コード例 #1
0
 def voi(self, prob, label):
     prob = np.int64(1 - prob)
     f_image, n_labels = mahotas.label(prob)
     f_image = np.int64(f_image.flatten())
     f_label = np.int64(label.flatten())
     return n_labels, partition_comparison.variation_of_information(
         f_image, f_label)
コード例 #2
0
ファイル: util.py プロジェクト: fagan2888/em-quality-metric
    def create_vi_plot(initial_segmentation,
                       new_segmentation,
                       vi_s,
                       filepath=None):

        # create plot
        new_segmentation_target = Util.propagate_max_overlap(
            new_segmentation, initial_segmentation)
        before_vi = partition_comparison.variation_of_information(
            new_segmentation.ravel(), initial_segmentation.ravel())
        target_vi = partition_comparison.variation_of_information(
            new_segmentation_target.ravel(), initial_segmentation.ravel())

        bins = np.arange(0, len(vi_s))

        before_vi = [before_vi] * len(vi_s)
        target_vi = [target_vi] * len(vi_s)

        fig, ax = plt.subplots()

        ax.plot(bins, target_vi, 'k--', label='Target VI')
        ax.plot(bins, vi_s_, 'k', label='Variation of Information')
        ax.plot(bins, before_vi, 'k:', label='VI before')
        # ax.set_yscale('log')

        # Now add the legend with some customizations.
        legend = ax.legend(loc='upper center', shadow=True)
        ax.set_ylim([-0.5, 1.5])

        # The frame is matplotlib.patches.Rectangle instance surrounding the legend.
        frame = legend.get_frame()
        frame.set_facecolor('0.90')

        # Set the fontsize
        for label in legend.get_texts():
            label.set_fontsize('large')

        for label in legend.get_lines():
            label.set_linewidth(1.5)  # the legend line width

        if filepath:
            plt.savefig(filepath)
コード例 #3
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    def apply(cls, label_array):
        '''
    Apply the metric to a label_array with shape Y,X,Z.
    '''

        vi_sum = 0.
        done = 0.

        print 'Calulating for', label_array.shape[0], 'slices.'

        for z in range(label_array.shape[0] - 1):
            vi_sum += partition_comparison.variation_of_information(
                label_array[z, :, :].astype(np.uint64).ravel(),
                label_array[z + 1, :, :].astype(np.uint64).ravel())

            done += 1
            percentage = int(((done) / (label_array.shape[0] - 1)) * 100)
            if (percentage % 10 == 0):
                print percentage, '% done'

        vi_sum /= label_array.shape[0]

        return vi_sum
コード例 #4
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    for thresh in thresh_range:

        below_thresh = smooth_output < np.uint16(max_smooth * thresh)

        #below_thresh = mahotas.morph.close(below_thresh.astype(np.bool), disc)
        #below_thresh = mahotas.morph.open(below_thresh.astype(np.bool), disc)

        seeds,nseeds = mahotas.label(below_thresh)

        if nseeds == 0:
            continue

        ws = np.uint32(mahotas.cwatershed(smooth_output, seeds))

        voi_score = partition_comparison.variation_of_information(target_segs.ravel(), ws.ravel())

        thresh_voi_results.append(voi_score)

        print 's={0:0.2f}, t={1:0.2f}, voi_score={2:0.4f}.'.format(smooth_sigma, thresh, voi_score)

        dx, dy = np.gradient(ws)
        result = np.logical_or(dx!=0, dy!=0)

        figsize(20,20)
        imshow(result, cmap=cm.gray)
        plt.show()

        if voi_score < best_score:
            best_score = voi_score
            best_sigma = smooth_sigma
コード例 #5
0
ファイル: vi_rav.py プロジェクト: oshaikh13/dojo
# print out.shape

# print np.where(segs.ravel() != 0)
# print np.where(gt.ravel() != 0)
# print np.where(out.ravel() != 0)

# crop the data to only image data
x = 210
y = 60
z = 50  # we dont need since we have already cut the z
dim_x = dim_y = 400
segs = segs[:, y:y + dim_y, x:x + dim_x]
gt = gt[:, y:y + dim_y, x:x + dim_x]
#out = out[:,y:y+dim_y,x:x+dim_x]

print 'VI groundtruth vs. groundtruth:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(),
    gt.astype(np.uint32).ravel())
print 'VI groundtruth vs. pipeline:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(), segs.ravel())
print 'VI groundtruth vs. proofread:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(), out.ravel())

print 'Adjusted RandIndex groundtruth vs. groundtruth:', metrics.adjusted_rand_score(
    gt.astype(np.uint32).ravel(),
    gt.astype(np.uint32).ravel())
print 'Adjusted RandIndex groundtruth vs. pipeline:', metrics.adjusted_rand_score(
    gt.astype(np.uint32).ravel(), segs.ravel())
print 'Adjusted RandIndex groundtruth vs. proofread:', metrics.adjusted_rand_score(
    gt.astype(np.uint32).ravel(), out.ravel())
コード例 #6
0
ファイル: vi_exp.py プロジェクト: 3Scan/dojo
import numpy as np
from sklearn import metrics

segs = tif.imread('/home/d/TMP/DOJO/mojo_400_5_orig/cut-train-segs.tif')
gt = tif.imread('/home/d/TMP/DOJO/mojo_400_5_orig/cut-train-labels.tif')


exp = tif.imread('/home/d/dong.tif')

exp[exp==0] = 2000

# crop the data to only image data
x = 210
y = 60
z = 50 # we dont need since we have already cut the z
dim_x = dim_y = 400
segs = segs[:,y:y+dim_y,x:x+dim_x]
gt = gt[:,y:y+dim_y,x:x+dim_x]
#out = out[:,y:y+dim_y,x:x+dim_x]


print 'VI groundtruth vs. groundtruth:', pc.variation_of_information(gt.astype(np.uint32).ravel(), gt.astype(np.uint32).ravel())
print 'VI groundtruth vs. pipeline:',pc.variation_of_information(gt.astype(np.uint32).ravel(), segs.ravel())
# print 'VI groundtruth vs. proofread:', pc.variation_of_information(gt.astype(np.uint32).ravel(), out.ravel())
print 'VI groundtruth vs. expert:', pc.variation_of_information(gt.astype(np.uint32).ravel(), exp.astype(np.uint32).ravel())

print 'Adjusted RandIndex groundtruth vs. groundtruth:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), gt.astype(np.uint32).ravel())
print 'Adjusted RandIndex groundtruth vs. pipeline:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), segs.ravel())
# print 'Adjusted RandIndex groundtruth vs. proofread:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), out.ravel())
print 'Adjusted RandIndex groundtruth vs. expert:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), exp.astype(np.uint32).ravel())
コード例 #7
0
ファイル: util.py プロジェクト: xcode2010/mlproof
 def vi(array1, array2):
     '''
 '''
     return partition_comparison.variation_of_information(
         array1.ravel(), array2.ravel())
コード例 #8
0
ファイル: vi_rav.py プロジェクト: 3Scan/dojo
  #   out = np.dstack([out, im])
  # else:
  #   out = im
  #   out_is_there = True

# print segs.shape
# print gt.shape
# print out.shape

# print np.where(segs.ravel() != 0)
# print np.where(gt.ravel() != 0)
# print np.where(out.ravel() != 0)

# crop the data to only image data
x = 210
y = 60
z = 50 # we dont need since we have already cut the z
dim_x = dim_y = 400
segs = segs[:,y:y+dim_y,x:x+dim_x]
gt = gt[:,y:y+dim_y,x:x+dim_x]
#out = out[:,y:y+dim_y,x:x+dim_x]


print 'VI groundtruth vs. groundtruth:', pc.variation_of_information(gt.astype(np.uint32).ravel(), gt.astype(np.uint32).ravel())
print 'VI groundtruth vs. pipeline:',pc.variation_of_information(gt.astype(np.uint32).ravel(), segs.ravel())
print 'VI groundtruth vs. proofread:', pc.variation_of_information(gt.astype(np.uint32).ravel(), out.ravel())

print 'Adjusted RandIndex groundtruth vs. groundtruth:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), gt.astype(np.uint32).ravel())
print 'Adjusted RandIndex groundtruth vs. pipeline:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), segs.ravel())
print 'Adjusted RandIndex groundtruth vs. proofread:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), out.ravel())
コード例 #9
0
def run(slice):

  DATA_PATH = '/Volumes/DATA1/EMQM_DATA/ac3x75/'
  GOLD_PATH = os.path.join(DATA_PATH,'gold/')
  RHOANA_PATH = os.path.join(DATA_PATH,'rhoana/')
  IMAGE_PATH = os.path.join(DATA_PATH,'input/')
  PROB_PATH = os.path.join(DATA_PATH,'prob/')
  PATCH_PATH = os.path.join(DATA_PATH,'test_rhoana/')
  OUTPUT_PATH = os.path.join(DATA_PATH,'fix_rhoana/')


  gold = _metrics.Util.read(GOLD_PATH+'*.tif')
  rhoana = _metrics.Util.read(RHOANA_PATH+'*.tif')
  images = _metrics.Util.read(IMAGE_PATH+'*.tif')
  probs = _metrics.Util.read(PROB_PATH+'*.tif')


  SLICE = slice

  print 'Running on slice', SLICE

  image = images[SLICE]
  prob = probs[SLICE]
  seg = _metrics.Util.normalize_labels(rhoana[SLICE])[0]

  # fill and normalize gold
  gold_zeros = _metrics.Util.threshold(gold[SLICE], 0)
  gold_filled = _metrics.Util.fill(gold[SLICE], gold_zeros.astype(np.bool))
  gold_filled_relabeled = skimage.measure.label(gold_filled).astype(np.uint64)
  gold_normalized = _metrics.Util.normalize_labels(gold_filled_relabeled)[0]

  # color both segmentations
  cm = _metrics.Util.load_colormap('/Volumes/DATA1/ac3x75/mojo/ids/colorMap.hdf5')
  colored_rhoana_normalized = cm[seg % len(cm)]
  colored_gold_normalized = cm[gold_normalized % len(cm)]



  print 'Find all patches'
  t0 = time.time()
  p = loop(image, prob, seg)
  print time.time()-t0, 'seconds for', len(p), 'patches'

  print 'Setting up CNN'
  val_fn = setup_n()  

  print 'Creating Matrix'
  m_new = create_matrix(val_fn, seg, p)
  out = seg
  sureness = 0.
  surenesses = []
  vi_s = []
  images = []
  before_VI = partition_comparison.variation_of_information(gold_normalized.ravel(), seg.ravel())

  max_counter = 0

  while m_new.max() >= sureness:
      max_counter += 1
      print 'Iteration', max_counter
      print 'Merging for sureness s=', m_new.max()
      surenesses.append(m_new.max())
      
      m_new, out = merge(val_fn, image, out, prob, m_new)
      
      vi = partition_comparison.variation_of_information(out.ravel(), gold_normalized.ravel())
      vi_s.append(vi)
      
      images.append(np.array(out))
      
      print 'New VI', vi
      print '-'*80
        
  before_VI = partition_comparison.variation_of_information(gold_normalized.ravel(), seg.ravel())

  index = vi_s.index(min(vi_s))
  print 'Before VI', before_VI
  print 'Lowest VI', vi_s[index]
  print 'Iterations', index
  print 'Sureness', surenesses[index]


  # create plot
  new_rhoana = propagate_max_overlap(seg, gold_normalized)
  target_vi = partition_comparison.variation_of_information(new_rhoana.ravel(), gold_normalized.ravel())

  bins = np.arange(0, len(vi_s))#np.arange(len(vi_s), 0, -1)
  # bins /= 100

  vi_s_ = vi_s #[x for (y,x) in sorted(zip(surenesses, vi_s))]
  surenesses_ = bins #surenesses#[y for (y,x) in sorted(zip(surenesses, vi_s))]

  original_vi = [before_VI]*len(vi_s)
  target_vi = [target_vi]*len(vi_s)

  fig, ax = plt.subplots()

  ax.plot(surenesses_, target_vi, 'k--', label='Target VI')
  ax.plot(surenesses_, vi_s_, 'k', label='Variation of Information')
  ax.plot(surenesses_, original_vi, 'k:', label='Rhoana VI before')
  # ax.set_yscale('log')

  # Now add the legend with some customizations.
  legend = ax.legend(loc='upper center', shadow=True)
  ax.set_ylim([0.6,1.5])

  # The frame is matplotlib.patches.Rectangle instance surrounding the legend.
  frame = legend.get_frame()
  frame.set_facecolor('0.90')

  # Set the fontsize
  for label in legend.get_texts():
      label.set_fontsize('large')

  for label in legend.get_lines():
      label.set_linewidth(1.5)  # the legend line width

  plt.savefig(OUTPUT_PATH+os.sep+'graph_'+str(slice)+'.png')

  mh.imsave(OUTPUT_PATH+os.sep+'best_'+str(slice)+'.tif', images[index])
  mh.imsave(OUTPUT_PATH+os.sep+'last_'+str(slice)+'.tif', images[-1])

  print 'All done.'
コード例 #10
0
def partition_VI(putative, gold):
    mask = gold != 0
    VI_res = partition_comparison.variation_of_information(gold[mask], putative[mask].astype(gold.dtype))
    return VI_res
コード例 #11
0
ファイル: vi_exp.py プロジェクト: oshaikh13/dojo
exp = tif.imread('/home/d/dong.tif')

exp[exp == 0] = 2000

# crop the data to only image data
x = 210
y = 60
z = 50  # we dont need since we have already cut the z
dim_x = dim_y = 400
segs = segs[:, y:y + dim_y, x:x + dim_x]
gt = gt[:, y:y + dim_y, x:x + dim_x]
#out = out[:,y:y+dim_y,x:x+dim_x]

print 'VI groundtruth vs. groundtruth:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(),
    gt.astype(np.uint32).ravel())
print 'VI groundtruth vs. pipeline:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(), segs.ravel())
# print 'VI groundtruth vs. proofread:', pc.variation_of_information(gt.astype(np.uint32).ravel(), out.ravel())
print 'VI groundtruth vs. expert:', pc.variation_of_information(
    gt.astype(np.uint32).ravel(),
    exp.astype(np.uint32).ravel())

print 'Adjusted RandIndex groundtruth vs. groundtruth:', metrics.adjusted_rand_score(
    gt.astype(np.uint32).ravel(),
    gt.astype(np.uint32).ravel())
print 'Adjusted RandIndex groundtruth vs. pipeline:', metrics.adjusted_rand_score(
    gt.astype(np.uint32).ravel(), segs.ravel())
# print 'Adjusted RandIndex groundtruth vs. proofread:', metrics.adjusted_rand_score(gt.astype(np.uint32).ravel(), out.ravel())
print 'Adjusted RandIndex groundtruth vs. expert:', metrics.adjusted_rand_score(