一. LightWeight概述
light weight openpose是openpose的简化版本，使用了openpose的大体流程。
Light weight openpose和openpose的区别是：
a 前者使用的是Mobilenet V1（到conv5_5），后者使用的是Vgg19（前10层）。
b 前者部分层使用了空洞卷积（dilated convolution）来提升感受视野，后者使用一般的卷积。
c 前者卷积核大小为3*3，后者为7*7。
d 前者只有一个refine stage，后者有5个stage。
e 前者的initial stage和refine stage里面的两个分支（hotmaps和pafs）使用权值共享，后者则是并行的两个分支
二. LightWeight的网络结构
openpose的每个stage使用下图中左侧的两个并行的分支，分别预测hotmaps和pafs，为了进一步降低计算量，light weight openpose中将前几层进行权值共享，如下图右侧所示。
其网络流程：
三. LightWeight的网络结构代码
import torch
from torch import nn
from modules.conv import conv, conv_dw, conv_dw_no_bn
class Cpm(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.align = conv(in_channels, out_channels, kernel_size=1, padding=0, bn=False)
self.trunk = nn.Sequential(
conv_dw_no_bn(out_channels, out_channels),
conv_dw_no_bn(out_channels, out_channels),
conv_dw_no_bn(out_channels, out_channels)
)
self.conv = conv(out_channels, out_channels, bn=False)
def forward(self, x):
x = self.align(x)
x = self.conv(x + self.trunk(x))
return x
class InitialStage(nn.Module):
def __init__(self, num_channels, num_heatmaps, num_pafs):
super().__init__()
self.trunk = nn.Sequential(
conv(num_channels, num_channels, bn=False),
conv(num_channels, num_channels, bn=False),
conv(num_channels, num_channels, bn=False)
)
self.heatmaps = nn.Sequential(
conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
conv(512, num_heatmaps, kernel_size=1, padding=0, bn=False, relu=False)
)
self.pafs = nn.Sequential(
conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
conv(512, num_pafs, kernel_size=1, padding=0, bn=False, relu=False)
)
def forward(self, x):
trunk_features = self.trunk(x)
heatmaps = self.heatmaps(trunk_features)
pafs = self.pafs(trunk_features)
return [heatmaps, pafs]
class RefinementStageBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.initial = conv(in_channels, out_channels, kernel_size=1, padding=0, bn=False)
self.trunk = nn.Sequential(
conv(out_channels, out_channels),
conv(out_channels, out_channels, dilation=2, padding=2)
)
def forward(self, x):
initial_features = self.initial(x)
trunk_features = self.trunk(initial_features)
return initial_features + trunk_features
class RefinementStage(nn.Module):
def __init__(self, in_channels, out_channels, num_heatmaps, num_pafs):
super().__init__()
self.trunk = nn.Sequential(
RefinementStageBlock(in_channels, out_channels),
RefinementStageBlock(out_channels, out_channels),
RefinementStageBlock(out_channels, out_channels),
RefinementStageBlock(out_channels, out_channels),
RefinementStageBlock(out_channels, out_channels)
)
self.heatmaps = nn.Sequential(
conv(out_channels, out_channels, kernel_size=1, padding=0, bn=False),
conv(out_channels, num_heatmaps, kernel_size=1, padding=0, bn=False, relu=False)
)
self.pafs = nn.Sequential(
conv(out_channels, out_channels, kernel_size=1, padding=0, bn=False),
conv(out_channels, num_pafs, kernel_size=1, padding=0, bn=False, relu=False)
)
def forward(self, x):
trunk_features = self.trunk(x)
heatmaps = self.heatmaps(trunk_features)
pafs = self.pafs(trunk_features)
return [heatmaps, pafs]
class PoseEstimationWithMobileNet(nn.Module):
def __init__(self, num_refinement_stages=1, num_channels=128, num_heatmaps=19, num_pafs=38):
super().__init__()
self.model = nn.Sequential(
conv( 3, 32, stride=2, bias=False),
conv_dw( 32, 64),
conv_dw( 64, 128, stride=2),
conv_dw(128, 128),
conv_dw(128, 256, stride=2),
conv_dw(256, 256),
conv_dw(256, 512), # conv4_2
conv_dw(512, 512, dilation=2, padding=2),
conv_dw(512, 512),
conv_dw(512, 512),
conv_dw(512, 512),
conv_dw(512, 512) # conv5_5
)
self.cpm = Cpm(512, num_channels)
self.initial_stage = InitialStage(num_channels, num_heatmaps, num_pafs)
self.refinement_stages = nn.ModuleList()
for idx in range(num_refinement_stages):
self.refinement_stages.append(RefinementStage(num_channels + num_heatmaps + num_pafs, num_channels,
num_heatmaps, num_pafs))
def forward(self, x):
backbone_features = self.model(x)
backbone_features = self.cpm(backbone_features)
stages_output = self.initial_stage(backbone_features)
for refinement_stage in self.refinement_stages:
stages_output.extend(
refinement_stage(torch.cat([backbone_features, stages_output[-2], stages_output[-1]], dim=1)))
return stages_output
四. LightWeight是怎么去识别行为呢
LightWeight可以检测到人体的关键点，所以可以通过两种方式来判断行为，第一种方法是通过计算角度，第二种方式，是通过判断整体的关键点（把抠出的关键点图送入到分类网络），本文的做法是第一种方式
# 计算姿态
def get_pos(keypoints):
str_pose = ""
# 计算左臂与水平方向的夹角
keypoints = np.array(keypoints)
v1 = keypoints[1] - keypoints[0]
v2 = keypoints[2] - keypoints[0]
angle_left_arm = get_angle(v1, v2)
#计算右臂与水平方向的夹角
v1 = keypoints[0] - keypoints[1]
v2 = keypoints[3] - keypoints[1]
angle_right_arm = get_angle(v1, v2)
if angle_left_arm>0 and angle_right_arm>0:
str_pose = "LEFT_UP"
elif angle_left_arm<0 and angle_right_arm<0:
str_pose = "RIGHT_UP"
elif angle_left_arm>0 and angle_right_arm<0:
str_pose = "ALL_HANDS_UP"
elif angle_left_arm>0 and angle_right_arm<0:
str_pose = "NORMAL"
return str_pose五. LightWeight的演示效果
六. 整个工程的内容
提供源代码，模型，提供GUI界面代码
代码的下载路径（新窗口打开链接）：基于深度学习LightWeight的人体姿态之行为识别系统源码
有问题可以私信或者留言，有问必答
