File size: 12,839 Bytes
a93afca
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
"""
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"):
        # 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)