vis.py 2.8 KB

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586
  1. import matplotlib
  2. matplotlib.use('nbagg')
  3. import numpy as np
  4. import matplotlib.pyplot as plt
  5. from p2ch11.dsets import Ct, LunaDataset
  6. clim=(0.0, 1.3)
  7. def findMalignantSamples(start_ndx=0, limit=10):
  8. ds = LunaDataset()
  9. malignantSample_list = []
  10. for sample_tup in ds.sample_list:
  11. if sample_tup[2]:
  12. print(len(malignantSample_list), sample_tup)
  13. malignantSample_list.append(sample_tup)
  14. if len(malignantSample_list) >= limit:
  15. break
  16. return malignantSample_list
  17. def showNodule(series_uid, batch_ndx=None, **kwargs):
  18. ds = LunaDataset(series_uid=series_uid, **kwargs)
  19. malignant_list = [i for i, x in enumerate(ds.sample_list) if x[2]]
  20. if batch_ndx is None:
  21. if malignant_list:
  22. batch_ndx = malignant_list[0]
  23. else:
  24. print("Warning: no malignant samples found; using first non-malignant sample.")
  25. batch_ndx = 0
  26. ct = Ct(series_uid)
  27. # ct_tensor, malignant_tensor, series_uid, center_irc = ds[batch_ndx]
  28. malignant_tensor, diameter_mm, series_uid, center_irc, nodule_tensor = ds[batch_ndx]
  29. ct_ary = nodule_tensor[1].numpy()
  30. fig = plt.figure(figsize=(15, 25))
  31. group_list = [
  32. #[0,1,2],
  33. [3,4,5],
  34. [6,7,8],
  35. [9,10,11],
  36. #[12,13,14],
  37. #[15]
  38. ]
  39. subplot = fig.add_subplot(len(group_list) + 2, 3, 1)
  40. subplot.set_title('index {}'.format(int(center_irc.index)))
  41. plt.imshow(ct.ary[int(center_irc.index)], clim=clim, cmap='gray')
  42. subplot = fig.add_subplot(len(group_list) + 2, 3, 2)
  43. subplot.set_title('row {}'.format(int(center_irc.row)))
  44. plt.imshow(ct.ary[:,int(center_irc.row)], clim=clim, cmap='gray')
  45. subplot = fig.add_subplot(len(group_list) + 2, 3, 3)
  46. subplot.set_title('col {}'.format(int(center_irc.col)))
  47. plt.imshow(ct.ary[:,:,int(center_irc.col)], clim=clim, cmap='gray')
  48. subplot = fig.add_subplot(len(group_list) + 2, 3, 4)
  49. subplot.set_title('index {}'.format(int(center_irc.index)))
  50. plt.imshow(ct_ary[ct_ary.shape[0]//2], clim=clim, cmap='gray')
  51. subplot = fig.add_subplot(len(group_list) + 2, 3, 5)
  52. subplot.set_title('row {}'.format(int(center_irc.row)))
  53. plt.imshow(ct_ary[:,ct_ary.shape[1]//2], clim=clim, cmap='gray')
  54. subplot = fig.add_subplot(len(group_list) + 2, 3, 6)
  55. subplot.set_title('col {}'.format(int(center_irc.col)))
  56. plt.imshow(ct_ary[:,:,ct_ary.shape[2]//2], clim=clim, cmap='gray')
  57. for row, index_list in enumerate(group_list):
  58. for col, index in enumerate(index_list):
  59. subplot = fig.add_subplot(len(group_list) + 2, 3, row * 3 + col + 7)
  60. subplot.set_title('slice {}'.format(index))
  61. plt.imshow(ct_ary[index*2], clim=clim, cmap='gray')
  62. print(series_uid, batch_ndx, bool(malignant_tensor[0]), malignant_list, ct.vxSize_xyz)
  63. return ct_ary