def _add_proposals_from_file(self, roidb, proposal_file, min_proposal_size,
top_k, crowd_thresh):
"""Add proposals from a proposals file to an roidb."""
logger.info('Loading proposals from: {}'.format(proposal_file))
with open(proposal_file, 'r') as f:
proposals = pickle.load(f)
id_field = 'indexes' if 'indexes' in proposals else 'ids' # compat fix
_sort_proposals(proposals, id_field)
box_list = []
for i, entry in enumerate(roidb):
if i % 2500 == 0:
logger.info(' {:d}/{:d}'.format(i + 1, len(roidb)))
boxes = proposals['boxes'][i]
# Sanity check that these boxes are for the correct image id
assert entry['id'] == proposals[id_field][i]
# Remove duplicate boxes and very small boxes and then take top k
boxes = box_utils.clip_boxes_to_image(boxes, entry['height'],
entry['width'])
keep = box_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = box_utils.filter_small_boxes(boxes, min_proposal_size)
boxes = boxes[keep, :]
if top_k > 0:
boxes = boxes[:top_k, :]
box_list.append(boxes)
_merge_proposal_boxes_into_roidb(roidb, box_list)
if crowd_thresh > 0:
_filter_crowd_proposals(roidb, crowd_thresh)
def _add_proposals_from_file(self, roidb, proposal_file, min_proposal_size,
top_k):
"""Add proposals from a proposals file to an roidb.
"""
logger.info('Loading proposals from: {}'.format(proposal_file))
with open(proposal_file, 'r') as f:
proposals = pickle.load(f)
#proposals[seq_idx][idx]
box_list = []
for i, entry in enumerate(roidb):
if i % 500 == 0:
logger.info(' {:d}/{:d}'.format(i + 1, len(roidb)))
seq_idx = entry['seq_idx']
idx = entry['idx']
boxes = proposals['boxes'][seq_idx][idx]
# Remove duplicate boxes and very small boxes and then take top k
boxes = box_utils.clip_boxes_to_image(boxes, entry['height'],
entry['width'])
keep = box_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = box_utils.filter_small_boxes(boxes, min_proposal_size)
boxes = boxes[keep, :]
if top_k > 0:
boxes = boxes[:top_k, :]
box_list.append(boxes)
_merge_proposal_boxes_into_roidb(roidb, box_list)
def _add_proposals_from_file(
self, roidb, proposal_file, min_proposal_size, top_k, crowd_thresh
):
"""Add proposals from a proposals file to an roidb."""
logger.info('Loading proposals from: {}'.format(proposal_file))
with open(proposal_file, 'r') as f:
proposals = pickle.load(f)
id_field = 'indexes' if 'indexes' in proposals else 'ids' # compat fix
_sort_proposals(proposals, id_field)
box_list = []
for i, entry in enumerate(roidb):
if i % 2500 == 0:
logger.info(' {:d}/{:d}'.format(i + 1, len(roidb)))
boxes = proposals['boxes'][i]
# Sanity check that these boxes are for the correct image id
assert entry['id'] == proposals[id_field][i]
# Remove duplicate boxes and very small boxes and then take top k
boxes = box_utils.clip_boxes_to_image(
boxes, entry['height'], entry['width']
)
keep = box_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = box_utils.filter_small_boxes(boxes, min_proposal_size)
boxes = boxes[keep, :]
if top_k > 0:
boxes = boxes[:top_k, :]
box_list.append(boxes)
_merge_proposal_boxes_into_roidb(roidb, box_list)
if crowd_thresh > 0:
_filter_crowd_proposals(roidb, crowd_thresh)
def __init__(self,
root,
year='2007',
image_set='trainval',
download=False,
transforms=None):
self.root = root
self.transforms = transforms
self.year = year
if year == "2007" and image_set == "test":
year = "2007-test"
self.url = DATASET_YEAR_DICT[year]['url']
self.filename = DATASET_YEAR_DICT[year]['filename']
self.md5 = DATASET_YEAR_DICT[year]['md5']
valid_sets = ["train", "trainval", "val"]
if year == "2007-test":
valid_sets.append("test")
self.image_set = verify_str_arg(image_set, "image_set", valid_sets)
base_dir = DATASET_YEAR_DICT[year]['base_dir']
voc_root = os.path.join(self.root, base_dir)
image_dir = os.path.join(voc_root, 'JPEGImages')
annotation_dir = os.path.join(voc_root, 'Annotations')
if download:
download_extract(self.url, self.root, self.filename, self.md5)
# pause()
if not os.path.isdir(voc_root):
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
splits_dir = os.path.join(voc_root, 'ImageSets/Main')
split_f = os.path.join(splits_dir, image_set.rstrip('\n') + '.txt')
with open(os.path.join(split_f), "r") as f:
file_names = [x.strip() for x in f.readlines()]
self.class_labels = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
self.num_classes = len(self.class_labels)
self.images = [os.path.join(image_dir, x + ".jpg") for x in file_names]
annotations = [os.path.join(annotation_dir, x + ".xml") for x in file_names]
assert (len(self.images) == len(annotations))
#load annotations:
self.annotations = []
for i, ann_path in enumerate(annotations):
raw_annot = self.parse_voc_xml(ET.parse(ann_path).getroot())['annotation']
self.annotations.append({
'img_key': int(raw_annot['filename'].replace('.jpg','')),
'width' : int(raw_annot['size']['width']),
'height' : int(raw_annot['size']['height']),
'object' : raw_annot['object']
})
##TODO ajust this to be more beauty =p
ss_data = self.root + 'selective_search_data/voc_' + self.year + '_' + self.image_set + '.pkl'
with open(ss_data, 'rb') as f:
proposals = pickle.load(f)
sort_proposals(proposals, 'indexes')
self.proposals = []
for i, boxes in enumerate(proposals['boxes']):
if i % 2500 == 0:
logger.info(' {:d}/{:d}'.format(i + 1, len(proposals['boxes'])))
annotation = self.annotations[i]
assert annotation['img_key'] == proposals['indexes'][i]
# Remove duplicate boxes and very small boxes and then take top k
boxes = box_utils.clip_boxes_to_image(boxes, annotation['height'], annotation['width'])
keep = box_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = box_utils.filter_small_boxes(boxes, cfg.FAST_RCNN.MIN_PROPOSAL_SIZE)
boxes = boxes[keep, :]
if cfg.FAST_RCNN.TOP_K > 0:
boxes = boxes[:cfg.FAST_RCNN.TOP_K, :]
self.proposals.append(boxes.astype(np.float))
