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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 nnfrom modules.conv import conv, conv_dw, conv_dw_no_bnclass 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 xclass 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_featuresclass 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_2conv_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

视频演示地址：基于深度学习LightWeight的人体姿态之行为识别系统源码_哔哩哔哩_bilibili

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

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