""" 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 itertools import json import os.path as osp import pickle import time import matplotlib.pyplot as plt import numpy as np import skimage.io as io from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon, Rectangle from pycocotools import mask class G_REFER: def __init__(self, data_root, dataset="grefcoco", splitBy="unc"): 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() 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") 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"] 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): print("creating index...") 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"] Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {} Sents, sentToRef, sentToTokens = {}, {}, {} availableSplits = [] for ref in self.data["refs"]: 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"]) 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] for sent in ref["sentences"]: Sents[sent["sent_id"]] = sent sentToRef[sent["sent_id"]] = ref sentToTokens[sent["sent_id"]] = sent["tokens"] 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] def showRef(self, ref, seg_box="seg"): ax = plt.gca() image = self.Imgs[ref["image_id"]] I = io.imread(osp.join(self.IMAGE_DIR, image["file_name"])) ax.imshow(I) for sid, sent in enumerate(ref["sentences"]): print("%s. %s" % (sid + 1, sent["sent"])) if seg_box == "seg": ann_id = ref["ann_id"] ann = self.Anns[ann_id] polygons = [] color = [] c = "none" if type(ann["segmentation"][0]) == list: 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) p = PatchCollection( polygons, facecolors=color, edgecolors=(1, 0, 0, 0), linewidths=1, alpha=1, ) ax.add_collection(p) else: 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))) 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: rle = mask.frPyObjects(ann["segmentation"], image["height"], image["width"]) else: rle = ann["segmentation"] m = mask.decode(rle) m = np.sum( m, axis=2 ) m = m.astype(np.uint8) area = sum(mask.area(rle)) 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)