将分割数据中的像素级多边形标注转换为目标检测的边界框（bbox）标注，其实并不复杂。每个分割目标的多边形标注由多个像素点的 [x, y] 坐标组成，只需找到这些坐标的 Xmin, Ymin, Xmax, 和 Ymax 值，就能确定该目标的边界框。

结果：

preprocess.py代码如下

import os
import glob
from shutil import copy2
from PIL import Image
import json
import numpy as np

def copy_file(src, src_ext, dst):
    # find all files ends up with ext
    flist = sorted(glob.glob(os.path.join(src, '*', src_ext)))
    for fname in flist:
        src_path = os.path.join(src, fname)
        copy2(src_path, dst)
        print('copied %s to %s' % (src_path, dst))

def construct_box(inst_root, inst_name, cls_name, dst):
    inst_list = sorted(glob.glob(os.path.join(inst_root, '*', inst_name)))
    cls_list = sorted(glob.glob(os.path.join(inst_root, '*', cls_name)))
    for inst, cls in zip(*(inst_list, cls_list)):
        inst_map = Image.open(os.path.join(inst_root, inst))
        inst_map = np.array(inst_map, dtype=np.int32)
        cls_map = Image.open(os.path.join(inst_root, cls))
        cls_map = np.array(cls_map, dtype=np.int32)
        H, W = inst_map.shape
        # get a list of unique instances
        inst_info = {'imgHeight':H, 'imgWidth':W, 'objects':{}}
        inst_ids = np.unique(inst_map)
        for iid in inst_ids: 
            if int(iid) < 1000: # filter out non-instance masks
                continue
            ys,xs = np.where(inst_map==iid)
            ymin, ymax, xmin, xmax = \
                    ys.min(), ys.max(), xs.min(), xs.max()
            cls_label = np.median(cls_map[inst_map==iid])
            inst_info['objects'][str(iid)] = {'bbox': [int(xmin), int(ymin), int(xmax), int(ymax)],
                                              'cls': int(cls_label)}
        # write a file to path
        filename = os.path.splitext(os.path.basename(inst))[0]
        savename = os.path.join(dst, filename + '.json')
        with open(savename, 'w') as f:
            json.dump(inst_info, f)
        print('wrote a bbox summary of %s to %s' % (inst, savename))

# organize image
if __name__ == '__main__':
    folder_name = 'datasets/cityscape/'
    train_img_dst = os.path.join(folder_name, 'train_img')
    train_label_dst = os.path.join(folder_name, 'train_label')
    train_inst_dst = os.path.join(folder_name, 'train_inst')
    train_bbox_dst = os.path.join(folder_name, 'train_bbox')
    val_img_dst = os.path.join(folder_name, 'val_img')
    val_label_dst = os.path.join(folder_name, 'val_label')
    val_inst_dst = os.path.join(folder_name, 'val_inst')
    val_bbox_dst = os.path.join(folder_name, 'val_bbox')

    if not os.path.exists(train_img_dst):
        os.makedirs(train_img_dst)
    if not os.path.exists(train_label_dst):
        os.makedirs(train_label_dst)
    if not os.path.exists(train_inst_dst):
        os.makedirs(train_inst_dst)
    if not os.path.exists(val_img_dst):
        os.makedirs(val_img_dst)
    if not os.path.exists(val_label_dst):
        os.makedirs(val_label_dst)
    if not os.path.exists(val_inst_dst):
        os.makedirs(val_inst_dst)

    root = "G:\Dataset\cityscapes"
    # # train_image
    # copy_file('G:/Dataset/cityscapes/leftImg8bit/train',\
    #         '*_leftImg8bit.png', train_img_dst)
    # # train_label
    # copy_file('G:/Dataset/cityscapes/gtFine/train',\
    #         '*_labelIds.png', train_label_dst)
    # # train_inst
    # copy_file('G:/Dataset/cityscapes/gtFine/train',\
    #         '*_instanceIds.png', train_inst_dst)
    # # val_image
    # copy_file('G:/Dataset/cityscapes/leftImg8bit/val',\
    #         '*_leftImg8bit.png', val_img_dst)
    # # val_label
    # copy_file('G:/Dataset/cityscapes/gtFine/val',\
    #         '*_labelIds.png', val_label_dst)
    # # val_inst
    # copy_file('G:/Dataset/cityscapes/gtFine/val',\
    #         '*_instanceIds.png', val_inst_dst)

    if not os.path.exists(train_bbox_dst):
        os.makedirs(train_bbox_dst)
    if not os.path.exists(val_bbox_dst):
        os.makedirs(val_bbox_dst)
    # wrote a bounding box summary 
    construct_box('G:/Dataset/cityscapes/gtFine/train',\
            '*_instanceIds.png', '*_labelIds.png', train_bbox_dst)
    construct_box('G:/Dataset/cityscapes/gtFine/val',\
            '*_instanceIds.png', '*_labelIds.png', val_bbox_dst)