def load_det_proposals(seq_name, start_frm_id, end_frm_id):
prop_file_path, prop_file_ext = io.get_dets_info(seq_name)
prop_arr = ld.load_det_proposals(
prop_file_path) ## load all the proposals(always)
frm_ids = np.arange(start_frm_id, end_frm_id + 1)
prop_arr, row_indexes = dets_filter_frms(prop_arr, frm_ids)
if debug_flag:
print 'prop_file_path:', prop_file_path
print 'prop_file_ext:', prop_file_ext
return prop_arr
def vis_picked_mult_instances():
print '=============================test vis_util================================='
track_mask_dir = io.get_track_mask_info(set_name)
prop_file_path, prop_file_ext = io.get_dets_info(set_name)
prop_arr = ld.load_det_proposals(prop_file_path)
if debug_flag:
print 'track_mask_dir:', track_mask_dir
print 'prop_file_path:', prop_file_path
print 'prop_file_ext:', prop_file_ext
print 'prop_arr.shape:', prop_arr.shape
##0> load image names
imgset_path, jpg_dir, im_ext = io.get_ims_info()
im_names = ld.get_im_names(imgset_path)
im_names = im_names[:test_frm_num]
##1> load color images
rgb_ims = ld.load_color_images(jpg_dir, im_names, im_ext)
vis_proposals_dir = io.get_vis_dir_info(vis_mult_prop_folder)
iter_name = 'iter' + str(4)
## draw bbox per person
frm_ids = np.unique(prop_arr[:, 0]).astype(int)
frm_num = len(frm_ids)
##for debug
for n_id in xrange(
frm_num): ## label is obj_id (42 has some problems, 56)
frm_id = frm_ids[n_id]
row_indexs = np.where(prop_arr[:, 0] == frm_id)[0] ##frame indexes
frm_prop_arr = prop_arr[row_indexs]
frm_labels = frm_prop_arr[:, 1].astype(int)
im = rgb_ims[n_id]
im_name = set_name + '_' + str(frm_ids[n_id] + shift_num).zfill(6)
if len(frm_labels) == 0:
continue
masks = load_frame_masks(track_mask_dir, frm_labels, iter_name,
im_name)
boxes = load_frame_boxes(track_mask_dir, frm_id, frm_labels, iter_name)
if debug_flag:
print 'frm_id:', frm_id
print 'frm_labels:', frm_labels.shape
print 'masks.shape:', len(masks)
print 'boxes.shape:', boxes.shape
vis_proposals_multi_instances(vis_proposals_dir, iter_name, im,
im_name, boxes, masks, frm_labels)
if debug_flag:
print 'track_mask_dir:', track_mask_dir
##print 'im_names:', im_names
print 'vis_proposals_dir:', vis_proposals_dir
def get_refine_dets():
track_mask_dir = io.get_track_mask_info(set_name)
prop_file_path, prop_file_ext = io.get_dets_info(set_name)
prop_arr = ld.load_det_proposals(prop_file_path)
if debug_flag:
print 'track_mask_dir:', track_mask_dir
print 'prop_file_path:', prop_file_path
print 'prop_file_ext:', prop_file_ext
print 'prop_arr.shape:', prop_arr.shape
det_frm_ids = []
det_obj_ids = []
det_boxes = []
det_masks = []
frm_ids = np.sort(np.unique(prop_arr[:, 0]).astype(int))
frm_num = len(frm_ids)
## calculate based on per-frame
for n_id in xrange(frm_num):
frm_id = frm_ids[n_id]
row_indexs = np.where(prop_arr[:, 0] == frm_id)[0] ##frame indexes
frm_prop_arr = prop_arr[row_indexs]
frm_labels = frm_prop_arr[:, 1].astype(int)
im_name = set_name + '_' + str(frm_ids[n_id] + shift_num).zfill(
6) ##im_name
boxes = load_frame_boxes(track_mask_dir, frm_id, frm_labels, iter_name)
masks = load_frame_masks(track_mask_dir, frm_labels, iter_name,
im_name)
##max_masks=get_max_masks(masks,boxes,img_box) ## not used
det_frm_ids.append(frm_id)
det_obj_ids.append(frm_labels)
det_boxes.append(boxes)
det_masks.append(masks)
#if debug_flag:
# print 'frm_id:', frm_id
# print 'frm_labels:', frm_labels.shape
# print 'len(masks):', len(masks)
# print 'len(boxes):', len(boxes)
return det_frm_ids, det_obj_ids, det_boxes, det_masks
def get_init_dets(cont_flag=False):
det_boxes = []
det_masks = []
det_obj_ids = []
det_frm_ids = []
bin_mask_dir = mcfg.MASK.BIN_LAB_DIR
init_mask_dir = os.path.join(bin_mask_dir, set_name)
prop_file_path, prop_file_ext = io.get_dets_info(set_name)
if not cont_flag:
init_mask_dir = init_mask_dir.replace('Continious', 'Discrete')
prop_file_path = prop_file_path.replace('Continious', 'Discrete')
prop_arr = ld.load_det_proposals(prop_file_path)
frm_ids = np.sort(np.unique(prop_arr[:, 0]).astype(int))
frm_num = len(frm_ids)
img_box = [0, 0, im_width - 1, im_height - 1] ## not used
## calculate based on per-frame
##for n_id in xrange(1):
for n_id in xrange(frm_num):
frm_id = frm_ids[n_id]
row_indexs = np.where(prop_arr[:, 0] == frm_id)[0] ##frame indexes
frm_prop_arr = prop_arr[row_indexs]
frm_labels = frm_prop_arr[:, 1].astype(int)
im_name = set_name + '_' + str(frm_ids[n_id] + shift_num).zfill(6)
frm_coords = frm_prop_arr[:, 2:6]
flt_frm_boxes = coords_to_boxes(frm_coords)
frm_masks = load_frame_masks(init_mask_dir, frm_labels, '', im_name)
int_frm_boxes = boxes_float_to_int(flt_frm_boxes, frm_masks)
##max_masks=get_max_masks(masks,boxes,img_box) ## not used
det_frm_ids.append(frm_id)
det_obj_ids.append(frm_labels)
det_boxes.append(int_frm_boxes)
det_masks.append(frm_masks)
# if debug_flag:
# print 'cont_flag:', cont_flag
# print 'init_mask_dir:', init_mask_dir
# print 'prop_file_path:', prop_file_path
# print 'prop_arr.shape:', prop_arr.shape
# print 'frm_num:', frm_num
return det_frm_ids, det_obj_ids, det_boxes, det_masks
def get_pre_cont_dets():
# print 'prop_file_path:', prop_file_path
# print 'prop_file_ext:', prop_file_ext
prop_file_path, prop_file_ext = io.get_dets_info(seq_name)
prop_arr = ld.load_det_proposals(prop_file_path)
return prop_arr