def _toMask(objs, coco):
# modify segmentation by reference
for obj in objs:
t = coco.imgs[obj['image_id']]
for region in obj['regions']:
if type(region['segmentation']) == list:
# format is xyxy, convert to RLE
region['segmentation'] = mask.frPyObjects(
[region['segmentation']], t['height'], t['width'])
if len(region['segmentation']) == 1:
region['segmentation'] = region['segmentation'][0]
else:
# an object can have multiple polygon regions
# merge them into one RLE mask
region['segmentation'] = mask.merge(
obj['segmentation'])
if 'area' not in region:
region['area'] = mask.area(
[region['segmentation']])[0]
elif type(region['segmentation']) == dict and type(
region['segmentation']['counts']) == list:
region['segmentation'] = mask.frPyObjects(
[region['segmentation']], t['height'],
t['width'])[0]
elif type(region['segmentation']) == dict and \
type(region['segmentation']['counts'] == unicode or type(region['segmentation']['counts']) == str):
# format is already RLE, do nothing
if 'area' not in region:
region['area'] = mask.area(
[region['segmentation']])[0]
else:
raise Exception('segmentation format not supported.')
def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
res.dataset['images'] = [img for img in self.dataset['images']]
print('Loading and preparing results...')
tic = time.time()
if type(resFile) == str or type(resFile) == unicode:
anns = json.load(open(resFile))
elif type(resFile) == np.ndarray:
anns = self.loadNumpyAnnotations(resFile)
else:
anns = resFile
assert type(anns) == list, 'results in not an array of objects'
annsImgIds = [ann['image_id'] for ann in anns]
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
'Results do not correspond to current coco set'
if 'caption' in anns[0]:
imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
for id, ann in enumerate(anns):
ann['id'] = id+1
elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
bb = ann['bbox']
x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
if not 'segmentation' in ann:
ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
ann['area'] = bb[2]*bb[3]
ann['id'] = id+1
ann['iscrowd'] = 0
elif 'segmentation' in anns[0]:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
# now only support compressed RLE format as segmentation results
ann['area'] = maskUtils.area(ann['segmentation'])
if not 'bbox' in ann:
ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
ann['id'] = id+1
ann['iscrowd'] = 0
elif 'keypoints' in anns[0]:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
s = ann['keypoints']
x = s[0::3]
y = s[1::3]
x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
ann['area'] = (x1-x0)*(y1-y0)
ann['id'] = id + 1
ann['bbox'] = [x0,y0,x1-x0,y1-y0]
print('DONE (t={:0.2f}s)'.format(time.time()- tic))
res.dataset['annotations'] = anns
res.createIndex()
return res
def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
res.dataset['images'] = [img for img in self.dataset['images']]
# res.dataset['info'] = copy.deepcopy(self.dataset['info'])
# res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses'])
print 'Loading and preparing results... '
tic = time.time()
if type(resFile) == str:
anns = json.load(open(resFile))
elif type(resFile) == np.ndarray:
anns = self.loadNumpyAnnotations(resFile)
else:
anns = resFile
assert type(anns) == list, 'results in not an array of objects'
annsImgIds = [ann['image_id'] for ann in anns]
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
'Results do not correspond to current coco set'
if 'caption' in anns[0]:
imgIds = set([img['id'] for img in res.dataset['images']]) & set(
[ann['image_id'] for ann in anns])
res.dataset['images'] = [
img for img in res.dataset['images'] if img['id'] in imgIds
]
for id, ann in enumerate(anns):
ann['id'] = id + 1
elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
res.dataset['categories'] = copy.deepcopy(
self.dataset['categories'])
for id, ann in enumerate(anns):
bb = ann['bbox']
x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
if not 'segmentation' in ann:
ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
ann['area'] = bb[2] * bb[3]
ann['id'] = id + 1
ann['iscrowd'] = 0
elif 'segmentation' in anns[0]:
res.dataset['categories'] = copy.deepcopy(
self.dataset['categories'])
for id, ann in enumerate(anns):
# now only support compressed RLE format as segmentation results
ann['area'] = mask.area([ann['segmentation']])[0]
if not 'bbox' in ann:
ann['bbox'] = mask.toBbox([ann['segmentation']])[0]
ann['id'] = id + 1
ann['iscrowd'] = 0
print 'DONE (t=%0.2fs)' % (time.time() - tic)
res.dataset['annotations'] = anns
res.createIndex()
return res
def loadRes(self, resFile):
"""
Load result file and return a result api object.
:param resFile (str) : file name of result file
:return: res (obj) : result api object
"""
res = COCO()
for img in self.dataset['images']:
print img
sys.exit(0)
res.dataset['images'] = [int(img) for img in self.dataset['images']]
# res.dataset['info'] = copy.deepcopy(self.dataset['info'])
# res.dataset['licenses'] = copy.deepcopy(self.dataset['licenses'])
print 'Loading and preparing results... '
tic = time.time()
anns = json.load(open(resFile))
print type(anns)
assert type(anns) == list, 'results in not an array of objects'
annsImgIds = [ann['image_id'] for ann in anns]
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
'Results do not correspond to current coco set'
if 'caption' in anns[0]:
imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
for id, ann in enumerate(anns):
ann['id'] = id+1
elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
bb = ann['bbox']
x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
if not 'segmentation' in ann:
ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
ann['area'] = bb[2]*bb[3]
ann['id'] = id+1
ann['iscrowd'] = 0
elif 'segmentation' in anns[0]:
res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
for id, ann in enumerate(anns):
# now only support compressed RLE format as segmentation results
ann['area'] = mask.area([ann['segmentation']])[0]
if not 'bbox' in ann:
ann['bbox'] = mask.toBbox([ann['segmentation']])[0]
ann['id'] = id+1
ann['iscrowd'] = 0
print 'DONE (t=%0.2fs)'%(time.time()- tic)
res.dataset['annotations'] = anns
res.createIndex()
return res
def _toMask(objs, coco):
# modify segmentation by reference
for obj in objs:
t = coco.imgs[obj['image_id']]
for region in obj['regions']:
if type(region['segmentation']) == list:
# format is xyxy, convert to RLE
region['segmentation'] = mask.frPyObjects(
[region['segmentation']], t['height'], t['width'])
region['segmentation'][0]['counts'] = region[
'segmentation'][0]['counts'].decode('utf-8')
if len(region['segmentation']) == 1:
region['segmentation'] = region['segmentation'][0]
else:
# an object can have multiple polygon regions
# merge them into one RLE mask
region['segmentation'] = mask.merge(
obj['segmentation'])
if 'area' not in region:
region['area'] = mask.area(
[region['segmentation']])[0]
elif type(region['segmentation']) == dict and type(
region['segmentation']['counts']) == list:
region['segmentation'] = mask.frPyObjects(
[region['segmentation']], t['height'],
t['width'])[0]
region['segmentation']['counts'] = region[
'segmentation']['counts'].decode('utf-8')
elif type(region['segmentation']) == dict and \
type(region['segmentation']['counts']) == str or type(region['segmentation']['counts']) == bytes:
# Python 3 renamed the unicode type to str, the old str type has been replaced by bytes.
# format is already RLE, do nothing
if 'area' not in region:
region['area'] = mask.area(
[region['segmentation']])[0]
else:
print('???')
raise Exception('segmentation format not supported.')
