一. LightWeight概述

????????light weight openpose是openpose的簡化版本，使用了openpose的大體流程。

????????Light weight openpose和openpose的區別是：

????????a 前者使用的是Mobilenet V1（到conv5_5），后者使用的是Vgg19（前10層）。

????????b 前者部分層使用了空洞卷積（dilated convolution）來提升感受視野，后者使用一般的卷積。

????????c 前者卷積核大小為3*3，后者為7*7。

????????d 前者只有一個refine stage，后者有5個stage。

????????e 前者的initial stage和refine stage里面的兩個分支（hotmaps和pafs）使用權值共享，后者則是并行的兩個分支

二. LightWeight的網絡結構

????????openpose的每個stage使用下圖中左側的兩個并行的分支，分別預測hotmaps和pafs，為了進一步降低計算量，light weight openpose中將前幾層進行權值共享，如下圖右側所示。

????????其網絡流程：

三. LightWeight的網絡結構代碼

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是怎么去識別跌倒呢

????????LightWeight可以檢測到人體的關鍵點，所以可以通過兩種方式來判斷是否跌倒，第一種方法是通過計算角度，第二種方式，是通過判斷整體的關鍵點（把摳出的關鍵點圖送入到分類網絡），本文的做法是第二種方式

五. LightWeight的演示效果（具有圖片和攝像頭識別功能）

六. 整個工程的內容

提供源代碼，模型，提供GUI界面代碼

代碼的下載路徑（新窗口打開鏈接）：基于深度學習LightWeight的人體姿態檢測跌倒系統源碼

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