直接划分数据集

import os
import random
 
trainval_percent = 0.2
train_percent = 0.8
xmlfilepath = 'Annotations'#xml文件
txtsavepath = 'ImageSets\Main'#存traintxt等
total_xml = os.listdir(xmlfilepath)
 
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
 
ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/val.txt', 'w')
 
for i in list:
    name = total_xml[i][:-4] + '\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)
 
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

划分后解析xml文件在label下生成完整带特征和标签的txt文件(下面代码的数据分割未操作，上面已执行)

import os
import pandas 
import shutil
import random


import cv2
import numpy as np
import xml.etree.ElementTree as ET


# 这部分休要修改


class Data_preprocess(object):
    '''
    解析xml数据
    '''
    def __init__(self,data_path):
        self.data_path = data_path
        self.image_size = 416
        self.batch_size = 32
        self.cell_size = 13
        self.classes = ["hat","person"]
        self.num_classes = len(self.classes)
        self.box_per_cell = 5
        self.class_to_ind = dict(zip(self.classes, range(self.num_classes)))

        self.count = 0
        self.epoch = 1
        self.count_t = 0

    def load_labels(self, model):
        if model == 'train':
            txtname = os.path.join(self.data_path, 'ImageSets/Main/train.txt')
        if model == 'test':
            txtname = os.path.join(self.data_path, 'ImageSets/Main/test.txt')

        if model == "val":
            txtname = os.path.join(self.data_path, 'ImageSets/Main/val.txt')


        with open(txtname, 'r') as f:
            image_ind = [x.strip() for x in f.readlines()] # 文件名去掉 .jpg

        
        my_index = 0
        for ind in image_ind:
            class_inds, x1s, y1s, x2s, y2s,img_width,img_height = self.load_data(ind)

            if len(class_inds) == 0:
                pass
            else:
                annotation_label = ""
                #box_x: label_index, x_min,y_min,x_max,y_max
                for label_i in range(len(class_inds)):

                    annotation_label += " " + str(class_inds[label_i])
                    annotation_label += " " + str(x1s[label_i])
                    annotation_label += " " + str(y1s[label_i])
                    annotation_label += " " + str(x2s[label_i])
                    annotation_label += " " + str(y2s[label_i])

                with open("./data/my_data/label/"+model+".txt","a") as f:
                    f.write(str(my_index) + " " + data_path+"/JPEGImages/"+ind+".jpg"+" "+str(img_width) +" "+str(img_height)+ annotation_label + "\n")

                my_index += 1

            print(my_index)



    def load_data(self, index):
        label = np.zeros([self.cell_size, self.cell_size, self.box_per_cell, 5 + self.num_classes])
        filename = os.path.join(self.data_path, 'Annotations', index + '.xml')
        tree = ET.parse(filename)
        image_size = tree.find('size')
        image_width = int(float(image_size.find('width').text))
        image_height = int(float(image_size.find('height').text))
        # h_ratio = 1.0 * self.image_size / image_height
        # w_ratio = 1.0 * self.image_size / image_width

        objects = tree.findall('object')

        class_inds = []
        x1s = []
        y1s = []
        x2s = []
        y2s = []

        for obj in objects:
            box = obj.find('bndbox')
            x1 = int(float(box.find('xmin').text))
            y1 = int(float(box.find('ymin').text))
            x2 = int(float(box.find('xmax').text))
            y2 = int(float(box.find('ymax').text))
            # x1 = max(min((float(box.find('xmin').text)) * w_ratio, self.image_size), 0)
            # y1 = max(min((float(box.find('ymin').text)) * h_ratio, self.image_size), 0)
            # x2 = max(min((float(box.find('xmax').text)) * w_ratio, self.image_size), 0)
            # y2 = max(min((float(box.find('ymax').text)) * h_ratio, self.image_size), 0)
            if obj.find('name').text in self.classes:
                class_ind = self.class_to_ind[obj.find('name').text]
                # class_ind = self.class_to_ind[obj.find('name').text.lower().strip()]

                # boxes = [0.5 * (x1 + x2) / self.image_size, 0.5 * (y1 + y2) / self.image_size, np.sqrt((x2 - x1) / self.image_size), np.sqrt((y2 - y1) / self.image_size)]
                # cx = 1.0 * boxes[0] * self.cell_size
                # cy = 1.0 * boxes[1] * self.cell_size
                # xind = int(np.floor(cx))
                # yind = int(np.floor(cy))
                
                # label[yind, xind, :, 0] = 1
                # label[yind, xind, :, 1:5] = boxes
                # label[yind, xind, :, 5 + class_ind] = 1

                if x1 >= x2 or y1 >= y2:
                    pass
                else:
                    class_inds.append(class_ind)
                    x1s.append(x1)
                    y1s.append(y1)
                    x2s.append(x2)
                    y2s.append(y2)

        return class_inds, x1s, y1s, x2s, y2s, image_width, image_height


def data_split(img_path):
    '''
    数据分割
    '''

    files = os.listdir(img_path)

    test_part = random.sample(files,int(351*0.2))

    val_part = random.sample(test_part,int(int(351*0.2)*0.5))

    val_index = 0
    test_index = 0
    train_index = 0
    for file in files:
        if file in val_part:

            with open("./data/my_data/ImageSets/Main/val.txt","a") as val_f:
                val_f.write(file[:-4] + "\n" )

            val_index += 1

        elif file in test_part:
            with open("./data/my_data/ImageSets/Main/test.txt","a") as test_f:
                test_f.write(file[:-4] + "\n")

            test_index += 1

        else:
            with open("./data/my_data/ImageSets/Main/train.txt","a") as train_f:
                train_f.write(file[:-4] + "\n")

            train_index += 1  


        print(train_index,test_index,val_index)



if __name__ == "__main__":
    
    # 分割train, val, test
    # img_path = "./data/my_data/ImageSets"
    # data_split(img_path)
    print("===========split data finish============")

    # 做需要的训练集
    base_path = "./"
    data_path = os.path.join(base_path,"data/my_data")  # 绝对路径

    data_p = Data_preprocess(data_path)
    data_p.load_labels("train")
    data_p.load_labels("test")
    data_p.load_labels("val")
    print("==========data pro finish===========")