# This file is made availabel under the Apache license
# This file is based on code availabel under Simplified BSD Licens:
#   https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305
#
# Copyright (c) 2014, Piotr Dollar and Tsung-Yi Lin
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def loadRes(coco_obj, anns):
    """
    Load result file and return a result api object.
    :param   resFile (str)     : file name of result file
    :return: res (obj)         : result api object
    """

    # This function has the same functionality as pycocotools.COCO.loadRes,
    # except that the input anns is list of results rather than a json file.
    # Refer to
    # https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305,

    # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
    # or matplotlib.backends is imported for the first time
    # pycocotools import matplotlib
    import matplotlib
    matplotlib.use('Agg')
    from pycocotools.coco import COCO
    import pycocotools.mask as maskUtils
    import time
    res = COCO()
    res.dataset['images'] = [img for img in coco_obj.dataset['images']]

    tic = time.time()
    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(coco_obj.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(coco_obj.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 not 'segmentation' 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
    elif 'segmentation' in anns[0]:
        res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
            'categories'])
        for id, ann in enumerate(anns):
            # now only support compressed RLE format as segmentation results
            ann['area'] = maskUtils.area(ann['segmentation'])
            if not 'bbox' in ann:
                ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
            ann['id'] = id + 1
            ann['iscrowd'] = 0
    elif 'keypoints' in anns[0]:
        res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
            'categories'])
        for id, ann in enumerate(anns):
            s = ann['keypoints']
            x = s[0::3]
            y = s[1::3]
            x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
            ann['area'] = (x1 - x0) * (y1 - y0)
            ann['id'] = id + 1
            ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]

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