def redo_quality2(block_path, dense_gt_path, minMemb, minSeg, sigMin, sigWeights, sigSmooth, edgeLengths=None,
nodeFeatures=None, nodeSizes=None, nodeLabels=None, nodeNumStop=None, beta=0, metric='l1',
wardness=0.2, out=None):
folder_list = [f for f in os.listdir(block_path) if not os.path.isfile(os.path.join(block_path,f))
and ("figure" not in f) and "redone" not in f]
print "folder_list", folder_list
outpath = block_path + "/redone_quality"
if not os.path.exists(outpath):
os.mkdir(outpath)
dense_gt_data = read_h5(dense_gt_path)
nodes_in_dgt = len(np.unique(dense_gt_data))
for f in folder_list:
folder_path = os.path.join(block_path, f)
prob_file = [h for h in os.listdir(folder_path) if "probs" in h]
prob_file_path = os.path.join(folder_path,prob_file[0])
print
print
print prob_file_path
predict_data = read_h5(prob_file_path)
adjusted_predict_data = adjust_predict(predict_data)
print "#nodes in dense gt", nodes_in_dgt
pmin = 0.5
seg, sup, wsDt_data, agglCl_data = get_segmentation(adjusted_predict_data, pmin, minMemb, minSeg, sigMin, sigWeights,
sigSmooth,True,False, edgeLengths,nodeFeatures, nodeSizes,
nodeLabels, nodeNumStop,beta, metric, wardness, out)
nodes_in_seg = len(np.unique(seg))
nodes_in_sup = len(np.unique(sup))
ri, voi = rand_index_variation_of_information(seg, dense_gt_data)
new_folder_path = outpath + "/" + f
if not os.path.exists(new_folder_path):
os.mkdir(new_folder_path)
key = "pmin_"+ str(pmin) + "_minMemb_" + str(minMemb)+ "_minSeg_"+ str(minSeg) +"_sigMin_" + str(sigMin) + \
"_sigWeights_" + str(sigWeights) + "_sigSmooth_" + str(sigSmooth) + "_nodeNumStop_" + str(nodeNumStop)
save_h5(seg, new_folder_path + "/segmentation.h5", key)
save_h5(sup, new_folder_path + "/super_pixels.h5", key)
seg_data = np.zeros((2,3))
seg_data[0,0] = ri
seg_data[0,1] = voi
seg_data[1,0] = nodes_in_dgt
seg_data[1,1] = nodes_in_sup
seg_data[1,2] = nodes_in_seg
save_h5(seg_data, new_folder_path + "/seg_data", key, None)
def test_wsDt_agglCl_configs(predict_path, dense_gt_path, pmin=0.5, minMemb=10, minSeg=10, sigMin=2, sigWeights=2, sigSmooth=0.1):
"""
:param predict_path:
:param dense_gt_path:
:param pmin:
:param minMemb:
:param minSeg:
:param sigMin:
:param sigWeights:
:param sigSmooth:
:return:
"""
#get rand index and variation of information
predict_data = read_h5(predict_path)
adjusted_predict_data = adjust_predict(predict_data)
dense_gt_data = read_h5(dense_gt_path)
seg, sup = get_segmentation(adjusted_predict_data,pmin,minMemb,minSeg,sigMin,sigWeights,sigSmooth)
ri, voi = rand_index_variation_of_information(seg, dense_gt_data)
print "ri, voi", ri, voi
config = predict_path.split("/")[-2]
filename = config + "_" + predict_path.split("/")[-1]
outpath_folder = "/home/stamylew/test_folder/q_data/wsDt_agglCl_tests/"
outpath = outpath_folder + filename
key = "sigMin_" + str(sigMin) + "_sigWeights_" + str(sigWeights) + "_sigSmooth_" + str(sigSmooth)
if not os.path.exists(outpath):
print "Output h5 file did not exist."
data = np.zeros((1,2))
data[0,0] = ri
data[0,1] = voi
save_h5(data, outpath, key, None)
else:
old_data = read_h5(outpath, key)
data = np.zeros((1,2))
data[0,0] = ri
data[0,1] = voi
new_data = np.vstack((old_data, data))
save_h5(new_data, outpath, key, None)
