# we add noise to the original position of the data
for j in range(numCuts):
# rotate image with normally distributed angle with sigma = 10 degrees
normalDistAngle = np.random.normal(0, config['angleStddev'])
cut.rotate(normalDistAngle)
# generate random image center point in range -12 - + 12 from the current ceiled center point
randomRadius = random.uniform(0, config['displacement'])
randomPhi = random.uniform(0, 360) # in degrees
images[i + j * numImages] = cut.cut_to(
(randomRadius * math.cos(math.radians(randomPhi)),
randomRadius * math.sin(math.radians(randomPhi))))
t = cut.targets_normalized()
tmp = []
for t_ in t:
for t__ in t_:
tmp.append(t__)
targets[i + j * numImages] = tmp
if i % 10 == 0:
print(f'\rcompleted input image {i} of {numImages} images')
print('Writing images and target information to file...')
# write the images and their corresponding head positions to hdf5
if args.mode == 'range':
h5_filename = cut[
'HDF5Directory'] + f'{numCuts:02d}_{args.start:08d}-{args.stop:08d}_{args.step:d}'
