利用openpose提取自建数据集骨骼点训练st-gcn，复现st-gcn-Toy模板网

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利用openpose提取自建数据集骨骼点训练st-gcn，复现st-gcn

0、下载st-gcn

参考：
gitbub上fork后导入到gitee快些: st-gcn下载
也可以直接下载zip文件后解压

1、处理准备自己数据集

数据集要求将相同类别的视频放到同一文件夹,我这里用到一个较老的数据集：training_lib_KTH.zip，六种行为放到六个不同文件夹。

用于st-gcn训练的数据集视频帧数不要超过300帧，5~6s的视频时长比较好，不要10几s的视频时长。要不然会报index 300 is out of bounds for axis 1 with size 300这种错误。因此对上面数据集进一步裁剪为6s的大概150帧（此视频帧率为25后面利用FFmpeg再次改变帧率为30时，时长会变长到8s）。裁剪后视频文件如下

原始数据集和裁剪为6s的数据集放在链接：
链接：https://pan.baidu.com/s/1oHQyo-c3e5YXb52b-O0STQ?pwd=x166
提取码：x166

2、准备环境，搭建openpose环境

搭建openpose环境是为了利用open pose提取knetics-skeleton视频的骨骼点，openpose环境的搭建可参考以下视频：
openpose环境搭建
以及参考博客：windows 10下，自编译openpose代码
此处注意如果用cuda，最好先下载vs后再下载cuda

3、利用openpose提取自己视频骨骼数据

st-gcn作者有提供他们整理好并使用的kinetics-skeleton数据集，他们数据集格式如下图所示：
openpose训练自己的数据集,深度学习,人工智能,计算机视觉
接下来参考：用自建kinetics-skeleton行为识别数据集训练st-gcn网络流程记录中的2部分对视频数据进行resize至340x256的大小，30fps的帧率。然后调用openpose的进行骨骼点数据的检测和输出。

#!/usr/bin/env python
# coding:gbk
import os
import argparse
import json
import shutil

import numpy as np
import torch
import skvideo.io

#from processor.io import IO
import tools
import tools.utils as utils


# if __name__ == '__main__':
#class PreProcess(IO):
class PreProcess():
    def start(self):

        work_dir = 'D:/st-gcn'
        type_number = 6
        action_filename_list = ['boxing', 'handclapping', 'handwaving', 'jogging', 'running', 'walking']


        for process_index in range(type_number):

            action_filename = action_filename_list[process_index]
            # 标签信息
            labelAction_name = '{}_{}'.format(action_filename,process_index)
            #labelAction_name = 'xxx_{}'.format(process_index)
            label_no = process_index

            # 视频所在文件夹
            originvideo_file = 'D:/dataSet/training_lib_KTH_cut_6s/{}/'.format(action_filename)
            # resized视频输出文件夹 需要自己创建几个动作的文件夹
            resizedvideo_file = './mydata/training_lib_KTH_cut_6s/resized/{}/'.format(action_filename)

            videos_file_names = os.listdir(originvideo_file)

            # 1. Resize文件夹下的视频到340x256 30fps
            for file_name in videos_file_names:
                video_path = '{}{}'.format(originvideo_file, file_name)
                outvideo_path = '{}{}'.format(resizedvideo_file, file_name)
                writer = skvideo.io.FFmpegWriter(outvideo_path,
                                                 outputdict={'-f': 'mp4','-vcodec': 'libx264', '-s': '340x256',
                                                             '-r': '30'})
                reader = skvideo.io.FFmpegReader(video_path)
                for frame in reader.nextFrame():
                    writer.writeFrame(frame)
                writer.close()
                print('{} resize success'.format(file_name))

            # 2. 利用openpose提取每段视频骨骼点数据
            resizedvideos_file_names = os.listdir(resizedvideo_file)
            for file_name in resizedvideos_file_names:
                outvideo_path = '{}{}'.format(resizedvideo_file, file_name)

                # openpose = '{}/examples/openpose/openpose.bin'.format(self.arg.openpose)
                #openpose = '{}/OpenPoseDemo.exe'.format(self.arg.openpose)
                openpose = 'D:/openpose-master/build/x64/Release/OpenPoseDemo.exe'

                video_name = file_name.split('.')[0]
                output_snippets_dir = './mydata/training_lib_KTH_cut_6s/resized/snippets/{}'.format(video_name)
                output_sequence_dir = './mydata/training_lib_KTH_cut_6s/resized/data'
                output_sequence_path = '{}/{}.json'.format(output_sequence_dir, video_name)

                #label_name_path = '{}/resource/kinetics_skeleton/label_name_action.txt'.format(work_dir)
				#自己创建好标签文档，里面文档内写好动作名称
                label_name_path = '{}/resource/kinetics_skeleton/label_name_action{}.txt'.format(work_dir,process_index)
                with open(label_name_path) as f:
                    label_name = f.readlines()
                    label_name = [line.rstrip() for line in label_name]

                # pose estimation
                openpose_args = dict(
                    video=outvideo_path,
                    write_json=output_snippets_dir,
                    display=0,
                    render_pose=0,
                    model_pose='COCO')
                command_line = openpose + ' '
                command_line += ' '.join(['--{} {}'.format(k, v) for k, v in openpose_args.items()])
                shutil.rmtree(output_snippets_dir, ignore_errors=True)
                os.makedirs(output_snippets_dir)
                os.system(command_line)

                # pack openpose ouputs
                video = utils.video.get_video_frames(outvideo_path)

                height, width, _ = video[0].shape

                # 这里可以修改label, label_index
                video_info = utils.openpose.json_pack(
                    output_snippets_dir, video_name, width, height, labelAction_name, label_no)

                if not os.path.exists(output_sequence_dir):
                    os.makedirs(output_sequence_dir)

                with open(output_sequence_path, 'w') as outfile:
                    json.dump(video_info, outfile)
                if len(video_info['data']) == 0:
                    print('{} Can not find pose estimation results.'.format(file_name))
                    return
                else:
                    print('{} pose estimation complete.'.format(file_name))

if __name__ == '__main__':
    p=PreProcess()
    p.start()

运行代码之前提前再st-gcn里面建好/mydata/training_lib_KTH_cut_6s_resized文件夹，并在文件夹中将六种行为的文件夹也创建好，‘boxing’, ‘handclapping’, ‘handwaving’, ‘jogging’, ‘running’, ‘walking’。运行之前也需要创建标签文档0到5：
openpose训练自己的数据集,深度学习,人工智能,计算机视觉
每个文档里面是100个动作标签
运行完如下所示：

此时打开data能看到所有视频的骨骼数据，snippets里面每个json文件存的是单帧骨骼数据，data里面每个json文件都是一个视频的所有骨骼点数据。data里面json文件打开如下图所示：
openpose训练自己的数据集,深度学习,人工智能,计算机视觉

4、整理骨骼点数据，生成st-gcn运行的格式

首先将data里面的数据分成训练集、验证集、测试集按照6：2：2划分.
openpose训练自己的数据集,深度学习,人工智能,计算机视觉
将01到15复制kinetics_train，16-20放到val，21-25放到test。

import json
import os

if __name__ == '__main__':
    train_json_path = './mydata/kinetics-skeleton/kinetics_train'
    val_json_path = './mydata/kinetics-skeleton/kinetics_val'
    test_json_path = './mydata/kinetics-skeleton/kinetics_test'

    output_train_json_path = './mydata/kinetics-skeleton/kinetics_train_label.json'
    output_val_json_path = './mydata/kinetics-skeleton/kinetics_val_label.json'
    output_test_json_path = './mydata/kinetics-skeleton/kinetics_test_label.json'
    #
    train_json_names = os.listdir(train_json_path)
    val_json_names = os.listdir(val_json_path)
    test_json_names = os.listdir(test_json_path)

    train_label_json = dict()
    val_label_json = dict()
    test_label_json = dict()


    for file_name in train_json_names:
        name = file_name.split('.')[0]
        json_file_path = '{}/{}'.format(train_json_path, file_name)
        json_file = json.load(open(json_file_path))

        file_label = dict()
        if len(json_file['data']) == 0:
            file_label['has_skeleton'] = False
        else:
            file_label['has_skeleton'] = True
        file_label['label'] = json_file['label']
        file_label['label_index'] = json_file['label_index']

        train_label_json['{}'.format(name)] = file_label

        print('{} success'.format(file_name))

    with open(output_train_json_path, 'w') as outfile:
        json.dump(train_label_json, outfile)

    for file_name in val_json_names:
        name = file_name.split('.')[0]
        json_file_path = '{}/{}'.format(val_json_path, file_name)
        json_file = json.load(open(json_file_path))

        file_label = dict()
        if len(json_file['data']) == 0:
            file_label['has_skeleton'] = False
        else:
            file_label['has_skeleton'] = True
        file_label['label'] = json_file['label']
        file_label['label_index'] = json_file['label_index']

        val_label_json['{}'.format(name)] = file_label

        print('{} success'.format(file_name))

    with open(output_val_json_path, 'w') as outfile:
        json.dump(val_label_json, outfile)

    for file_name in test_json_names:
        name = file_name.split('.')[0]
        json_file_path = '{}/{}'.format(test_json_path, file_name)
        json_file = json.load(open(json_file_path))

        file_label = dict()
        if len(json_file['data']) == 0:
            file_label['has_skeleton'] = False
        else:
            file_label['has_skeleton'] = True
        file_label['label'] = json_file['label']
        file_label['label_index'] = json_file['label_index']

        test_label_json['{}'.format(name)] = file_label

        print('{} success'.format(file_name))

        with open(output_test_json_path, 'w') as outfile:
            json.dump(test_label_json, outfile)

生成如下：
openpose训练自己的数据集,深度学习,人工智能,计算机视觉
再利用stgcn训练代码中自带了数据转换代码tools/kinetics_gendata.py,使用该脚本将kinetics-skleton数据集转换为训练使用的npy与pkl文件。
这里参考博客：数据转换中的三数据转换。以下地方需要修改：

        num_person_in=1,  #observe the first 5 persons
        num_person_out=1,  #then choose 2 persons with the highest score

    part = ['train', 'val','test']

frame不用修改即可前面视频已经裁剪过
运行脚本后：
openpose训练自己的数据集,深度学习,人工智能,计算机视觉

5、训练st-gcn网络

这部分参考st-gcn训练自建行为识别数据集中的5、6部分即可
我的train.yaml是这样修改的：

work_dir: ./work_dir/recognition/kinetics_skeleton/ST_GCN

# feeder
feeder: feeder.feeder.Feeder
train_feeder_args:
  random_choose: True
  random_move: True
  window_size: 150 
#  data_path: ./data/Kinetics/kinetics-skeleton/train_data.npy
#  label_path: ./data/Kinetics/kinetics-skeleton/train_label.pkl
  data_path: ./mydata/kinetics-skeleton/train_data.npy
  label_path: ./mydata/kinetics-skeleton/train_label.pkl

test_feeder_args:
#  data_path: ./data/Kinetics/kinetics-skeleton/val_data.npy
#  label_path: ./data/Kinetics/kinetics-skeleton/val_label.pkl
  data_path: ./mydata/kinetics-skeleton/val_data.npy
  label_path: ./mydata/kinetics-skeleton/val_label.pkl

# model
model: net.st_gcn.Model
model_args:
  in_channels: 3
  num_class: 6
  edge_importance_weighting: True
  graph_args:
    layout: 'openpose'
    strategy: 'spatial'

# training
#device: [0,1,2,3]
device: [0]
batch_size: 32
test_batch_size: 32

#optim
base_lr: 0.1
step: [20, 30, 40, 50]
num_epoch: 50

执行训练代码：

python main.py recognition -c config/st_gcn/kinetics-skeleton/train.yaml

6、测试

可修改test.yaml利用刚刚自己的测试集进行测试

weights: ./work_dir/recognition/kinetics_skeleton/ST_GCN/epoch50_model.pt
#weights: ./models/st_gcn.kinetics.pt

# feeder
feeder: feeder.feeder.Feeder
test_feeder_args:
#  data_path: ./data/Kinetics/kinetics-skeleton/val_data.npy
#  label_path: ./data/Kinetics/kinetics-skeleton/val_label.pkl

#  data_path: ./mydata/kinetics-skeleton/val_data.npy
#  label_path: ./mydata/kinetics-skeleton/val_label.pkl
  data_path: ./mydata/kinetics-skeleton/test_data.npy
  label_path: ./mydata/kinetics-skeleton/test_label.pkl

# model
model: net.st_gcn.Model
model_args:
  in_channels: 3
  num_class: 6
  edge_importance_weighting: True
  graph_args:
    layout: 'openpose'
    strategy: 'spatial'

# test 
phase: test
device: 0
test_batch_size: 32

然后执行

python main.py recognition -c config/st_gcn/kinetics-skeleton/test.yaml

注：作者也是一位初学者，此文章供参考讨论，有什么问题欢迎讨论多多指教！文章来源地址https://www.toymoban.com/news/detail-797857.html

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利用openpose提取自建数据集骨骼点训练st-gcn，复现st-gcn