def loadRes()

in src/sagemaker_defect_detection/utils/coco_eval.py [0:0]

• 32 lines of code
• 15 McCabe index (conditional complexity)

def loadRes(self, resFile):
    """
    Load result file and return a result api object.
    :param   resFile (str)     : file name of result file
    :return: res (obj)         : result api object
    """
    res = COCO()
    res.dataset["images"] = [img for img in self.dataset["images"]]

    # print('Loading and preparing results...')
    # tic = time.time()
    if isinstance(resFile, torch._six.string_classes):
        anns = json.load(open(resFile))
    elif type(resFile) == np.ndarray:
        anns = self.loadNumpyAnnotations(resFile)
    else:
        anns = resFile
    assert type(anns) == list, "results in not an array of objects"
    annsImgIds = [ann["image_id"] for ann in anns]
    assert set(annsImgIds) == (
        set(annsImgIds) & set(self.getImgIds())
    ), "Results do not correspond to current coco set"
    if "caption" in anns[0]:
        imgIds = set([img["id"] for img in res.dataset["images"]]) & set([ann["image_id"] for ann in anns])
        res.dataset["images"] = [img for img in res.dataset["images"] if img["id"] in imgIds]
        for id, ann in enumerate(anns):
            ann["id"] = id + 1
    elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
        res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
        for id, ann in enumerate(anns):
            bb = ann["bbox"]
            x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
            if "segmentation" not in ann:
                ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
            ann["area"] = bb[2] * bb[3]
            ann["id"] = id + 1
            ann["iscrowd"] = 0

    res.dataset["annotations"] = anns
    createIndex(res)
    return res