def get_quality_values(predict, gt, dense_gt, minMemb, minSeg, sigMin, sigWeights, sigSmooth,edgeLengths,nodeFeatures,
nodeSizes, nodeLabels, nodeNumStop, beta, metric, wardness,out):
"""
"""
adjusted_predict = adjust_predict(predict)
# save_h5(adjusted_predict, "/home/stamylew/delme/prob.h5", "data")
relevant_predict, relevant_gt = exclude_ignore_label(adjusted_predict, gt)
acc, pre, rec = accuracy_precision_recall(relevant_predict, relevant_gt)
auc_score = calculate_roc_auc_score(relevant_predict, relevant_gt)
no_of_true_pos, no_of_false_pos = true_and_false_pos(relevant_predict, relevant_gt)
no_of_true_neg, no_of_false_neg = true_and_false_neg(relevant_predict, relevant_gt)
print "#nodes in dense gt", len(np.unique(dense_gt))
pmin= 0.5
segmentation, super_pixels, wsDt_data, agglCl_data = get_segmentation(adjusted_predict,pmin, minMemb, minSeg, sigMin,
sigWeights, sigSmooth, True,False, edgeLengths,
nodeFeatures, nodeSizes, nodeLabels, nodeNumStop,
beta, metric, wardness, out)
ri_data = skl.randIndex(segmentation.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32), True)
voi_data = skl.variationOfInformation(segmentation.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32), True)
sp_ri = skl.randIndex(super_pixels.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32), True)
sp_voi = skl.variationOfInformation(super_pixels.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32), True)
print
print "super pixels values", (sp_ri, sp_voi)
print
print "segmentation values", (ri_data, voi_data)
return acc, pre, rec, auc_score, ri_data, voi_data, no_of_true_pos, no_of_false_pos, no_of_true_neg, no_of_false_neg, \
wsDt_data, agglCl_data
def rand_index_variation_of_information(segmentation, dense_gt):
ri = skl.randIndex(segmentation.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32),True)
voi = skl.variationOfInformation(segmentation.flatten().astype(np.uint32), dense_gt.flatten().astype(np.uint32), True)
return ri, voi
