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146
sam3/eval/ytvis_coco_wrapper.py
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146
sam3/eval/ytvis_coco_wrapper.py
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# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
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# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
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import copy
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import json
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import logging
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import numpy as np
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import pycocotools.mask as mask_util
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from pycocotools.coco import COCO
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from typing_extensions import override
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class YTVIS(COCO):
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"""
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Helper class for reading YT-VIS annotations
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"""
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@override
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def __init__(self, annotation_file: str = None, ignore_gt_cats: bool = True):
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"""
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Args:
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annotation_file: Path to the annotation file
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ignore_gt_cats: If True, we ignore the ground truth categories and replace them with a dummy "object" category. This is useful for Phrase AP evaluation.
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"""
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self.ignore_gt_cats = ignore_gt_cats
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super().__init__(annotation_file=annotation_file)
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@override
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def createIndex(self):
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# We rename some keys to match the COCO format before creating the index.
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if "annotations" in self.dataset:
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for ann in self.dataset["annotations"]:
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if "video_id" in ann:
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ann["image_id"] = int(ann.pop("video_id"))
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if self.ignore_gt_cats:
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ann["category_id"] = -1
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else:
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ann["category_id"] = int(ann["category_id"])
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if "bboxes" in ann:
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# note that in some datasets we load under this YTVIS class,
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# some "bboxes" could be None for when the GT object is invisible,
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# so we replace them with [0, 0, 0, 0]
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ann["bboxes"] = [
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bbox if bbox is not None else [0, 0, 0, 0]
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for bbox in ann["bboxes"]
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]
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if "areas" in ann:
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# similar to "bboxes", some areas could be None for when the GT
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# object is invisible, so we replace them with 0
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areas = [a if a is not None else 0 for a in ann["areas"]]
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# Compute average area of tracklet
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ann["area"] = np.mean(areas)
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if "videos" in self.dataset:
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for vid in self.dataset["videos"]:
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vid["id"] = int(vid["id"])
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self.dataset["images"] = self.dataset.pop("videos")
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if self.ignore_gt_cats:
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self.dataset["categories"] = [
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{"supercategory": "object", "id": -1, "name": "object"}
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]
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else:
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for cat in self.dataset["categories"]:
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cat["id"] = int(cat["id"])
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super().createIndex()
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@override
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def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
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if len(areaRng) > 0:
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logging.warning(
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"Note that we filter out objects based on their *average* area across the video, not per frame area"
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)
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return super().getAnnIds(imgIds=imgIds, catIds=catIds, iscrowd=iscrowd)
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@override
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def showAnns(self, anns, draw_bbox=False):
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raise NotImplementedError("Showing annotations is not supported")
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@override
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def loadRes(self, resFile):
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# Adapted from COCO.loadRes to support tracklets/masklets
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res = YTVIS(ignore_gt_cats=self.ignore_gt_cats)
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res.dataset["images"] = [img for img in self.dataset["images"]]
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if type(resFile) == str:
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with open(resFile) as f:
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anns = json.load(f)
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elif type(resFile) == np.ndarray:
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anns = self.loadNumpyAnnotations(resFile)
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else:
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anns = resFile
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assert type(anns) == list, "results is not an array of objects"
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annsImgIds = [ann["image_id"] for ann in anns]
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assert set(annsImgIds) == (
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set(annsImgIds) & set(self.getImgIds())
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), "Results do not correspond to current coco set"
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if "bboxes" in anns[0] and not anns[0]["bboxes"] == []:
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res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
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for id, ann in enumerate(anns):
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bbs = [(bb if bb is not None else [0, 0, 0, 0]) for bb in ann["bboxes"]]
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xxyy = [[bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]] for bb in bbs]
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if not "segmentations" in ann:
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ann["segmentations"] = [
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[[x1, y1, x1, y2, x2, y2, x2, y1]] for (x1, x2, y1, y2) in xxyy
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]
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ann["areas"] = [bb[2] * bb[3] for bb in bbs]
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# NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.
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ann["area"] = np.mean(ann["areas"])
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ann["id"] = id + 1
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ann["iscrowd"] = 0
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elif "segmentations" in anns[0]:
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res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
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for id, ann in enumerate(anns):
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ann["bboxes"] = [
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mask_util.toBbox(segm) for segm in ann["segmentations"]
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]
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if "areas" not in ann:
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ann["areas"] = [
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mask_util.area(segm) for segm in ann["segmentations"]
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]
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# NOTE: We also compute average area of a tracklet across video, allowing us to compute area based mAP.
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ann["area"] = np.mean(ann["areas"])
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ann["id"] = id + 1
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ann["iscrowd"] = 0
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res.dataset["annotations"] = anns
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res.createIndex()
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return res
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@override
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def download(self, tarDir=None, imgIds=[]):
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raise NotImplementedError
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@override
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def loadNumpyAnnotations(self, data):
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raise NotImplementedError("We don't support numpy annotations for now")
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@override
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def annToRLE(self, ann):
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raise NotImplementedError("We expect masks to be already in RLE format")
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@override
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def annToMask(self, ann):
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raise NotImplementedError("We expect masks to be already in RLE format")
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