- def load_gt_roidb(dataset_name, image_set_name, root_path, dataset_path, result_path=None, flip=False):
- def load_proposal_roidb(dataset_name, image_set_name, root_path, dataset_path, result_path=None, proposal=‘rpn’, append_gt=True, flip=False,proposal_path=‘proposals’, only_gt=False, get_imdb=False, load_mask=False):
imdb = eval(dataset_name)(image_set_name, root_path, dataset_path, result_path,load_mask=load_mask)
roidb = imdb.gt_roidb()
imdb.gt_roidb
imdb.py
def gt_roidb(self):
raise NotImplementedError
在子类中实现 coco.py coco继承自IMDB
class coco(IMDB):
def __init__(self, image_set, root_path, data_path, result_path=None, mask_size=-1, binary_thresh=None, load_mask=False):
gt_roidb作用：返回值 gt_roidb，存储三个文件_gt_roidb.pkl, _index_roidb.pkl, _sindex_roidb.pkl, 返回值gt_roidb通过遍历image_set_index中的index，调用self._load_coco_annotation(index)，也就是说, roidb其实就是每个图片的coco标注的集合，每个集合元素的形式如下
gt_roidb实现代码
def gt_roidb(self):
cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
index_file = os.path.join(self.cache_path, self.name + '_index_roidb.pkl')
sindex_file = os.path.join(self.cache_path, self.name + '_sindex_roidb.pkl')
if os.path.exists(cache_file) and os.path.exists(index_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
with open(index_file, 'rb') as fid:
self.image_set_index = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = []
valid_id = []
vids = []
ct = 0
for index in self.image_set_index:
roientry,flag = self._load_coco_annotation(index)
if flag:
gt_roidb.append(roientry)
valid_id.append(index)
vids.append(ct)
ct = ct + 1
self.image_set_index = valid_id
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
with open(index_file, 'wb') as fid:
cPickle.dump(valid_id, fid, cPickle.HIGHEST_PROTOCOL)
with open(sindex_file, 'wb') as fid:
cPickle.dump(vids, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
coco.py
def _load_coco_annotation(self, index):
def _polys2boxes(polys):
boxes_from_polys = np.zeros((len(polys), 4), dtype=np.float32)
for i in range(len(polys)):
poly = polys[i]
x0 = min(min(p[::2]) for p in poly)
x1 = max(max(p[::2]) for p in poly)
y0 = min(min(p[1::2]) for p in poly)
y1 = max(max(p[1::2]) for p in poly)
boxes_from_polys[i, :] = [x0, y0, x1, y1]
return boxes_from_polys
"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
iscrowd:
crowd instances are handled by marking their overlaps with all categories to -1
and later excluded in training
bbox:
[x1, y1, w, h]
:param index: coco image id
:return: roidb entry
"""
im_ann = self.coco.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False)
objs = self.coco.loadAnns(annIds)
annIds = self.coco.getAnnIds(imgIds=index, iscrowd=True)
objsc = self.coco.loadAnns(annIds)
# sanitize bboxes
valid_objs = []
for obj in objs:
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
#yujie
#x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
#y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
x2 = np.min((width, x1 + np.max((0, w))))
y2 = np.min((height, y1 + np.max((0, h))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
valid_objsc = []
for obj in objsc:
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
#yujie
x2 = np.min((width, x1 + np.max((0, w))))
y2 = np.min((height, y1 + np.max((0, h))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objsc.append(obj)
objs = valid_objs
objc = valid_objsc
num_objs = len(objs)
num_objsc = len(objsc)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
boxesc = np.zeros((num_objsc, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
#for ix, obj in enumerate(objsc):
# boxesc[ix, :] = obj['clean_bbox']
for ix, obj in enumerate(objs):
cls = self._coco_ind_to_class_ind[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
if obj['iscrowd']:
overlaps[ix, :] = -1.0
else:
overlaps[ix, cls] = 1.0
ws = boxes[:, 2] - boxes[:, 0]
hs = boxes[:, 3] - boxes[:, 1]
flag = True
roi_rec = {'image': self.image_path_from_index(index),
'height': height,
'width': width,
'boxes': boxes,
'boxesc': boxesc,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'max_classes': overlaps.argmax(axis=1), #[2]
'max_overlaps': overlaps.max(axis=1),
'flipped': False}
#print '>>>>>> roi_rec'
#print roi_rec
if self.load_mask:
# we only care about valid polygons
print '>>>>>>> load mask'
segs = []
for obj in objs:
if not isinstance(obj['segmentation'], list):
# This is a crowd box
segs.append([])
else:
segs.append([np.array(p) for p in obj['segmentation'] if len(p)>=6])
roi_rec['gt_masks'] = segs
# Uncomment if you need to compute gts based on segmentation masks
# seg_boxes = _polys2boxes(segs)
# roi_rec['mask_boxes'] = seg_boxes
return roi_rec, flag
- def merge_roidb(roidbs):
- add_chip_data(raid, chip_meta_data_path=’’):
- remove_small_boxes(roidb, max_scale=3, mn_size=10):
- filter_roidb(roidb, config):
- load_gt_segdb(dataset_name, image_set_name, root_path, dataset_path, result_path=None, flip=False):
- def merge_segdb(segdbs):
参考博文
[1]python eval
[2]argmax
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