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import copy |
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import io |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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from importlib import import_module |
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MODULE_MAPPING = { |
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'DetResizeForTest': '.db_resize_for_test', |
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'CopyPaste': '.crop_paste', |
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'IaaAugment': '.iaa_augment', |
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'EastRandomCropData': '.crop_resize', |
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'DetLabelEncode': '.db_label_encode', |
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'MakeBorderMap': '.db_label_encode', |
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'MakeShrinkMap': '.db_label_encode', |
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} |
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class NormalizeImage(object): |
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"""normalize image such as substract mean, divide std""" |
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def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs): |
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if isinstance(scale, str): |
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scale = eval(scale) |
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) |
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mean = mean if mean is not None else [0.485, 0.456, 0.406] |
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std = std if std is not None else [0.229, 0.224, 0.225] |
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shape = (3, 1, 1) if order == 'chw' else (1, 1, 3) |
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self.mean = np.array(mean).reshape(shape).astype('float32') |
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self.std = np.array(std).reshape(shape).astype('float32') |
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def __call__(self, data): |
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img = data['image'] |
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from PIL import Image |
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if isinstance(img, Image.Image): |
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img = np.array(img) |
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assert isinstance(img, |
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np.ndarray), "invalid input 'img' in NormalizeImage" |
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data['image'] = (img.astype('float32') * self.scale - |
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self.mean) / self.std |
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return data |
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class ToCHWImage(object): |
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"""convert hwc image to chw image""" |
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def __init__(self, **kwargs): |
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pass |
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def __call__(self, data): |
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img = data['image'] |
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from PIL import Image |
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if isinstance(img, Image.Image): |
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img = np.array(img) |
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data['image'] = img.transpose((2, 0, 1)) |
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return data |
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class KeepKeys(object): |
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def __init__(self, keep_keys, **kwargs): |
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self.keep_keys = keep_keys |
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def __call__(self, data): |
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data_list = [] |
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for key in self.keep_keys: |
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data_list.append(data[key]) |
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return data_list |
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def transform(data, ops=None): |
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"""transform.""" |
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if ops is None: |
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ops = [] |
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for op in ops: |
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data = op(data) |
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if data is None: |
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return None |
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return data |
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class DecodeImage(object): |
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"""decode image.""" |
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def __init__(self, |
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img_mode='RGB', |
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channel_first=False, |
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ignore_orientation=False, |
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**kwargs): |
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self.img_mode = img_mode |
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self.channel_first = channel_first |
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self.ignore_orientation = ignore_orientation |
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def __call__(self, data): |
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img = data['image'] |
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assert type(img) is bytes and len( |
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img) > 0, "invalid input 'img' in DecodeImage" |
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img = np.frombuffer(img, dtype='uint8') |
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if self.ignore_orientation: |
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img = cv2.imdecode( |
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img, cv2.IMREAD_IGNORE_ORIENTATION | cv2.IMREAD_COLOR) |
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else: |
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img = cv2.imdecode(img, 1) |
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if img is None: |
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return None |
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if self.img_mode == 'GRAY': |
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
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elif self.img_mode == 'RGB': |
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % ( |
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img.shape) |
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img = img[:, :, ::-1] |
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if self.channel_first: |
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img = img.transpose((2, 0, 1)) |
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data['image'] = img |
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return data |
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class DecodeImagePIL(object): |
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"""decode image.""" |
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def __init__(self, img_mode='RGB', **kwargs): |
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self.img_mode = img_mode |
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def __call__(self, data): |
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img = data['image'] |
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assert type(img) is bytes and len( |
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img) > 0, "invalid input 'img' in DecodeImage" |
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img = data['image'] |
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buf = io.BytesIO(img) |
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img = Image.open(buf).convert('RGB') |
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if self.img_mode == 'Gray': |
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img = img.convert('L') |
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elif self.img_mode == 'BGR': |
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img = np.array(img)[:, :, ::-1] |
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img = Image.fromarray(np.uint8(img)) |
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data['image'] = img |
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return data |
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def dynamic_import(class_name): |
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module_path = MODULE_MAPPING.get(class_name) |
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if not module_path: |
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raise ValueError(f'Unsupported class: {class_name}') |
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module = import_module(module_path, package=__package__) |
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return getattr(module, class_name) |
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def create_operators(op_param_list, global_config=None): |
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ops = [] |
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for op_info in op_param_list: |
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op_name = list(op_info.keys())[0] |
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param = copy.deepcopy(op_info[op_name]) or {} |
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if global_config: |
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param.update(global_config) |
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if op_name in globals(): |
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op_class = globals()[op_name] |
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else: |
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op_class = dynamic_import(op_name) |
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ops.append(op_class(**param)) |
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return ops |
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