def from_dict(cls, src_dict):
assert 'counts' in src_dict
assert 'size' in src_dict
h = src_dict['size'][0]
w = src_dict['size'][1]
rle = mask.frPyObjects([src_dict], h, w)
assert isinstance(rle, list) and isinstance(rle[0], dict)
return cls(w, h, rle[0]['counts'])
def from_list(cls, w, h, src_list):
rle = mask.frPyObjects(src_list, h, w)
assert isinstance(rle, list)
if len(rle) > 1:
rle_bytes = mask.merge(rle)['counts']
else:
rle_bytes = rle[0]['counts']
return cls(w, h, rle_bytes)
def getMask(self, ref):
# return mask, area and mask-center
ann = self.refToAnn[ref['ref_id']]
image = self.Imgs[ref['image_id']]
if type(ann['segmentation'][0]) == list: # polygon
rle = mask.frPyObjects(ann['segmentation'], image['height'],
image['width'])
else:
rle = ann['segmentation']
return {'rle': rle}
def getMask(self, ref):
# return mask, area and mask-center
ann = self.refToAnn[ref['ref_id']]
image = self.Imgs[ref['image_id']]
if type(ann['segmentation'][0]) == list: # polygon
rle = mask.frPyObjects(ann['segmentation'], image['height'], image['width'])
else:
rle = ann['segmentation']
m = mask.decode(rle)
m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
# compute area
area = sum(mask.area(rle)) # should be close to ann['area']
return {'mask': m, 'area': area}
def getMask(self, ref):
# return mask, area and mask-center
ann = self.refToAnn[ref['ref_id']]
image = self.Imgs[ref['image_id']]
if type(ann['segmentation'][0]) == list: # polygon
rle = mask.frPyObjects(ann['segmentation'], image['height'], image['width'])
else:
rle = ann['segmentation']
m = mask.decode(rle)
m = np.sum(m, axis=2) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
# compute area
area = sum(mask.area(rle)) # should be close to ann['area']
return {'mask': m, 'area': area}
def generateMasksAndQueries(MASK_DIR, QUERY_FILE_NAME, refer_obj):
img_sg_counts = {}
query_file_str = {}
print "Creating {} mask files..".format(len(refer_obj.data['annotations']))
actual_processed = 0
for i,ann in enumerate(refer_obj.data['annotations']):
if i % 1000 == 0:
with open(QUERY_FILE_NAME, 'w') as fp:
json.dump(query_file_str, fp)
print "On annotation #{}, processed = {}".format(i,actual_processed)
if ann['iscrowd'] == 1 or ann['id'] not in refer_obj.annToRef:
continue
actual_processed += 1
image = refer_obj.Imgs[ann['image_id']]
if type(ann['segmentation']) == list: # polygon
rle = mask.frPyObjects(ann['segmentation'], image['height'], image['width'])
else:
rle = ann['segmentation']
m = mask.decode(rle)
m = np.sum(m, axis=2)
matlab_dict = {'segimg_t': m}
if ann['image_id'] in img_sg_counts:
img_sg_counts[ann['image_id']] += 1
filename = "{}_{}".format(ann['image_id'],img_sg_counts[ann['image_id']])
else:
img_sg_counts[ann['image_id']] = 1
filename = "{}_{}".format(ann['image_id'],img_sg_counts[ann['image_id']])
io.savemat(osp.join(MASK_DIR,filename),matlab_dict)
ref = refer_obj.annToRef[ann['id']]
if filename in query_file_str:
val = query_file_str[filename]
else:
val = []
for sentence in ref['sentences']:
val.append(sentence['raw'])
query_file_str[filename] = val