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-# This file is made availabel under the Apache license
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-# This file is based on code availabel under Simplified BSD Licens:
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-# https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305
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-#
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-# Copyright (c) 2014, Piotr Dollar and Tsung-Yi Lin
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-# All rights reserved.
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-#
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-# Redistribution and use in source and binary forms, with or without
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-# modification, are permitted provided that the following conditions are met:
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-#
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-# 1. Redistributions of source code must retain the above copyright notice, this
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-# list of conditions and the following disclaimer.
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-# 2. Redistributions in binary form must reproduce the above copyright notice,
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-# this list of conditions and the following disclaimer in the documentation
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-# and/or other materials provided with the distribution.
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-#
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-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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-# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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-# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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-# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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-# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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-# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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-#
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-# The views and conclusions contained in the software and documentation are those
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-# of the authors and should not be interpreted as representing official policies,
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-# either expressed or implied, of the FreeBSD Project.
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-
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-
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-def loadRes(coco_obj, anns):
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- """
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- Load result file and return a result api object.
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- :param resFile (str) : file name of result file
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- :return: res (obj) : result api object
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- """
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-
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- # This function has the same functionality as pycocotools.COCO.loadRes,
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- # except that the input anns is list of results rather than a json file.
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- # Refer to
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- # https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305,
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-
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- # matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
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- # or matplotlib.backends is imported for the first time
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- # pycocotools import matplotlib
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- import matplotlib
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- matplotlib.use('Agg')
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- from pycocotools.coco import COCO
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- import pycocotools.mask as maskUtils
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- import time
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- res = COCO()
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- res.dataset['images'] = [img for img in coco_obj.dataset['images']]
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-
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- tic = time.time()
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- assert type(anns) == list, 'results in not an array of objects'
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- annsImgIds = [ann['image_id'] for ann in anns]
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- assert set(annsImgIds) == (set(annsImgIds) & set(coco_obj.getImgIds())), \
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- 'Results do not correspond to current coco set'
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- if 'caption' in anns[0]:
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- imgIds = set([img['id'] for img in res.dataset['images']]) & set(
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- [ann['image_id'] for ann in anns])
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- res.dataset['images'] = [
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- img for img in res.dataset['images'] if img['id'] in imgIds
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- ]
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- for id, ann in enumerate(anns):
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- ann['id'] = id + 1
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- elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
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- res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
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- 'categories'])
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- for id, ann in enumerate(anns):
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- bb = ann['bbox']
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- x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
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- if not 'segmentation' in ann:
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- ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
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- ann['area'] = bb[2] * bb[3]
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- ann['id'] = id + 1
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- ann['iscrowd'] = 0
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- elif 'segmentation' in anns[0]:
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- res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
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- 'categories'])
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- for id, ann in enumerate(anns):
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- # now only support compressed RLE format as segmentation results
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- ann['area'] = maskUtils.area(ann['segmentation'])
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- if not 'bbox' in ann:
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- ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
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- ann['id'] = id + 1
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- ann['iscrowd'] = 0
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- elif 'keypoints' in anns[0]:
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- res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[
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- 'categories'])
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- for id, ann in enumerate(anns):
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- s = ann['keypoints']
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- x = s[0::3]
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- y = s[1::3]
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- x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y)
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- ann['area'] = (x1 - x0) * (y1 - y0)
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- ann['id'] = id + 1
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- ann['bbox'] = [x0, y0, x1 - x0, y1 - y0]
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-
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- res.dataset['annotations'] = anns
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- res.createIndex()
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- return res
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