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Commit 28a6a795 authored by gospodnetic's avatar gospodnetic
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Get discarded average per method and model_method, generate performance tables... 

Get discarded average per method and model_method, generate performance tables with or without discarded viewpoints, fix max_coverage and min_VPC functions
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Benchmark.py
+ 120
− 36
View file @ 28a6a795
import utilities as util import utilities as util
from LogContainer import LogContainer from LogContainer import LogContainer
...@@ -23,13 +22,22 @@ class Benchmark: ...@@ -23,13 +22,22 @@ class Benchmark:
else: else:
self.methods_per_approach[approach] = [method] self.methods_per_approach[approach] = [method]
def generate_performance_tex_table(self): def generate_performance_tex_table(self, output_path = "./", coverage_threshold=0.99, with_discarded=True):
tex_file = open("performance_table.tex", "w") filename = output_path
if with_discarded:
filename += "performance_table_discarded.tex"
else:
filename += "performance_table.tex"
tex_file = open(filename, "w")
tex_file.write("\n\\begin{table*}\n") tex_file.write("\n\\begin{table*}\n")
tex_file.write("\\centering\n")
tex_file.write("\\begin{tabular}{|c| c|") tex_file.write("\\begin{tabular}{|c| c|")
for model in self.log_container_per_model: for model in self.log_container_per_model:
tex_file.write(" c c c c|") if with_discarded:
tex_file.write(" c c c c|")
else:
tex_file.write(" c c c|")
tex_file.write("}\n") tex_file.write("}\n")
tex_file.write("\\hline\n") tex_file.write("\\hline\n")
...@@ -38,48 +46,84 @@ class Benchmark: ...@@ -38,48 +46,84 @@ class Benchmark:
# Put models into array to ensure the order is always maintained. # Put models into array to ensure the order is always maintained.
models = [] models = []
for model in self.log_container_per_model: if with_discarded:
tex_file.write(" & \\multicolumn{{4}}{{|c|}}{{{}}}".format(model)) for model in self.log_container_per_model:
models.append(model) tex_file.write(" & \\multicolumn{{4}}{{|c|}}{{{}}}".format(model))
tex_file.write("\\\\\n") models.append(model)
tex_file.write("\\\\\n")
# Header - column names
tex_file.write("\\hline\n") # Header - column names
tex_file.write("Approach & Method") tex_file.write("\\hline\n")
for model in models: tex_file.write("Approach & Method")
tex_file.write(" & \\#VPC & \\makecell{\\#VPC\\\\used} & \\#OVP & \\%") for model in models:
tex_file.write("\\\\\n") tex_file.write(" & \\#VPC & \\makecell{\\#VPC\\\\used} & \\#OVP & \\%")
tex_file.write("\\\\\n")
for approach in self.methods_per_approach: for approach in self.methods_per_approach:
method_count = len(self.methods_per_approach[approach]) method_count = len(self.methods_per_approach[approach])
tex_file.write("\n\\hline\n") tex_file.write("\n\\hline\n")
tex_file.write("\\multirow{{{}}}{{*}}{{\\makecell{{{}}}}}".format(method_count, approach)) tex_file.write("\\multirow{{{}}}{{*}}{{\\makecell{{{}}}}}".format(method_count, approach))
for method in self.methods_per_approach[approach]: for method in self.methods_per_approach[approach]:
tex_file.write("\n& \makecell{{{}}}".format(method)) tex_file.write("\n& \makecell{{{}}}".format(method))
for model in models:
try:
best_log = self.log_container_per_model[model].get_best_log(method, coverage_threshold)
except Exception as e:
tex_file.write(" & - & - & - & -")
continue
VPC_count = best_log.VPC["count"] + best_log.VPC["discarded_count"]
VPC_used = best_log.VPC["count"]
OVP = len(best_log.optimization["OVP"])
coverage = util.set_precision(best_log.coverage["percent_fraction"] * 100, 2)
tex_file.write(" & {} & {} & {} & {}".format(VPC_count, VPC_used, OVP, coverage))
tex_file.write("\\\\")
else:
for model in self.log_container_per_model:
tex_file.write(" & \\multicolumn{{3}}{{|c|}}{{{}}}".format(model))
models.append(model)
tex_file.write("\\\\\n")
# Header - column names
tex_file.write("\\hline\n")
tex_file.write("Approach & Method")
for model in models:
tex_file.write(" & \\#VPC & \\#OVP & \\%")
tex_file.write("\\\\\n")
for model in models:
try:
best_log = self.log_container_per_model[model].get_best_log(method)
except:
tex_file.write(" & - & - & - & -")
continue
VPC_count = best_log.VPC["count"] + best_log.VPC["discarded_count"] for approach in self.methods_per_approach:
VPC_used = best_log.VPC["count"] method_count = len(self.methods_per_approach[approach])
OVP = len(best_log.optimization["OVP"]) tex_file.write("\n\\hline\n")
coverage = util.set_precision(best_log.coverage["percent_fraction"] * 100, 2) tex_file.write("\\multirow{{{}}}{{*}}{{\\makecell{{{}}}}}".format(method_count, approach))
tex_file.write(" & {} & {} & {} & {}".format(VPC_count, VPC_used, OVP, coverage))
tex_file.write("\\\\")
tex_file.write("\n\\end{tabular}") for method in self.methods_per_approach[approach]:
tex_file.write("\n& \makecell{{{}}}".format(method))
for model in models:
try:
best_log = self.log_container_per_model[model].get_best_log(method, coverage_threshold)
except Exception as e:
tex_file.write(" & - & - & -")
continue
VPC_count = best_log.VPC["count"] + best_log.VPC["discarded_count"]
OVP = len(best_log.optimization["OVP"])
coverage = util.set_precision(best_log.coverage["percent_fraction"] * 100, 2)
tex_file.write(" & {} & {} & {}".format(VPC_count, OVP, coverage))
tex_file.write("\\\\")
tex_file.write("\n\\hline\n")
tex_file.write("\\end{tabular}")
tex_file.write("\n\\end{table*}\n") tex_file.write("\n\\end{table*}\n")
tex_file.close() tex_file.close()
# \usepackage{longtable} needed. # \usepackage{longtable} needed.
def generate_complete_tex_table(self): def generate_complete_tex_table(self, output_path = "./"):
tex_file = open("complete_table.tex", "w") tex_file = open(output_path + "complete_table.tex", "w")
for model in self.log_container_per_model: for model in self.log_container_per_model:
tex_file.write("\n\\begin{longtable}{|c c c c c c c c|}\n") tex_file.write("\n\\begin{longtable}{|c c c c c c c c|}\n")
...@@ -138,6 +182,28 @@ class Benchmark: ...@@ -138,6 +182,28 @@ class Benchmark:
print("discarded_per_approach: {}".format(discarded_per_approach)) print("discarded_per_approach: {}".format(discarded_per_approach))
return discarded_per_approach return discarded_per_approach
def get_average_discarded_per_method(self):
discarded_per_method_list = {}
for approach in self.methods_per_approach:
for method in self.methods_per_approach[approach]:
for model in self.log_container_per_model:
log_container = LogContainer(self.log_container_per_model[model].get_methods_per_approach())
log_container.add_logs(self.log_container_per_model[model].get_logs_by_method(method))
if log_container.size() == 0:
continue
container_avg = log_container.get_avg_discarded()
if method in discarded_per_method_list:
discarded_per_method_list[method].append(container_avg)
else:
discarded_per_method_list[method] = [container_avg]
discarded_per_method = {}
for method in discarded_per_method_list:
discarded_per_method[method] = np.sum(discarded_per_method_list[method]) / len(discarded_per_method_list[method])
print ("discarded_per_method: {}".format(discarded_per_method))
return discarded_per_method
def get_average_discarded_per_model(self): def get_average_discarded_per_model(self):
discarded_per_model = {} discarded_per_model = {}
for model in self.log_container_per_model: for model in self.log_container_per_model:
...@@ -146,6 +212,24 @@ class Benchmark: ...@@ -146,6 +212,24 @@ class Benchmark:
print("discarded_per_model {}".format(discarded_per_model)) print("discarded_per_model {}".format(discarded_per_model))
return discarded_per_model return discarded_per_model
def get_average_discarded_per_model_per_method(self):
discarded_per_model_method = {}
for model in self.log_container_per_model:
for approach in self.methods_per_approach:
for method in self.methods_per_approach[approach]:
log_container = LogContainer(self.log_container_per_model[model].get_methods_per_approach())
log_container.add_logs(self.log_container_per_model[model].get_logs_by_method(method))
if log_container.size() == 0:
continue
if model in discarded_per_model_method:
discarded_per_model_method[model][method] = log_container.get_avg_discarded()
else:
discarded_per_model_method[model] = {}
discarded_per_model_method[model][method] = log_container.get_avg_discarded()
print("discarded_per_model_method {}".format(discarded_per_model_method))
return discarded_per_model_method
def get_average_discarded_per_model_per_approach(self): def get_average_discarded_per_model_per_approach(self):
discarded_per_model_approach = {} discarded_per_model_approach = {}
for model in self.log_container_per_model: for model in self.log_container_per_model:
......
LogContainer.py
+ 18
− 4
View file @ 28a6a795
...@@ -77,16 +77,15 @@ class LogContainer: ...@@ -77,16 +77,15 @@ class LogContainer:
# Log which obtains coverage over 99% with minimal number of viewpoint candidates # Log which obtains coverage over 99% with minimal number of viewpoint candidates
# If no log obrains coverage over 99%, the log with the greatest coverage is considered. # If no log obrains coverage over 99%, the log with the greatest coverage is considered.
def get_best_log(self, method): def get_best_log(self, method, coverage_threshold=0.99):
method_logs = self.get_logs_by_method(method) method_logs = self.get_logs_by_method(method)
if len(method_logs) == 0: if len(method_logs) == 0:
raise Exception("Error: No logs available for given method ({})".format(method)) raise Exception("Error: No logs available for given method ({})".format(method))
# Find logs with coverage >99%. # Find logs with coverage >99%.
high_coverage_logs = self.__filter_coverage_threshold(method_logs, 0.99, ComparisonType.GEQ) high_coverage_logs = self.__filter_coverage_threshold(method_logs, coverage_threshold, ComparisonType.GEQ)
if len(high_coverage_logs) > 0: if len(high_coverage_logs) > 0:
return self.max_coverage_log(high_coverage_logs) return self.min_used_vpc_log(high_coverage_logs)
else: else:
return self.max_coverage_log(method_logs) return self.max_coverage_log(method_logs)
...@@ -118,8 +117,23 @@ class LogContainer: ...@@ -118,8 +117,23 @@ class LogContainer:
for log in input_logs: for log in input_logs:
if log.coverage["percent_fraction"] > max_coverage: if log.coverage["percent_fraction"] > max_coverage:
max_log = log max_log = log
max_coverage = log.coverage["percent_fraction"]
return max_log return max_log
def min_used_vpc_log(self, input_logs=None):
if not input_logs:
input_logs = self.logs
if len(input_logs) == 0:
raise Exception("Error: no logs available.")
min_VPC = input_logs[0].VPC["count"]
min_log = input_logs[0]
for log in input_logs:
if log.VPC["count"] < min_VPC:
min_log = log
min_VPC = log.VPC["count"]
return min_log
def __parse_methods_per_approach(self): def __parse_methods_per_approach(self):
self.approaches_per_method = {} self.approaches_per_method = {}
for approach in self.methods_per_approach: for approach in self.methods_per_approach:
......
object_space_exploration_per_approach_methods.json
+ 7
− 10
View file @ 28a6a795
{ {
"SphereSampling": [ "SphereSampling": [
"UniformSphereAvgBbox", "AvgBbox",
"UniformSphereConvexHull", "ConvexHull",
"UniformSphereFocusRepositioned", "FocusRepositioned",
"UniformSphereMaxBbox", "MaxBbox",
"UniformSphereMinBbox", "MinBbox",
"UniformSphereMixedRepositioned", "MixedRepositioned",
"UniformSphereNoDisplacement", "NoDisplacement"],
"UniformSphere",
"UniformSphereCentroid"],
"VertexBased": [ "VertexBased": [
"VertexCentroid",
"VertexBBoxCenter", "VertexBBoxCenter",
"VertexNormal"], "VertexNormal"],
"GeometryBased": [ "GeometryBased": [
......
ovp_paths.json
+ 4
− 1
View file @ 28a6a795
[ [
"/home/pastrva/Projects/VirtualImageProcessing/Fileserver/Papers/2020_Generate_and_Test_Comparison/Results/WithFiltering/face_shield" "/home/pastrva/Projects/VirtualImageProcessing/Fileserver/Papers/2020_Generate_and_Test_Comparison/Results/WithoutFiltering/gear",
"/home/pastrva/Projects/VirtualImageProcessing/Fileserver/Papers/2020_Generate_and_Test_Comparison/Results/WithoutFiltering/hirth",
"/home/pastrva/Projects/VirtualImageProcessing/Fileserver/Papers/2020_Generate_and_Test_Comparison/Results/WithoutFiltering/time_shaft",
"/home/pastrva/Projects/VirtualImageProcessing/Fileserver/Papers/2020_Generate_and_Test_Comparison/Results/WithoutFiltering/face_shield"
] ]
vob.py
+ 5
− 2
View file @ 28a6a795
...@@ -82,12 +82,15 @@ def main(): ...@@ -82,12 +82,15 @@ def main():
benchmark = Benchmark() benchmark = Benchmark()
benchmark.set_log_containers(log_container_per_model) benchmark.set_log_containers(log_container_per_model)
benchmark.generate_performance_tex_table() benchmark.generate_performance_tex_table(output_path="./data/", coverage_threshold=0.98, with_discarded=True)
benchmark.generate_complete_tex_table() benchmark.generate_performance_tex_table(output_path="./data/", coverage_threshold=0.98, with_discarded=False)
benchmark.generate_complete_tex_table(output_path="./data/")
benchmark.get_average_RT_duration_per_model() benchmark.get_average_RT_duration_per_model()
benchmark.get_average_discarded_per_approach() benchmark.get_average_discarded_per_approach()
benchmark.get_average_discarded_per_method()
benchmark.get_average_discarded_per_model() benchmark.get_average_discarded_per_model()
benchmark.get_average_discarded_per_model_per_approach() benchmark.get_average_discarded_per_model_per_approach()
benchmark.get_average_discarded_per_model_per_method()
if __name__ == "__main__": if __name__ == "__main__":
......
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