def make_seg_map(prob_map_path, dense_gt_path):
prob_map = read_h5(prob_map_path)
dense_gt = read_h5(dense_gt_path)
nodes_in_dgt = len(np.unique(dense_gt))
print "nodes in dgt", nodes_in_dgt
seed_map = wsDtSegmentation(prob_map, pmin=0.5, minMembraneSize=10, minSegmentSize=0, sigmaMinima=6, sigmaWeights=1, cleanCloseSeeds=True,
returnSeedsOnly=True)
seg_map, sup_map, wsDt_data, agglCl_data = get_segmentation(prob_map, pmin=0.5, minMemb=10, minSeg=10, sigMin=6, sigWeights=1, sigSmooth=0.1,
cleanCloseSeeds=True, returnSeedsOnly=False, edgeLengths=None, nodeFeatures=None,
nodeSizes=None, nodeLabels=None, nodeNumStop=nodes_in_dgt, beta=0, metric='l1',
wardness=0.2, out=None)
print "seeds", len(np.unique(seed_map))
print "seg", len(np.unique(seg_map))
#make folder for seeds
seed_folder = prob_map_path.split("prob")[0] + "seeds/"
if not os.path.exists(seed_folder):
os.mkdir(seed_folder)
seed_map_outpath = seed_folder + prob_map_path.split("/")[-1]
#make folder for super pixels
sup_folder = prob_map_path.split("prob")[0] + "sup_maps/"
if not os.path.exists(sup_folder):
os.mkdir(sup_folder)
sup_map_outpath = sup_folder + prob_map_path.split("/")[-1]
#make folder for segmentations
seg_folder = prob_map_path.split("prob")[0] + "seg_maps/"
if not os.path.exists(seg_folder):
os.mkdir(seg_folder)
seg_map_outpath = seg_folder + prob_map_path.split("/")[-1]
#make folder for segmentation data
data_folder = prob_map_path.split("prob")[0] + "seg_data/"
if not os.path.exists(data_folder):
os.mkdir(data_folder)
data_outpath = data_folder + prob_map_path.split("/")[-1]
#get segmentation data
ri, voi = rand_index_variation_of_information(seg_map, dense_gt)
print "ri, voi", ri , voi
ri_data = np.array((ri))
voi_data = np.array((voi))
ri_voi_data = np.array((ri, voi))
#save seeds, superpixels and segmentations
# save_h5(ri_voi_data, data_outpath, key)
# save_h5(ri_data, data_outpath, "rand index", None)
# save_h5(voi_data, data_outpath, "variation of information", None)
# save_h5(seed_map, seed_map_outpath, "data", None)
save_h5(sup_map,sup_map_outpath, "data", None)
save_h5(seg_map,seg_map_outpath, "data", None)
def get_segmentation(predict, pmin=0.5, minMemb=10, minSeg=10, sigMin=6, sigWeights=1, sigSmooth=0.1, cleanCloseSeeds=True,
returnSeedsOnly=False, edgeLengths=None,nodeFeatures=None, nodeSizes=None, nodeLabels=None,
nodeNumStop=None, beta=0, metric='l1', wardness=0.2, out=None):
""" Get segmentation through watershed and agglomerative clustering
:param predict: prediction map
:return: segmentation map
"""
#use watershed and save superpixels map
super_pixels = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, returnSeedsOnly)
# seeds = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, True)
# save_h5(seeds, "/home/stamylew/delme/seeds.h5", "data")
print
print "#Nodes in superpixels", len(np.unique(super_pixels))
# save_h5(super_pixels, "/home/stamylew/delme/super_pixels.h5", "data")
#smooth prediction map
probs = vf.gaussianSmoothing(predict, sigSmooth)
# save_h5(probs, "/home/stamylew/delme/probs.h5", "data")
#make grid graph
grid_graph = vg.gridGraph(super_pixels.shape, False)
grid_graph_edge_indicator = vg.edgeFeaturesFromImage(grid_graph, probs)
#make region adjacency graph
rag = vg.regionAdjacencyGraph(grid_graph, super_pixels)
#accumulate edge features from grid graph node map
edge_weights = rag.accumulateEdgeFeatures(grid_graph_edge_indicator)
edge_weights_tag = "mean of the probabilities"
#do agglomerative clustering
labels = vg.agglomerativeClustering(rag, edge_weights, edgeLengths, nodeFeatures, nodeSizes,
nodeLabels, nodeNumStop, beta, metric, wardness, out)
#segmentation data
wsDt_data = np.zeros((8,1))
wsDt_data[:,0] = (pmin, minMemb, minSeg, sigMin, sigWeights, sigSmooth, cleanCloseSeeds, returnSeedsOnly)
agglCl_data = edge_weights_tag, str(edgeLengths), str(nodeFeatures), str(nodeSizes), str(nodeLabels), str(nodeNumStop), \
str(beta), metric, str(wardness), str(out)
#project labels back to data
segmentation = rag.projectLabelsToBaseGraph(labels)
print "#nodes in segmentation", len(np.unique(segmentation))
# save_h5(segmentation, "/home/stamylew/delme/segmap.h5", "data", None)
print "seg", np.unique(segmentation)
return segmentation, super_pixels, wsDt_data, agglCl_data
def get_segmentation(predict, pmin=0.5, minMemb=10, minSeg=10, sigMin=6, sigWeights=1, sigSmooth=0.1, cleanCloseSeeds=True,
returnSeedsOnly=False, edgeLengths=None,nodeFeatures=None, nodeSizes=None, nodeLabels=None, nodeNumStop=None,
beta=0, metric='l1', wardness=0.2, out=None):
""" Get segmentation through watershed and agglomerative clustering
:param predict: prediction map
:return: segmentation map
"""
#use watershed and save superpixels map
super_pixels = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, returnSeedsOnly)
# seeds = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, True)
# save_h5(seeds, "/home/stamylew/delme/seeds.h5", "data")
print
print "#Nodes in superpixels", len(np.unique(super_pixels))
# save_h5(super_pixels, "/home/stamylew/delme/super_pixels.h5", "data")
#smooth prediction map
probs = vf.gaussianSmoothing(predict, sigSmooth)
# save_h5(probs, "/home/stamylew/delme/probs.h5", "data")
#make grid graph
grid_graph = vg.gridGraph(super_pixels.shape, False)
grid_graph_edge_indicator = vg.edgeFeaturesFromImage(grid_graph, probs)
#make region adjacency graph
rag = vg.regionAdjacencyGraph(grid_graph, super_pixels)
#accumulate edge features from grid graph node map
edge_weights = rag.accumulateEdgeFeatures(grid_graph_edge_indicator)
edge_weights_tag = "mean of the probabilities"
#do agglomerative clustering
def agglomerativeClustering_beststop_th05(graph, edgeWeights=None, edgeStoppers=None,
edgeLengths=None, nodeFeatures=None, nodeSizes=None,
nodeLabels=None, beta=0, metric=None,
wardness=1.0, sameLabelMultiplier=1.0, out=None):
vg.ac(graph, edgeWeights=edgeWeights, edgeStoppers=edgeStoppers,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures,
nodeSizes=nodeSizes, nodeLabels=nodeLabels, nodeNumStop=0,
beta=beta, wardness=wardness, sameLabelMultiplier=sameLabelMultiplier, out=out)
f = open('/tmp/ac_bestNodenumstop.txt', 'r')
nodeNumStop = f.readline()
f.close()
print "best nodeNumStop:", nodeNumStop
# in experiments if n of the groundtruth is n the file contains n-1. probably in the hc.hxx the
# number is written to file too late. does this make any sense??
nodeNumStop = int(nodeNumStop)
nodeNumStop += 1
ac_res, nodelabel_out_1 = vg.ac(graph, edgeWeights=edgeWeights, edgeStoppers=edgeStoppers,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures,
nodeSizes=nodeSizes, nodeLabels=nodeLabels, nodeNumStop=nodeNumStop,
beta=beta, wardness=wardness, sameLabelMultiplier=sameLabelMultiplier,
out=out)
return ac_res, nodeNumStop, nodelabel_out_1
labels, _, _ = agglomerativeClustering_beststop_th05(rag, edgeWeights=edge_weights, edgeStoppers=edge_weights,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures, nodeSizes=nodeSizes,
nodeLabels=nodeLabels, beta=beta, metric=metric,
wardness=0.1, sameLabelMultiplier=1.0, out=out)
# labels = vg.agglomerativeClustering(rag, edge_weights, edgeLengths, nodeFeatures, nodeSizes,
# nodeLabels, nodeNumStop, beta, metric, wardness, out)
#segmentation data
wsDt_data = np.zeros((8,1))
wsDt_data[:,0] = (pmin, minMemb, minSeg, sigMin, sigWeights, sigSmooth, cleanCloseSeeds, returnSeedsOnly)
agglCl_data = edge_weights_tag, str(edgeLengths), str(nodeFeatures), str(nodeSizes), str(nodeLabels), str(nodeNumStop), str(beta), metric, str(wardness), str(out)
#project labels back to data
segmentation = rag.projectLabelsToBaseGraph(labels)
print "#nodes in segmentation", len(np.unique(segmentation))
# save_h5(segmentation, "/home/stamylew/delme/segmap.h5", "data", None)
return segmentation, super_pixels, wsDt_data, agglCl_data
