Openpose的整体网络结构采用VGG网络作为骨架进行预训练处理。该网络分为两个部分,两部分能同时对提取到的关键点进行预测置信图、编码相邻关键点间的关联向量场并分别回归S和L。 图中上半部分即第一分支用以预测置信图,下半部分即第二分支用以预测关联向量场。每回归一次S和L即为完成一轮迭代预测,通过连续的t∈(1...
2.1 Openpose网络Openpose的整体网络结构采用VGG网络作为骨架进行预训练处理。该网络分为两个部分,两部分能同时对提取到的关键点进行预测置信图、编码相邻关键点间的关联向量场并分别回归S和L。图中上半部分即第一分支用以预测置信图,下半部分即第二分支用以预测关联向量场。每回归一次S和L即为完成一轮迭代预测,通过...
print('--> Building model') # do_quantization=True进行量化 # 量化会减小模型的体积和提升运算速度,但是会有精度的丢失 # 开启预编译,提高load速度 ret = rknn.build(do_quantization=True, dataset='./dataset.txt', pre_compile=True) if ret != 0: print('Build model failed!') exit(ret...
Download the openpose VGG tf-model with command line./download.sh(/Pose/graph_models/VGG_origin) or forkhere, and place it under the corresponding folder; python main.py, it willstart the webcam. (you can choose to test video with commandpython main.py --video=test.mp4, however I just...
Cloning into 'tf-openpose'... remote: Counting objects: 1238, done. remote: Total 1238 (delta 0), reused 0 (delta 0), pack-reused 1238 Receiving objects: 100% (1238/1238), 84.36 MiB | 5.52 MiB/s, done. Resolving deltas: 100% (650/650), done. ...
train.pyautomatically download MSCOCO 2017 dataset intodataset/coco17. The default model is VGG19 used in the OpenPose paper. To customize the model, simply changing it inmodels.py. You can usetrain_config.pyto configure the training.config.DATA.train_datacan be: ...
# 关键点连接数量×2 self.trainable = trainable self.PAF = [] self.CPM = [] self.__build_network(input_data) def __build_network(self, input_data): with tf.variable_scope("MODEL"): with tf.variable_scope("base"): # 原文中选择使用Vgg19前10层输出feature map,在此使用相同的网络结构...
train.pyautomatically download MSCOCO 2017 dataset intodataset/coco17. The default model is VGG19 used in the OpenPose paper. To customize the model, simply changing it inmodels.py. You can usetrain_config.pyto configure the training.config.DATA.train_datacan be: ...