def stat():
df = read(queries.stats)
df.columns = [column.upper() for column in df.columns]
df = df[df.CATEGORY != 'miscellaneous']
df['CATEGORY'] = [item.upper() for item in df['CATEGORY']]
df['SUBCATEGORY'] = [item.capitalize() for item in df['SUBCATEGORY']]
df = df.pivot_table(
index=['CATEGORY', 'SUBCATEGORY'],
margins=True,
margins_name='======= TOTAL =======', # defaults to 'All'
aggfunc=sum)
return render_template(
'stats.html',
tables=[df.to_html(table_id='stats', classes='data', header="true")])
def make_dataset_1(path):
data = read(queries.select_dataset)
with open(os.path.join(path, 'label.csv'), 'w') as f:
for idx, item in tqdm(data.iterrows()):
filename = "%08d" % item['id'] + '.obj'
if os.path.isfile(item['file']):
copyfile(item['file'], os.path.join(path, filename))
item['subcategory'] = item['subcategory'] if item[
'category'] else 'null'
f.write(','.join([
filename, 'null',
purify(item['category']),
purify(item['subcategory'])
]) + '\n')
else:
print(filename, item['id'])
def get_zip(id):
obj_path = read(queries.select_object_by_id.format(id=id))['file'][0]
zip_path = obj_path.replace('.obj', 'zip')
with ZipFile(zip_path, 'w') as zip:
zip.write(obj_path)
@after_this_request
def remove_zipfile(response):
try:
os.remove(zip_path)
except Exception as e:
print(e)
return response
return send_from_directory(os.path.dirname(zip_path),
os.path.basename(zip_path))
def make_dataset_2(path, categories=None):
data = read(queries.select_dataset)
if categories:
data = data[data['category'].isin(categories)]
eval_portion = 0.1
test_portion = 0.2
data['subcategory'] = [
item if item else data.loc[idx, 'category']
for idx, item in enumerate(data['subcategory'])
]
subcategories = list(set(data['subcategory'].tolist()))
f_train = open(os.path.join(path, 'train.csv'), 'w')
f_test = open(os.path.join(path, 'test.csv'), 'w')
f_eval = open(os.path.join(path, 'eval.csv'), 'w')
for subcategory in tqdm(subcategories):
if subcategory == 'None':
continue
subcat_set = data[data['subcategory'] == subcategory]
subcat_set = shuffle(subcat_set)
num_subcat_set = len(subcat_set)
num_subset_eval_set = int(num_subcat_set * eval_portion)
num_subset_test_set = int(num_subcat_set * test_portion)
if num_subset_eval_set < 3:
continue
for idx, (_, item) in enumerate(subcat_set.iterrows()):
filename = "%08d" % item['id'] + '.obj'
item['subcategory'] = purify(item['subcategory'])
item['category'] = purify(item['category'])
if idx < num_subset_eval_set:
data_type = 'eval'
file = f_eval
elif idx < num_subset_eval_set + num_subset_test_set:
data_type = 'test'
file = f_test
else:
data_type = 'train'
file = f_train
dst_path = os.path.join(path, data_type, item['subcategory'])
make_dir(dst_path)
obj_file = os.path.join(dst_path, filename)
copyfile(item['file'], obj_file)
normalized_mesh = normalize_mesh(obj_file)
with open(obj_file, 'w') as f:
f.write(normalized_mesh)
file.write(','.join(
[filename, 'null', item['category'], item['subcategory']]) +
'\n')
f_test.close()
f_train.close()
f_eval.close()
def get_keywords():
return read(queries.select_keywords)
def update_thumbnail():
agent.read('select * from ')
def is_model(cadid):
return not agent.read(
f"SELECT * from cad_file WHERE source=2 and source_id='{cadid}'").empty
def get_img(id):
img_path = read(queries.select_object_by_id.format(id=id))['image'][0]
return send_from_directory(os.path.dirname(img_path),
os.path.basename(img_path))
def get_obj(id):
obj_path = read(queries.select_object_by_id.format(id=id))['file'][0]
return send_from_directory(os.path.dirname(obj_path),
os.path.basename(obj_path))
def get_miscellaneous():
return read("SELECT * FROM keyword WHERE parent=214")
def get_subcategory(parent):
return read(queries.select_category.format(parent=parent))
def get_dataset():
return read(queries.select_taxonomy)
def get_category():
return read(queries.select_category.format(parent=0))
def get_unlabeled_imgs():
return read(queries.select_unlabeled)
def get_keyword_id(keyword):
return read(f"SELECT * from keyword where name = '{keyword}'")['id'][0]
def get_exist_files(output_dir):
return agent.read(queries.check_trace_part_data.format(file=output_dir))['name'].to_list()
def get_cad_imgs(label, cad_type):
return read(queries.select_images.format(label=label, cad_type=cad_type))
