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"""
grefer v0.1
This interface provides access to gRefCOCO.
The following API functions are defined:
G_REFER - REFER api class
getRefIds - get ref ids that satisfy given filter conditions.
getAnnIds - get ann ids that satisfy given filter conditions.
getImgIds - get image ids that satisfy given filter conditions.
getCatIds - get category ids that satisfy given filter conditions.
loadRefs - load refs with the specified ref ids.
loadAnns - load anns with the specified ann ids.
loadImgs - load images with the specified image ids.
loadCats - load category names with the specified category ids.
getRefBox - get ref's bounding box [x, y, w, h] given the ref_id
showRef - show image, segmentation or box of the referred object with the ref
getMaskByRef - get mask and area of the referred object given ref or ref ids
getMask - get mask and area of the referred object given ref
showMask - show mask of the referred object given ref
"""
import os.path as osp
import json
import pickle
import time
import itertools
import skimage.io as io
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon, Rectangle
import numpy as np
from pycocotools import mask
class G_REFER:
def __init__(self, data_root, dataset='grefcoco', splitBy='unc'):
# provide data_root folder which contains grefcoco
print('loading dataset %s into memory...' % dataset)
self.ROOT_DIR = osp.abspath(osp.dirname(__file__))
self.DATA_DIR = osp.join(data_root, dataset)
if dataset in ['grefcoco']:
self.IMAGE_DIR = osp.join(data_root, 'images/train2014')
else:
raise KeyError('No refer dataset is called [%s]' % dataset)
tic = time.time()
# load refs from data/dataset/refs(dataset).json
self.data = {}
self.data['dataset'] = dataset
ref_file = osp.join(self.DATA_DIR, f'grefs({splitBy}).p')
if osp.exists(ref_file):
self.data['refs'] = pickle.load(open(ref_file, 'rb'),fix_imports=True)
else:
ref_file = osp.join(self.DATA_DIR, f'grefs({splitBy}).json')
if osp.exists(ref_file):
self.data['refs'] = json.load(open(ref_file, 'rb'))
else:
raise FileNotFoundError('JSON file not found')
# load annotations from data/dataset/instances.json
instances_file = osp.join(self.DATA_DIR, 'instances.json')
instances = json.load(open(instances_file, 'r'))
self.data['images'] = instances['images']
self.data['annotations'] = instances['annotations']
self.data['categories'] = instances['categories']
# create index
self.createIndex()
print('DONE (t=%.2fs)' % (time.time()-tic))
@staticmethod
def _toList(x):
return x if isinstance(x, list) else [x]
@staticmethod
def match_any(a, b):
a = a if isinstance(a, list) else [a]
b = b if isinstance(b, list) else [b]
return set(a) & set(b)
def createIndex(self):
# create sets of mapping
# 1) Refs: {ref_id: ref}
# 2) Anns: {ann_id: ann}
# 3) Imgs: {image_id: image}
# 4) Cats: {category_id: category_name}
# 5) Sents: {sent_id: sent}
# 6) imgToRefs: {image_id: refs}
# 7) imgToAnns: {image_id: anns}
# 8) refToAnn: {ref_id: ann}
# 9) annToRef: {ann_id: ref}
# 10) catToRefs: {category_id: refs}
# 11) sentToRef: {sent_id: ref}
# 12) sentToTokens: {sent_id: tokens}
print('creating index...')
# fetch info from instances
Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {}
Anns[-1] = None
for ann in self.data['annotations']:
Anns[ann['id']] = ann
imgToAnns[ann['image_id']] = imgToAnns.get(ann['image_id'], []) + [ann]
for img in self.data['images']:
Imgs[img['id']] = img
for cat in self.data['categories']:
Cats[cat['id']] = cat['name']
# fetch info from refs
Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {}
Sents, sentToRef, sentToTokens = {}, {}, {}
availableSplits = []
for ref in self.data['refs']:
# ids
ref_id = ref['ref_id']
ann_id = ref['ann_id']
category_id = ref['category_id']
image_id = ref['image_id']
if ref['split'] not in availableSplits:
availableSplits.append(ref['split'])
# add mapping related to ref
if ref_id in Refs:
print('Duplicate ref id')
Refs[ref_id] = ref
imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref]
category_id = self._toList(category_id)
added_cats = []
for cat in category_id:
if cat not in added_cats:
added_cats.append(cat)
catToRefs[cat] = catToRefs.get(cat, []) + [ref]
ann_id = self._toList(ann_id)
refToAnn[ref_id] = [Anns[ann] for ann in ann_id]
for ann_id_n in ann_id:
annToRef[ann_id_n] = annToRef.get(ann_id_n, []) + [ref]
# add mapping of sent
for sent in ref['sentences']:
Sents[sent['sent_id']] = sent
sentToRef[sent['sent_id']] = ref
sentToTokens[sent['sent_id']] = sent['tokens']
# create class members
self.Refs = Refs
self.Anns = Anns
self.Imgs = Imgs
self.Cats = Cats
self.Sents = Sents
self.imgToRefs = imgToRefs
self.imgToAnns = imgToAnns
self.refToAnn = refToAnn
self.annToRef = annToRef
self.catToRefs = catToRefs
self.sentToRef = sentToRef
self.sentToTokens = sentToTokens
self.availableSplits = availableSplits
print('index created.')
def getRefIds(self, image_ids=[], cat_ids=[], split=[]):
image_ids = self._toList(image_ids)
cat_ids = self._toList(cat_ids)
split = self._toList(split)
for s in split:
if s not in self.availableSplits:
raise ValueError(f'Invalid split name: {s}')
refs = self.data['refs']
if len(image_ids) > 0:
lists = [self.imgToRefs[image_id] for image_id in image_ids]
refs = list(itertools.chain.from_iterable(lists))
if len(cat_ids) > 0:
refs = [ref for ref in refs if self.match_any(ref['category_id'], cat_ids)]
if len(split) > 0:
refs = [ref for ref in refs if ref['split'] in split]
ref_ids = [ref['ref_id'] for ref in refs]
return ref_ids
def getAnnIds(self, image_ids=[], ref_ids=[]):
image_ids = self._toList(image_ids)
ref_ids = self._toList(ref_ids)
if any([len(image_ids), len(ref_ids)]):
if len(image_ids) > 0:
lists = [self.imgToAnns[image_id] for image_id in image_ids if image_id in self.imgToAnns]
anns = list(itertools.chain.from_iterable(lists))
else:
anns = self.data['annotations']
ann_ids = [ann['id'] for ann in anns]
if len(ref_ids) > 0:
lists = [self.Refs[ref_id]['ann_id'] for ref_id in ref_ids]
anns_by_ref_id = list(itertools.chain.from_iterable(lists))
ann_ids = list(set(ann_ids).intersection(set(anns_by_ref_id)))
else:
ann_ids = [ann['id'] for ann in self.data['annotations']]
return ann_ids
def getImgIds(self, ref_ids=[]):
ref_ids = self._toList(ref_ids)
if len(ref_ids) > 0:
image_ids = list(set([self.Refs[ref_id]['image_id'] for ref_id in ref_ids]))
else:
image_ids = self.Imgs.keys()
return image_ids
def getCatIds(self):
return self.Cats.keys()
def loadRefs(self, ref_ids=[]):
return [self.Refs[ref_id] for ref_id in self._toList(ref_ids)]
def loadAnns(self, ann_ids=[]):
if isinstance(ann_ids, str):
ann_ids = int(ann_ids)
return [self.Anns[ann_id] for ann_id in self._toList(ann_ids)]
def loadImgs(self, image_ids=[]):
return [self.Imgs[image_id] for image_id in self._toList(image_ids)]
def loadCats(self, cat_ids=[]):
return [self.Cats[cat_id] for cat_id in self._toList(cat_ids)]
def getRefBox(self, ref_id):
anns = self.refToAnn[ref_id]
return [ann['bbox'] for ann in anns] # [x, y, w, h]
def showRef(self, ref, seg_box='seg'):
ax = plt.gca()
# show image
image = self.Imgs[ref['image_id']]
I = io.imread(osp.join(self.IMAGE_DIR, image['file_name']))
ax.imshow(I)
# show refer expression
for sid, sent in enumerate(ref['sentences']):
print('%s. %s' % (sid+1, sent['sent']))
# show segmentations
if seg_box == 'seg':
ann_id = ref['ann_id']
ann = self.Anns[ann_id]
polygons = []
color = []
c = 'none'
if type(ann['segmentation'][0]) == list:
# polygon used for refcoco*
for seg in ann['segmentation']:
poly = np.array(seg).reshape((len(seg)/2, 2))
polygons.append(Polygon(poly, True, alpha=0.4))
color.append(c)
p = PatchCollection(polygons, facecolors=color, edgecolors=(1,1,0,0), linewidths=3, alpha=1)
ax.add_collection(p) # thick yellow polygon
p = PatchCollection(polygons, facecolors=color, edgecolors=(1,0,0,0), linewidths=1, alpha=1)
ax.add_collection(p) # thin red polygon
else:
# mask used for refclef
rle = ann['segmentation']
m = mask.decode(rle)
img = np.ones( (m.shape[0], m.shape[1], 3) )
color_mask = np.array([2.0,166.0,101.0])/255
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack( (img, m*0.5) ))
# show bounding-box
elif seg_box == 'box':
ann_id = ref['ann_id']
ann = self.Anns[ann_id]
bbox = self.getRefBox(ref['ref_id'])
box_plot = Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], fill=False, edgecolor='green', linewidth=3)
ax.add_patch(box_plot)
def getMask(self, ann):
if not ann:
return None
if ann['iscrowd']:
raise ValueError('Crowd object')
image = self.Imgs[ann['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 getMaskByRef(self, ref=None, ref_id=None, merge=False):
if not ref and not ref_id:
raise ValueError
if ref:
ann_ids = ref['ann_id']
ref_id = ref['ref_id']
else:
ann_ids = self.getAnnIds(ref_ids=ref_id)
if ann_ids == [-1]:
img = self.Imgs[self.Refs[ref_id]['image_id']]
return {
'mask': np.zeros([img['height'], img['width']], dtype=np.uint8),
'empty': True
}
anns = self.loadAnns(ann_ids)
mask_list = [self.getMask(ann) for ann in anns if not ann['iscrowd']]
if merge:
merged_masks = sum([mask['mask'] for mask in mask_list])
merged_masks[np.where(merged_masks>1)] = 1
return {
'mask': merged_masks,
'empty': False
}
else:
return mask_list
def showMask(self, ref):
M = self.getMask(ref)
msk = M['mask']
ax = plt.gca()
ax.imshow(msk)