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annotate rowit.py @ 1:41e8efe8df43 draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/S3segmenter commit c8f72e04db2cc6cc26f0359d5aa3b1a972bc6d53"
author watsocam
date Fri, 11 Mar 2022 23:37:49 +0000
parents 37acf42a824b
children
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1 import numpy as np
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2 from skimage.util import view_as_windows, montage
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5 class WindowView(object):
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6
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7 def __init__(
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8 self, img_shape, block_size, overlap_size
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9 ):
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10 self.img_shape = img_shape
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11 self.block_size = block_size
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12 self.overlap_size = overlap_size
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14 self.step_size = block_size - overlap_size
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16 def window_view_list(self, img, pad_mode='constant'):
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17 half = int(self.overlap_size / 2)
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18 img = np.pad(img, (
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19 (half, self.padded_shape[0] - self.img_shape[0] - half),
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20 (half, self.padded_shape[1] - self.img_shape[1] - half),
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21 ), mode=pad_mode)
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23 return self._window_view_list(img)
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24
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25 def padding_mask(self):
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26 half = int(self.overlap_size / 2)
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27 padding_mask = np.ones(self.img_shape, dtype=np.bool)
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28 padding_mask = np.pad(padding_mask, (
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29 (half, self.padded_shape[0] - self.img_shape[0] - half),
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30 (half, self.padded_shape[1] - self.img_shape[1] - half),
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31 ), mode='constant', constant_values=0)
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32 return self._window_view_list(padding_mask)
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34 def reconstruct(self, img_window_view_list):
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35 grid_shape = self.window_view_shape[:2]
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37 start = int(self.overlap_size / 2)
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38 end = int(self.block_size - start)
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40 img_window_view_list = img_window_view_list[..., start:end, start:end]
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41
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42 return montage(
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43 img_window_view_list, grid_shape=grid_shape
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44 )[:self.img_shape[0], :self.img_shape[1]]
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45
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46 @property
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47 def padded_shape(self):
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48 padded_shape = np.array(self.img_shape) + self.overlap_size
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49 n = np.ceil((padded_shape - self.block_size) / self.step_size)
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50 padded_shape = (self.block_size + (n * self.step_size)).astype(np.int)
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51 return tuple(padded_shape)
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52
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53 @property
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54 def window_view_shape(self):
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55 return view_as_windows(
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56 np.empty(self.padded_shape),
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57 self.block_size, self.step_size
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58 ).shape
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59
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60 def _window_view_list(self, img):
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61 return (
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62 view_as_windows(img, self.block_size, self.step_size)
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63 .reshape(-1, self.block_size, self.block_size)
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64 )
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65
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66 def crop_with_padding_mask(img, padding_mask, return_mask=False):
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67 if np.all(padding_mask == 1):
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68 return (img, padding_mask) if return_mask else img
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69 (r_s, r_e), (c_s, c_e) = [
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70 (i.min(), i.max() + 1)
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71 for i in np.where(padding_mask == 1)
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72 ]
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73 padded = np.zeros_like(img)
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74 img = img[r_s:r_e, c_s:c_e]
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75 padded[r_s:r_e, c_s:c_e] = 1
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76 return (img, padded) if return_mask else img