原文地址：https://www.learnopencv.com/deep-learning-based-human-pose-estimation-using-opencv-cpp-python/
COCO输出格式：
鼻子– 0，脖子– 1，右肩– 2，右肘– 3，右腕– 4，左肩– 5，左肘– 6，左腕– 7，右臀部– 8，右膝盖– 9 ，右脚踝– 10，左髋– 11，左膝– 12，LAnkle – 13，右眼– 14，左眼– 15，右耳– 16，左耳– 17，背景– 18
模型文件：
input: "image" input_dim: 1 input_dim: 3 input_dim: 1 # This value will be defined at runtime input_dim: 1 # This value will be defined at runtime layer { name: "conv1_1" type: "Convolution" bottom: "image" top: "conv1_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1_stage1" type: "Pooling" bottom: "conv1_2" top: "pool1_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1_stage1" top: "conv2_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2_stage1" type: "Pooling" bottom: "conv2_2" top: "pool2_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2_stage1" top: "conv3_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "conv3_4" type: "Convolution" bottom: "conv3_3" top: "conv3_4" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu3_4" type: "ReLU" bottom: "conv3_4" top: "conv3_4" } layer { name: "pool3_stage1" type: "Pooling" bottom: "conv3_4" top: "pool3_stage1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3_stage1" top: "conv4_1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3_CPM" type: "Convolution" bottom: "conv4_2" top: "conv4_3_CPM" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_3_CPM" type: "ReLU" bottom: "conv4_3_CPM" top: "conv4_3_CPM" } layer { name: "conv4_4_CPM" type: "Convolution" bottom: "conv4_3_CPM" top: "conv4_4_CPM" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu4_4_CPM" type: "ReLU" bottom: "conv4_4_CPM" top: "conv4_4_CPM" } layer { name: "conv5_1_CPM_L1" type: "Convolution" bottom: "conv4_4_CPM" top: "conv5_1_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_1_CPM_L1" type: "ReLU" bottom: "conv5_1_CPM_L1" top: "conv5_1_CPM_L1" } layer { name: "conv5_1_CPM_L2" type: "Convolution" bottom: "conv4_4_CPM" top: "conv5_1_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_1_CPM_L2" type: "ReLU" bottom: "conv5_1_CPM_L2" top: "conv5_1_CPM_L2" } layer { name: "conv5_2_CPM_L1" type: "Convolution" bottom: "conv5_1_CPM_L1" top: "conv5_2_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_2_CPM_L1" type: "ReLU" bottom: "conv5_2_CPM_L1" top: "conv5_2_CPM_L1" } layer { name: "conv5_2_CPM_L2" type: "Convolution" bottom: "conv5_1_CPM_L2" top: "conv5_2_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_2_CPM_L2" type: "ReLU" bottom: "conv5_2_CPM_L2" top: "conv5_2_CPM_L2" } layer { name: "conv5_3_CPM_L1" type: "Convolution" bottom: "conv5_2_CPM_L1" top: "conv5_3_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_3_CPM_L1" type: "ReLU" bottom: "conv5_3_CPM_L1" top: "conv5_3_CPM_L1" } layer { name: "conv5_3_CPM_L2" type: "Convolution" bottom: "conv5_2_CPM_L2" top: "conv5_3_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_3_CPM_L2" type: "ReLU" bottom: "conv5_3_CPM_L2" top: "conv5_3_CPM_L2" } layer { name: "conv5_4_CPM_L1" type: "Convolution" bottom: "conv5_3_CPM_L1" top: "conv5_4_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_4_CPM_L1" type: "ReLU" bottom: "conv5_4_CPM_L1" top: "conv5_4_CPM_L1" } layer { name: "conv5_4_CPM_L2" type: "Convolution" bottom: "conv5_3_CPM_L2" top: "conv5_4_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 512 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "relu5_4_CPM_L2" type: "ReLU" bottom: "conv5_4_CPM_L2" top: "conv5_4_CPM_L2" } layer { name: "conv5_5_CPM_L1" type: "Convolution" bottom: "conv5_4_CPM_L1" top: "conv5_5_CPM_L1" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "conv5_5_CPM_L2" type: "Convolution" bottom: "conv5_4_CPM_L2" top: "conv5_5_CPM_L2" param { lr_mult: 1.0 decay_mult: 1 } param { lr_mult: 2.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage2" type: "Concat" bottom: "conv5_5_CPM_L1" bottom: "conv5_5_CPM_L2" bottom: "conv4_4_CPM" top: "concat_stage2" concat_param { axis: 1 } } layer { name: "Mconv1_stage2_L1" type: "Convolution" bottom: "concat_stage2" top: "Mconv1_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage2_L1" type: "ReLU" bottom: "Mconv1_stage2_L1" top: "Mconv1_stage2_L1" } layer { name: "Mconv1_stage2_L2" type: "Convolution" bottom: "concat_stage2" top: "Mconv1_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage2_L2" type: "ReLU" bottom: "Mconv1_stage2_L2" top: "Mconv1_stage2_L2" } layer { name: "Mconv2_stage2_L1" type: "Convolution" bottom: "Mconv1_stage2_L1" top: "Mconv2_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage2_L1" type: "ReLU" bottom: "Mconv2_stage2_L1" top: "Mconv2_stage2_L1" } layer { name: "Mconv2_stage2_L2" type: "Convolution" bottom: "Mconv1_stage2_L2" top: "Mconv2_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage2_L2" type: "ReLU" bottom: "Mconv2_stage2_L2" top: "Mconv2_stage2_L2" } layer { name: "Mconv3_stage2_L1" type: "Convolution" bottom: "Mconv2_stage2_L1" top: "Mconv3_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage2_L1" type: "ReLU" bottom: "Mconv3_stage2_L1" top: "Mconv3_stage2_L1" } layer { name: "Mconv3_stage2_L2" type: "Convolution" bottom: "Mconv2_stage2_L2" top: "Mconv3_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage2_L2" type: "ReLU" bottom: "Mconv3_stage2_L2" top: "Mconv3_stage2_L2" } layer { name: "Mconv4_stage2_L1" type: "Convolution" bottom: "Mconv3_stage2_L1" top: "Mconv4_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage2_L1" type: "ReLU" bottom: "Mconv4_stage2_L1" top: "Mconv4_stage2_L1" } layer { name: "Mconv4_stage2_L2" type: "Convolution" bottom: "Mconv3_stage2_L2" top: "Mconv4_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage2_L2" type: "ReLU" bottom: "Mconv4_stage2_L2" top: "Mconv4_stage2_L2" } layer { name: "Mconv5_stage2_L1" type: "Convolution" bottom: "Mconv4_stage2_L1" top: "Mconv5_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage2_L1" type: "ReLU" bottom: "Mconv5_stage2_L1" top: "Mconv5_stage2_L1" } layer { name: "Mconv5_stage2_L2" type: "Convolution" bottom: "Mconv4_stage2_L2" top: "Mconv5_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage2_L2" type: "ReLU" bottom: "Mconv5_stage2_L2" top: "Mconv5_stage2_L2" } layer { name: "Mconv6_stage2_L1" type: "Convolution" bottom: "Mconv5_stage2_L1" top: "Mconv6_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage2_L1" type: "ReLU" bottom: "Mconv6_stage2_L1" top: "Mconv6_stage2_L1" } layer { name: "Mconv6_stage2_L2" type: "Convolution" bottom: "Mconv5_stage2_L2" top: "Mconv6_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage2_L2" type: "ReLU" bottom: "Mconv6_stage2_L2" top: "Mconv6_stage2_L2" } layer { name: "Mconv7_stage2_L1" type: "Convolution" bottom: "Mconv6_stage2_L1" top: "Mconv7_stage2_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage2_L2" type: "Convolution" bottom: "Mconv6_stage2_L2" top: "Mconv7_stage2_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage3" type: "Concat" bottom: "Mconv7_stage2_L1" bottom: "Mconv7_stage2_L2" bottom: "conv4_4_CPM" top: "concat_stage3" concat_param { axis: 1 } } layer { name: "Mconv1_stage3_L1" type: "Convolution" bottom: "concat_stage3" top: "Mconv1_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage3_L1" type: "ReLU" bottom: "Mconv1_stage3_L1" top: "Mconv1_stage3_L1" } layer { name: "Mconv1_stage3_L2" type: "Convolution" bottom: "concat_stage3" top: "Mconv1_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage3_L2" type: "ReLU" bottom: "Mconv1_stage3_L2" top: "Mconv1_stage3_L2" } layer { name: "Mconv2_stage3_L1" type: "Convolution" bottom: "Mconv1_stage3_L1" top: "Mconv2_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage3_L1" type: "ReLU" bottom: "Mconv2_stage3_L1" top: "Mconv2_stage3_L1" } layer { name: "Mconv2_stage3_L2" type: "Convolution" bottom: "Mconv1_stage3_L2" top: "Mconv2_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage3_L2" type: "ReLU" bottom: "Mconv2_stage3_L2" top: "Mconv2_stage3_L2" } layer { name: "Mconv3_stage3_L1" type: "Convolution" bottom: "Mconv2_stage3_L1" top: "Mconv3_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage3_L1" type: "ReLU" bottom: "Mconv3_stage3_L1" top: "Mconv3_stage3_L1" } layer { name: "Mconv3_stage3_L2" type: "Convolution" bottom: "Mconv2_stage3_L2" top: "Mconv3_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage3_L2" type: "ReLU" bottom: "Mconv3_stage3_L2" top: "Mconv3_stage3_L2" } layer { name: "Mconv4_stage3_L1" type: "Convolution" bottom: "Mconv3_stage3_L1" top: "Mconv4_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage3_L1" type: "ReLU" bottom: "Mconv4_stage3_L1" top: "Mconv4_stage3_L1" } layer { name: "Mconv4_stage3_L2" type: "Convolution" bottom: "Mconv3_stage3_L2" top: "Mconv4_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage3_L2" type: "ReLU" bottom: "Mconv4_stage3_L2" top: "Mconv4_stage3_L2" } layer { name: "Mconv5_stage3_L1" type: "Convolution" bottom: "Mconv4_stage3_L1" top: "Mconv5_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage3_L1" type: "ReLU" bottom: "Mconv5_stage3_L1" top: "Mconv5_stage3_L1" } layer { name: "Mconv5_stage3_L2" type: "Convolution" bottom: "Mconv4_stage3_L2" top: "Mconv5_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage3_L2" type: "ReLU" bottom: "Mconv5_stage3_L2" top: "Mconv5_stage3_L2" } layer { name: "Mconv6_stage3_L1" type: "Convolution" bottom: "Mconv5_stage3_L1" top: "Mconv6_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage3_L1" type: "ReLU" bottom: "Mconv6_stage3_L1" top: "Mconv6_stage3_L1" } layer { name: "Mconv6_stage3_L2" type: "Convolution" bottom: "Mconv5_stage3_L2" top: "Mconv6_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage3_L2" type: "ReLU" bottom: "Mconv6_stage3_L2" top: "Mconv6_stage3_L2" } layer { name: "Mconv7_stage3_L1" type: "Convolution" bottom: "Mconv6_stage3_L1" top: "Mconv7_stage3_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage3_L2" type: "Convolution" bottom: "Mconv6_stage3_L2" top: "Mconv7_stage3_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage4" type: "Concat" bottom: "Mconv7_stage3_L1" bottom: "Mconv7_stage3_L2" bottom: "conv4_4_CPM" top: "concat_stage4" concat_param { axis: 1 } } layer { name: "Mconv1_stage4_L1" type: "Convolution" bottom: "concat_stage4" top: "Mconv1_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage4_L1" type: "ReLU" bottom: "Mconv1_stage4_L1" top: "Mconv1_stage4_L1" } layer { name: "Mconv1_stage4_L2" type: "Convolution" bottom: "concat_stage4" top: "Mconv1_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage4_L2" type: "ReLU" bottom: "Mconv1_stage4_L2" top: "Mconv1_stage4_L2" } layer { name: "Mconv2_stage4_L1" type: "Convolution" bottom: "Mconv1_stage4_L1" top: "Mconv2_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage4_L1" type: "ReLU" bottom: "Mconv2_stage4_L1" top: "Mconv2_stage4_L1" } layer { name: "Mconv2_stage4_L2" type: "Convolution" bottom: "Mconv1_stage4_L2" top: "Mconv2_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage4_L2" type: "ReLU" bottom: "Mconv2_stage4_L2" top: "Mconv2_stage4_L2" } layer { name: "Mconv3_stage4_L1" type: "Convolution" bottom: "Mconv2_stage4_L1" top: "Mconv3_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage4_L1" type: "ReLU" bottom: "Mconv3_stage4_L1" top: "Mconv3_stage4_L1" } layer { name: "Mconv3_stage4_L2" type: "Convolution" bottom: "Mconv2_stage4_L2" top: "Mconv3_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage4_L2" type: "ReLU" bottom: "Mconv3_stage4_L2" top: "Mconv3_stage4_L2" } layer { name: "Mconv4_stage4_L1" type: "Convolution" bottom: "Mconv3_stage4_L1" top: "Mconv4_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage4_L1" type: "ReLU" bottom: "Mconv4_stage4_L1" top: "Mconv4_stage4_L1" } layer { name: "Mconv4_stage4_L2" type: "Convolution" bottom: "Mconv3_stage4_L2" top: "Mconv4_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage4_L2" type: "ReLU" bottom: "Mconv4_stage4_L2" top: "Mconv4_stage4_L2" } layer { name: "Mconv5_stage4_L1" type: "Convolution" bottom: "Mconv4_stage4_L1" top: "Mconv5_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage4_L1" type: "ReLU" bottom: "Mconv5_stage4_L1" top: "Mconv5_stage4_L1" } layer { name: "Mconv5_stage4_L2" type: "Convolution" bottom: "Mconv4_stage4_L2" top: "Mconv5_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage4_L2" type: "ReLU" bottom: "Mconv5_stage4_L2" top: "Mconv5_stage4_L2" } layer { name: "Mconv6_stage4_L1" type: "Convolution" bottom: "Mconv5_stage4_L1" top: "Mconv6_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage4_L1" type: "ReLU" bottom: "Mconv6_stage4_L1" top: "Mconv6_stage4_L1" } layer { name: "Mconv6_stage4_L2" type: "Convolution" bottom: "Mconv5_stage4_L2" top: "Mconv6_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage4_L2" type: "ReLU" bottom: "Mconv6_stage4_L2" top: "Mconv6_stage4_L2" } layer { name: "Mconv7_stage4_L1" type: "Convolution" bottom: "Mconv6_stage4_L1" top: "Mconv7_stage4_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage4_L2" type: "Convolution" bottom: "Mconv6_stage4_L2" top: "Mconv7_stage4_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage5" type: "Concat" bottom: "Mconv7_stage4_L1" bottom: "Mconv7_stage4_L2" bottom: "conv4_4_CPM" top: "concat_stage5" concat_param { axis: 1 } } layer { name: "Mconv1_stage5_L1" type: "Convolution" bottom: "concat_stage5" top: "Mconv1_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage5_L1" type: "ReLU" bottom: "Mconv1_stage5_L1" top: "Mconv1_stage5_L1" } layer { name: "Mconv1_stage5_L2" type: "Convolution" bottom: "concat_stage5" top: "Mconv1_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage5_L2" type: "ReLU" bottom: "Mconv1_stage5_L2" top: "Mconv1_stage5_L2" } layer { name: "Mconv2_stage5_L1" type: "Convolution" bottom: "Mconv1_stage5_L1" top: "Mconv2_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage5_L1" type: "ReLU" bottom: "Mconv2_stage5_L1" top: "Mconv2_stage5_L1" } layer { name: "Mconv2_stage5_L2" type: "Convolution" bottom: "Mconv1_stage5_L2" top: "Mconv2_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage5_L2" type: "ReLU" bottom: "Mconv2_stage5_L2" top: "Mconv2_stage5_L2" } layer { name: "Mconv3_stage5_L1" type: "Convolution" bottom: "Mconv2_stage5_L1" top: "Mconv3_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage5_L1" type: "ReLU" bottom: "Mconv3_stage5_L1" top: "Mconv3_stage5_L1" } layer { name: "Mconv3_stage5_L2" type: "Convolution" bottom: "Mconv2_stage5_L2" top: "Mconv3_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage5_L2" type: "ReLU" bottom: "Mconv3_stage5_L2" top: "Mconv3_stage5_L2" } layer { name: "Mconv4_stage5_L1" type: "Convolution" bottom: "Mconv3_stage5_L1" top: "Mconv4_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage5_L1" type: "ReLU" bottom: "Mconv4_stage5_L1" top: "Mconv4_stage5_L1" } layer { name: "Mconv4_stage5_L2" type: "Convolution" bottom: "Mconv3_stage5_L2" top: "Mconv4_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage5_L2" type: "ReLU" bottom: "Mconv4_stage5_L2" top: "Mconv4_stage5_L2" } layer { name: "Mconv5_stage5_L1" type: "Convolution" bottom: "Mconv4_stage5_L1" top: "Mconv5_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage5_L1" type: "ReLU" bottom: "Mconv5_stage5_L1" top: "Mconv5_stage5_L1" } layer { name: "Mconv5_stage5_L2" type: "Convolution" bottom: "Mconv4_stage5_L2" top: "Mconv5_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage5_L2" type: "ReLU" bottom: "Mconv5_stage5_L2" top: "Mconv5_stage5_L2" } layer { name: "Mconv6_stage5_L1" type: "Convolution" bottom: "Mconv5_stage5_L1" top: "Mconv6_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage5_L1" type: "ReLU" bottom: "Mconv6_stage5_L1" top: "Mconv6_stage5_L1" } layer { name: "Mconv6_stage5_L2" type: "Convolution" bottom: "Mconv5_stage5_L2" top: "Mconv6_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage5_L2" type: "ReLU" bottom: "Mconv6_stage5_L2" top: "Mconv6_stage5_L2" } layer { name: "Mconv7_stage5_L1" type: "Convolution" bottom: "Mconv6_stage5_L1" top: "Mconv7_stage5_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage5_L2" type: "Convolution" bottom: "Mconv6_stage5_L2" top: "Mconv7_stage5_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage6" type: "Concat" bottom: "Mconv7_stage5_L1" bottom: "Mconv7_stage5_L2" bottom: "conv4_4_CPM" top: "concat_stage6" concat_param { axis: 1 } } layer { name: "Mconv1_stage6_L1" type: "Convolution" bottom: "concat_stage6" top: "Mconv1_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage6_L1" type: "ReLU" bottom: "Mconv1_stage6_L1" top: "Mconv1_stage6_L1" } layer { name: "Mconv1_stage6_L2" type: "Convolution" bottom: "concat_stage6" top: "Mconv1_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu1_stage6_L2" type: "ReLU" bottom: "Mconv1_stage6_L2" top: "Mconv1_stage6_L2" } layer { name: "Mconv2_stage6_L1" type: "Convolution" bottom: "Mconv1_stage6_L1" top: "Mconv2_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage6_L1" type: "ReLU" bottom: "Mconv2_stage6_L1" top: "Mconv2_stage6_L1" } layer { name: "Mconv2_stage6_L2" type: "Convolution" bottom: "Mconv1_stage6_L2" top: "Mconv2_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu2_stage6_L2" type: "ReLU" bottom: "Mconv2_stage6_L2" top: "Mconv2_stage6_L2" } layer { name: "Mconv3_stage6_L1" type: "Convolution" bottom: "Mconv2_stage6_L1" top: "Mconv3_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage6_L1" type: "ReLU" bottom: "Mconv3_stage6_L1" top: "Mconv3_stage6_L1" } layer { name: "Mconv3_stage6_L2" type: "Convolution" bottom: "Mconv2_stage6_L2" top: "Mconv3_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu3_stage6_L2" type: "ReLU" bottom: "Mconv3_stage6_L2" top: "Mconv3_stage6_L2" } layer { name: "Mconv4_stage6_L1" type: "Convolution" bottom: "Mconv3_stage6_L1" top: "Mconv4_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage6_L1" type: "ReLU" bottom: "Mconv4_stage6_L1" top: "Mconv4_stage6_L1" } layer { name: "Mconv4_stage6_L2" type: "Convolution" bottom: "Mconv3_stage6_L2" top: "Mconv4_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu4_stage6_L2" type: "ReLU" bottom: "Mconv4_stage6_L2" top: "Mconv4_stage6_L2" } layer { name: "Mconv5_stage6_L1" type: "Convolution" bottom: "Mconv4_stage6_L1" top: "Mconv5_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage6_L1" type: "ReLU" bottom: "Mconv5_stage6_L1" top: "Mconv5_stage6_L1" } layer { name: "Mconv5_stage6_L2" type: "Convolution" bottom: "Mconv4_stage6_L2" top: "Mconv5_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 3 kernel_size: 7 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu5_stage6_L2" type: "ReLU" bottom: "Mconv5_stage6_L2" top: "Mconv5_stage6_L2" } layer { name: "Mconv6_stage6_L1" type: "Convolution" bottom: "Mconv5_stage6_L1" top: "Mconv6_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage6_L1" type: "ReLU" bottom: "Mconv6_stage6_L1" top: "Mconv6_stage6_L1" } layer { name: "Mconv6_stage6_L2" type: "Convolution" bottom: "Mconv5_stage6_L2" top: "Mconv6_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 128 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mrelu6_stage6_L2" type: "ReLU" bottom: "Mconv6_stage6_L2" top: "Mconv6_stage6_L2" } layer { name: "Mconv7_stage6_L1" type: "Convolution" bottom: "Mconv6_stage6_L1" top: "Mconv7_stage6_L1" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 38 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "Mconv7_stage6_L2" type: "Convolution" bottom: "Mconv6_stage6_L2" top: "Mconv7_stage6_L2" param { lr_mult: 4.0 decay_mult: 1 } param { lr_mult: 8.0 decay_mult: 0 } convolution_param { num_output: 19 pad: 0 kernel_size: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" } } } layer { name: "concat_stage7" type: "Concat" bottom: "Mconv7_stage6_L2" bottom: "Mconv7_stage6_L1" # top: "concat_stage7" top: "net_output" concat_param { axis: 1 } }
下载模型权重
步骤一：
我们正在使用在Caffe深度学习框架上训练的模型。Caffe模型具有2个文件–
- .prototxt文件，指定了神经网络的体系结构–不同层的排列方式等。
- .caffemodel文件，用于存储训练后的模型的权重
我们将使用这两个文件将网络加载到内存中。
protoFile = "model/pose_deploy_linevec.prototxt" weightsFile = "model/pose_iter_440000.caffemodel"net=cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
步骤二：
读取图像并准备输入网络
我们使用OpenCV读取的输入帧应转换为输入Blob（例如Caffe），以便可以将其输入到网络。这是使用blobFromImage函数完成的，该函数将图像从OpenCV格式转换为Caffe blob格式。这些参数将在blobFromImage函数中提供。首先，我们将像素值标准化为（0,1）。然后，我们指定图像的尺寸。接下来，要减去的平均值为（0,0,0）。由于OpenCV和Caffe都使用BGR格式，因此无需交换R和B通道。
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) # 读取caffe模型 inWidth = 368 inHeight = 368 inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False) #将输入图片转成相应模型识别的blob数据 net.setInput(inpBlob) # 放进网络
步骤三：
进行预测并解析关键点
一旦将图像传递到模型，就可以使用单行代码进行预测。OpenCV中DNN类的正向方法通过网络进行正向传递，这只是说它正在做出预测的另一种方式。
output = net.forward() # 向前传播，进行预测
输出为4D矩阵：
- 第一维是图像ID（如果您将多个图像传递到网络）。
- 第二个维度指示关键点的索引。该模型将生成所有连接在一起的置信度图和零件亲和度图。对于COCO模型，它由57个部分组成– 18个关键点置信度图+ 1个背景+ 19 * 2个部分亲和度图。同样，对于MPI，它会产生44点。我们将仅使用与关键点相对应的前几个点。
- 第三维是输出图的高度。
- 第四个维度是输出图的宽度。
我们检查图像中是否存在每个关键点。我们通过找到关键点的置信度图的最大值来获得关键点的位置。我们还使用阈值来减少错误检测。
H = out.shape[2] W = out.shape[3] # Empty list to store the detected keypoints points = [] for i in range(len()): # confidence map of corresponding body's part. probMap = output[0, i, :, :] # Find global maxima of the probMap. minVal, prob, minLoc, point = cv2.minMaxLoc(probMap) # Scale the point to fit on the original image x = (frameWidth * point[0]) / W y = (frameHeight * point[1]) / H if prob > threshold : cv2.circle(frame, (int(x), int(y)), 15, (0, 255, 255), thickness=-1, lineType=cv.FILLED) cv2.putText(frame, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 0, 255), 3, lineType=cv2.LINE_AA) # Add the point to the list if the probability is greater than the threshold points.append((int(x), int(y))) else : points.append(None) cv2.imshow("Output-Keypoints",frame) cv2.waitKey(0) cv2.destroyAllWindows()
由于我们事先知道了点的索引，因此只要有关键点，我们就可以通过仅加入对来绘制骨架。这是使用下面给出的代码完成的。
for pair in POSE_PAIRS: partA = pair[0] partB = pair[1] if points[partA] and points[partB]: cv2.line(frameCopy, points[partA], points[partB], (0, 255, 0), 3)
完整代码：
import cv2 import time import numpy as np MODE = "COCO" if MODE is "COCO": protoFile = "model/pose_deploy_linevec.prototxt" weightsFile = "model/pose_iter_440000.caffemodel" nPoints = 18 POSE_PAIRS = [[1, 0], [1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [8, 9], [9, 10], [1, 11], [11, 12], [12, 13], [0, 14], [0, 15], [14, 16], [15, 17]] frame = cv2.imread("image.jpg") frameCopy = np.copy(frame) frameWidth = frame.shape[1] frameHeight = frame.shape[0] threshold = 0.1 net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) # 读取caffe模型 t = time.time() inWidth = 368 inHeight = 368 inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False) #将输入图片转成相应模型识别的blob数据 net.setInput(inpBlob) # 放进网络 output = net.forward() # 向前传播，进行预测 print("time taken by network : {:.3f}".format(time.time() - t)) # print(output.shape, output) #输出4D举证: # 第一维是图像ID（如果您将多个图像传递到网络）。 # 第二个维度指示关键点的索引。该模型将生成所有连接在一起的置信度图和零件亲和度图。对于COCO模型，它由57个部分组成– 18个关键点置信度图+ 1个背景+ 19 * 2个部分亲和度图。同样，对于MPI，它会产生44点。我们将仅使用与关键点相对应的前几个点。 # 第三维是输出图的高度。 # 第四个维度是输出图的宽度。 H = output.shape[2] # 输出的图像的高度 W = output.shape[3] # 输出图像的宽度 # Empty list to store the detected keypoints points = [] for i in range(nPoints): # confidence map of corresponding body's part. probMap = output[0, i, :, :] # 获取关键点 # Find global maxima of the probMap. minVal, prob, minLoc, point = cv2.minMaxLoc(probMap) # 通过minMaxLoc得出该矩阵中的最小值、最大值、最小值索引，最大值索引 print(minVal, prob, minLoc, point) # Scale the point to fit on the original image将输出图像中的关键点映射到原始图片上 x = (frameWidth / W) * point[0] y = (frameHeight / H) * point[1] if prob > threshold: cv2.circle(frameCopy, (int(x), int(y)), 4, (0, 255, 255), thickness=-1, lineType=cv2.FILLED) cv2.putText(frameCopy, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), lineType=cv2.LINE_AA) # Add the point to the list if the probability is greater than the threshold points.append((int(x), int(y))) else: points.append(None) # Draw Skeleton for pair in POSE_PAIRS: partA = pair[0] partB = pair[1] if points[partA] and points[partB]: cv2.line(frame, points[partA], points[partB], (0, 255, 255), 2) cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED) cv2.imshow('Output-Keypoints', frameCopy) cv2.imshow('Output-Skeleton', frame) print("Total time taken : {:.3f}".format(time.time() - t)) cv2.waitKey(0)