def load_vox(vox_path): # 用于加载单个体素，参数是体素的路径，返回以np.ndarray存储的体素
with open(vox_path, 'rb') as f: # rb是以二进制读形式打开
# third_party.binvox_rw.read_as_3d_array().data将体素数据读取成三维数组，具体进入.\third_party\binvox_rw.py中查看
# 其中，三维数组代表体素的x，y，z。占用的体素值为true，非占用的体素值为false
# keras.utils.to_categorical()用于将数据转换成0，1独热编码的形式
# 在这个例子中，to_categorical将false转换成[1,0],将true转换成[0,1]
return to_categorical(
binvox_rw.read_as_3d_array(f).data) # 返回转换成独热编码的数组
def load_label(label_samples):
if isinstance(label_samples, str):
label_samples = [label_samples]
ret = []
for voxel_path in label_samples:
with open(voxel_path, 'rb') as f:
ret.append(binvox_rw.read_as_3d_array(f).data)
return (np.stack(ret) if len(ret) != 1 else ret[0])
def load_vox(vox_path):
with open(vox_path, 'rb') as f:
return to_categorical(binvox_rw.read_as_3d_array(f).data)