def CollapseGraph(prefix, segmentation, vertex_ones, vertex_twos, maintained_edges, algorithm, evaluate): # get the number of edges nedges = maintained_edges.shape[0] # create the union find data structure and collapse the graph max_label = np.amax(segmentation) + 1 union_find = [unionfind.UnionFindElement(iv) for iv in range(max_label)] # go through all of the edges for ie in range(nedges): # skip if the edge should not collapse if maintained_edges[ie]: continue # merge these vertices vertex_one = vertex_ones[ie] vertex_two = vertex_twos[ie] unionfind.Union(union_find[vertex_one], union_find[vertex_two]) # create the mapping and save the result mapping = np.zeros(max_label, dtype=np.int64) for iv in range(max_label): mapping[iv] = unionfind.Find(union_find[iv]).label # apply the mapping and save the result seg2seg.MapLabels(segmentation, mapping) segmentation_filename = 'segmentations/{}-{}.h5'.format(prefix, algorithm) dataIO.WriteH5File(segmentation, segmentation_filename, 'main') # spawn a new meta file dataIO.SpawnMetaFile(prefix, segmentation_filename, 'main') # evaluate if gold data exists if evaluate: # get the variation of information for this result new_prefix = segmentation_filename.split('/')[1][:-3] # read in the new gold data gold = dataIO.ReadGoldData(prefix) rand_error, vi = comparestacks.VariationOfInformation( new_prefix, segmentation, gold) print 'Rand Error Full: {}'.format(rand_error[0] + rand_error[1]) print 'Rand Error Merge: {}'.format(rand_error[0]) print 'Rand Error Split: {}'.format(rand_error[1]) print 'Variation of Information Full: {}'.format(vi[0] + vi[1]) print 'Variation of Information Merge: {}'.format(vi[0]) print 'Variation of Information Split: {}'.format(vi[1]) if not os.path.exists('results'): os.mkdir('results') with open('results/{}-{}.txt'.format(prefix, algorithm), 'w') as fd: fd.write('Rand Error Full: {}\n'.format(rand_error[0] + rand_error[1])) fd.write('Rand Error Merge: {}\n'.format(rand_error[0])) fd.write('Rand Error Split: {}\n'.format(rand_error[1])) fd.write('Variation of Information Full: {}\n'.format(vi[0] + vi[1])) fd.write('Variation of Information Merge: {}\n'.format(vi[0])) fd.write('Variation of Information Split: {}\n'.format(vi[1]))
from biologicalgraphs.utilities import dataIO from biologicalgraphs.graphs.biological import node_generation, edge_generation from biologicalgraphs.evaluation import comparestacks #from biologicalgraphs.cnns.biological import nodes, edges from biologicalgraphs.cnns.biological.nodes import forward #from biologicalgraphs.cnns.biological.edges import forward from biologicalgraphs.transforms import seg2seg, seg2gold from biologicalgraphs.skeletonization import generate_skeletons from biologicalgraphs.algorithms import lifted_multicut # the prefix name corresponds to the meta file in meta/{PREFIX}.meta prefix = 'Kasthuri-test' # read the ground truth for this data gold = dataIO.ReadGoldData(prefix) # read the input segmentation data segmentation = dataIO.ReadSegmentationData(prefix) # subset is either training, validation, or testing subset = 'testing' # remove the singleton slices node_generation.RemoveSingletons(prefix, segmentation) print("Step 1 done") # need to update the prefix and segmentation # removesingletons writes a new h5 file to disk prefix = '{}-segmentation-wos'.format(prefix) segmentation = dataIO.ReadSegmentationData(prefix) # need to rerun seg2gold mapping since segmentation changed seg2gold_mapping = seg2gold.Mapping(prefix, segmentation, gold)
def MergeGroundTruth(prefix, model_prefix): # read the segmentation data segmentation = dataIO.ReadSegmentationData(prefix) # get the multicut filename (with graph weights) multicut_filename = 'multicut/{}-{}.graph'.format(model_prefix, prefix) # read the gold data gold = dataIO.ReadGoldData(prefix) # read in the segmentation to gold mapping mapping = seg2gold.Mapping(segmentation, gold) # get the maximum segmentation value max_value = np.amax(segmentation) + 1 # create union find data structure union_find = [UnionFind.UnionFindElement(iv) for iv in range(max_value)] # read in all of the labels with open(multicut_filename, 'rb') as fd: # read the number of vertices and edges nvertices, nedges, = struct.unpack('QQ', fd.read(16)) # read in all of the edges for ie in range(nedges): # read in the two labels label_one, label_two, = struct.unpack('QQ', fd.read(16)) # skip over the reduced labels and edge weight fd.read(24) # if the labels are the same and the mapping is non zero if mapping[label_one] == mapping[label_two] and mapping[label_one]: UnionFind.Union(union_find[label_one], union_find[label_two]) # create a mapping mapping = np.zeros(max_value, dtype=np.int64) # update the segmentation for iv in range(max_value): label = UnionFind.Find(union_find[iv]).label mapping[iv] = label merged_segmentation = seg2seg.MapLabels(segmentation, mapping) gold_filename = 'gold/{}_gold.h5'.format(prefix) # TODO fix this code temporary filename truth_filename = 'multicut/{}-truth.h5'.format(prefix) # temporary write h5 file dataIO.WriteH5File(merged_segmentation, truth_filename, 'stack') import time start_time = time.time() print ('Ground truth: ') # create the command line command = '~/software/PixelPred2Seg/comparestacks --stack1 {} --stackbase {} --dilate1 1 --dilatebase 1 --relabel1 --relabelbase --filtersize 100 --anisotropic'.format(truth_filename, gold_filename) # execute the command os.system(command) print (time.time() - start_time)
def Forward(prefix, model_prefix, segmentation, subset, seg2gold_mapping=None, evaluate=False): # optimal parameters from paper (width starts with 3 channels) width = (3, 20, 60, 60) radius = 400 threshold_volume = 10368000 # read in the trained model model = model_from_json(open('{}.json'.format(model_prefix), 'r').read()) model.load_weights('{}-best-loss.h5'.format(model_prefix)) # get all of the examples examples, npositives, nnegatives = CollectExamples(prefix, width, radius, subset) # get all of the large-small pairings pairings = CollectLargeSmallPairs(prefix, width, radius, subset) #assert (len(pairings) == examples.shape[0]) # get the threshold in terms of number of voxels resolution = dataIO.Resolution(prefix) threshold = int(threshold_volume / (resolution[IB_Z] * resolution[IB_Y] * resolution[IB_X])) # get the list of nodes over and under the threshold small_segments, large_segments = FindSmallSegments(segmentation, threshold) # get all of the probabilities probabilities = model.predict_generator(NodeGenerator(examples, width), examples.shape[0], max_q_size=1000) # save the probabilities to a file output_filename = '{}-{}.probabilities'.format(model_prefix, prefix) with open(output_filename, 'wb') as fd: fd.write(struct.pack('q', examples.shape[0])) for ie, (label_one, label_two) in enumerate(pairings): fd.write( struct.pack('qqd', label_one, label_two, probabilities[ie])) # create the correct labels for the ground truth ground_truth = np.zeros(npositives + nnegatives, dtype=np.bool) for iv in range(npositives): ground_truth[iv] = True # get the results with labeled data predictions = Prob2Pred(np.squeeze(probabilities[:npositives + nnegatives])) # print the confusion matrix if not seg2gold_mapping is None: output_filename = '{}-{}-inference.txt'.format(model_prefix, prefix) PrecisionAndRecall(ground_truth, predictions, output_filename) # create a mapping small_segment_predictions = dict() for small_segment in small_segments: small_segment_predictions[small_segment] = set() # go through each pairing for pairing, probability in zip(pairings, probabilities): label_one, label_two = pairing # make sure that either label one or two is small and the other is large assert ((label_one in small_segments) ^ (label_two in small_segments)) if label_one in small_segments: small_segment = label_one large_segment = label_two else: small_segment = label_two large_segment = label_one small_segment_predictions[small_segment].add( (large_segment, probability[0])) # begin to map the small labels max_label = np.amax(segmentation) + 1 mapping = [iv for iv in range(max_label)] if not seg2gold_mapping is None: ncorrect_merges = 0 nincorrect_merges = 0 # go through all of the small segments for small_segment in small_segments: best_probability = -1 best_large_segment = -1 # go through all the neighboring large segments for large_segment, probability in small_segment_predictions[ small_segment]: if probability > best_probability: best_probability = probability best_large_segment = large_segment # this should almost never happen but if it does just continue if best_large_segment == -1 or best_probability < 0.5: mapping[small_segment] = small_segment continue # get all of the best large segments else: mapping[small_segment] = best_large_segment if not seg2gold_mapping is None: # don't consider undetermined locations if seg2gold_mapping[small_segment] < 1 or seg2gold_mapping[ best_large_segment] < 1: continue if seg2gold_mapping[small_segment] == seg2gold_mapping[ best_large_segment]: ncorrect_merges += 1 else: nincorrect_merges += 1 if not seg2gold_mapping is None: print '\nResults:' print ' Correctly Merged: {}'.format(ncorrect_merges) print ' Incorrectly Merged: {}'.format(nincorrect_merges) with open(output_filename, 'a') as fd: fd.write('\nResults:\n') fd.write(' Correctly Merged: {}\n'.format(ncorrect_merges)) fd.write(' Incorrectly Merged: {}\n'.format(nincorrect_merges)) # save the node mapping in the cache for later end2end_mapping = [mapping[iv] for iv in range(max_label)] # initiate the mapping to eliminate small segments seg2seg.MapLabels(segmentation, mapping) # reduce the labels and map again mapping, _ = seg2seg.ReduceLabels(segmentation) seg2seg.MapLabels(segmentation, mapping) # update the end to end mapping with the reduced labels for iv in range(max_label): end2end_mapping[iv] = mapping[end2end_mapping[iv]] # get the model name (first component is architecture and third is node-) model_name = model_prefix.split('/')[1] segmentation_filename = 'segmentations/{}-reduced-{}.h5'.format( prefix, model_name) dataIO.WriteH5File(segmentation, segmentation_filename, 'main') # spawn a new meta file dataIO.SpawnMetaFile(prefix, segmentation_filename, 'main') # save the end to end mapping in the cache if not os.path.exists('cache'): os.mkdir('cache') mapping_filename = 'cache/{}-reduced-{}-end2end.map'.format( prefix, model_name) with open(mapping_filename, 'wb') as fd: fd.write(struct.pack('q', max_label)) for label in range(max_label): fd.write(struct.pack('q', end2end_mapping[label])) if evaluate: gold = dataIO.ReadGoldData(prefix) new_prefix = segmentation_filename.split('/')[-1].split('.')[0] # run the evaluation framework rand_error, vi = comparestacks.VariationOfInformation( new_prefix, segmentation, gold) # write the output file if not os.path.exists('results'): os.mkdir('results') with open('results/{}-reduced-{}.txt'.format(prefix, model_name), 'w') as fd: fd.write('Rand Error Full: {}\n'.format(rand_error[0] + rand_error[1])) fd.write('Rand Error Merge: {}\n'.format(rand_error[0])) fd.write('Rand Error Split: {}\n'.format(rand_error[1])) fd.write('Variation of Information Full: {}\n'.format(vi[0] + vi[1])) fd.write('Variation of Information Merge: {}\n'.format(vi[0])) fd.write('Variation of Information Split: {}\n'.format(vi[1]))