PyTorch VOC数据加载

• 这是一个读取VOC数据集的例子，供大家参考
• 但是我想这更适合作为一个云备份，哈哈

"""
因为我打算先搞一下目标检测
所以先用VOC这个轻量级的来做
"""

import torch
import torch.utils.data as data
import numpy as np
import cv2
import xml  # 标注是xml格式

try:
    import xml.etree.cElementTree as ET  # 解析xml的c语言版的模块
except ImportError:
    import xml.etree.ElementTree as ET


VOC_CLASSES = {
    'aeroplane', 'bicycle', 'bird', 'boat',
    'bottle', 'bus', 'car', 'cat', 'chair',
    'cow', 'diningtable', 'dog', 'horse',
    'motorbike', 'person', 'pottedplant',
    'sheep', 'sofa', 'train', 'tvmonitor'
}

# 把str映射为int
dict_classes = dict(zip(VOC_CLASSES, range(len(VOC_CLASSES))))
# print(dict_classes['aeroplane'])


class ReadVOC(data.Dataset):
    def __init__(self, root):
        print("reading voc...")
        self.root = root

        self.img_idx = []
        self.ano_idx = []

        self.bbox = []
        self.obj_name = []  # 类别

        train_txt_path = self.root + "/ImageSets/Main/train_val.txt"  # train这个文件夹里面数量太少 换掉

        self.img_path = self.root + "/JPEGImages/"
        self.ano_path = self.root + "/Annotations/"

        # 首先读取txt文件进行训练集图片索引
        train_txt = open(train_txt_path)

        lines = train_txt.readlines()

        for line in lines:
            name = line.strip().split()[0]
            # print(name)  # name is in str type

            self.img_idx.append(self.img_path + name + '.jpg')
            self.ano_idx.append(self.ano_path + name + '.xml')  # 最好是在这直接解析出bbox

    def __getitem__(self, item):
        # print("getitem...")
        # print(self.img_idx[item])
        img = cv2.imread(self.img_idx[item])
        height, width, channels = img.shape

        targrts = ET.parse(self.ano_idx[item])  # .getroot()   # 运行时解析 逻辑更加清晰
        res = []  # 标注输出

        # find all obj in xml
        for obj in targrts.iter("object"):  # 便利物体
            name = obj.find('name').text.lower().strip()
            class_idx = dict_classes[name]

            bbox = obj.find('bndbox')

            pts = ['xmin', 'ymin', 'xmax', 'ymax']
            obj_bbox = []
            for i, pt in enumerate(pts):
                cur_pt = int(bbox.find(pt).text)
                cur_pt = cur_pt / width if i % 2 == 0 else cur_pt / height  # scale height or width
                obj_bbox.append(cur_pt)

            res.append(obj_bbox)  # 当前obj的所有bboxdf
            res.append(class_idx)

        img, res = self.data_trans(img, res)

        return img, res

    def __len__(self):
        data_lenth = len(self.img_idx)
        # print('data lenth is ', data_lenth)
        return data_lenth

    # 标注输入使用w h归一化的相对坐标
    def data_trans(self, img_input, bbox_input):
        # print("trans...")

        goal_size = (400, 400)

        # 在这时候,图像尺寸可以变化,只要目标不发生平移等等
        img = cv2.resize(img_input, goal_size)

        # pre-process input img
        img = torch.from_numpy(img).permute(2, 0, 1).float()

        # 把bbox转换成绝对坐标
        # bbox = [bbox_input[0] * goal_size[0], bbox_input[1] * goal_size[1], bbox_input[2] * goal_size[0], bbox_input[3] * goal_size[1]]
        # bbox = list(map(int, bbox))
        bbox = torch.tensor(bbox_input[0])

        return img, bbox


if __name__ == "__main__":
    ReadVOC(root='/home/.../data/VOCdevkit/VOC2012')

本文地址：https://blog.csdn.net/m0_38139098/article/details/107426408