def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
figure_set = FigureSet()
figure_set.load_list(args.list_path)
with open(args.annotation_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
for idx, file in enumerate(figure_set.files):
# if '1465-9921-6-55-4' not in file:
# continue
print('Processing Image {:d}: {:s}.'.format(idx, file))
# Create figure object
figure = Figure(file)
# Load image
figure.load_image()
# Load annotation
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
figure.load_annotation_iphotodraw(xml_path)
# write to CSV file
# The format is:
# image_path,label_x1,label_y1,label_x2,label_y2,label
# if there is no label, the format becomes:
# image_path,,,,,
# TODO : factorize this
has_label = False
for panel in figure.panels:
if panel.label_rect is not None:
label = map_label(panel.label)
if len(label
) == 1: # We care for single letter label for now
row = list()
# add image_path
row.append(figure.image_path)
row.append(str(panel.label_rect[0]))
row.append(str(panel.label_rect[1]))
row.append(str(panel.label_rect[2]))
row.append(str(panel.label_rect[3]))
row.append(label)
csv_writer.writerow(row)
has_label = True
if not has_label:
row = list()
row.append(figure.image_path) # add image_path
row.append('')
row.append('')
row.append('')
row.append('')
row.append('')
csv_writer.writerow(row)
def save_gt_preview(self):
for idx, file in enumerate(self.files):
# if idx != 5:
# continue
logging.info('Generate GT Annotation Preview for Image %d: %s.',
idx, file)
try:
if not os.path.exists(file):
raise PanelSegError('Could not find {}.'.format(file))
figure = Figure()
figure.load_image(file)
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
if not os.path.exists(xml_path):
raise PanelSegError('Could not find {}.'.format(xml_path))
figure.load_annotation_iphotodraw(xml_path)
except PanelSegError as ex:
logging.warning(ex.message)
continue
# Save preview
folder, file = os.path.split(figure.image_path)
folder = os.path.join(folder, "prev")
figure.save_preview(folder)
def eval_mAP(args):
# Read all figures to be evaluated
with open(args.eval_list) as f:
lines = f.readlines()
all_boxes = []
all_scores = []
all_num = []
figures = []
with open(args.eval_file, 'a') as f:
for idx, filepath in enumerate(lines):
print(str(idx) + ': ' + filepath)
filepath = filepath.strip()
figure = Figure(filepath, padding=0)
figure.load_gt_annotation(which_annotation='panel')
pickle_file = os.path.join(args.auto_dir,
figure.file.replace('.jpg', '.pickle'))
with open(pickle_file, 'rb') as handle:
boxes, scores, classes, num = pickle.load(handle)
all_boxes.append(boxes[0])
all_scores.append(scores[0])
all_num.append(num[0])
figures.append(figure)
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
figure_set = FigureSet()
figure_set.load_list(args.list_path)
with open(args.annotation_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
for idx, file in enumerate(figure_set.files):
# if '1465-9921-6-55-4' not in file:
# continue
print('Processing Image {:d}: {:s}.'.format(idx, file))
figure = Figure(file)
figure.load_image()
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
figure.load_annotation_iphotodraw(xml_path)
# write to CSV file
for panel in figure.panels:
csv_writer.writerow([
figure.image_path,
str(panel.panel_rect[0]),
str(panel.panel_rect[1]),
str(panel.panel_rect[2]),
str(panel.panel_rect[3]), 'panel'
])
def main(args=None):
# parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
figure_set = FigureSet()
figure_set.load_list(args.list_path)
with open(args.annotation_path, 'w', newline='') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
for idx, file in enumerate(figure_set.files):
# if '1465-9921-6-55-4' not in file:
# continue
print('Processing Image {:d}: {:s}.'.format(idx, file))
figure = Figure(file)
figure.load_image()
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
figure.load_annotation_iphotodraw(xml_path)
# write to CSV file
# The format is:
# image_path,panel_x1,panel_y1,panel_x2,panel_y2,label_x1,label_y1,label_x2,label_y2,label
# if there is no label, the format becomes:
# image_path,panel_x1,panel_y1,panel_x2,panel_y2,,,,,
for panel in figure.panels:
row = list()
row.append(figure.image_path) # add image_path
row.append(str(panel.panel_rect[0]))
row.append(str(panel.panel_rect[1]))
row.append(str(panel.panel_rect[2]))
row.append(str(panel.panel_rect[3]))
row.append('panel')
if panel.label_rect is None:
row.append('')
row.append('')
row.append('')
row.append('')
row.append('')
else:
label = map_label(panel.label)
row.append(str(panel.label_rect[0]))
row.append(str(panel.label_rect[1]))
row.append(str(panel.label_rect[2]))
row.append(str(panel.label_rect[3]))
row.append(label)
csv_writer.writerow(row)
def validate_annotation(self):
for idx, file in enumerate(self.files):
# if idx != 5:
# continue
logging.info('Validate Annotation of Image %d: %s.', idx, file)
try:
if not os.path.exists(file):
raise PanelSegError('Could not find {}.'.format(file))
figure = Figure()
figure.load_image(file)
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
if not os.path.exists(xml_path):
raise PanelSegError('Could not find {}.'.format(xml_path))
figure.load_annotation_iphotodraw(xml_path)
except PanelSegError as ex:
logging.warning(ex.message)
continue
def eval_ImageCLEF(args):
def compute_metric(gt_panels, auto_panels):
correct = 0
picked_auto_idx = [False] * len(auto_panels)
for gt_idx, gt_panel in enumerate(gt_panels):
max_overlapping = -1
max_auto_idx = -1
for auto_idx, auto_panel in enumerate(auto_panels):
if picked_auto_idx[auto_idx]:
continue
intersection = intersect(auto_panel.panel_rect,
gt_panel.panel_rect)
if intersection is None:
continue
intersection_area = (intersection[2] - intersection[0]) * (
intersection[3] - intersection[1])
auto_area = (
auto_panel.panel_rect[2] - auto_panel.panel_rect[0]) * (
auto_panel.panel_rect[3] - auto_panel.panel_rect[1])
overlapping = intersection_area / auto_area
if overlapping > max_overlapping:
max_overlapping = overlapping
max_auto_idx = auto_idx
# if max_overlapping > 0.66:
if max_overlapping > 0.5:
correct += 1
picked_auto_idx[max_auto_idx] = True
return correct
# Read all figures to be evaluated
with open(args.eval_list) as f:
lines = f.readlines()
with open(args.eval_file, 'a') as f:
overall_correct_count = 0
overall_gt_count = 0
overall_auto_count = 0
overall_accuracy = 0.0
overall_recall = 0.0
overall_precision = 0.0
for idx, filepath in enumerate(lines):
# if '1471-213X-8-30-5' not in filepath:
# continue
print(str(idx) + ': ' + filepath)
filepath = filepath.strip()
figure = Figure(filepath)
if 'clef_eval' in args.eval_list:
csv_path = os.path.join(
figure.image_path.replace('.jpg', '.csv'))
figure.load_annotation_csv(csv_path)
else:
figure.load_image()
xml_path = os.path.join(
figure.image_path.replace('.jpg', '_data.xml'))
figure.load_annotation_iphotodraw(xml_path)
gt_panels = figure.panels
image_folder, image_file = os.path.split(figure.image_path)
auto_file = os.path.join(args.auto_dir,
image_file.replace('.jpg', '.csv'))
figure.load_annotation_csv(auto_file)
auto_panels = figure.panels
correct = compute_metric(gt_panels, auto_panels)
k = max(len(auto_panels), len(gt_panels))
accuracy = correct / k
recall = correct / len(gt_panels)
if len(auto_panels) == 0:
precision = 0
else:
precision = correct / len(auto_panels)
f.write("{0}: {1}\t{2}\t{3}\n".format(figure.image_path, accuracy,
precision, recall))
overall_accuracy += accuracy
overall_recall += recall
overall_precision += precision
overall_correct_count += correct
overall_gt_count += len(gt_panels)
overall_auto_count += len(auto_panels)
overall_accuracy /= len(lines)
overall_precision /= len(lines)
overall_recall /= len(lines)
f.write("Overall ImageCLEF Accuracy: {}, Precision: {}, Recall: {}\n".
format(overall_accuracy, overall_precision, overall_recall))
overall_accuracy = overall_correct_count / max(overall_auto_count,
overall_gt_count)
overall_recall = overall_correct_count / overall_gt_count
overall_precision = overall_correct_count / overall_auto_count
f.write("Overall Accuracy: {}, Precision: {}, Recall: {}\n".format(
overall_accuracy, overall_precision, overall_recall))