def evaluate_from_pred_dict(predictions,
refvg_split,
analyze_subset=True,
exp_name_in_summary=None,
save_result_to_path=None,
update_predictions=False,
verbose=False):
evaluator = Evaluator(refvg_split=refvg_split,
analyze_subset=analyze_subset)
mb = list()
for img_id, im_preds in predictions.items():
pred_mask_tag = None
pred_boxes_tag = None
correct_tag = None
for task_id, task_pred in im_preds.items():
if 'pred_mask' in task_pred:
pred_mask_tag = 'pred_mask'
if 'pred_boxlist' in task_pred:
pred_boxes_tag = 'pred_boxlist'
if 'correct' in task_pred:
correct_tag = 'correct'
break
if len(mb) == 0:
if pred_mask_tag is not None:
mb.append('mask')
if pred_boxes_tag is not None:
mb.append('box')
im, ib = evaluator.eval_single_img(img_id=img_id,
im_pred_dict=im_preds,
pred_mask_tag=pred_mask_tag,
pred_boxes_tag=pred_boxes_tag,
correct_tag=correct_tag,
verbose=verbose)
if update_predictions:
for task_id, task_pred in im_preds.items():
if task_id in im:
task_pred['mask_iou'] = im[task_id]
if task_id in ib:
task_pred['box_iou'] = ib[task_id]
evaluator.analyze_stats(mask_box=mb,
exp_name_in_summary=exp_name_in_summary,
save_result_to_path=save_result_to_path)
return predictions
def evaluate_from_pred_folder(pred_folder,
refvg_split,
analyze_subset=True,
exp_name_in_summary=None,
save_result_to_path=None,
verbose=False):
print(exp_name_in_summary)
evaluator = Evaluator(refvg_split=refvg_split,
analyze_subset=analyze_subset)
fnames = os.listdir(pred_folder)
fnames.sort()
img_preds = dict()
cur_img_id = ''
for fname in fnames:
if not fname.endswith('.png'):
continue
task_id = fname.split('.')[0]
img_id = task_id.split('__')[0]
pred_mask = png_to_pred_mask(os.path.join(pred_folder, fname))
if img_id == cur_img_id:
img_preds[task_id] = {'pred_mask': pred_mask}
else:
if len(img_preds) > 0:
# print(img_preds)
evaluator.eval_single_img(img_id=int(cur_img_id),
im_pred_dict=img_preds,
pred_mask_tag='pred_mask',
verbose=verbose)
cur_img_id = img_id
img_preds = dict()
img_preds[task_id] = {'pred_mask': pred_mask}
if len(img_preds) > 0:
evaluator.eval_single_img(img_id=int(cur_img_id),
im_pred_dict=img_preds,
pred_mask_tag='pred_mask',
verbose=verbose)
evaluator.analyze_stats(mask_box=['mask'],
exp_name_in_summary=exp_name_in_summary,
save_result_to_path=save_result_to_path)
return
