Esempio n. 1
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 def _do_detection_eval(self, res_file):
     coco_dt = self._COCO.loadRes(res_file)
     coco_eval = COCOeval(self._COCO, coco_dt, 'bbox')
     coco_eval.evaluate()
     coco_eval.accumulate()
     coco_eval.summarize()
     results = OrderedDict()
     results['bbox'] = self._derive_coco_results(
         coco_eval, 'bbox', class_names=self.class_name)
     print_csv_format(results)
Esempio n. 2
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 def _do_detection_eval(self, res_file, output_dir):
     ann_type = 'bbox'
     coco_dt = self._COCO.loadRes(res_file)
     coco_eval = COCOeval(self._COCO, coco_dt)
     coco_eval.params.useSegm = (ann_type == 'segm')
     coco_eval.evaluate()
     coco_eval.accumulate()
     self._print_detection_eval_metrics(coco_eval)
     eval_file = os.path.join(output_dir, 'detection_results.pkl')
     with open(eval_file, 'wb') as fid:
         pickle.dump(coco_eval, fid, pickle.HIGHEST_PROTOCOL)
     print('Wrote COCO eval results to: {}'.format(eval_file))
Esempio n. 3
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 def _do_detection_eval(self, res_file, output_dir):
     ann_type = 'bbox'
     coco_dt = self._COCO.loadRes(res_file)
     coco_eval = COCOeval(self._COCO, coco_dt)
     if self.classes == "youtube_bb":
         coco_eval.params.setDetParams()
         coco_eval.params.catIds = self._COCO.getCatIds(
             catNms=classes_youtubebb)
         coco_eval.params.imgIds = self.image_indexes
         print(len(coco_eval.params.imgIds))
     elif self.classes == "youtube_bb_sub":
         coco_eval.params.setDetParams()
         coco_eval.params.catIds = self._COCO.getCatIds(
             catNms=classes_youtubebb_sub)
         coco_eval.params.imgIds = self.image_indexes
         print(len(coco_eval.params.imgIds))
     coco_eval.params.useSegm = (ann_type == 'segm')
     coco_eval.evaluate()
     coco_eval.accumulate()
     self._print_detection_eval_metrics(coco_eval)
     eval_file = os.path.join(output_dir, 'detection_results.pkl')
     with open(eval_file, 'wb') as fid:
         pickle.dump(coco_eval, fid, pickle.HIGHEST_PROTOCOL)
     print('Wrote COCO eval results to: {}'.format(eval_file))
Esempio n. 4
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def evaluate_detections(FPS=None):

    print('Running demo for *%s* results.' % (annType))
    # initialize COCO ground truth api
    annFile = '%s/annotations/%s_%s.json' % (dataDir, prefix, dataType)
    print(annFile)
    cocoGt = COCO(annFile)
    # initialize COCO detections api
    cocoDt = cocoGt.loadRes(resFile)
    imgIds = cocoGt.getImgIds()
    # imgIds = imgIds[0:100]
    # imgId = imgIds[np.random.randint(100)]

    # running evaluation
    cocoEval = COCOeval(cocoGt, cocoDt, annType)
    cocoEval.params.imgIds = imgIds
    cocoEval.evaluate()
    cocoEval.accumulate()
    means = cocoEval.summarize()
    with open(os.path.join(output_dir,
                           str(int(means[0] * 10000)) + '.txt'),
              'w') as res_file:
        res_file.write('CUDA: ' + str(args.cuda) + '\n')
        res_file.write('model_dir: ' + args.model_dir + '\n')
        res_file.write('iteration: ' + args.iteration + '\n')
        res_file.write('model_name: ' + args.model_name + '\n')
        res_file.write('backbone : ' + args.backbone + '\n')
        if args.backbone in ['RefineDet_VGG']:
            res_file.write('refine : ' + str(args.refine) + '\n')
            res_file.write('deform : ' + str(args.deform) + '\n')
            res_file.write('multi-head : ' + str(args.multihead) + '\n')
        res_file.write('ssd_dim: ' + str(args.ssd_dim) + '\n')
        res_file.write('confidence_threshold: ' +
                       str(args.confidence_threshold) + '\n')
        res_file.write('nms_threshold: ' + str(args.nms_threshold) + '\n')
        res_file.write('top_k: ' + str(args.top_k) + '\n')
        res_file.write('dataset_name: ' + str(args.dataset_name) + '\n')
        res_file.write('set_file_name: ' + str(args.set_file_name) + '\n')
        res_file.write('detection: ' + str(args.detection) + '\n')
        res_file.write('~~~~~~~~~~~~~~~~~\n')
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = {:.4f}\n'
            .format(means[0]))
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = {:.4f}\n'
            .format(means[1]))
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = {:.4f}\n'
            .format(means[2]))
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = {:.4f}\n'
            .format(means[3]))
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = {:.4f}\n'
            .format(means[4]))
        res_file.write(
            'Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = {:.4f}\n'
            .format(means[5]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = {:.4f}\n'
            .format(means[6]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = {:.4f}\n'
            .format(means[7]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = {:.4f}\n'
            .format(means[8]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = {:.4f}\n'
            .format(means[8]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = {:.4f}\n'
            .format(means[10]))
        res_file.write(
            'Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = {:.4f}\n'
            .format(means[11]))
        if FPS:
            for i, f in enumerate(FPS):
                res_file.write(str(i) + ': FPS = {:.4f}\n'.format(f))