def load_brushes_size(self):
blend_files = common.get_files_from_directory(
self.root_blend_files + self.model_name + "/", ['blend'], "snap")
brush_sizes = []
for file in blend_files:
bpy.ops.wm.open_mainfile(filepath=file[0],
filter_blender=True,
filemode=8,
display_type='FILE_DEFAULTDISPLAY',
load_ui=False,
use_scripts=True)
try:
if bpy.data.scenes["Scene"]:
brush_sizes.append([
bpy.data.scenes["Scene"].tool_settings.
unified_paint_settings.size, bpy.data.scenes["Scene"].
tool_settings.unified_paint_settings.unprojected_radius
])
else:
brush_sizes.append(brush_sizes[-1])
except KeyError:
print('modifier not found')
for el in brush_sizes:
print(el)
bs_file = open("../steps/" + self.model_name + "/b_size", "wb+")
pickle.dump(brush_sizes, bs_file)
bs_file.close()
def parse_dir(files_path):
onlyfiles = common.get_files_from_directory(files_path, ['obj'])
for file in onlyfiles:
print(file)
data = parse_single_obj(file[0])
print(data)
print()
def __init__(self,
model_name,
obj_path="../obj2_files/",
steps_path="../steps/"):
self.model_name = model_name
self.obj_path = obj_path + self.model_name + "/"
self.mesh_no = len(
common.get_files_from_directory(self.obj_path, ["obj"],
start_with="snap"))
self.steps_path = steps_path
def __init__(self,
blend_root_dir,
obj_root_dir,
model_name,
start,
stop,
basemesh_name,
mode="obj"):
self.blend_files_path = blend_root_dir + model_name
self.obj_files_path = obj_root_dir + model_name
self.start = start
self.stop = stop
self.basemesh_name = basemesh_name
self.mode = mode
self.blend_files = common.get_files_from_directory(
self.blend_files_path, ['blend'], "snap")
self.blend_files.sort()
def parse_dir(files_path):
onlyfiles = common.get_files_from_directory(files_path, ['obj'])
for file in onlyfiles:
print(file)
loadOBJModel(file[0], True)
def distance_compressing(model_name, single_file=False):
'''
Produces the JSON for the data on mesh distances between steps
For every step, it saves:
- distance mean;
- distance variance;
- distance skewness;
- distance curtosis;
'''
if single_file:
file_name = "../steps/" + model_name[0] + "/distance_data.txt"
fh = open(file_name, 'r')
i = 0
distances = {}
idx = 0
for line in fh:
if i % 2 == 0:
idx = int(line)
else:
data = line.split(' ')
data = [
float(el) for idx, el in enumerate(data) if idx % 2 == 1
]
if data:
np_data = numpy.array(data)
distances[idx] = {}
distances[idx]["distance_mean"] = numpy.mean(np_data,
axis=0)
distances[idx]["distance_variance"] = numpy.var(np_data,
axis=0)
distances[idx]["distance_skewness"] = scs.skew(np_data,
axis=0)
distances[idx]["distance_curtosis"] = scs.kurtosis(np_data,
axis=0)
else:
distances[idx] = {}
distances[idx]["distance_mean"] = None
distances[idx]["distance_variance"] = None
distances[idx]["distance_skewness"] = None
distances[idx]["distance_curtosis"] = None
i += 1
fh.close()
common.save_json(distances,
"../steps/" + model_name[0] + "/distance_data.json",
compressed=False)
else:
dir_name = "../steps/" + model_name[0] + "/dist_data/"
files = common.get_files_from_directory(dir_name)
distances = {}
for path, filename in files:
step = int(filename[4:])
fh = open(path, 'r')
dists = []
for line in fh:
data = line.split(' ')
dists.append(float(data[1].strip()))
if dists:
distances[step] = {}
distances[step]["distance_mean"] = numpy.mean(dists, axis=0)
distances[step]["distance_variance"] = numpy.var(dists, axis=0)
distances[step]["distance_skewness"] = scs.skew(dists, axis=0)
distances[step]["distance_curtosis"] = scs.kurtosis(dists,
axis=0)
else:
distances[step] = {}
distances[step]["distance_mean"] = None
distances[step]["distance_variance"] = None
distances[step]["distance_skewness"] = None
distances[step]["distance_curtosis"] = None
common.save_json(distances,
"../steps/" + model_name[0] + "/distance_data.json",
compressed=False)
def __init__(self, model_name):
self.model_name = model_name
self.root_blend_files = "/Volumes/PART FAT/3ddata/"
self.blend_files = common.get_files_from_directory(self.root_blend_files + self.model_name + "/", ['blend'], "snap")
self.blend_files.sort()