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)
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))
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))
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))
