def augments(cls,
image_folder_path,
dest_folder_path,
aug_per_image,
include_base=False):
"""
:param include_base: Thêm ảnh ban đầu vào thư mục được aug
:param aug_per_image: Số ảnh aug ra từ ảnh ban đầu
:param image_folder_path: đường dẫn tới thư mục lưu các hình ban đầu
image_folder_path:
label_1:
image_1
label_2:
image_2
:param dest_folder_path: đường dẫn tới thư mục lưu các hình được aug
des_folder_path
label_1
image_1
augmented_image_1_1
augmented_image_1_2
label_2
image_2
augmented_image_2_1
augmented_image_2_2
:return: None
"""
FileUtils.remove_dirs(dest_folder_path)
FileUtils.make_dirs(dest_folder_path)
dataloaders = cls.data_block.dataloaders(Path(image_folder_path),
bs=cls.batch_size)
dataloader = dataloaders.train
vocab = dataloaders.train_ds.vocab
for aug_index in range(aug_per_image):
for image_batch, y_batch in dataloader:
for index, (image, y) in enumerate(zip(image_batch, y_batch)):
image = cls.to_pillow_image(image)
label = vocab[y]
FileUtils.make_dirs(FileUtils.join(dest_folder_path,
label))
image.save(
FileUtils.join(
dest_folder_path, label,
f"{label}_augmented_{aug_index + 1}.jpg"))
if include_base:
for label_name in FileUtils.list_top_folders_names(
image_folder_path):
des_label_folder_path = FileUtils.join(dest_folder_path,
label_name)
FileUtils.make_dirs(des_label_folder_path)
for file_name in FileUtils.list_top_files_names(
FileUtils.join(image_folder_path, label_name)):
source_file_path = FileUtils.join(image_folder_path,
label_name, file_name)
des_file_path = FileUtils.join(des_label_folder_path,
file_name)
Image.open(FileUtils.join(source_file_path)).resize(
(cls.size, cls.size)).save(des_file_path)
def extract_faces(portrait_folder_path="portrait", face_folder_path="face"):
print("extract_faces", "start")
FileUtils.remove_dirs(face_folder_path)
FileUtils.make_dirs(face_folder_path)
for label in tqdm(FileUtils.list_top_folders_names(portrait_folder_path)):
for file_name in FileUtils.list_top_files_names(FileUtils.join(portrait_folder_path, label)):
portrait = Image.open(FileUtils.join(portrait_folder_path, label, file_name))
face = FaceExtracter.extract(portrait)
if not face:
print(f"Không tìm thấy khuôn mặt nào trong chân dung {file_name} của {label}")
continue
label_face_folder_path = FileUtils.join(face_folder_path, label)
face_file_path = FileUtils.join(label_face_folder_path, file_name)
FileUtils.make_dirs(label_face_folder_path)
face.save(face_file_path)
print("extract_faces", "completed")
def train_classifier(data_folder_path="face_augmented"):
print("train_classifier", "start")
X = []
y = []
print("train_classifier", "embed_faces", "start")
for label in tqdm(FileUtils.list_top_folders_names(data_folder_path)):
faces = []
for file_name in FileUtils.list_top_files_names(FileUtils.join(data_folder_path, label)):
face = Image.open(FileUtils.join(data_folder_path, label, file_name))
faces.append(face)
embeds = ImageEmbedder.embeds(faces)
X += [embed for embed in embeds]
y += [label] * len(faces)
print("train_classifier", "embed_faces", "completed")
print("train_classifier", "fit_classifier", "start")
FaceClassifier.fit(numpy.array(X), numpy.array(y))
print("train_classifier", "fit_classifier", "completed")
