def evaluate(self):
        """
        Run per image evaluation on given images and store results in self.evalImgs_cpp, a
        datastructure that isn't readable from Python but is used by a c++ implementation of
        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure
        self.evalImgs because this datastructure is a computational bottleneck.
        :return: None
        """
        tic = time.time()

        p = self.params
        # add backward compatibility if useSegm is specified in params
        if p.useSegm is not None:
            p.iouType = "segm" if p.useSegm == 1 else "bbox"
        logger.info("Evaluate annotation type *{}*".format(p.iouType))
        p.imgIds = list(np.unique(p.imgIds))
        if p.useCats:
            p.catIds = list(np.unique(p.catIds))
        p.maxDets = sorted(p.maxDets)
        self.params = p

        self._prepare()  # bottleneck

        # loop through images, area range, max detection number
        catIds = p.catIds if p.useCats else [-1]

        if p.iouType == "segm" or p.iouType == "bbox":
            computeIoU = self.computeIoU
        elif p.iouType == "keypoints":
            computeIoU = self.computeOks
        self.ious = {
            (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds
        }  # bottleneck

        maxDet = p.maxDets[-1]

        # <<<< Beginning of code differences with original COCO API
        def convert_instances_to_cpp(instances, is_det=False):
            # Convert annotations for a list of instances in an image to a format that's fast
            # to access in C++
            instances_cpp = []
            for instance in instances:
                instance_cpp = _C.InstanceAnnotation(
                    int(instance["id"]),
                    instance["score"] if is_det else instance.get("score", 0.0),
                    instance["area"],
                    bool(instance.get("iscrowd", 0)),
                    bool(instance.get("ignore", 0)),
                )
                instances_cpp.append(instance_cpp)
            return instances_cpp

        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++
        ground_truth_instances = [
            [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds]
            for imgId in p.imgIds
        ]
        detected_instances = [
            [convert_instances_to_cpp(self._dts[imgId, catId], is_det=True) for catId in p.catIds]
            for imgId in p.imgIds
        ]
        ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds]

        if not p.useCats:
            # For each image, flatten per-category lists into a single list
            ground_truth_instances = [[[o for c in i for o in c]] for i in ground_truth_instances]
            detected_instances = [[[o for c in i for o in c]] for i in detected_instances]

        # Call C++ implementation of self.evaluateImgs()
        self._evalImgs_cpp = _C.COCOevalEvaluateImages(
            p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances
        )
        self._evalImgs = None

        self._paramsEval = copy.deepcopy(self.params)
        toc = time.time()
        logger.info("COCOeval_opt.evaluate() finished in {:0.2f} seconds.".format(toc - tic))
        # >>>> End of code differences with original COCO API

        Run per image evaluation on given images and store results in self.evalImgs_cpp, a
        datastructure that isn't readable from Python but is used by a c++ implementation of
        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure
        self.evalImgs because this datastructure is a computational bottleneck.
        :return: None
        
video_chat_text/video_chat_with_ChatGPT/models/grit_src/third_party/CenterNet2/detectron2/evaluation/fast_eval_api.py