elia / refer /refer.py
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"""
This interface provides access to four datasets:
1) refclef
2) refcoco
3) refcoco+
4) refcocog
split by unc and google
The following API functions are defined:
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
getMask - get mask and area of the referred object given ref
showMask - show mask of the referred object given ref
"""
import sys
import os.path as osp
import json
import pickle as 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
from pprint import pprint
import numpy as np
from pycocotools import mask
class REFER:
def __init__(self, data_root, dataset='refcoco', splitBy='unc'):
# provide data_root folder which contains refclef, refcoco, refcoco+ and refcocog
# also provide dataset name and splitBy information
# e.g., dataset = 'refcoco', splitBy = 'unc'
print('loading dataset %s into memory...' % dataset)
if dataset == 'refcocog':
print('Split by {}!'.format(splitBy))
self.ROOT_DIR = osp.abspath(osp.dirname(__file__))
self.DATA_DIR = osp.join(data_root, dataset)
if dataset in ['refcoco', 'refcoco+', 'refcocog']:
self.IMAGE_DIR = osp.join(data_root, 'images/mscoco/images/train2014')
elif dataset == 'refclef':
self.IMAGE_DIR = osp.join(data_root, 'images/saiapr_tc-12')
else:
print('No refer dataset is called [%s]' % dataset)
sys.exit()
# load refs from data/dataset/refs(dataset).json
tic = time.time()
ref_file = osp.join(self.DATA_DIR, 'refs(' + splitBy + ').p')
self.data = {}
self.data['dataset'] = dataset
f = open(ref_file, 'r')
self.data['refs'] = pickle.load(open(ref_file, 'rb'))
# 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))
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 = {}, {}, {}, {}
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 = {}, {}, {}
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']
# add mapping related to ref
Refs[ref_id] = ref
imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref]
catToRefs[category_id] = catToRefs.get(category_id, []) + [ref]
refToAnn[ref_id] = Anns[ann_id]
annToRef[ann_id] = 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
print('index created.')
def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=''):
image_ids = image_ids if type(image_ids) == list else [image_ids]
cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]
ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
if len(image_ids) == len(cat_ids) == len(ref_ids) == len(split) == 0:
refs = self.data['refs']
else:
if not len(image_ids) == 0:
refs = [self.imgToRefs[image_id] for image_id in image_ids]
else:
refs = self.data['refs']
if not len(cat_ids) == 0:
refs = [ref for ref in refs if ref['category_id'] in cat_ids]
if not len(ref_ids) == 0:
refs = [ref for ref in refs if ref['ref_id'] in ref_ids]
if not len(split) == 0:
if split in ['testA', 'testB', 'testC']:
refs = [ref for ref in refs if split[-1] in ref['split']] # we also consider testAB, testBC, ...
elif split in ['testAB', 'testBC', 'testAC']:
refs = [ref for ref in refs if ref['split'] == split] # rarely used I guess...
elif split == 'test':
refs = [ref for ref in refs if 'test' in ref['split']]
elif split == 'train' or split == 'val':
refs = [ref for ref in refs if ref['split'] == split]
else:
print('No such split [%s]' % split)
sys.exit()
ref_ids = [ref['ref_id'] for ref in refs]
return ref_ids
def getAnnIds(self, image_ids=[], cat_ids=[], ref_ids=[]):
image_ids = image_ids if type(image_ids) == list else [image_ids]
cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]
ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
if len(image_ids) == len(cat_ids) == len(ref_ids) == 0:
ann_ids = [ann['id'] for ann in self.data['annotations']]
else:
if not len(image_ids) == 0:
lists = [self.imgToAnns[image_id] for image_id in image_ids if
image_id in self.imgToAnns] # list of [anns]
anns = list(itertools.chain.from_iterable(lists))
else:
anns = self.data['annotations']
if not len(cat_ids) == 0:
anns = [ann for ann in anns if ann['category_id'] in cat_ids]
ann_ids = [ann['id'] for ann in anns]
if not len(ref_ids) == 0:
ids = set(ann_ids).intersection(set([self.Refs[ref_id]['ann_id'] for ref_id in ref_ids]))
return ann_ids
def getImgIds(self, ref_ids=[]):
ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
if not 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=[]):
if type(ref_ids) == list:
return [self.Refs[ref_id] for ref_id in ref_ids]
elif type(ref_ids) == int:
return [self.Refs[ref_ids]]
def loadAnns(self, ann_ids=[]):
if type(ann_ids) == list:
return [self.Anns[ann_id] for ann_id in ann_ids]
elif type(ann_ids) == int or type(ann_ids) == unicode:
return [self.Anns[ann_ids]]
def loadImgs(self, image_ids=[]):
if type(image_ids) == list:
return [self.Imgs[image_id] for image_id in image_ids]
elif type(image_ids) == int:
return [self.Imgs[image_ids]]
def loadCats(self, cat_ids=[]):
if type(cat_ids) == list:
return [self.Cats[cat_id] for cat_id in cat_ids]
elif type(cat_ids) == int:
return [self.Cats[cat_ids]]
def getRefBox(self, ref_id):
ref = self.Refs[ref_id]
ann = self.refToAnn[ref_id]
return ann['bbox'] # [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, 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 showMask(self, ref):
M = self.getMask(ref)
msk = M['mask']
ax = plt.gca()
ax.imshow(msk)
if __name__ == '__main__':
refer = REFER(dataset='refcocog', splitBy='google')
ref_ids = refer.getRefIds()
ref_ids = refer.getRefIds(split='train')
print('There are %s training referred objects.' % len(ref_ids))
for ref_id in ref_ids:
ref = refer.loadRefs(ref_id)[0]
if len(ref['sentences']) < 2:
continue
print('The label is %s.' % refer.Cats[ref['category_id']])
plt.figure()
refer.showRef(ref, seg_box='box')
plt.show()