Paddlepaddle使用自己的VOC数据集训练目标检测（0废话简易教程）

一安装paddlepaddle和paddledection（略）

笔者使用的是自己的数据集

二在dataset目录下新建自己的数据集文件，如下：

其中

xml文件内容如下：

另外新建一个createList.py文件：


# -- coding: UTF-8 --
import os
import os.path as osp
import re
import random
 
devkit_dir = '../smoke/'
years = ['2007', '2012']
 
 
def get_dir(devkit_dir,  type):
    return osp.join(devkit_dir, type)
 
 
def walk_dir(devkit_dir):
    filelist_dir = get_dir(devkit_dir, 'ImageSets/Main')
    annotation_dir = get_dir(devkit_dir, 'annotations')
    img_dir = get_dir(devkit_dir, 'images')
    trainval_list = []
    test_list = []
    added = set()
 
    for _, _, files in os.walk(filelist_dir):
        for fname in files:
            img_ann_list = []
            if re.match('train\.txt', fname):
                img_ann_list = trainval_list
            elif re.match('val\.txt', fname):
                img_ann_list = test_list
            else:
                continue
            fpath = osp.join(filelist_dir, fname)
            for line in open(fpath):
                name_prefix = line.strip().split()[0]
                if name_prefix in added:
                    continue
                added.add(name_prefix)
                ann_path = osp.join(annotation_dir, name_prefix + '.xml')
                img_path = osp.join(img_dir, name_prefix + '.jpg')
                assert os.path.isfile(ann_path), 'file %s not found.' % ann_path
                assert os.path.isfile(img_path), 'file %s not found.' % img_path
                img_ann_list.append((img_path, ann_path))
 
    return trainval_list, test_list
 
 
def prepare_filelist(devkit_dir, output_dir):
    trainval_list = []
    test_list = []
    trainval, test = walk_dir(devkit_dir)
    trainval_list.extend(trainval)
    test_list.extend(test)
    random.shuffle(trainval_list)
    with open(osp.join(output_dir, 'trainval.txt'), 'w') as ftrainval:
        for item in trainval_list:
            ftrainval.write(item[0] + ' ' + item[1] + '\n')
 
    with open(osp.join(output_dir, 'test.txt'), 'w') as ftest:
        for item in test_list:
            ftest.write(item[0] + ' ' + item[1] + '\n')
 
 
if __name__ == '__main__':
    prepare_filelist(devkit_dir, '../smoke')

一个data2tarin.py文件：


# -- coding: UTF-8 --
import os
import random
 
 
trainval_percent = 0.9
train_percent = 0.9
xml = r"D:\Coding\PaddleDetection-release-2.7\dataset\smoke\annotations"
save_path = r"D:\Coding\PaddleDetection-release-2.7\dataset\smoke\ImageSets\Main"
 
if not os.path.exists(save_path):
    os.makedirs(save_path)
 
total_xml = os.listdir(xml)
 
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)
 
print("train and val size", tv)
print("traub size", tr)
ftrainval = open(os.path.join(save_path, 'trainval.txt'), 'w')
ftest = open(os.path.join(save_path, 'test.txt'), 'w')
ftrain = open(os.path.join(save_path, 'train.txt'), 'w')
fval = open(os.path.join(save_path, 'val.txt'), 'w')
 
for i in list:
    name = total_xml[i][:-4]+'\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)
 
ftrainval.close()
ftrain.close()
fval.close()
ftest .close()

运行以上两个脚本，结果如图：

新建label_list.txt文件，内容如下，为标签文件：

三新建smoke.yml文件

内容如下：


metric: VOC
map_type: 11point
num_classes: 4
 
TrainDataset:
  name: VOCDataSet
  dataset_dir: dataset/smoke
  anno_path: trainval.txt
  label_list: label_list.txt
  data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
 
EvalDataset:
  name: VOCDataSet
  dataset_dir: dataset/smoke
  anno_path: test.txt
  label_list: label_list.txt
  data_fields: ['image', 'gt_bbox', 'gt_class', 'difficult']
 
TestDataset:
  name: ImageFolder
  anno_path: dataset/smoke/label_list.txt