| import torch, torchvision, imageio, os |
| import imageio.v3 as iio |
| from PIL import Image |
|
|
|
|
| class DataProcessingPipeline: |
| def __init__(self, operators=None): |
| self.operators: list[DataProcessingOperator] = [] if operators is None else operators |
| |
| def __call__(self, data): |
| for operator in self.operators: |
| data = operator(data) |
| return data |
| |
| def __rshift__(self, pipe): |
| if isinstance(pipe, DataProcessingOperator): |
| pipe = DataProcessingPipeline([pipe]) |
| return DataProcessingPipeline(self.operators + pipe.operators) |
|
|
|
|
| class DataProcessingOperator: |
| def __call__(self, data): |
| raise NotImplementedError("DataProcessingOperator cannot be called directly.") |
| |
| def __rshift__(self, pipe): |
| if isinstance(pipe, DataProcessingOperator): |
| pipe = DataProcessingPipeline([pipe]) |
| return DataProcessingPipeline([self]).__rshift__(pipe) |
|
|
|
|
| class DataProcessingOperatorRaw(DataProcessingOperator): |
| def __call__(self, data): |
| return data |
|
|
|
|
| class ToInt(DataProcessingOperator): |
| def __call__(self, data): |
| return int(data) |
|
|
|
|
| class ToFloat(DataProcessingOperator): |
| def __call__(self, data): |
| return float(data) |
|
|
|
|
| class ToStr(DataProcessingOperator): |
| def __init__(self, none_value=""): |
| self.none_value = none_value |
| |
| def __call__(self, data): |
| if data is None: data = self.none_value |
| return str(data) |
|
|
|
|
| class LoadImage(DataProcessingOperator): |
| def __init__(self, convert_RGB=True): |
| self.convert_RGB = convert_RGB |
| |
| def __call__(self, data: str): |
| image = Image.open(data) |
| if self.convert_RGB: image = image.convert("RGB") |
| return image |
|
|
|
|
| class ImageCropAndResize(DataProcessingOperator): |
| def __init__(self, height=None, width=None, max_pixels=None, height_division_factor=1, width_division_factor=1): |
| self.height = height |
| self.width = width |
| self.max_pixels = max_pixels |
| self.height_division_factor = height_division_factor |
| self.width_division_factor = width_division_factor |
|
|
| def crop_and_resize(self, image, target_height, target_width): |
| width, height = image.size |
| scale = max(target_width / width, target_height / height) |
| image = torchvision.transforms.functional.resize( |
| image, |
| (round(height*scale), round(width*scale)), |
| interpolation=torchvision.transforms.InterpolationMode.BILINEAR |
| ) |
| image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) |
| return image |
| |
| def get_height_width(self, image): |
| if self.height is None or self.width is None: |
| width, height = image.size |
| if width * height > self.max_pixels: |
| scale = (width * height / self.max_pixels) ** 0.5 |
| height, width = int(height / scale), int(width / scale) |
| height = height // self.height_division_factor * self.height_division_factor |
| width = width // self.width_division_factor * self.width_division_factor |
| else: |
| height, width = self.height, self.width |
| return height, width |
| |
| def __call__(self, data: Image.Image): |
| image = self.crop_and_resize(data, *self.get_height_width(data)) |
| return image |
|
|
|
|
| class ToList(DataProcessingOperator): |
| def __call__(self, data): |
| return [data] |
| |
|
|
| class LoadVideo(DataProcessingOperator): |
| def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x): |
| self.num_frames = num_frames |
| self.time_division_factor = time_division_factor |
| self.time_division_remainder = time_division_remainder |
| |
| self.frame_processor = frame_processor |
| |
| def get_num_frames(self, reader): |
| num_frames = self.num_frames |
| if int(reader.count_frames()) < num_frames: |
| num_frames = int(reader.count_frames()) |
| while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: |
| num_frames -= 1 |
| return num_frames |
| |
| def __call__(self, data: str): |
| reader = imageio.get_reader(data) |
| num_frames = self.get_num_frames(reader) |
| frames = [] |
| for frame_id in range(num_frames): |
| frame = reader.get_data(frame_id) |
| frame = Image.fromarray(frame) |
| frame = self.frame_processor(frame) |
| frames.append(frame) |
| reader.close() |
| return frames |
|
|
|
|
| class SequencialProcess(DataProcessingOperator): |
| def __init__(self, operator=lambda x: x): |
| self.operator = operator |
| |
| def __call__(self, data): |
| return [self.operator(i) for i in data] |
|
|
|
|
| class LoadGIF(DataProcessingOperator): |
| def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x): |
| self.num_frames = num_frames |
| self.time_division_factor = time_division_factor |
| self.time_division_remainder = time_division_remainder |
| |
| self.frame_processor = frame_processor |
| |
| def get_num_frames(self, path): |
| num_frames = self.num_frames |
| images = iio.imread(path, mode="RGB") |
| if len(images) < num_frames: |
| num_frames = len(images) |
| while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: |
| num_frames -= 1 |
| return num_frames |
| |
| def __call__(self, data: str): |
| num_frames = self.get_num_frames(data) |
| frames = [] |
| images = iio.imread(data, mode="RGB") |
| for img in images: |
| frame = Image.fromarray(img) |
| frame = self.frame_processor(frame) |
| frames.append(frame) |
| if len(frames) >= num_frames: |
| break |
| return frames |
|
|
|
|
| class RouteByExtensionName(DataProcessingOperator): |
| def __init__(self, operator_map): |
| self.operator_map = operator_map |
| |
| def __call__(self, data: str): |
| file_ext_name = data.split(".")[-1].lower() |
| for ext_names, operator in self.operator_map: |
| if ext_names is None or file_ext_name in ext_names: |
| return operator(data) |
| raise ValueError(f"Unsupported file: {data}") |
|
|
|
|
| class RouteByType(DataProcessingOperator): |
| def __init__(self, operator_map): |
| self.operator_map = operator_map |
| |
| def __call__(self, data): |
| for dtype, operator in self.operator_map: |
| if dtype is None or isinstance(data, dtype): |
| return operator(data) |
| raise ValueError(f"Unsupported data: {data}") |
|
|
|
|
| class LoadTorchPickle(DataProcessingOperator): |
| def __init__(self, map_location="cpu"): |
| self.map_location = map_location |
| |
| def __call__(self, data): |
| return torch.load(data, map_location=self.map_location, weights_only=False) |
|
|
|
|
| class ToAbsolutePath(DataProcessingOperator): |
| def __init__(self, base_path=""): |
| self.base_path = base_path |
| |
| def __call__(self, data): |
| return os.path.join(self.base_path, data) |
|
|
|
|
| class LoadAudio(DataProcessingOperator): |
| def __init__(self, sr=16000): |
| self.sr = sr |
| def __call__(self, data: str): |
| import librosa |
| input_audio, sample_rate = librosa.load(data, sr=self.sr) |
| return input_audio |
|
|
