""" 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)