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是怎么去识别跌倒呢
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是否跌倒，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），本文的做法是第二种方式
五. LightWeight的演示效果（具有图片和摄像头识别功能）
演示视频：基于深度学习LightWeight的人体姿态检测跌倒系统源码_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
项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷
https://www.bilibili.com/video/BV1CBz5YDEgU/
