annotate projective_transformation_points.py @ 3:a84822a0060c draft

"planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit 220e44642b9998e649b51904cf973b61a8161c18"
author imgteam
date Fri, 08 May 2020 05:22:34 -0400
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ed8a71e13f7b planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit c3f4b766f03770f094fda6bda0a5882c0ebd4581
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1 from skimage.transform import ProjectiveTransform
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2 from scipy.ndimage import map_coordinates
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3 import numpy as np
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4 import pandas as pd
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5 import argparse
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8 def _stackcopy(a, b):
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9 if a.ndim == 3:
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10 a[:] = b[:, :, np.newaxis]
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11 else:
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12 a[:] = b
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15 def warp_img_coords_batch(coord_map, shape, dtype=np.float64, batch_size=1000000):
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16 rows, cols = shape[0], shape[1]
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17 coords_shape = [len(shape), rows, cols]
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18 if len(shape) == 3:
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19 coords_shape.append(shape[2])
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20 coords = np.empty(coords_shape, dtype=dtype)
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22 tf_coords = np.indices((cols, rows), dtype=dtype).reshape(2, -1).T
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24 for i in range(0, (tf_coords.shape[0]//batch_size+1)):
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25 tf_coords[batch_size*i:batch_size*(i+1)] = coord_map(tf_coords[batch_size*i:batch_size*(i+1)])
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26 tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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28 _stackcopy(coords[1, ...], tf_coords[0, ...])
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29 _stackcopy(coords[0, ...], tf_coords[1, ...])
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30 if len(shape) == 3:
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31 coords[2, ...] = range(shape[2])
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33 return coords
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36 def warp_coords_batch(coord_map, coords, dtype=np.float64, batch_size=1000000):
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37 tf_coords = coords.astype(np.float32)[:, ::-1]
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39 for i in range(0, (tf_coords.shape[0]//batch_size)+1):
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40 tf_coords[batch_size*i:batch_size*(i+1)] = coord_map(tf_coords[batch_size*i:batch_size*(i+1)])
ed8a71e13f7b planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/projective_transformation_points/ commit c3f4b766f03770f094fda6bda0a5882c0ebd4581
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42 return tf_coords[:, ::-1]
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45 def transform(coords, warp_matrix, out):
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46 roi_coords = np.array(pd.read_csv(coords, delimiter="\t"))
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47 trans_matrix = np.array(pd.read_csv(warp_matrix, delimiter="\t", header=None))
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48
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49 tol = 10
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50 moving = np.zeros(np.max(roi_coords,axis=0)+tol, dtype=np.int8)
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51 idx_roi_coords = (roi_coords[:,0]-1) * moving.shape[1] + roi_coords[:,1] - 1
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52 moving.flat[idx_roi_coords] = 1
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54 transP = ProjectiveTransform(matrix=trans_matrix)
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55 roi_coords_warped_direct = warp_coords_batch(transP, roi_coords)
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56 shape_fixed = np.round(np.max(roi_coords_warped_direct,axis=0)).astype(roi_coords.dtype)+tol
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58 transI = ProjectiveTransform(matrix=np.linalg.inv(trans_matrix))
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59 img_coords_warped = warp_img_coords_batch(transI, shape_fixed)
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61 moving_warped = map_coordinates(moving, img_coords_warped, mode='constant', cval=0)
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62 idx_roi_coords_warped = np.where(moving_warped==1)
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64 df = pd.DataFrame()
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65 df['x'] = idx_roi_coords_warped[0] + 1
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66 df['y'] = idx_roi_coords_warped[1] + 1
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67 df.to_csv(out, index = False, sep="\t")
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70 if __name__ == "__main__":
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71 parser = argparse.ArgumentParser(description="Transform coordinates")
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72 parser.add_argument("coords", help="Paste path to .csv with coordinates to transform (tab separated)")
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73 parser.add_argument("warp_matrix", help="Paste path to .csv that should be used for transformation (, separated)")
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74 parser.add_argument("out", help="Paste path to file in which transformed coords should be saved (tab separated)")
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75 args = parser.parse_args()
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76 transform(args.coords, args.warp_matrix, args.out)