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
relative_image_filepath = []
image_filenames = []
image_filenames.append(prefix + '_view.png')
image_filenames.append(prefix + '_input.png')
image_filenames.append(prefix + '_symm_detection_recon.png')
image_filenames.append(prefix + '_symm_detection_accuracy.png')
image_filenames.append(prefix + '_symm_detection_completeness.png')
# FIXME
if not os.path.exists(dirname + '/../../output/' + prefix + '/' +
prefix + '_input.png'):
continue
if not os.path.exists(dirname + '/../../output/' + prefix + '/' +
prefix + '_view.png'):
continue
shutil.copy(
dirname + '/../../output/' + prefix + '/' + prefix + '_input.png',
dirname + '/' + prefix + '_input.png')
shutil.copy(
dirname + '/../../output/' + prefix + '/' + prefix + '_view.png',
dirname + '/' + prefix + '_view.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
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append('_symm_detection_recon')
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + '/' + prefix + csv_filename_postfix + '.csv'
if symemtry_part_index >= 0:
# Real 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, relative_image_filepath[0],
relative_image_filepath[1],
relative_image_filepath[2],
relative_image_filepath[3],
relative_image_filepath[4],
accuracy_values[0], completeness_values[0])
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, 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
relative_image_filepath = []
image_filenames = []
#image_filenames.append(prefix + '_view.png')
#image_filenames.append(prefix + '_input.png')
#image_filenames.append(prefix + '_symm_detection_recon.png')
#image_filenames.append(prefix + '_symm_detection_accuracy.png')
#image_filenames.append(prefix + '_symm_detection_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
accuracy_values = []
completeness_values = []
csv_filename_postfixes = []
csv_filename_postfixes.append('_baseline')
for csv_filename_postfix in csv_filename_postfixes:
csv_filename = dirname + '/' + prefix + csv_filename_postfix + '.csv'
if symemtry_part_index >= 0:
# Real 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:
print('Warning: NaN values (' + prefix + ')')
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, #relative_image_filepath[0], relative_image_filepath[1], relative_image_filepath[2],
#relative_image_filepath[3], relative_image_filepath[4],
accuracy_values[0], completeness_values[0])
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