def verify_gt_annots(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,write_res_flag=False):
print '===========================m1.verify gt annots============================'
## switch 1: for gt
# ## 5> postprocessing masks
gt_boxes=gt_boxes.astype(int)
gt_list_masks=masks_align_boxes(gt_masks,gt_boxes) ## tailor masks
gt_masks=np.asarray(gt_list_masks)
# ## 6> verify the gt_annots and measurement algorithm
prec,rec,tp_cnt,fp_cnt,miss_gt_cnt=get_precision_recall(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks)
ave_mask_iou_arr,ave_inst_iou=get_average_maskiou(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks)
if debug_flag:
print 'tp_cnt:', tp_cnt
print 'fp_cnt:', fp_cnt
print 'miss_gt_cnt:', miss_gt_cnt
print 'prec:', prec
print 'rec:', rec
print 'ave_mask_iou_arr:', ave_mask_iou_arr
print 'ave_inst_iou:', ave_inst_iou
## write metrics in local disk
if write_res_flag:
metric_dir=get_metric_dir()
gt_metric_dir=os.path.join(metric_dir,'gt.txt')
res_content='-------------------------gt----------------------------'
res_content+='\n'+'obj_ids:'+str(np.sort(np.unique(gt_obj_ids)))
res_content+='\n'+'tp_cnt:'+str(tp_cnt)
res_content+='\n'+'fp_cnt:'+str(fp_cnt)
res_content+='\n'+'miss_gt_cnt:'+str(miss_gt_cnt)
res_content+='\n'+'prec:'+str(prec)
res_content+='\n'+'rec:'+str(rec)
res_content+='\n'+'ave_mask_iou_arr:'+str(ave_mask_iou_arr)
res_content+='\n'+'ave_inst_iou:'+str(ave_inst_iou)
save_verify_res(gt_metric_dir,res_content)
def verify_ref_annots(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,ref_boxes,ref_masks,write_res_flag):
print '===========================m3.verify refine annots============================'
## switch3: for refine one
# preprocessing gt annots
gt_boxes=gt_boxes.astype(int)
gt_list_masks=masks_align_boxes(gt_masks,gt_boxes) ## tailor masks
gt_masks=np.asarray(gt_list_masks)
# preprocessing corrup annots
ref_boxes=ref_boxes.astype(int)
tailor_masks=masks_align_boxes(ref_masks,ref_boxes)
tailor_masks=np.asarray(tailor_masks)
# verify the corrup_annots and measurement algorithm
prec,rec,tp_cnt,fp_cnt,miss_gt_cnt=get_precision_recall(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,gt_frm_ids,gt_obj_ids,ref_boxes,tailor_masks)
ave_mask_iou_arr,ave_inst_iou=get_average_maskiou(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,gt_frm_ids,gt_obj_ids,ref_boxes,tailor_masks)
if debug_flag:
print 'prec:', prec
print 'rec:', rec
print 'ave_mask_iou_arr:', ave_mask_iou_arr
print 'ave_inst_iou:', ave_inst_iou
## write metrics in local disk
if write_res_flag:
metric_dir=get_metric_dir()
gt_metric_dir=os.path.join(metric_dir,'refine.txt')
res_content='-------------------------refine----------------------------'
res_content+='\n'+'obj_ids:'+str(np.sort(np.unique(gt_obj_ids)))
res_content+='\n'+'tp_cnt:'+str(tp_cnt)
res_content+='\n'+'fp_cnt:'+str(fp_cnt)
res_content+='\n'+'miss_gt_cnt:'+str(miss_gt_cnt)
res_content+='\n'+'prec:'+str(prec)
res_content+='\n'+'rec:'+str(rec)
res_content+='\n'+'ave_mask_iou_arr:'+str(ave_mask_iou_arr)
res_content+='\n'+'ave_inst_iou:'+str(ave_inst_iou)
save_verify_res(gt_metric_dir,res_content)
def get_enlarge_masks(track_mask_dir, cont_dets):
## mini_masks for ref-masks
mini_masks = get_ref_masks(track_mask_dir, cont_dets)
# transfer small masks(same size with boxes) to large masks(same size with color image)
mask_num = len(mini_masks)
masks = np.zeros((mask_num, im_height, im_width), dtype=int)
## mini_boxes
dets = get_ref_dets(track_mask_dir, cont_dets)
coords = dets[:, 2:6]
boxes = coords_to_boxes(coords)
boxes = boxes.astype(int)
## max_boxes
max_boxes = np.zeros((mask_num, 4))
max_boxes[:, 2] = im_width - 1
max_boxes[:, 3] = im_height - 1
max_boxes = max_boxes.astype(int)
## max_masks
max_masks = masks_align_boxes(mini_masks, boxes, max_boxes)
arr_masks = np.asarray(max_masks)
return arr_masks
def cal_per_label(rgb_ims,im_names,forward_flows,backward_flows,filt_obj_ids,gt_dets,gt_masks,corrup_boxes,corrup_masks):
print '===============calculate per label================='
## parameters
start_frm_id=1
for f_obj_id in filt_obj_ids:
print '---------------------------------------------',f_obj_id
gt_masks_dir,corrup_masks_dir,pick_masks_dir=create_masks_dirs(f_obj_id)
o_gt_dets,row_indexes=dets_filter_ids(gt_dets,[f_obj_id])
o_gt_frm_ids,o_gt_obj_ids,o_gt_boxes=parse_dets1(o_gt_dets)
o_gt_masks=gt_masks[row_indexes]
o_corrup_boxes=corrup_boxes[row_indexes]
o_corrup_masks=corrup_masks[row_indexes]
## filter color images
filt_indexes=o_gt_frm_ids-start_frm_id
im_arr=np.asarray(rgb_ims)
cur_ims=im_arr[filt_indexes]
cur_im_names=im_names[filt_indexes]
fw_flows=forward_flows[filt_indexes[:-1]]
bw_flows=backward_flows[filt_indexes[:-1]]
save_vis_masks(cur_ims,cur_im_names,gt_masks_dir,o_gt_masks,corrup_masks_dir,o_corrup_masks) #v1
if debug_flag:
print 'o_gt_dets:', o_gt_dets
print 'gt_masks_dir:',gt_masks_dir
print 'corrup_masks_dir:', corrup_masks_dir
print 'pick_masks_dir:', pick_masks_dir
print 'o_gt_frm_ids:', o_gt_frm_ids
#----------------------------------------------------------------------------------------
## prepare dataset for 'refine-masks' algorithm
# preprocessing corrup_masks and corrup_boxes(corrup_dets): make masks has the same size with boxes
o_corrup_boxes=o_corrup_boxes.astype(int)
tailor_masks=masks_align_boxes(o_corrup_masks,o_corrup_boxes)
tailor_masks=np.asarray(tailor_masks)
## prepare (corrup) dets
corrup_coords=boxes_to_coords(o_corrup_boxes)
corrup_dets=o_gt_dets
corrup_dets[:,2:6]=corrup_coords
# 6> refine masks
##input: prop_arr,masks,obj_ids, forward_flows, backward_flows
refine_masks(pick_masks_dir,rgb_ims,forward_flows,backward_flows,tailor_masks,corrup_dets)
ref_dets=get_ref_dets(pick_masks_dir,corrup_dets) ## verified
# ref_masks=get_ref_masks(pick_masks_dir,corrup_dets)
ref_masks=get_enlarge_masks(pick_masks_dir,corrup_dets)
ref_coords=ref_dets[:,2:6]
ref_boxes=coords_to_boxes(ref_coords)
verify_ref_annots(gt_frm_ids,gt_obj_ids,gt_boxes,gt_masks,ref_boxes,ref_masks,write_res_flag)
# g_obj_masks=gt_masks[n_id]
# d_obj_ids=dt_obj_ids[n_id] ## org
# d_obj_boxes=dt_boxes[n_id]
# d_obj_masks=dt_masks[n_id]
if __name__ == '__main__':
print '=================tud_crossing_eval==============================='
## input1: gt_dets, gt_masks
##im_names=get_im_names()
gt_dets = load_dets_new()
gt_masks = load_masks_new()
gt_frm_ids, gt_obj_ids, gt_boxes = parse_dets1(gt_dets)
gt_boxes = gt_boxes.astype(int)
gt_list_masks = masks_align_boxes(gt_masks, gt_boxes) ## tailor masks
gt_masks = np.asarray(gt_list_masks)
## input2: dt_dets, dt_masks
dt_dets = load_dets_ref()
dt_masks = load_masks_ref()
dt_frm_ids, dt_obj_ids, dt_boxes = parse_dets1(dt_dets)
dt_boxes = dt_boxes.astype(int)
dt_list_masks = masks_align_boxes(dt_masks, dt_boxes) ## tailor masks
dt_masks = np.asarray(dt_list_masks)
# if debug_flag:
# print 'gt_dets.shape:',gt_dets.shape
# print 'gt_masks.shape:', gt_masks[0].shape
# print 'gt_boxes:',gt_boxes.shape