nlist]
return img_list, params_list
def makedata_inner(config, settype='train', nframes=None):
# create directories and filenames
directory = os.path.abspath(os.path.expanduser(config['directory'])+settype)
if nframes is None:
nframes = config[settype]['nframes']
start = 0
tempnum = nframes
for dir in ('images', 'params'):
path = os.path.join(directory, dir)
if not os.path.exists(path):
os.makedirs(path)
already_files = len(os.listdir(path))
if already_files < tempnum: #if there are fewer than the number of files desired
tempnum = already_files
if not config['overwrite']:
start = tempnum
if start >= nframes:
return
with open(directory + '/config.json', 'w') as f:
json.dump(config, f)
filetxtname = os.path.join(directory, 'filenames.txt')
imgname = os.path.join(directory, 'images', 'image{:04d}.' + config['imgtype'])
jsonname = os.path.join(directory, 'params', 'image{:04d}.json')
filetxt = open(filetxtname, 'w')
#always only one particle per stamp
config['particle']['nspheres'] = [1,2]
shape = config['shape']
for n in range(start, nframes): # for each frame ...
print(imgname.format(n))
sample = make_sample(config) # ... get params for particles
s = sample[0]
# ext = feature_extent(s, config)
# #introduce 1% noise to ext
# ext = np.random.normal(ext, 0.01*ext)
# extsize = ext*2
# shapesize = shape[0]
# if extsize <= shapesize:
# scale = 1
# else:
# scale = int(np.floor(extsize/shapesize) + 1)
# newshape = [i * scale for i in shape]
# holo = LMHologram(coordinates=coordinates(newshape))
# holo.instrument.properties = config['instrument']
# # ... calculate hologram
# frame = np.random.normal(0, config['noise'], newshape)
# if len(sample) > 0:
# holo.particle = sample[0]
# holo.particle.x_p += (scale-1)*100
# holo.particle.y_p += (scale-1)*100
# frame += holo.hologram().reshape(newshape)
# else:
# frame += 1.
# frame = np.clip(100 * frame, 0, 255).astype(np.uint8)
# #decimate
# frame = frame[::scale, ::scale]
if config['scale_integer']:
frame, scale = scale_int(s, config)
else:
frame, scale = scale_float(s, config)
# ... and save the results
cv2.imwrite(imgname.format(n), frame)
with open(jsonname.format(n), 'w') as fp:
fp.write(format_json(sample, config, scale))
filetxt.write(imgname.format(n) + '\n')
return
def makedata(settype='train/', nframes=10):
# create directories and filenames
directory = os.path.expanduser(config['directory'])+settype
for dir in ('images', 'params'):
if not os.path.exists(os.path.join(directory, dir)):
os.makedirs(os.path.join(directory, dir))
shutil.copy2(configfile, directory)
filetxtname = os.path.join(directory, 'filenames.txt')
imgname = os.path.join(directory, 'images', 'image{:04d}.' + imgtype)
jsonname = os.path.join(directory, 'params', 'image{:04d}.json')
filetxt = open(filetxtname, 'w')
img_list = []
scale_list = []
zlist = []
alist = []
nlist = []
for n in range(nframes): # for each frame ...
print(imgname.format(n))
sample = make_sample(config) # ... get params for particles
s = sample[0]
zlist.append(s.z_p)
alist.append(s.a_p)
nlist.append(s.n_p)
ext = feature_extent(s, config)
#introduce 1% noise to ext
ext = np.random.normal(ext, 0.01*ext)
extsize = ext*2
shapesize = shape[0]
if extsize <= shapesize:
scale = 1
else:
scale = int(np.floor(extsize/shapesize) + 1)
newshape = [i * scale for i in shape]
holo = LMHologram(coordinates=coordinates(newshape))
holo.instrument.properties = config['instrument']
# ... calculate hologram
frame = np.random.normal(0, config['noise'], newshape)
if len(sample) > 0:
holo.particle = sample[0]
holo.particle.x_p += (scale-1)*100
holo.particle.y_p += (scale-1)*100
frame += holo.hologram().reshape(newshape)
else:
frame += 1.
frame = np.clip(100 * frame, 0, 255).astype(np.uint8)
#decimate
frame = frame[::scale, ::scale]
img_list.append(frame)
scale_list.append(scale)
# ... and save the results
#do we need?
cv2.imwrite(imgname.format(n), frame)
with open(jsonname.format(n), 'w') as fp:
fp.write(format_json(sample, config, scale))
filetxt.write(imgname.format(n) + '\n')
img_list = np.array(img_list).astype('float32')
img_list *= 1./255
zlist = np.array(zlist).astype('float32')
zlist = rescale(zmin, zmax, zlist)
alist = np.array(alist).astype('float32')
alist = rescale(amin, amax, alist)
nlist = np.array(nlist).astype('float32')
nlist = rescale(nmin, nmax, nlist)
scale_list = np.array(scale_list).astype(int)
params_list = [zlist, alist, nlist]
return img_list, params_list, scale_list