def dataset_Leaf(self):
"""
Create the data for the Foliage Dataset
:return:
"""
self.create_data_base()
self.make_directories("other")
print("Create id Text File ...")
id_d = Make_Id_Species.Set_Id_Species(self.p + "train/",
self.path_database["data"])
id_d.set_list_id()
id_d.set_id_dic()
print("Create File ...")
for directories_data in ["normalization", "augmentation"]:
self.m_dir.make_directories(id_d.path_dict,
self.path_database[directories_data])
print("Normalize ...")
norm_train = Normalization.Normalize(
self.p + "train/", self.path_database["normalization"])
norm_train.normalize_dimension_image()
print("Augmentation ...")
d_augm = Data_Augmentation.Data_Augmentation(
self.path_database["normalization"],
self.path_database["augmentation"])
d_augm.create_augmentation(False)
print("Normalize Validation...")
shutil.rmtree(self.path_database["normalization"])
os.makedirs(self.a + "normalization/")
self.m_dir.make_directories(id_d.path_dict,
self.path_database["normalization"])
norm_test = Normalization.Normalize(
self.p + "validation/", self.path_database["normalization"])
norm_test.normalize_dimension_image()
norm_test.reduce()
m_file = Make_train_val_file()
print("Create Text File ...")
path_for_val_train = {
"train": "augmentation",
"validation": "normalization"
}
for i in path_for_val_train:
m_file.make_file(self.path_database[path_for_val_train[i]],
self.path_database["data"] + i + ".txt",
id_d.path_dict)
self.lmdb_build.set_lmdb(self.path_database, self.a,
self.caffe_path + "build/tools")
mean = Create_Mean()
mean.make_mean(self.a, self.a + "lmdb/",
self.caffe_path + "build/tools")
def main():
print('My name is Abhishek Sinha')
obj = Normalization.Normalize()
obj.generateData()
