# load parameter-json file
# with open(parameter_dir + parameterJsonPath) as parameter_file:
# json_data = json.load(parameter_file)
# x.append(json_data['Position']['X'])
# y.append(json_data['Position']['Y'])
# z.append(json_data['Position']['Z'])
# load json and image file
cut.load(dicom_file=imagePath, tracked_file=trackedJsonPath)
# 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
