__author__ = "licheng" """ 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 itertools import json import os.path as osp import pickle import sys import time from pprint import pprint 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 REFER: def __init__(self, data_root, dataset="refcoco", 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 ["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") elif dataset == 'rs_reason' or dataset == 'rrsisd': self.IMAGE_DIR = osp.join(data_root, "images/rrsisd/JPEGImages") else: print("No refer dataset is called [%s]" % dataset) sys.exit() self.dataset = dataset tic = time.time() ref_file = osp.join(self.DATA_DIR,"refs(" + splitBy + ").p") self.data = {} self.data["dataset"] = dataset self.data["refs"] = pickle.load(open(ref_file, "rb")) instances_file = osp.join(self.DATA_DIR, "instances.json") instances = json.load(open(instances_file, "rb")) self.data["images"] = instances["images"] self.data["annotations"] = instances["annotations"] self.data["categories"] = instances["categories"] self.createIndex() print("DONE (t=%.2fs)" % (time.time() - tic)) def createIndex(self): print("creating index...") 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"] Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {} Sents, sentToRef, sentToTokens = {}, {}, {} 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"] 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 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 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"] ] elif split in ["testAB", "testBC", "testAC"]: refs = [ ref for ref in refs if ref["split"] == split ] 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 ] 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"] 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, ref): ann = self.refToAnn[ref["ref_id"]] image = self.Imgs[ref["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 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() print(len(ref_ids)) print(len(refer.Imgs)) print(len(refer.imgToRefs)) 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 pprint(ref) print("The label is %s." % refer.Cats[ref["category_id"]]) plt.figure() refer.showRef(ref, seg_box="box") plt.show()