def load_instances(input_filepath, output_filepath, mesh_list, symemtry_part_index):
dirnames = glob.glob(input_filepath + "/*")
exp_name = os.path.basename(os.path.normpath(input_filepath + "/../"))
exp_output_filepath = output_filepath + "/images/" + exp_name
print (exp_name)
if not os.path.isdir(output_filepath + "/images/"):
os.mkdir(output_filepath + "/images/")
if not os.path.isdir(exp_output_filepath):
os.mkdir(exp_output_filepath)
instances = []
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
# print prefix
if not prefix in mesh_list:
continue
abs_dirname = os.path.abspath(dirname)
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
# print('Candidate index: ' + str(candidate_index))
absolute_image_filepath = []
image_filenames = []
# image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + "_input.png")
image_filenames.append("../../part_assembly/" + prefix + "/" + prefix + "_assembly_cuboid.png")
image_filenames.append("../../part_assembly/" + prefix + "/" + prefix + "_assembly_accuracy.png")
image_filenames.append(prefix + "_" + str(candidate_index) + ".png")
# image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_accuracy.png')
# image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_completeness.png')
image_filenames.append(prefix + "_" + str(candidate_index) + "_database_accuracy.png")
# image_filenames.append(prefix + '_' + str(candidate_index) + '_database_completeness.png')
# image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_accuracy.png')
# image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + "/" + image_filename):
print 'Warning: File does not exist: "' + (dirname + "/" + image_filename) + '"'
is_loaded = False
break
if not os.path.exists(output_filepath + "/" + image_filename):
# Copy the image.
shutil.copy(dirname + "/" + image_filename, exp_output_filepath)
"""
# Create a thumbnail.
librr.create_thumbnails(output_filepath + '/' + image_filename, librr.thumbname_width)
"""
# Get relative file path.
# absolute_image_filepath.append(abs_dirname + '/' + image_filename)
absolute_image_filepath.append("images/" + exp_name + "/" + os.path.basename(image_filename))
if not is_loaded:
continue
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append("_" + str(candidate_index) + "_symmetry")
csv_filename_postfixes.append("_" + str(candidate_index) + "_database")
csv_filename_postfixes.append("_" + str(candidate_index) + "_fusion")
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + "/" + prefix + csv_filename_postfix + ".csv"
if symemtry_part_index >= 0:
# Read per-part files.
csv_filename = dirname + "/" + prefix + csv_filename_postfix + "_" + str(symemtry_part_index) + ".csv"
all_values = librr.get_csv_value(csv_filename, librr.threshold)
if not all_values:
accuracy_values.append(float("NaN"))
completeness_values.append(float("NaN"))
else:
accuracy_values.append(all_values[0])
completeness_values.append(all_values[1])
instance = OutputInstance( # prefix,
absolute_image_filepath[0],
absolute_image_filepath[1],
absolute_image_filepath[2],
absolute_image_filepath[3],
absolute_image_filepath[4],
# absolute_image_filepath[5],
# absolute_image_filepath[6],
# absolute_image_filepath[7],
# absolute_image_filepath[8],
# accuracy_values[0], completeness_values[0],
# accuracy_values[1], completeness_values[1],
# accuracy_values[2], completeness_values[2]
)
instances.append(instance)
return instances
def load_instances(input_filepath, output_filepath, symemtry_part_index):
dirnames = glob.glob(input_filepath + '/*')
instances = []
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
print prefix
abs_dirname = os.path.abspath(dirname)
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
print('Candidate index: ' + str(candidate_index))
absolute_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + '/' + image_filename):
print 'Warning: File does not exist: "' + (dirname + '/' + image_filename) + '"'
is_loaded = False
break
if not os.path.exists(output_filepath + '/' + image_filename):
'''
# Copy the image.
shutil.copy(dirname + '/' + image_filename, output_filepath)
# Create a thumbnail.
librr.create_thumbnails(output_filepath + '/' + image_filename, librr.thumbname_width)
'''
# Get relative file path.
absolute_image_filepath.append(abs_dirname + '/' + image_filename)
if not is_loaded:
continue
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append('_' + str(candidate_index) + '_symmetry')
csv_filename_postfixes.append('_' + str(candidate_index) + '_database')
csv_filename_postfixes.append('_' + str(candidate_index) + '_fusion')
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + '/' + prefix + csv_filename_postfix + '.csv'
if symemtry_part_index >= 0:
# Read per-part files.
csv_filename = dirname + '/' + prefix + csv_filename_postfix\
+ '_' + str(symemtry_part_index) + '.csv'
all_values = librr.get_csv_value(csv_filename, librr.threshold)
if not all_values:
accuracy_values.append(float("NaN"))
completeness_values.append(float("NaN"))
else:
accuracy_values.append(all_values[0])
completeness_values.append(all_values[1])
instance = OutputInstance(prefix, absolute_image_filepath[0],
absolute_image_filepath[1], absolute_image_filepath[2],
absolute_image_filepath[3], absolute_image_filepath[4],
absolute_image_filepath[5], absolute_image_filepath[6],
absolute_image_filepath[7], absolute_image_filepath[8],
accuracy_values[0], completeness_values[0],
accuracy_values[1], completeness_values[1],
accuracy_values[2], completeness_values[2])
instances.append(instance)
return instances
def load_instances(input_filepath, output_filepath, symemtry_part_index):
dirnames = glob.glob(input_filepath + '/*')
instances = []
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
print prefix
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
print('Candidate index: ' + str(candidate_index))
# Read images.
relative_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_symmetry_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_symmetry_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_database_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_database_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_fusion_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) +
'_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + '/' + image_filename):
print 'Warning: File does not exist: "' + (
dirname + '/' + image_filename) + '"'
is_loaded = False
break
if not os.path.exists(output_filepath + '/' + image_filename):
# Copy the image.
shutil.copy(dirname + '/' + image_filename, output_filepath)
# Create a thumbnail.
librr.create_thumbnails(output_filepath + '/' + image_filename,
librr.thumbname_width)
# Get relative file path.
relative_image_filepath.append('./' + image_filename)
if not is_loaded:
continue
# Read stats.
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append('_' + str(candidate_index) + '_symmetry')
csv_filename_postfixes.append('_' + str(candidate_index) + '_database')
csv_filename_postfixes.append('_' + str(candidate_index) + '_fusion')
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + '/' + prefix + csv_filename_postfix + '.csv'
if symemtry_part_index >= 0:
# Read per-part files.
csv_filename = dirname + '/' + prefix + csv_filename_postfix\
+ '_' + str(symemtry_part_index) + '.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
is_loaded = False
break
else:
all_values = librr.get_csv_value(csv_filename, librr.threshold)
if not all_values:
accuracy_values.append(float("NaN"))
completeness_values.append(float("NaN"))
else:
accuracy_values.append(all_values[0])
completeness_values.append(all_values[1])
if not is_loaded:
continue
# Read per-point labeling accuracy.
csv_filename = dirname + '/' + prefix + '_' + str(
candidate_index) + '_labeling_stats.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
continue
else:
per_point_labeling_accuracy = read_value_from_csv_file(
csv_filename, symemtry_part_index, 3)
# Read cuboid distance to ground truth.
csv_filename = dirname + '/' + prefix + '_' + str(
candidate_index) + '_cuboid_distance.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
continue
else:
cuboid_distance_to_ground_truth = read_value_from_csv_file(
csv_filename, symemtry_part_index, 1)
instance = OutputInstance(
prefix, relative_image_filepath[0], relative_image_filepath[1],
relative_image_filepath[2], relative_image_filepath[3],
relative_image_filepath[4], relative_image_filepath[5],
relative_image_filepath[6], relative_image_filepath[7],
relative_image_filepath[8], accuracy_values[0],
completeness_values[0], accuracy_values[1], completeness_values[1],
accuracy_values[2], completeness_values[2],
per_point_labeling_accuracy, cuboid_distance_to_ground_truth)
instances.append(instance)
return instances
def load_instances(input_filepath, output_filepath):
dirnames = glob.glob(input_filepath + '/*')
all_accu_values = [[], [], [], [], []]
all_comp_values = [[], [], [], [], []]
x_values = []
count_instances = 0
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
#print prefix
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
#print('Candidate index: ' + str(candidate_index))
relative_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + '/' + image_filename):
print 'Warning: File does not exist: "' + (dirname + '/' + image_filename) + '"'
is_loaded = False
break
if not is_loaded:
continue
csv_filename_postfixes = []
csv_filename_postfixes.append(prefix + '_' + str(candidate_index) + '_symmetry')
csv_filename_postfixes.append(prefix + '_' + str(candidate_index) + '_database')
csv_filename_postfixes.append(prefix + '_' + str(candidate_index) + '_fusion')
csv_filename_postfixes.append('/../../part_assembly/' + prefix + '/' + prefix +'_assembly')
csv_filename_postfixes.append('/../../symmetry_detection/' + prefix + '/' + prefix +'_symm_detection_recon')
file_exist = True
for i in range(len(csv_filename_postfixes)):
csv_filename_postfix = csv_filename_postfixes[i]
csv_filename = dirname + '/' + csv_filename_postfix + '.csv'
if not os.path.isfile(csv_filename):
file_exist = False
break
if not file_exist:
continue
for i in range(len(csv_filename_postfixes)):
csv_filename_postfix = csv_filename_postfixes[i]
csv_filename = dirname + '/' + csv_filename_postfix + '.csv'
if not os.path.isfile(csv_filename):
continue
(accu_values, comp_values, x_values) = librr.get_csv_all_value(csv_filename)
if not all_accu_values[i]:
all_accu_values[i] = accu_values
if not all_comp_values[i]:
all_comp_values[i] = comp_values
all_accu_values[i] = [x+y for x,y in zip(all_accu_values[i], accu_values)]
all_comp_values[i] = [x+y for x,y in zip(all_comp_values[i], comp_values)]
count_instances += 1
if count_instances > 0:
for i in range(len(all_accu_values)):
all_accu_values[i] = [x / count_instances for x in all_accu_values[i]]
for i in range(len(all_comp_values)):
all_comp_values[i] = [x / count_instances for x in all_comp_values[i]]
return (all_accu_values, all_comp_values, x_values)
def load_instances(input_filepath, output_filepath, symemtry_part_index):
dirnames = glob.glob(input_filepath + '/*')
instances = []
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
print prefix
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
print('Candidate index: ' + str(candidate_index))
# Read images.
relative_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + '/' + image_filename):
print 'Warning: File does not exist: "' + (dirname + '/' + image_filename) + '"'
is_loaded = False
break
if not os.path.exists(output_filepath + '/' + image_filename):
# Copy the image.
shutil.copy(dirname + '/' + image_filename, output_filepath)
# Create a thumbnail.
librr.create_thumbnails(output_filepath + '/' + image_filename, librr.thumbname_width)
# Get relative file path.
relative_image_filepath.append('./' + image_filename)
if not is_loaded:
continue
# Read stats.
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append('_' + str(candidate_index) + '_symmetry')
csv_filename_postfixes.append('_' + str(candidate_index) + '_database')
csv_filename_postfixes.append('_' + str(candidate_index) + '_fusion')
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + '/' + prefix + csv_filename_postfix + '.csv'
if symemtry_part_index >= 0:
# Read per-part files.
csv_filename = dirname + '/' + prefix + csv_filename_postfix\
+ '_' + str(symemtry_part_index) + '.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
is_loaded = False
break
else:
all_values = librr.get_csv_value(csv_filename, librr.threshold)
if not all_values:
accuracy_values.append(float("NaN"))
completeness_values.append(float("NaN"))
else:
accuracy_values.append(all_values[0])
completeness_values.append(all_values[1])
if not is_loaded:
continue
# Read per-point labeling accuracy.
csv_filename = dirname + '/' + prefix + '_' + str(candidate_index) + '_labeling_stats.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
continue
else:
per_point_labeling_accuracy = read_value_from_csv_file(csv_filename, symemtry_part_index, 3)
# Read cuboid distance to ground truth.
csv_filename = dirname + '/' + prefix + '_' + str(candidate_index) + '_cuboid_distance.csv'
if not os.path.exists(csv_filename):
print 'Warning: File does not exist: "' + csv_filename + '"'
continue
else:
cuboid_distance_to_ground_truth = read_value_from_csv_file(csv_filename, symemtry_part_index, 1)
instance = OutputInstance(prefix, relative_image_filepath[0],
relative_image_filepath[1], relative_image_filepath[2],
relative_image_filepath[3], relative_image_filepath[4],
relative_image_filepath[5], relative_image_filepath[6],
relative_image_filepath[7], relative_image_filepath[8],
accuracy_values[0], completeness_values[0],
accuracy_values[1], completeness_values[1],
accuracy_values[2], completeness_values[2],
per_point_labeling_accuracy, cuboid_distance_to_ground_truth)
instances.append(instance)
return instances
def load_instances(input_filepath, output_filepath, symemtry_part_index):
output_file_prefix = os.path.basename(os.path.normpath(input_filepath + '/../'))
dirnames = glob.glob(input_filepath + '/*')
print(output_file_prefix + '_ours.csv')
print(output_file_prefix + '_podolak.csv')
ours_file = open(output_file_prefix + '_ours.csv', 'w')
podolak_file = open(output_file_prefix + '_podolak.csv', 'w')
for dirname in dirnames:
if not os.path.isdir(dirname):
continue
prefix = os.path.basename(dirname)
print prefix
is_loaded = True
candidate_index = librr.find_best_candidate(dirname, prefix)
print('Candidate index: ' + str(candidate_index))
relative_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_symmetry_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_database_completeness.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_accuracy.png')
image_filenames.append(prefix + '_' + str(candidate_index) + '_fusion_completeness.png')
for image_filename in image_filenames:
if not os.path.exists(dirname + '/' + image_filename):
print 'Warning: File does not exist: "' + (dirname + '/' + image_filename) + '"'
is_loaded = False
break
# Get relative file path.
relative_image_filepath.append('./' + image_filename)
if not is_loaded:
continue
ours_symm_filename_postfix = '_' + str(candidate_index) + '_symmetry_info.txt'
ours_symm_filename = dirname + '/' + prefix + ours_symm_filename_postfix
if os.path.exists(ours_symm_filename):
with open(ours_symm_filename, 'r') as csv_file:
data = csv.reader(csv_file, delimiter=',')
x_data = data.next()
n = np.array([float(x_data[1]), float(x_data[2]), float(x_data[3])])
t = float(x_data[4])
n = n / np.linalg.norm(n)
ours_file.write(str(n[0]) + ',' + str(n[1]) + ',' + str(n[2]) + ',' + str(t) + '\n')
podolak_symm_filename_postfix = '_symmetry_info.txt'
podolak_symm_filename = dirname + '/../../symmetry_detection/' + prefix + '/' + prefix + podolak_symm_filename_postfix
print(podolak_symm_filename)
if os.path.exists(podolak_symm_filename):
with open(podolak_symm_filename, 'r') as csv_file:
data = csv.reader(csv_file, delimiter=',')
x_data = data.next()
n = np.array([float(x_data[1]), float(x_data[2]), float(x_data[3])])
t = float(x_data[4])
n = n / np.linalg.norm(n)
podolak_file.write(str(n[0]) + ',' + str(n[1]) + ',' + str(n[2]) + ',' + str(t) + '\n')
ours_file.close()
podolak_file.close()
