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from typing import Optional, Tuple, Union |
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import matplotlib.pyplot as plt |
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import tifffile |
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import torch |
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import numpy as np |
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import mmcv |
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from mmcv.transforms import to_tensor |
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from mmcv.transforms.base import BaseTransform |
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from mmengine.structures import InstanceData, PixelData |
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from mmdet.registry import TRANSFORMS |
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from mmdet.structures import DetDataSample |
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from mmdet.structures.bbox import BaseBoxes |
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import mmengine.fileio as fileio |
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def irflyfromfile(img_path, img_num=1,repeat =1) -> np.ndarray: |
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"""Read an image from bytes. |
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Args: |
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backend (str | None): The image decoding backend type. |
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Returns: |
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ndarray: Loaded image array. |
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Examples: |
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""" |
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with tifffile.TiffFile(img_path) as tif: |
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img = tif.asarray() |
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img = np.expand_dims(img, axis=-1) |
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img_name = img_path.split('/')[-1] |
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try: |
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img_id = int(img_name.split('.')[0]) |
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except: |
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img_id = 1 |
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for i in range(1, img_num): |
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img_id_i = img_id - 1 |
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img_name_i = str(img_id_i).zfill(6)+'.tiff' |
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img_path_i = img_path.replace(img_name, img_name_i) |
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with tifffile.TiffFile(img_path_i) as tif: |
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img_i = tif.asarray() |
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img_i = np.expand_dims(img_i, axis=-1) |
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img = np.concatenate((img_i, img), axis=-1) |
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img = np.repeat(img,repeat,axis=-1) |
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return img |
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@TRANSFORMS.register_module() |
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class LoadIRFlyImageFromFiles(BaseTransform): |
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"""Load multi-channel images from a list of separate channel files. |
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Required Keys: |
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- img_path |
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Modified Keys: |
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- img |
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- img_shape |
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- ori_shape |
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Args: |
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to_float32 (bool): Whether to convert the loaded image to a float32 |
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numpy array. If set to False, the loaded image is an uint8 array. |
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Defaults to False. |
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""" |
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def __init__( |
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self, |
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to_float32: bool = False, |
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image_num: int = 1, |
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repeat: int =1, |
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normalized_basis = None, |
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) -> None: |
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self.to_float32 = to_float32 |
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self.image_num = image_num |
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self.repeat = repeat |
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self.normalized_basis = normalized_basis |
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def transform(self, results: dict) -> dict: |
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"""Transform functions to load multiple images and get images meta |
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information. |
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Args: |
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results (dict): Result dict from :obj:`mmdet.CustomDataset`. |
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Returns: |
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dict: The dict contains loaded images and meta information. |
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""" |
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img = irflyfromfile(results['img_path'],img_num = self.image_num, repeat=self.repeat) |
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if self.normalized_basis == None: |
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img = img/1500 |
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else: |
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img = img/np.array(self.normalized_basis) |
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if self.to_float32: |
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img = img.astype(np.float32) |
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results['img'] = img |
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results['img_shape'] = img.shape[:2] |
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results['ori_shape'] = img.shape[:2] |
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return results |
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def __repr__(self): |
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repr_str = (f'{self.__class__.__name__}(' |
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f'to_float32={self.to_float32}, ') |
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return repr_str |
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