提取visdrone2019数据集数据

1、visdrone2019数据集数据这通过只提取类跟踪数据集，因此对其更改，如果需要提取多类的直接打开即可，同时呢这个转换为多累跟踪数据集，其中数据的格式为:,其中的类别为：ignored regions(0), pedestrian(1),people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9),motor(10),others(11)# import shutil# -110 类是我们需要检测和跟踪的标# van# truck# tricycle# awning-tricy

2、cle (8), - 7# bus# motor# -# others(11)# We need 10 classes to detect and trackingvan: 4,:param img: opencv(numpy array): HWC:param boxes: a list of boxes: left(box0), top(box1), width(box2), height(box3)box = list(map(lambda x: int(x + 0.5), box) # 四舍五imgbox1: box1 + box3, box0: box0 + box2 = 0, 0,

3、 0return imgdef gen_dot_train_file(data_root, rel_path, out_root, f_name=detrac.train):param rel_path:param out_root:param f_name:return:if not (os.path.isdir(data_root) and os.path.isdir(out_root):out_f_path = out_root + / + f_namecnt = 0with open(out_f_path, w) as f:root = data_root + rel_pathseqs

4、 = x for x in os.listdir(root)seqs.sort()# seqs = sorted(seqs, key=lambda x: int(x.split(_)-1)for seq in tqdm(seqs):img_dir = root + / + seq # + /img1img_list = x for x in os.listdir(img_dir)img_list.sort()for img in img_list:if img.endswith(.jpg):f.write(item + n)cnt += 1print(Total :d images for t

5、raining.format(cnt)def gen_track_dataset(src_root, dst_root, viz_root=None):dst_img_root = dst_root + /imagesdst_txt_root = dst_root + /labels_with_idsif not os.path.isdir(dst_img_root):os.makedirs(dst_img_root)# id, 0# track_start_id = 0track_start_id_dict = defaultdict(int) # start id0开始for cls_id

6、 in id2cls.keys():# 遍历每个视频序列for seq in tqdm(seq_names):print(Processing :.format(seq)seq_img_dir = src_root + /sequences/ + seqseq_txt_f_path = src_root + /annotations/ + seq + .txt# ()dst_seq_img_dir = dst_img_root + / + seqif not os.path.isdir(dst_seq_img_dir):os.makedirs(dst_seq_img_dir)dst_seq_t

7、xt_dir = dst_txt_root + / + seqif not os.path.isdir(dst_seq_txt_dir):os.makedirs(dst_seq_txt_dir)# seqtrack_id# seq_max_tar_id = 0seq_max_tra_id_dictk = 0# 视频序列seq_frame_names = os.listdir(seq_img_dir)seq_frame_names.sort()label_n_lines = len(label_lines)for line_i, line in enumerate(label_lines):li

8、ne = int(x) for x in line.strip().split(,)seq_label_arrayline_i = lineseq_label_arrayline_i = line# 这需要注意的是，数据集的格式为：# ,# 数据集的类别为：# ignored regions(0), pedestrian(1),people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10),others(11)# 0boxbox，因此分两部分处理，把忽略的和真

9、正的类别进区分处理# , seq_objs_label_dict = defaultdict(list)# seqdictclasstarget idseq_cls_target_ids_dict = defaultdict(list)tmp_ids_dict = defaultdict(set)cls_id = label7 - 1target_id = label1#for cls_id in tmp_ids_dict.keys():track_ids = tmp_ids_dictcls_id# track_ids = set(track_ids)track_ids = list(trac

10、k_ids)track_ids.sort()seq_cls_target_ids_dictcls_id = track_ids# track_ids = tmp_ids_dict9# track_ids = list(track_ids)# track_ids.sort()for k, v in seq_cls_target_ids_dict.items():seq_max_tra_id_dictk = len(v)# print( max track id: :d, start id: :d#.format(id2cls9, seq_max_tra_id_dict0, track_start

11、_id_dict0)for k in id2cls.keys():# 读取每帧for fr_id in seq_objs_label_dict.keys():# -fr_labels = seq_objs_label_dictfr_idprint(Err: empty image.)continueviz_path = viz_dir + / + fr_name# img数据作为可视化输出img_viz = copy.deepcopy(img)# - label(txt)# label_str(or)fr_label_strs = for label in fr_labels:# cls_id

12、 and cls_nameobj_type = label7assert 0 obj_type 11cls_id = obj_type - 1 # 0开始# cls_name = id2clscls_idtarget_id = label1score = label6truncation = label8 # no truncation = 0 (truncation ratio 0%), and partial truncation = 1 (truncation ratio 1% 50%)occlusion = label9# - label(label/): 在归化之前if not (v

13、iz_root is None): # 如果可视化录不为空# targetbboxcv2.rectangle(img_viz,pt_1,pt_2,cls_str = id2clscls_idveh_type_str_size = cv2.getTextSize(cls_str,cv2.FONT_HERSHEY_PLAIN,1.3,1)0(pt_10,cv2.FONT_HERSHEY_PLAIN,1.3,225, 255, 255,1)tr_id_str_size = cv2.getTextSize(tr_id_str,cv2.FONT_HERSHEY_PLAIN,1.3,1)0cv2.putT

14、ext(img_viz,tr_id_str,(pt_10,pt_11 + veh_type_str_size1 + tr_id_str_size1 + 8),cv2.FONT_HERSHEY_PLAIN,1.3,225, 255, 255,1)# bbox中点坐标bbox_center_x = bbox_left + bbox_width * 0.5bbox_center_y = bbox_top + bbox_height * 0.5# bbox(0.0, 1.0)bbox_center_x /= Wbbox_center_y /= Hbbox_width /= Wbbox_height /

15、= H# label, label成完成才输出# class_id, track_id, bbox_center_x, box_center_y, bbox_width, bbox_height# label_str = :d :d :.6f :.6f :.6f :.6fn.format(cls_id,track_id,bbox_center_x,bbox_center_y,bbox_width,bbox_height)label_str = :d :d :.6f :.6f :.6f :.6fn.format(0,track_id,bbox_center_x,bbox_center_y,bbo

16、x_width,bbox_height)fr_label_strs.append(label_str)# seq, track_start_idfor cls_id in id2cls.keys():print(Total :d frames.format(frame_cnt)viz_root=E:/MOTdata/VisDrone2020/viz_result_val)转换完成以后，进成.train件：88 . 88(| -_- |)0 = /0_/-_. | |/ ./ | : |/ / _| -:- |- | | - / | |佛祖保佑Version: 1.0FilePath: srcg

17、en_data_path copy.pyLastEditTime: 2021-05-28 15:23:35import globprint(image22:, file=f)f.close()def gen_dot_train_file(data_root, rel_path, out_root, f_name=VisDrone2019.train):To generate the dot train file:param data_root:param rel_path:param out_root:param f_name:return:if not (os.path.isdir(data

18、_root) and os.path.isdir(out_root):out_f_path = out_root + / + f_namecnt = 0with open(out_f_path, w) as f:root = data_root +/+ rel_pathseqs = x for x in os.listdir(root)seqs.sort()# seqs = sorted(seqs, key=lambda x: int(x.split(_)-1)for seq in tqdm(seqs):img_dir = root + / + seq # + /img1img_list = x for x in os.listdir(img_dir)img_list.sort()print(Total :d images for training.format(cnt)def clean_train_set(im