def convert(self, devkit_dir):
"""
Args:
devkit_dir: path to VOC2012 devkit dir (e.g. VOCdevkit/VOC2012)
"""
devkit_dir = get_path(devkit_dir, is_directory=True)
image_set_file = devkit_dir / 'ImageSets' / 'Segmentation' / 'test.txt'
mask_dir = Path('SegmentationClass')
image_dir = Path('JPEGImages')
annotations = []
for image in read_txt(image_set_file):
annotation = SegmentationAnnotation(
str(image_dir / '{}.jpg'.format(image)),
str(mask_dir / '{}.png'.format(image)),
mask_loader=GTMaskLoader.SCIPY)
annotations.append(annotation)
meta = {
'label_map': dict(enumerate(_VOC_CLASSES_SEGMENTATION)),
'background_label': 0,
'segmentation_colors': _SEGMENTATION_COLORS
}
return annotations, meta
def prepare_annotation(self,
ann_file: Path,
train=False,
landmarks_map=None):
positive_pairs, negative_pairs = [], []
ann_lines = read_txt(ann_file)
for line in ann_lines[1:]: # skip header
pair = line.strip().split()
if len(pair) == 3:
positive_pairs.append(pair)
elif len(pair) == 4:
negative_pairs.append(pair)
all_images = set()
positive_data, all_images = self.convert_positive(
positive_pairs, all_images)
negative_data, all_images = self.convert_negative(
negative_pairs, all_images)
annotations = []
for image in all_images:
annotation = ReIdentificationClassificationAnnotation(
image, positive_data[image], negative_data[image])
if landmarks_map:
image_landmarks = landmarks_map.get(image)
annotation.metadata['keypoints'] = image_landmarks
if train:
annotation.metadata['train'] = True
annotations.append(annotation)
return annotations
def convert(self, devkit_dir, has_background=True):
"""
Args:
devkit_dir: path to VOC2007 devkit dir (e.g. .../VOCdevkit/VOC2007)
has_background: allows to add background label to label map
"""
if isinstance(has_background, str):
has_background = string_to_bool(has_background)
class_to_ind = prepare_detection_labels(has_background)
devkit_dir = get_path(devkit_dir, is_directory=True)
annotation_directory = get_path(devkit_dir / 'Annotations',
is_directory=True)
images_directory = get_path(devkit_dir / 'JPEGImages',
is_directory=True)
detections = []
image_set_file = devkit_dir / 'ImageSets' / 'Main' / 'test.txt'
for image in tqdm(read_txt(image_set_file, sep=None)):
file_path = annotation_directory / '{}.xml'.format(image)
tree = ET.parse(str(file_path))
identifier = tree.find('.//filename').text
image_path = images_directory / identifier
if not image_path.is_file():
raise FileNotFoundError("{}: {}".format(
os.strerror(errno.ENOENT), image_path))
labels, x_mins, y_mins, x_maxs, y_maxs = [], [], [], [], []
difficult_indices = []
for entry in tree.getroot():
if not entry.tag.startswith('object'):
continue
bbox = entry.find('bndbox')
difficult = int(entry.find('difficult').text)
if difficult == 1:
difficult_indices.append(len(labels))
labels.append(class_to_ind[entry.find('name').text])
x_mins.append(float(bbox.find('xmin').text) - 1)
y_mins.append(float(bbox.find('ymin').text) - 1)
x_maxs.append(float(bbox.find('xmax').text) - 1)
y_maxs.append(float(bbox.find('ymax').text) - 1)
image_annotation = DetectionAnnotation(identifier, labels, x_mins,
y_mins, x_maxs, y_maxs)
image_annotation.metadata['difficult_boxes'] = difficult_indices
detections.append(image_annotation)
meta = {'label_map': reverse_label_map(class_to_ind)}
if has_background:
meta['background_label'] = 0
return detections, meta
def convert(self, pairs_file, train_file=None, landmarks_file=None):
landmarks_map = {}
if landmarks_file:
for landmark_line in read_txt(landmarks_file):
landmark_line = landmark_line.split('\t')
landmarks_map[landmark_line[0]] = [
int(point) for point in landmark_line[1:]
]
test_annotations = self.prepare_annotation(pairs_file, True,
landmarks_map)
if train_file:
train_annotations = self.prepare_annotation(
train_file, True, landmarks_map)
test_annotations += train_annotations
return test_annotations, {}
def convert(self, wider_annotation: str, label_start=1):
"""
Args:
wider_annotation: path to wider validation file
label_start: start index for labels
"""
wider_annotation = get_path(wider_annotation)
image_annotations = read_txt(wider_annotation)
image_ids = []
for image_id, line in enumerate(image_annotations):
if '.jpg' in line:
image_ids.append(image_id)
annotations = []
for image_id in image_ids:
identifier = image_annotations[image_id]
bbox_count = image_annotations[image_id + 1]
bbox_lines = image_annotations[image_id + 2:image_id + 2 +
int(bbox_count)]
x_mins, y_mins, x_maxs, y_maxs = [], [], [], []
for bbox in bbox_lines:
x_min, y_min, x_max, y_max = convert_bboxes_xywh_to_x1y1x2y2(
*(map(float, (bbox.split(' ')[0:4]))))
x_mins.append(x_min)
y_mins.append(y_min)
x_maxs.append(x_max)
y_maxs.append(y_max)
annotations.append(
DetectionAnnotation(identifier,
[int(label_start)] * len(x_mins), x_mins,
y_mins, x_maxs, y_maxs))
return annotations, {
'label_map': {
0: '__background__',
int(label_start): 'face'
},
'background_label': 0
}
def _read_labels(labels_file):
"""Extract label names from labels.txt file"""
return read_txt(labels_file)
def rename_identifiers(annotation_list, images_file: str):
for annotation, image in zip(annotation_list, read_txt(images_file)):
annotation.identifier = image
return annotation_list