在上一章，我们了解了demo，现在开始准备根据自己的场景去训练自己的数据，实现自己的目标检测。
在前面我们知道机器要识别一个问题，需要一个训练的过程，所以我们首先第一步是要对物体进行标注。这里就不使用官方的数据集了，先自己准备一个自己需要学习的图片集合（大概每个种类100张左右）
第一步：建立图片文件夹

mkdir images

然后把需要学习的图片放到文件夹里面
第二步：用labelme进行标记（如何安装，请自行百度）,标注结束后，会在images下新增和图片一一对应的json文件

image.png

第三步：把labelme格式的文件转为coco格式（PaddleDetection不支持labelme格式），这里可以使用PaddleDetection下的工具tools/x2coco.py，

python3 tools/tc_x2coco.py \
                --dataset_type labelme \
                --json_input_dir dataset/lables/images_yt \
                --image_input_dir dataset/lables/images_yt \
                --output_dir dataset/lables/images_yt/output \
                --train_proportion 0.8 \
                --val_proportion 0.2 \
                --test_proportion 0.0

这里存在两个坑。

image.png

坑一：labelme生成的json和图片默认是在同一个文件夹下，这里用tools/x2coco.py会导致生成的训练文件和验证文件减少一半,主要是由于上面图中的代码导致，并且容易出现json和图片不对应的问题，导致最终训练结果失败，我开始就是被这里坑了，以为是自己标注问题，来来回回删除了好几次标注由重新标，最后发现不是自己的问题，是被这里的bug坑了

坑二，如果你有幸json文件和图片文件不在一个文件夹，是分为两个文件夹，这里还有一个小坑，不过问题不严重，你可以看到上图的count=1，这里导致生成的train比应该生成的少了一个，应该为count=0

填完上面的两个坑后，会在输出文件夹下得到三个文件夹

Annotation、train、val

这里就是需要训练的素材了,按照自己的实际路径修改configs/yolov3_mobilenet_v1_coco.yml的配置，其中分类数量也需要改为自己的实际分类数量，然后开始训练

python3 tools/train.py -c configs/yolov3_mobilenet_v1_coco.yml --eval -o use_gpu=false

漫长的训练过程…………

image.png

这里其实有几个地方需要注意：
1、batch_size 每次训练的批次大小，和内存相关，内存大的可以调整大一点，但是需要控制内存使用率不能超过80%，否则更加慢，我自己的是设置batch_size: 2，
2、worker_num，和自己的cpu核数有关，略小于自己的核数比较合适，我的是i5是6C，我这里设置worker_num: 4，否则容易cpu跑满，夯住
这里附上我自己修改后的x2coco

#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import glob
import json
import os
import os.path as osp
import sys
import shutil
import xml.etree.ElementTree as ET
from tqdm import tqdm
import re

import numpy as np
import PIL.ImageDraw

label_to_num = {}
categories_list = []
labels_list = []


class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return super(MyEncoder, self).default(obj)


def getbbox(self, points):
    polygons = points
    mask = self.polygons_to_mask([self.height, self.width], polygons)
    return self.mask2box(mask)


def images_labelme(data, num):
    image = {}
    image['height'] = data['imageHeight']
    image['width'] = data['imageWidth']
    image['id'] = num + 1
    image['file_name'] = data['imagePath'].split('/')[-1]
    return image


def images_cityscape(data, num, img_file):
    image = {}
    image['height'] = data['imgHeight']
    image['width'] = data['imgWidth']
    image['id'] = num + 1
    image['file_name'] = img_file
    return image


def categories(label, labels_list):
    category = {}
    category['supercategory'] = 'component'
    category['id'] = len(labels_list) + 1
    category['name'] = label
    return category


def annotations_rectangle(points, label, image_num, object_num, label_to_num):
    annotation = {}
    seg_points = np.asarray(points).copy()
    seg_points[1, :] = np.asarray(points)[2, :]
    seg_points[2, :] = np.asarray(points)[1, :]
    annotation['segmentation'] = [list(seg_points.flatten())]
    annotation['iscrowd'] = 0
    annotation['image_id'] = image_num + 1
    annotation['bbox'] = list(
        map(float, [
            points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
                1] - points[0][1]
        ]))
    annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
    annotation['category_id'] = label_to_num[label]
    annotation['id'] = object_num + 1
    return annotation


def annotations_polygon(height, width, points, label, image_num, object_num,
                        label_to_num):
    annotation = {}
    annotation['segmentation'] = [list(np.asarray(points).flatten())]
    annotation['iscrowd'] = 0
    annotation['image_id'] = image_num + 1
    annotation['bbox'] = list(map(float, get_bbox(height, width, points)))
    annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
    annotation['category_id'] = label_to_num[label]
    annotation['id'] = object_num + 1
    return annotation


def get_bbox(height, width, points):
    polygons = points
    mask = np.zeros([height, width], dtype=np.uint8)
    mask = PIL.Image.fromarray(mask)
    xy = list(map(tuple, polygons))
    PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
    mask = np.array(mask, dtype=bool)
    index = np.argwhere(mask == 1)
    rows = index[:, 0]
    clos = index[:, 1]
    left_top_r = np.min(rows)
    left_top_c = np.min(clos)
    right_bottom_r = np.max(rows)
    right_bottom_c = np.max(clos)
    return [
        left_top_c, left_top_r, right_bottom_c - left_top_c,
        right_bottom_r - left_top_r
    ]


def deal_json(ds_type, img_path, json_path):
    data_coco = {}
    images_list = []
    annotations_list = []
    image_num = -1
    object_num = -1
    for img_file in get_filelist_from_dir(img_path,support_ext=".jpg|.jpeg|.png"):
        img_label = os.path.splitext(img_file)[0]
        if img_file.split('.')[
                -1] not in ['bmp', 'jpg', 'jpeg', 'png', 'JPEG', 'JPG', 'PNG']:
            continue
        label_file = osp.join(json_path, img_label + '.json')
        print('Generating dataset from:', label_file)
        image_num = image_num + 1
        with open(label_file) as f:
            data = json.load(f)
            if ds_type == 'labelme':
                images_list.append(images_labelme(data, image_num))
            elif ds_type == 'cityscape':
                images_list.append(images_cityscape(data, image_num, img_file))
            if ds_type == 'labelme':
                for shapes in data['shapes']:
                    object_num = object_num + 1
                    label = shapes['label']
                    if label not in labels_list:
                        categories_list.append(categories(label, labels_list))
                        labels_list.append(label)
                        label_to_num[label] = len(labels_list)
                    p_type = shapes['shape_type']
                    if p_type == 'polygon':
                        points = shapes['points']
                        annotations_list.append(
                            annotations_polygon(data['imageHeight'], data[
                                'imageWidth'], points, label, image_num,
                                                object_num, label_to_num))

                    if p_type == 'rectangle':
                        (x1, y1), (x2, y2) = shapes['points']
                        x1, x2 = sorted([x1, x2])
                        y1, y2 = sorted([y1, y2])
                        points = [[x1, y1], [x2, y2], [x1, y2], [x2, y1]]
                        annotations_list.append(
                            annotations_rectangle(points, label, image_num,
                                                  object_num, label_to_num))
            elif ds_type == 'cityscape':
                for shapes in data['objects']:
                    object_num = object_num + 1
                    label = shapes['label']
                    if label not in labels_list:
                        categories_list.append(categories(label, labels_list))
                        labels_list.append(label)
                        label_to_num[label] = len(labels_list)
                    points = shapes['polygon']
                    annotations_list.append(
                        annotations_polygon(data['imgHeight'], data[
                            'imgWidth'], points, label, image_num, object_num,
                                            label_to_num))
    data_coco['images'] = images_list
    data_coco['categories'] = categories_list
    data_coco['annotations'] = annotations_list
    return data_coco


def voc_get_label_anno(ann_dir_path, ann_ids_path, labels_path):
    with open(labels_path, 'r') as f:
        labels_str = f.read().split()
    labels_ids = list(range(1, len(labels_str) + 1))

    with open(ann_ids_path, 'r') as f:
        ann_ids = f.read().split()
    ann_paths = []
    for aid in ann_ids:
        if aid.endswith('xml'):
            ann_path = os.path.join(ann_dir_path, aid)
        else:
            ann_path = os.path.join(ann_dir_path, aid + '.xml')
        ann_paths.append(ann_path)

    return dict(zip(labels_str, labels_ids)), ann_paths


def voc_get_image_info(annotation_root, im_id):
    filename = annotation_root.findtext('filename')
    assert filename is not None
    img_name = os.path.basename(filename)

    size = annotation_root.find('size')
    width = float(size.findtext('width'))
    height = float(size.findtext('height'))

    image_info = {
        'file_name': filename,
        'height': height,
        'width': width,
        'id': im_id
    }
    return image_info


def voc_get_coco_annotation(obj, label2id):
    label = obj.findtext('name')
    assert label in label2id, "label is not in label2id."
    category_id = label2id[label]
    bndbox = obj.find('bndbox')
    xmin = float(bndbox.findtext('xmin')) - 1
    ymin = float(bndbox.findtext('ymin')) - 1
    xmax = float(bndbox.findtext('xmax'))
    ymax = float(bndbox.findtext('ymax'))
    assert xmax > xmin and ymax > ymin, "Box size error."
    o_width = xmax - xmin
    o_height = ymax - ymin
    anno = {
        'area': o_width * o_height,
        'iscrowd': 0,
        'bbox': [xmin, ymin, o_width, o_height],
        'category_id': category_id,
        'ignore': 0,
    }
    return anno


def voc_xmls_to_cocojson(annotation_paths, label2id, output_dir, output_file):
    output_json_dict = {
        "images": [],
        "type": "instances",
        "annotations": [],
        "categories": []
    }
    bnd_id = 1  # bounding box start id
    im_id = 0
    print('Start converting !')
    for a_path in tqdm(annotation_paths):
        # Read annotation xml
        ann_tree = ET.parse(a_path)
        ann_root = ann_tree.getroot()

        img_info = voc_get_image_info(ann_root, im_id)
        im_id += 1
        img_id = img_info['id']
        output_json_dict['images'].append(img_info)

        for obj in ann_root.findall('object'):
            ann = voc_get_coco_annotation(obj=obj, label2id=label2id)
            ann.update({'image_id': img_id, 'id': bnd_id})
            output_json_dict['annotations'].append(ann)
            bnd_id = bnd_id + 1

    for label, label_id in label2id.items():
        category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
        output_json_dict['categories'].append(category_info)
    output_file = os.path.join(output_dir, output_file)
    with open(output_file, 'w') as f:
        output_json = json.dumps(output_json_dict)
        f.write(output_json)

def get_filelist_from_dir(dir, support_ext=".jpg|.jpeg|.png"):
    '''
    获取指定目录下的指定后缀文件名，返回列表，不带路径
    '''
    if (not os.path.exists(dir) or not os.path.isdir(dir)):
        raise Exception("Image Directory [%s] invalid" % dir)
    files = []
    for item in os.listdir(dir):
        ext = os.path.splitext(item)[1][1:].strip().lower()
        if (len(ext) > 0 and ext in support_ext):        
            files.append(item)
    return files


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--dataset_type', help='the type of dataset')
    parser.add_argument('--json_input_dir', help='input annotated directory')
    parser.add_argument('--image_input_dir', help='image directory')
    parser.add_argument(
        '--output_dir', help='output dataset directory', default='./')
    parser.add_argument(
        '--train_proportion',
        help='the proportion of train dataset',
        type=float,
        default=1.0)
    parser.add_argument(
        '--val_proportion',
        help='the proportion of validation dataset',
        type=float,
        default=0.0)
    parser.add_argument(
        '--test_proportion',
        help='the proportion of test dataset',
        type=float,
        default=0.0)
    parser.add_argument(
        '--voc_anno_dir',
        help='In Voc format dataset, path to annotation files directory.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_anno_list',
        help='In Voc format dataset, path to annotation files ids list.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_label_list',
        help='In Voc format dataset, path to label list. The content of each line is a category.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_out_name',
        type=str,
        default='voc.json',
        help='In Voc format dataset, path to output json file')
    args = parser.parse_args()
    try:
        assert args.dataset_type in ['voc', 'labelme', 'cityscape']
    except AssertionError as e:
        print(
            '当前只支持 voc, cityscape dataset and labelme 的数据格式转换!!')
        os._exit(0)

    if args.dataset_type == 'voc':
        assert args.voc_anno_dir and args.voc_anno_list and args.voc_label_list
        label2id, ann_paths = voc_get_label_anno(
            args.voc_anno_dir, args.voc_anno_list, args.voc_label_list)
        voc_xmls_to_cocojson(
            annotation_paths=ann_paths,
            label2id=label2id,
            output_dir=args.output_dir,
            output_file=args.voc_out_name)
    else:
        try:
            assert os.path.exists(args.json_input_dir)
        except AssertionError as e:
            print('The json folder does not exist!')
            os._exit(0)
        try:
            assert os.path.exists(args.image_input_dir)
        except AssertionError as e:
            print('The image folder does not exist!')
            os._exit(0)
        try:
            assert abs(args.train_proportion + args.val_proportion \
                    + args.test_proportion - 1.0) < 1e-5
        except AssertionError as e:
            print(
                'The sum of pqoportion of training, validation and test datase must be 1!'
            )
            os._exit(0)

        # Allocate the dataset.
        total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
        if args.train_proportion != 0:
            train_num = int(total_num * args.train_proportion)
            # 增加判断，判断输出的文件夹是否存在
            exist=os.path.exists(args.output_dir)
            if exist:
                print('目标文件夹已经存在，正在删除!')
                shutil.rmtree(args.output_dir)
            os.makedirs(args.output_dir + '/train')
        else:
            train_num = 0
        if args.val_proportion == 0.0:
            val_num = 0
            test_num = total_num - train_num
            if args.test_proportion != 0.0:
                os.makedirs(args.output_dir + '/test')
        else:
            val_num = int(total_num * args.val_proportion)
            test_num = total_num - train_num - val_num
            os.makedirs(args.output_dir + '/val')
            if args.test_proportion != 0.0:
                os.makedirs(args.output_dir + '/test')
        count = 0
        # 这里有bug，没有过滤文件夹下的文件类型，如果json文件和图片文件在一个文件夹下会导致训练样本和验证样本不准确
        for img_name in get_filelist_from_dir(args.image_input_dir,support_ext=".jpg|.jpeg|.png"):
            jsonfilename=os.path.basename(img_name).rsplit('.',1)[0]+'.json'
            if count <= train_num:
                if osp.exists(args.output_dir + '/train/'):
                    shutil.copyfile(
                        osp.join(args.image_input_dir, img_name),
                        osp.join(args.output_dir + '/train/', img_name))
                    shutil.copyfile(
                        osp.join(args.json_input_dir, jsonfilename),
                        osp.join(args.output_dir + '/train/', jsonfilename))    
            else:
                if count <= train_num + val_num:
                    if osp.exists(args.output_dir + '/val/'):
                        shutil.copyfile(
                            osp.join(args.image_input_dir, img_name),
                            osp.join(args.output_dir + '/val/', img_name))
                        shutil.copyfile(
                            osp.join(args.json_input_dir, jsonfilename),
                            osp.join(args.output_dir + '/val/', jsonfilename)) 
                else:
                    if osp.exists(args.output_dir + '/test/'):
                        shutil.copyfile(
                            osp.join(args.image_input_dir, img_name),
                            osp.join(args.output_dir + '/test/', img_name))
                        shutil.copyfile(
                            osp.join(args.json_input_dir, jsonfilename),
                            osp.join(args.output_dir + '/test/', jsonfilename)) 
            count = count + 1

        # Deal with the json files.
        if not os.path.exists(args.output_dir + '/annotations'):
            os.makedirs(args.output_dir + '/annotations')
        if args.train_proportion != 0:
            train_data_coco = deal_json(args.dataset_type,
                                        args.output_dir + '/train',
                                        args.json_input_dir)
            train_json_path = osp.join(args.output_dir + '/annotations',
                                       'train.json')
            json.dump(
                train_data_coco,
                open(train_json_path, 'w'),
                indent=4,
                cls=MyEncoder)
        if args.val_proportion != 0:
            val_data_coco = deal_json(args.dataset_type,
                                      args.output_dir + '/val',
                                      args.json_input_dir)
            val_json_path = osp.join(args.output_dir + '/annotations',
                                     'val.json')
            json.dump(
                val_data_coco,
                open(val_json_path, 'w'),
                indent=4,
                cls=MyEncoder)
        if args.test_proportion != 0:
            test_data_coco = deal_json(args.dataset_type,
                                       args.output_dir + '/test',
                                       args.json_input_dir)
            test_json_path = osp.join(args.output_dir + '/annotations',
                                      'test.json')
            json.dump(
                test_data_coco,
                open(test_json_path, 'w'),
                indent=4,
                cls=MyEncoder)


if __name__ == '__main__':
    main()