def _set_model(self):
"""
Attempt to parse the model file and divine some useful
information about it.
"""
with open(self.process_filename, 'rb') as model_file:
self.model = model_loader.auto_detect(model_file, self.ext)
def _set_model(self):
"""
Attempt to parse the model file and divine some useful
information about it.
"""
with open(self.process_filename, 'rb') as model_file:
self.model = model_loader.auto_detect(model_file, self.ext)
def process_stl(proc_state):
"""Code to process an stl or obj model. Will be run by celery.
A Workbench() represents a local tempory dir. It is automatically
cleaned up when this function exits.
"""
entry = proc_state.entry
workbench = proc_state.workbench
queued_filepath = entry.queued_media_file
queued_filename = workbench.localized_file(
mgg.queue_store, queued_filepath, 'source')
name_builder = FilenameBuilder(queued_filename)
ext = queued_filename.lower().strip()[-4:]
if ext.startswith("."):
ext = ext[1:]
else:
ext = None
# Attempt to parse the model file and divine some useful
# information about it.
with open(queued_filename, 'rb') as model_file:
model = model_loader.auto_detect(model_file, ext)
# generate preview images
greatest = [model.width, model.height, model.depth]
greatest.sort()
greatest = greatest[-1]
def snap(name, camera, width=640, height=640, project="ORTHO"):
filename = name_builder.fill(name)
workbench_path = workbench.joinpath(filename)
shot = {
"model_path": queued_filename,
"model_ext": ext,
"camera_coord": camera,
"camera_focus": model.average,
"camera_clip": greatest*10,
"greatest": greatest,
"projection": project,
"width": width,
"height": height,
"out_file": workbench_path,
}
blender_render(shot)
# make sure the image rendered to the workbench path
assert os.path.exists(workbench_path)
# copy it up!
with open(workbench_path, 'rb') as rendered_file:
public_path = create_pub_filepath(entry, filename)
with mgg.public_store.get_file(public_path, "wb") as public_file:
public_file.write(rendered_file.read())
return public_path
thumb_path = snap(
"{basename}.thumb.jpg",
[0, greatest*-1.5, greatest],
mgg.global_config['media:thumb']['max_width'],
mgg.global_config['media:thumb']['max_height'],
project="PERSP")
perspective_path = snap(
"{basename}.perspective.jpg",
[0, greatest*-1.5, greatest], project="PERSP")
topview_path = snap(
"{basename}.top.jpg",
[model.average[0], model.average[1], greatest*2])
frontview_path = snap(
"{basename}.front.jpg",
[model.average[0], greatest*-2, model.average[2]])
sideview_path = snap(
"{basename}.side.jpg",
[greatest*-2, model.average[1], model.average[2]])
## Save the public file stuffs
model_filepath = create_pub_filepath(
entry, name_builder.fill('{basename}{ext}'))
with mgg.public_store.get_file(model_filepath, 'wb') as model_file:
with open(queued_filename, 'rb') as queued_file:
model_file.write(queued_file.read())
# Remove queued media file from storage and database.
# queued_filepath is in the task_id directory which should
# be removed too, but fail if the directory is not empty to be on
# the super-safe side.
mgg.queue_store.delete_file(queued_filepath) # rm file
mgg.queue_store.delete_dir(queued_filepath[:-1]) # rm dir
entry.queued_media_file = []
# Insert media file information into database
media_files_dict = entry.setdefault('media_files', {})
media_files_dict[u'original'] = model_filepath
media_files_dict[u'thumb'] = thumb_path
media_files_dict[u'perspective'] = perspective_path
media_files_dict[u'top'] = topview_path
media_files_dict[u'side'] = sideview_path
media_files_dict[u'front'] = frontview_path
# Put model dimensions into the database
dimensions = {
"center_x" : model.average[0],
"center_y" : model.average[1],
"center_z" : model.average[2],
"width" : model.width,
"height" : model.height,
"depth" : model.depth,
"file_type" : ext,
}
entry.media_data_init(**dimensions)
