challenge/2019_COCO_DensePose/densepose_cocoeval.py [152:218]:
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    def _prepare(self):
        '''
        Prepare ._gts and ._dts for evaluation based on params
        :return: None
        '''

        def _toMask(anns, coco):
            # modify ann['segmentation'] by reference
            for ann in anns:
                rle = coco.annToRLE(ann)
                ann['segmentation'] = rle

        def _getIgnoreRegion(iid, coco):
            img = coco.imgs[iid]

            if not 'ignore_regions_x' in img.keys():
                return None

            if len(img['ignore_regions_x']) == 0:
                return None

            rgns_merged = []
            for region_x, region_y in zip(img['ignore_regions_x'], img['ignore_regions_y']):
                rgns = [iter(region_x), iter(region_y)]
                rgns_merged.append(list(it.next() for it in itertools.cycle(rgns)))
            rles = maskUtils.frPyObjects(rgns_merged, img['height'], img['width'])
            rle = maskUtils.merge(rles)
            
            return maskUtils.decode(rle)

        def _checkIgnore(dt, iregion):
            if iregion is None:
                return True

            bb = np.array(dt['bbox']).astype(np.int)
            x1,y1,x2,y2 = bb[0],bb[1],bb[0]+bb[2],bb[1]+bb[3]
            x2 = min([x2,iregion.shape[1]])
            y2 = min([y2,iregion.shape[0]])

            if bb[2]* bb[3] == 0:
                return False

            crop_iregion = iregion[y1:y2, x1:x2]

            if crop_iregion.sum() == 0:
                return True

            if not 'uv' in dt.keys(): # filtering boxes
                return crop_iregion.sum()/bb[2]/bb[3] < self.ignoreThrBB

            # filtering UVs
            ignoremask = np.require(crop_iregion, requirements=['F'])
            uvmask = np.require(np.asarray(dt['uv'][0]>0), dtype = np.uint8,
                    requirements=['F'])
            uvmask_ = maskUtils.encode(uvmask)
            ignoremask_ = maskUtils.encode(ignoremask)
            uviou = maskUtils.iou([uvmask_], [ignoremask_], [1])[0]
            return uviou < self.ignoreThrUV

        p = self.params

        if p.useCats:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
        else:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
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detectron/datasets/densepose_cocoeval.py [137:202]:
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    def _prepare(self):
        '''
        Prepare ._gts and ._dts for evaluation based on params
        :return: None
        '''

        def _toMask(anns, coco):
            # modify ann['segmentation'] by reference
            for ann in anns:
                rle = coco.annToRLE(ann)
                ann['segmentation'] = rle

        def _getIgnoreRegion(iid, coco):
            img = coco.imgs[iid]

            if not 'ignore_regions_x' in img.keys():
                return None

            if len(img['ignore_regions_x']) == 0:
                return None

            rgns_merged = []
            for region_x, region_y in zip(img['ignore_regions_x'], img['ignore_regions_y']):
                rgns = [iter(region_x), iter(region_y)]
                rgns_merged.append(list(it.next() for it in itertools.cycle(rgns)))
            rles = maskUtils.frPyObjects(rgns_merged, img['height'], img['width'])
            rle = maskUtils.merge(rles)
            return maskUtils.decode(rle)

        def _checkIgnore(dt, iregion):
            if iregion is None:
                return True

            bb = np.array(dt['bbox']).astype(np.int)
            x1,y1,x2,y2 = bb[0],bb[1],bb[0]+bb[2],bb[1]+bb[3]
            x2 = min([x2,iregion.shape[1]])
            y2 = min([y2,iregion.shape[0]])

            if bb[2]* bb[3] == 0:
                return False

            crop_iregion = iregion[y1:y2, x1:x2]

            if crop_iregion.sum() == 0:
                return True

            if not 'uv' in dt.keys(): # filtering boxes
                return crop_iregion.sum()/bb[2]/bb[3] < self.ignoreThrBB

            # filtering UVs
            ignoremask = np.require(crop_iregion, requirements=['F'])
            uvmask = np.require(np.asarray(dt['uv'][0]>0), dtype = np.uint8,
                    requirements=['F'])
            uvmask_ = maskUtils.encode(uvmask)
            ignoremask_ = maskUtils.encode(ignoremask)
            uviou = maskUtils.iou([uvmask_], [ignoremask_], [1])[0]
            return uviou < self.ignoreThrUV

        p = self.params

        if p.useCats:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
        else:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
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