def extract_attr()

in scripts/attr_prep_tag_NP.py [0:0]

• 32 lines of code
• 16 McCabe index (conditional complexity)

def extract_attr(database, splits):

    split_dict = {}
    for split in splits:
        split_dict.update({s:s for s in split})
    print('Object classes defined on {} videos, freq threshold is {}'.format(len(split_dict), args.freq_thresh))

    attr_all = [] # all the attributes

    for vid_id, vid in database.items():
        if split_dict.get(vid_id, -1) != -1:
            for seg_id, seg in vid['segments'].items():
                for obj in seg['objects']:
                    assert(len(obj['frame_ind']) == 1)
                    for box_id, box in obj['frame_ind'].items():
                        tmp = []
                        attr_lst = []
                        sorted_attr = sorted(box['attributes'], key=lambda x:x[0]) # the attributes are unordered
    
                        for ind, attr in enumerate(sorted_attr):
                            assert(attr[0] >= 0)
                            if len(tmp) == 0:
                                tmp.append(attr[1].lower()) # convert to lowercase
                            else:
                                if attr[0] == (sorted_attr[ind-1][0]+1):
                                    tmp.append(attr[1].lower())
                                else:
                                    attr_lst.append(tmp)
                                    tmp = [attr[1].lower()]
                        if len(tmp) > 0: # the last one
                            attr_lst.append(tmp)
    
                        # exclude empty box (no attribute)
                        # crowd boxes are ok for now
                        if len(attr_lst) == 0: # or box['crowds'] == 1
                            pass
                            # print('empty attribute at video {}, segment {}, box {}'.format(vid_id, seg_id, box_id))
                        else:
                            attr_all.extend([' '.join(i) for i in attr_lst])
    return attr_all