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：

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：

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：

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.

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：

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：

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！