def crop_xml(args, xml, sub_set_crop_path, instance_size=511):
"""
Dataset curation
Parameters
----------
xml: str , xml
sub_set_crop_path: str, xml crop path
instance_size: int, instance_size
"""
cv2 = try_import_cv2()
xmltree = ET.parse(xml)
objects = xmltree.findall('object')
frame_crop_base_path = os.path.join(sub_set_crop_path, xml.split('/')[-1].split('.')[0])
if not os.path.isdir(frame_crop_base_path):
makedirs(frame_crop_base_path)
img_path = xml.replace('xml', 'JPEG').replace('Annotations', 'Data')
im = cv2.imread(img_path)
avg_chans = np.mean(im, axis=(0, 1))
for id, object_iter in enumerate(objects):
bndbox = object_iter.find('bndbox')
bbox = [int(bndbox.find('xmin').text), int(bndbox.find('ymin').text),
int(bndbox.find('xmax').text), int(bndbox.find('ymax').text)]
z, x = crop_like_SiamFC(im, bbox, instance_size=instance_size, padding=avg_chans)
cv2.imwrite(os.path.join(args.download_dir, frame_crop_base_path, '{:06d}.{:02d}.z.jpg'.format(0, id)), z)
cv2.imwrite(os.path.join(args.download_dir, frame_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(0, id)), x)
def crop_video(args, sub_set, video, crop_path, ann_base_path):
"""
Dataset curation
Parameters
----------
sub_set: str , sub_set
video: str, video number
crop_path: str, crop_path
ann_base_path: str, Annotations base path
"""
cv2 = try_import_cv2()
video_crop_base_path = os.path.join(crop_path, sub_set, video)
if not os.path.isdir(video_crop_base_path):
makedirs(video_crop_base_path)
sub_set_base_path = os.path.join(ann_base_path, sub_set)
xmls = sorted(glob.glob(os.path.join(sub_set_base_path, video, '*.xml')))
for xml in xmls:
xmltree = ET.parse(xml)
objects = xmltree.findall('object')
objs = []
filename = xmltree.findall('filename')[0].text
im = cv2.imread(xml.replace('xml', 'JPEG').replace('Annotations', 'Data'))
avg_chans = np.mean(im, axis=(0, 1))
for object_iter in objects:
trackid = int(object_iter.find('trackid').text)
bndbox = object_iter.find('bndbox')
bbox = [int(bndbox.find('xmin').text), int(bndbox.find('ymin').text),
int(bndbox.find('xmax').text), int(bndbox.find('ymax').text)]
z, x = crop_like_SiamFC(im, bbox, instance_size=args.instance_size, padding=avg_chans)
cv2.imwrite(os.path.join(args.download_dir, video_crop_base_path, '{:06d}.{:02d}.z.jpg'.format(int(filename), trackid)), z)
cv2.imwrite(os.path.join(args.download_dir, video_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(int(filename), trackid)), x)
def crop_img(img, anns, set_crop_base_path, set_img_base_path, instance_size=511):
"""
Dataset curation
Parameters
----------
img: dic, img
anns: str, video number
set_crop_base_path: str, crop result path
set_img_base_path: str, ori image path
"""
frame_crop_base_path = os.path.join(set_crop_base_path, img['file_name'].split('/')[-1].split('.')[0])
if not os.path.isdir(frame_crop_base_path): makedirs(frame_crop_base_path)
cv2 = try_import_cv2()
im = cv2.imread('{}/{}'.format(set_img_base_path, img['file_name']))
avg_chans = np.mean(im, axis=(0, 1))
for trackid, ann in enumerate(anns):
rect = ann['bbox']
bbox = [rect[0], rect[1], rect[0] + rect[2], rect[1] + rect[3]]
if rect[2] <= 0 or rect[3] <= 0:
continue
z, x = crop_like_SiamFC(im, bbox, instance_size=instance_size, padding=avg_chans)
cv2.imwrite(os.path.join(args.download_dir, frame_crop_base_path, '{:06d}.{:02d}.z.jpg'.format(0, trackid)), z)
cv2.imwrite(os.path.join(args.download_dir, frame_crop_base_path, '{:06d}.{:02d}.x.jpg'.format(0, trackid)), x)