json多边形标签转yolo标签，yolo标签画框到图片上并保存

 

将json中标注的多边形提取为矩形标注并保存为txt格式，代码如下，适合一种类别，多种自己修改:

import json
import os
from os import listdir, getcwd
from os.path import join
import os.path

rootdir = '/home/lwf/下载/images' # 写自己存放图片的数据地址
def position(pos):
    # 该函数用来找出xmin,ymin,xmax,ymax即bbox包围框
    x = []
    y = []
    nums = len(pos)
    for i in range(nums):
        x.append(pos[i][0])
        y.append(pos[i][1])
    x_max = max(x)
    x_min = min(x)
    y_max = max(y)
    y_min = min(y)
    b = (float(x_min), float(x_max), float(y_min), float(y_max))
    return b

def convert(size, box):
    # 该函数将xmin,ymin,xmax,ymax转为x,y,w,h中心点坐标和宽高
    dw = 1. / (size[0])
    dh = 1. / (size[1])
    x = (box[0] + box[1]) / 2.0 - 1
    y = (box[2] + box[3]) / 2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)

def convert_annotation(image_id):
    load_f = open("/home/lwf/下载/json/%s.json" % (image_id), 'r')  # 导入json标签的地址
    load_dict = json.load(load_f)
    out_file = open('/home/lwf/下载/txt/%s.txt' % (image_id), 'w')  # 输出标签的地址
    # keys=tuple(load_dict.keys())
    w = load_dict['imageWidth']  # 原图的宽，用于归一化
    h = load_dict['imageHeight']
    # print(h)
    objects = load_dict['shapes']
    nums = len(objects)
    # print(nums)
    # object_key=tuple(objects.keys()

    for i in range(0, nums):
        labels = objects[i]['label']
        # print(i)
        if (labels in ['apple']):
            # print(labels)
            pos = objects[i]['points']
            b = position(pos)
            bb = convert((w, h), b)
            cls_id = 0  # 类别设为0
            out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
            # print(type(pos))
        #以下用不到
        elif (labels in ['car', 'truck', 'bus', 'caravan', 'trailer']):
            # print(labels)
            pos = objects[i]['polygon']
            b = position(pos)
            bb = convert((w, h), b)
            cls_id = 1  # 我这里把各种类型的车都设为类别１
            out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

def image_id(rootdir):
    a = []
    for parent, dirnames, filenames in os.walk(rootdir):
        for filename in filenames:
            filename = filename.strip('.jpg')
            # print(filename)
            a.append(filename)
    return a

names = image_id(rootdir)
for image_id in names:
    convert_annotation(image_id)

得到的格式如下:

----------------------------------------分割线-----------------------------------------------------------------------------

再将yolo标签画在图片上，可以预览，也可以批量保存下来，代码如下:

 

import os
import cv2

def main():
	# 总的检测根目录
    path_root_labels = '/home/lwf/下载/txt'
    # 总的检测根目录
    path_root_imgs ='/home/lwf/下载/数据/JPEGImages'
    type_object = '.txt'
    #保存画框后的图片路径
    path_save_imgs ='/home/lwf/下载/数据/save_image'

    for ii in os.walk(path_root_imgs):
        for j in ii[2]:
            type = j.split(".")[1]
            if type != 'jpg' and type != 'png':
                continue
            path_img = os.path.join(path_root_imgs, j)
            print(path_img)
            label_name = j[:-4]+type_object
            path_label = os.path.join(path_root_labels, label_name)

            if os.path.exists(path_label) == True:
                f = open(path_label, 'r+', encoding='utf-8')
                img = cv2.imread(path_img)
                img_tmp = img.copy()
                w = img.shape[1]
                h = img.shape[0]
                new_lines = []
                while True:
                    line = f.readline()
                    if line:

                        msg = line.split(" ")
                        # print(x_center,",",y_center,",",width,",",height)
                        x1 = int((float(msg[1]) - float(msg[3]) / 2) * w)  # x_center - width/2
                        y1 = int((float(msg[2]) - float(msg[4]) / 2) * h)  # y_center - height/2
                        x2 = int((float(msg[1]) + float(msg[3]) / 2) * w)  # x_center + width/2
                        y2 = int((float(msg[2]) + float(msg[4]) / 2) * h)  # y_center + height/2
                        print(x1,",",y1,",",x2,",",y2)
                        cv2.rectangle(img_tmp,(x1,y1),(x2,y2),(0,0,255),2)
                    else :
                        break
                # cv2.imwrite(path_save_imgs + '/'+j[:-4]+'.jpg', img_tmp)#保存图片
                cv2.imshow('test',img_tmp)#预览画框的图片
                cv2.waitKey(0)
                print(img_tmp)


if __name__ == '__main__':
    main()

效果如下:

参考博文:

将实例分割数据集转为目标检测数据集_逍遥王可爱的博客-CSDN博客