def evaluate(iou_type_evaluator: COCOeval) -> Tuple:
"""
Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None
"""
p = iou_type_evaluator.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"
print(
f"useSegm (deprecated) is not None. Running {p.iouType} evaluation"
)
# print('Evaluate annotation type *{}*'.format(p.iouType))
p.imgIds = list(numpy.unique(p.imgIds))
if p.useCats:
p.catIds = list(numpy.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
iou_type_evaluator.params = p
iou_type_evaluator._prepare()
# loop through images, area range, max detection number
cat_ids = p.catIds if p.useCats else [-1]
compute_iou = None
if p.iouType == "segm" or p.iouType == "bbox":
compute_iou = iou_type_evaluator.computeIoU
elif p.iouType == "keypoints":
compute_iou = iou_type_evaluator.computeOks
iou_type_evaluator.ious = {(imgId, catId): compute_iou(imgId, catId)
for imgId in p.imgIds for catId in cat_ids}
evaluate_img = iou_type_evaluator.evaluateImg
max_det = p.maxDets[-1]
eval_imgs = [
evaluate_img(img_id, cat_id, area_rng, max_det) for cat_id in cat_ids
for area_rng in p.areaRng for img_id in p.imgIds
]
eval_imgs = numpy.asarray(
eval_imgs
).reshape( # this is NOT in the pycocotools code, but could be done outside
len(cat_ids), len(p.areaRng), len(p.imgIds))
iou_type_evaluator._paramsEval = copy.deepcopy(iou_type_evaluator.params)
return p.imgIds, eval_imgs
