__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"): # 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) 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() self.dataset = dataset # load refs from data/dataset/refs(dataset).json tic = time.time() ref_file = osp.join(self.DATA_DIR, "refs(" + splitBy + ").p") print("ref_file: ", ref_file) self.data = {} self.data["dataset"] = dataset 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, "rb")) 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} # # position # position_x = np.mean(np.where(m==1)[1]) # [1] means columns (matlab style) -> x (c style) # position_y = np.mean(np.where(m==1)[0]) # [0] means rows (matlab style) -> y (c style) # # mass position (if there were multiple regions, we use the largest one.) # label_m = label(m, connectivity=m.ndim) # regions = regionprops(label_m) # if len(regions) > 0: # largest_id = np.argmax(np.array([props.filled_area for props in regions])) # largest_props = regions[largest_id] # mass_y, mass_x = largest_props.centroid # else: # mass_x, mass_y = position_x, position_y # # if centroid is not in mask, we find the closest point to it from mask # if m[mass_y, mass_x] != 1: # print('Finding closes mask point ...') # kernel = np.ones((10, 10),np.uint8) # me = cv2.erode(m, kernel, iterations = 1) # points = zip(np.where(me == 1)[0].tolist(), np.where(me == 1)[1].tolist()) # row, col style # points = np.array(points) # dist = np.sum((points - (mass_y, mass_x))**2, axis=1) # id = np.argsort(dist)[0] # mass_y, mass_x = points[id] # # return # return {'mask': m, 'area': area, 'position_x': position_x, 'position_y': position_y, 'mass_x': mass_x, 'mass_y': mass_y} # # show image and mask # I = io.imread(osp.join(self.IMAGE_DIR, image['file_name'])) # plt.figure() # plt.imshow(I) # ax = plt.gca() # 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) )) # plt.show() 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() # plt.figure() # refer.showMask(ref) # plt.show()