目录
#txt转xml
#xml转txt
#json转xml
#xml转json
#txt转xml
# coding: utf-8
# author: HXY
# 2020-4-17"""
该脚本用于visdrone数据处理；
将annatations文件夹中的txt标签文件转换为XML文件；
txt标签内容为：
<bbox_left>,<bbox_top>,<bbox_width>,<bbox_height>,<score>,<object_category>,<truncation>,<occlusion>
类别：
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 os
import cv2
import time
from xml.dom import minidomname_dict = {'0': 'ignored regions', '1': 'pedestrian', '2': 'people','3': 'bicycle', '4': 'car', '5': 'van', '6': 'truck','7': 'tricycle', '8': 'awning-tricycle', '9': 'bus','10': 'motor', '11': 'others'}def transfer_to_xml(pic, txt, file_name):xml_save_path = 'F:/bling/data/VisDrone2019-DET-train/Annotations_XML' # 生成的xml文件存储的文件夹if not os.path.exists(xml_save_path):os.mkdir(xml_save_path)img = cv2.imread(pic)img_w = img.shape[1]img_h = img.shape[0]img_d = img.shape[2]doc = minidom.Document()annotation = doc.createElement("annotation")doc.appendChild(annotation)folder = doc.createElement('folder')folder.appendChild(doc.createTextNode('visdrone'))annotation.appendChild(folder)filename = doc.createElement('filename')filename.appendChild(doc.createTextNode(file_name))annotation.appendChild(filename)source = doc.createElement('source')database = doc.createElement('database')database.appendChild(doc.createTextNode("Unknown"))source.appendChild(database)annotation.appendChild(source)size = doc.createElement('size')width = doc.createElement('width')width.appendChild(doc.createTextNode(str(img_w)))size.appendChild(width)height = doc.createElement('height')height.appendChild(doc.createTextNode(str(img_h)))size.appendChild(height)depth = doc.createElement('depth')depth.appendChild(doc.createTextNode(str(img_d)))size.appendChild(depth)annotation.appendChild(size)segmented = doc.createElement('segmented')segmented.appendChild(doc.createTextNode("0"))annotation.appendChild(segmented)with open(txt, 'r') as f:lines = [f.readlines()]for line in lines:for boxes in line:box = boxes.strip('/n')box = box.split(',')x_min = box[0]y_min = box[1]x_max = int(box[0]) + int(box[2])y_max = int(box[1]) + int(box[3])object_name = name_dict[box[5]]# if object_name is 'ignored regions' or 'others':# continueobject = doc.createElement('object')nm = doc.createElement('name')nm.appendChild(doc.createTextNode(object_name))object.appendChild(nm)pose = doc.createElement('pose')pose.appendChild(doc.createTextNode("Unspecified"))object.appendChild(pose)truncated = doc.createElement('truncated')truncated.appendChild(doc.createTextNode("1"))object.appendChild(truncated)difficult = doc.createElement('difficult')difficult.appendChild(doc.createTextNode("0"))object.appendChild(difficult)bndbox = doc.createElement('bndbox')xmin = doc.createElement('xmin')xmin.appendChild(doc.createTextNode(x_min))bndbox.appendChild(xmin)ymin = doc.createElement('ymin')ymin.appendChild(doc.createTextNode(y_min))bndbox.appendChild(ymin)xmax = doc.createElement('xmax')xmax.appendChild(doc.createTextNode(str(x_max)))bndbox.appendChild(xmax)ymax = doc.createElement('ymax')ymax.appendChild(doc.createTextNode(str(y_max)))bndbox.appendChild(ymax)object.appendChild(bndbox)annotation.appendChild(object)with open(os.path.join(xml_save_path, file_name + '.xml'), 'w') as x:x.write(doc.toprettyxml())x.close()f.close()if __name__ == '__main__':t = time.time()print('Transfer .txt to .xml...ing....')txt_folder = 'F:/bling/data/VisDrone2019-DET-train/annotations' # visdrone txt标签文件夹txt_file = os.listdir(txt_folder)img_folder = 'F:/bling/data/VisDrone2019-DET-train/images' # visdrone 照片所在文件夹for txt in txt_file:txt_full_path = os.path.join(txt_folder, txt)img_full_path = os.path.join(img_folder, txt.split('.')[0] + '.jpg')try:transfer_to_xml(img_full_path, txt_full_path, txt.split('.')[0])except Exception as e:print(e)print("Transfer .txt to .XML sucessed. costed: {:.3f}s...".format(time.time() - t))
#xml转txt
import os
import xml.etree.ElementTree as ETdirpath = 'F:/bling/data/xml/' #原来存放xml文件的目录
newdir = 'F:/bling/data/txt/' #修改label后形成的txt目录if not os.path.exists(newdir):os.makedirs(newdir)for fp in os.listdir(dirpath):root = ET.parse(os.path.join(dirpath,fp)).getroot()xmin, ymin, xmax, ymax = 0,0,0,0sz = root.find('size')width = float(sz[0].text)height = float(sz[1].text)filename = root.find('filename').textfor child in root.findall('object'): #找到图片中的所有框#print(child.find('name').text)sub = child.find('bndbox') #找到框的标注值并进行读取label = child.find('name').textxmin = float(sub[0].text)ymin = float(sub[1].text)xmax = float(sub[2].text)ymax = float(sub[3].text)try: #转换成yolov3的标签格式，需要归一化到（0-1）的范围内x_center = (xmin + xmax) / (2 * width)y_center = (ymin + ymax) / (2 * height)w = (xmax - xmin) / widthh = (ymax - ymin) / heightexcept ZeroDivisionError:print(filename,'的 width有问题')with open(os.path.join(newdir, fp.split('.')[0]+'.txt'), 'a+') as f:f.write(' '.join([str(label), str(x_center), str(y_center), str(w), str(h) + '\n']))print('ok')#json转xml
import xmltodict
import json
import os
# json to xml
def jsonToXml(json_str):try:xml_str=""xml_str = xmltodict.unparse(json_str, encoding='utf-8')except:xml_str = xmltodict.unparse({'request': json_str}, encoding='utf-8')finally:return xml_strdef json_to_xml(json_path,xml_path):if(os.path.exists(xml_path)==False):os.makedirs(xml_path)dir = os.listdir(json_path)for file in dir:file_list=file.split(".")with open(os.path.join(json_path,file), 'r') as load_f:load_dict = json.load(load_f)json_result = jsonToXml(load_dict)f = open(os.path.join(xml_path,file_list[0]+".xml"), 'w', encoding="UTF-8")f.write(json_result)f.close()
if __name__ == '__main__':json_path=r"F:/bling/data/json" #该目录为存放json文件的路径 ps:目录中只能存放json文件xml_path=r"F:/bling/data/train" #该目录为放xml文件的路径json_to_xml(json_path,xml_path)
#xml转json
import cv2
import xml.etree.ElementTree as ET
import numpy as np
import os
import json
import shutil
import base64
'''
该脚本实现将xml类型标签(或者yolo格式标签)转为json格式标签
需要的数据：原始图像 原始xml标签（原始txt标签）'''# 解析数据集，输入单张图片路径，图片路径不能出现中文，因为是cv2读取的。和对应xml文件的路径
# 返回图片 该图所有的目标框[[x1,y1,x2,y2],....] 每个框的类别[label1, label2, label3,.....] 注意是label而不是索引
def parse_img_label(img_path, xml_path): # 绝对路径img = cv2.imread(img_path)tree = ET.parse(xml_path) root = tree.getroot()objs = root.findall('object')bboxes = [] # 坐标框h ,w = img.shape[0], img.shape[1]#gt_labels = [] # 标签名for obj in objs: # 遍历所有的目标label = obj[0].text # <name>这个tag的值，即标签label = label.strip(' ')box = [int(obj[4][i].text) for i in range(4)]box.append(label) # box的元素 x1 y1 x2 y2 类别bboxes.append(box)return img, bboxes# 该函数用于将yolo的标签转回xml需要的标签。。即将归一化后的坐标转为原始的像素坐标
def convert_yolo_xml(box,img): # x,y,w,h = box[0], box[1], box[2], box[3]# 求出原始的x1 x2 y1 y2x2 = (2*x + w)*img.shape[1] /2x1 = x2 - w*img.shape[1]y2 = (2*y+h)*img.shape[0] /2y1 = y2 - h* img.shape[0]new_box = [x1,y1, x2, y2]new_box = list(map(int,new_box))return new_box# 该函数用于解析yolo格式的数据集，即txt格式的标注 返回图像 边框坐标 真实标签名（不是索引，因此需要预先定义标签）
def parse_img_txt(img_path, txt_path):name_label = ['class0','class1','class2'] # 需要自己预先定义,它的顺序要和实际yolo格式的标签中0 1 2 3的标签对应 yolo标签的类别是索引 而不是名字img = cv2.imread(img_path)f = open(txt_path)bboxes = []for line in f.readlines():line = line.split(" ")if len(line) == 5:obj_label = name_label[int(line[0])] # 将类别索引转成其名字x = float(line[1])y = float(line[2])w = float(line[3])h = float(line[4])box = convert_yolo_xml([x,y,w,h], img)box.append(obj_label)bboxes.append(box)return img, bboxes# 制作labelme格式的标签
# 参数说明 img_name： 图像文件名称
# txt_name: 标签文件的绝对路径，注意是绝对路径
# prefix： 图像文件的上级目录名。即形如/home/xjzh/data/ 而img_name是其下的文件名，如00001.jpg
# prefix+img_name即为图像的绝对路径。不该路径能出现中文，否则cv2读取会有问题
#
def get_json(img_name, txt_name, prefix, yolo=False):# 图片名 标签名 前缀label_dict = {} # json字典，依次填充它的value label_dict["imagePath"] = prefix + img_name # 图片路径label_dict["fillColor"] = [255,0,0,128] # 目标区域的填充颜色 RGBAlabel_dict["lineColor"] = [0,255,0,128] # 线条颜色label_dict["flag"] = {}label_dict["version"] = "3.16.7" # 版本号，随便with open(prefix + img_name,"rb") as f:img_data = f.read()base64_data = base64.b64encode(img_data)base64_str = str(base64_data, 'utf-8')label_dict["imageData"] = base64_str # labelme的json文件存放了图像的base64编码。这样如果图像路径有问题仍然能够打开文件img, gt_box = parse_img_label(prefix + img_name, txt_name) if not yolo else parse_img_txt(prefix + img_name, txt_name) # 读取真实数据label_dict["imageHeight"] = img.shape[0] # 高度label_dict["imageWidth"] = img.shape[1]shape_list = [] # 存放标注信息的列表，它是 shapes这个键的值。里面是一个列表，每个元素又是一个字典，字典内容是该标注的类型 颜色 坐标点等等#label_dict["shapes"] = [] # 列表，每个元素是字典。# box的元素 x1 y1 x2 y2 类别for box in gt_box:shape_dict = {} # 表示一个目标的字典shape_dict["shape_type"] = "rectangle" # 因为xml或yolo格式标签是矩形框标注，因此是rectangleshape_dict["fill_color"] = None #该类型的填充颜色 shape_dict["line_color"] = None # 线条颜色 可以设置，或者根据标签名自己预先设定labe_color_dictshape_dict["flags"] = {}shape_dict["label"] = box[-1] # 标签名 shape_dict["points"] = [[box[0],box[1]], [box[2], box[3]]] # 通常contours是长度为1的列表，如果有分块，可能就有多个 # [[x1,y1], [x2,y2]...]的列表shape_list.append(shape_dict)label_dict["shapes"] = shape_list #return label_dictimgs_path = "F:/bling/data/images/" # 图像路径
xmls_path ="F:/bling/data/xml/" # xml文件路径img_path = os.listdir(imgs_path)
out_json = 'F:/bling/data/json/' # 保存的json文件路径for nums, path in enumerate(img_path):if nums %200==0:print(f"processed {nums} images")xml_path = xmls_path + path.replace('jpg','xml') # xml文件的绝对路径label_dict = get_json(path, xml_path,prefix=imgs_path) # with open(out_json + path.replace("jpg","json"),'w') as f: # 写入一个json文件f.write(json.dumps(label_dict, ensure_ascii=False, indent=4, separators=(',', ':')))