在上一章，我們了解了demo，現在開始準備根據自己的場景去訓練自己的數據，實現自己的目標檢測。
在前面我們知道機器要識別一個問題，需要一個訓練的過程，所以我們首先第一步是要對物體進行標注。這里就不使用官方的數據集了，先自己準備一個自己需要學習的圖片集合（大概每個種類100張左右）
第一步：建立圖片文件夾

mkdir images

然后把需要學習的圖片放到文件夾里面
第二步：用labelme進行標記（如何安裝，請自行百度）,標注結束后，會在images下新增和圖片一一對應的json文件

image.png

第三步：把labelme格式的文件轉為coco格式（PaddleDetection不支持labelme格式），這里可以使用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

這里存在兩個坑。

image.png

坑一：labelme生成的json和圖片默認是在同一個文件夾下，這里用tools/x2coco.py會導致生成的訓練文件和驗證文件減少一半,主要是由于上面圖中的代碼導致，并且容易出現json和圖片不對應的問題，導致最終訓練結果失敗，我開始就是被這里坑了，以為是自己標注問題，來來回回刪除了好幾次標注由重新標，最后發現不是自己的問題，是被這里的bug坑了

坑二，如果你有幸json文件和圖片文件不在一個文件夾，是分為兩個文件夾，這里還有一個小坑，不過問題不嚴重，你可以看到上圖的count=1，這里導致生成的train比應該生成的少了一個，應該為count=0

填完上面的兩個坑后，會在輸出文件夾下得到三個文件夾

Annotation、train、val

這里就是需要訓練的素材了,按照自己的實際路徑修改configs/yolov3_mobilenet_v1_coco.yml的配置，其中分類數量也需要改為自己的實際分類數量，然后開始訓練

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

漫長的訓練過程…………

image.png

這里其實有幾個地方需要注意：
1、batch_size 每次訓練的批次大小，和內存相關，內存大的可以調整大一點，但是需要控制內存使用率不能超過80%，否則更加慢，我自己的是設置batch_size: 2，
2、worker_num，和自己的cpu核數有關，略小于自己的核數比較合適，我的是i5是6C，我這里設置worker_num: 4，否則容易cpu跑滿，夯住
這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"):
    '''
    獲取指定目錄下的指定后綴文件名，返回列表，不帶路徑
    '''
    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(
            '當前只支持 voc, cityscape dataset and labelme 的數據格式轉換!!')
        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)
            # 增加判斷，判斷輸出的文件夾是否存在
            exist=os.path.exists(args.output_dir)
            if exist:
                print('目標文件夾已經存在，正在刪除!')
                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
        # 這里有bug，沒有過濾文件夾下的文件類型，如果json文件和圖片文件在一個文件夾下會導致訓練樣本和驗證樣本不準確
        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()