def __init__(self, gt, predict, threshold = 0.5):
self.gt = gt
self.adjusted_predict = adjust_predict(predict)
self.relevant_data = exclude_ignore_label(gt, self.adjusted_predict)
self.nlables = get_number_of_labels(gt)
self.apr = apr(self.relevant_data[0], self.relevant_data[1])
self.auc_score = roc_auc(self.relevant_data[0], self.relevant_data[1])
self.quality = np.array((self.apr[0], self.apr[1], self.apr[2], self.auc_score))
self.roc_curve = draw_roc_curve(self.relevant_data[0], self.relevant_data[1])
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)
def test(ilp, files, gt_path, dense_gt_path, labels="", loops=3, weights="", repeats=1, outpath= "",
t_cache = "", p_cache = ""):
""" Run test
:param: ilp : path to ilp project to use for training
:param: files : path to file to do batch prediction on
:param: gt_path : path to trimap groundtruth
:param: dense_gt_path : path to dense groundtruth
:param: labels : amount of labeled pixels to use in training
:param: loops : amount of autocontext loops
:param: weights : weighting of the labels over the autocontext loops
:param: repeats : amount of repeat runs of the test
:param: outpath : outpath for test outputs
:param: t_cache : path to training data cache
:param: p_cache : path to prediction data cache
"""
hostname = socket.gethostname()
print "test information:"
print
print "hostname:", hostname
print
print "ilp_file:", ilp
print
print "files to predict:", files
print
print "groundtruth:", gt_path
print
print "dense groundtruth:", dense_gt_path
print
print "labels:", labels
print
print "loops:", loops
print
print "weights:", weights
print
print "repeats:", repeats
# Collect the test specifications
ilp_split = ilp.split(".")[-2]
training_file = "training file: "+ilp_split.split("/")[-1]
gt_split = gt_path.split(".")[-2]
trimap_file = "trimap file: " + gt_split.split("/")[-1]
# Assign paths
filesplit = files.split(".")[-2]
filename = filesplit.split("/")[-1]
prediction_file = "prediction file: "+ filename
test_folder_path = assign_path(hostname)[5]
if t_cache == "":
t_cache = test_folder_path + "/t_cache"
if p_cache == "":
p_cache = test_folder_path + "/p_cache/" + filename
if not os.path.exists(p_cache):
os.mkdir(p_cache)
if outpath == "":
output = test_folder_path + "/q_data"
else:
output = outpath
print
print "outpath:", outpath
print
print "t_cache:", t_cache
print
print "p_cache:", p_cache
# Create file tags
if labels == "":
label_tag = "all"
else:
# assert labels == check_ilp_labels(ilp)
true_labels = check_ilp_labels(ilp)
label_tag = true_labels
if weights == "":
weight_tag = "none"
else:
weight_tag = str(weights)
# Make folder for quality data
if "manual" in ilp:
filename += "_manual"
if "hand_drawn" in ilp:
filename += "_hand_drawn"
if "clever" in ilp:
filename += "_clever"
if "less_feat" in ilp:
filename += "_less_feat"
#change labels for every test
# if labels != "":
# print
# print "reducing labels to " + str(labels)
# ilp = reduce_labels_in_ilp(ilp, labels)
file_dir = output + "/" + filename
if not os.path.exists(file_dir):
os.mkdir(file_dir)
# Overwrite folder directory
# file_dir = "/mnt/CLAWS1/stamilev/delme"
# Check if file directory exists, if not make such directory
if not os.path.exists(file_dir):
print
print "Output folder did not exist."
os.mkdir(file_dir)
print
print "New one named " + file_dir + " was created."
q_outpath = file_dir + "/n_" + str(loops) + "_l_" + str(label_tag) + "_w_" + weight_tag
prob_folder = q_outpath + "/prob_files"
q_data_outpath = q_outpath + "/n_" + str(loops) + "_l_" + str(label_tag) + "_w_" + weight_tag + ".h5"
# Check if test directory exists, if not make such directory
if not os.path.exists(q_outpath):
print
print "Output h5 file did not exist"
os.mkdir(q_outpath)
os.mkdir(prob_folder)
print
print "New one named " + q_outpath + " was created."
if not os.path.exists(prob_folder):
os.mkdir(prob_folder)
# Run the test
for i in range(repeats):
print
print "round of repeats %d of %d" % (i+1, repeats)
#train on ilp project
ac_train(ilp, labels, loops, weights, t_cache, outpath)
print
print "training completed"
#batch predict files
ac_batch_predict(files, t_cache, p_cache, overwrite = "")
print
print "batch prediction completed"
#save prob files and quality data
prob_file = [x for x in os.listdir(p_cache) if ("probs" in x)]
predict_path = p_cache + "/" + prob_file[0]
predict_data = adjust_predict(read_h5(predict_path))
prob_path = prob_folder + "/prob_"+ str(i+1)+ ".h5"
# if os.path.exists(prob_path):
# # TODO:not working yet
# prob_path = prob_path.split("/")[-2] + "/new_" + prob_path.split("/")[-1]
# print prob_path
save_h5(predict_data, prob_path, "data")
save_quality_values(predict_path, gt_path, dense_gt_path, q_data_outpath, (0,49,99))
#save test specification data
save_h5([training_file, prediction_file, trimap_file], q_data_outpath, "used files", None)
save_h5([labels],q_data_outpath, "autocontext_parameters/#labels")
save_h5([loops], q_data_outpath, "autocontext_parameters/#loops")
save_h5([str(weight_tag)], q_data_outpath, "autocontext_parameters/weights")
print
print "quality data saved"
#save configuration data
# call(["cp", predict_path, q_outpath])
save_h5(["pmin", "minMemb", "minSeg", "sigMin", "sigWeights", "sigSmooth", "cleanCloseSeeds", "returnSeedsOnly"],
q_data_outpath, "segmentation/wsDt parameters", None)
save_h5(["edge_weights", "edgeLengths", "nodeFeatures", "nodeSizes", "nodeLabels", "nodeNumStop", "beta", "metric",
"wardness", "out"], q_data_outpath, "segmentation/agglCl parameters", None)
print
print "quality data saved"
#save_h5(q, "/home/stamylew/volumes/training_data/50cube3_bp.h5", "all_labels/n3/w_1_2_3")
#q = adjust_predict_file(q)
#q = exclude_ignore_label(g, q)
#
#
#
#c = config(p[0], p[1])
#c.show_quality()
#
#d = config(q[0], q[1])
#d.show_quality()
I = read_h5("/home/stamylew/volumes/trimaps/50cube2_tri.h5", "50cube2_tri")
II = read_h5("/home/stamylew/src/autocontext/prediction/cache/smallcubes_probs.h5", "exported_data")
II = adjust_predict(II)
q = exclude_ignore_label(I,II)
c = predict_class(q[0], q[1])
c.show_quality()
print c.return_quality()
#III = read_h5("/home/stamylew/volumes/training_data/50cube3_bp.h5", "all_labels/n3/w_3_2_1")
#III = adjust_predict_file(III)
#plt.figure()
#plt.imshow(I[11])
#
#plt.figure()
#plt.imshow(II[11])
