实时人体姿态评估算法学习笔记

0. 实时人体姿态评估算法学习笔记

本文是以 2017 CVPR的一篇论文为基础的,采用深度学习算法,实时评估人体姿态的代码重构的学习笔记.

叠加生成人体姿态

本文采用keras,以TensorFlow为backend.

import keras
from keras.models import Sequential
from keras.models import Model
from keras.layers import Input, Dense, Activation
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.merge import Concatenate
from config_reader import config_reader
import scipy
from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot

Using TensorFlow backend.

1.定义模型

模型基于VGG,1-4层采用VGG的模型,在VGG后面增加了非VGG的层.

def relu(x): 
    return Activation('relu')(x)

def conv(x, nf, ks, name):
    x1 = Conv2D(nf, (ks, ks), padding='same', name=name)(x)
    return x1

def pooling(x, ks, st, name):
    x = MaxPooling2D((ks, ks), strides=(st, st), name=name)(x)
    return x

def vgg_block(x):
     
    # Block 1
    x = conv(x, 64, 3, "conv1_1")
    x = relu(x)
    x = conv(x, 64, 3, "conv1_2")
    x = relu(x)
    x = pooling(x, 2, 2, "pool1_1")

    # Block 2
    x = conv(x, 128, 3, "conv2_1")
    x = relu(x)
    x = conv(x, 128, 3, "conv2_2")
    x = relu(x)
    x = pooling(x, 2, 2, "pool2_1")
    
    # Block 3
    x = conv(x, 256, 3, "conv3_1")
    x = relu(x)    
    x = conv(x, 256, 3, "conv3_2")
    x = relu(x)    
    x = conv(x, 256, 3, "conv3_3")
    x = relu(x)    
    x = conv(x, 256, 3, "conv3_4")
    x = relu(x)    
    x = pooling(x, 2, 2, "pool3_1")
    
    # Block 4
    x = conv(x, 512, 3, "conv4_1")
    x = relu(x)    
    x = conv(x, 512, 3, "conv4_2")
    x = relu(x)    
    
    # Additional non vgg layers
    x = conv(x, 256, 3, "conv4_3_CPM")
    x = relu(x)
    x = conv(x, 128, 3, "conv4_4_CPM")
    x = relu(x)
    
    return x

def stage1_block(x, num_p, branch):
    
    # Block 1        
    x = conv(x, 128, 3, "conv5_1_CPM_L%d" % branch)
    x = relu(x)
    x = conv(x, 128, 3, "conv5_2_CPM_L%d" % branch)
    x = relu(x)
    x = conv(x, 128, 3, "conv5_3_CPM_L%d" % branch)
    x = relu(x)
    x = conv(x, 512, 1, "conv5_4_CPM_L%d" % branch)
    x = relu(x)
    x = conv(x, num_p, 1, "conv5_5_CPM_L%d" % branch)
    
    return x

def stageT_block(x, num_p, stage, branch):
        
    # Block 1        
    x = conv(x, 128, 7, "Mconv1_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, 128, 7, "Mconv2_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, 128, 7, "Mconv3_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, 128, 7, "Mconv4_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, 128, 7, "Mconv5_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, 128, 1, "Mconv6_stage%d_L%d" % (stage, branch))
    x = relu(x)
    x = conv(x, num_p, 1, "Mconv7_stage%d_L%d" % (stage, branch))
    
    return x

weights_path = "model/keras/model.h5"

input_shape = (None,None,3)

img_input = Input(shape=input_shape)

stages = 6
np_branch1 = 38
np_branch2 = 19

# VGG
stage0_out = vgg_block(img_input)

# stage 1
stage1_branch1_out = stage1_block(stage0_out, np_branch1, 1)
stage1_branch2_out = stage1_block(stage0_out, np_branch2, 2)
x = Concatenate()([stage1_branch1_out, stage1_branch2_out, stage0_out])

# stage t >= 2
for sn in range(2, stages + 1):
    stageT_branch1_out = stageT_block(x, np_branch1, sn, 1)
    stageT_branch2_out = stageT_block(x, np_branch2, sn, 2)
    if (sn < stages):
        x = Concatenate()([stageT_branch1_out, stageT_branch2_out, stage0_out])

model = Model(img_input, [stageT_branch1_out, stageT_branch2_out])
model.load_weights(weights_path)

model.summary()
SVG(model_to_dot(model,show_shapes=True).create(prog='dot', format='svg'))

#模型的结构如下列表和图形

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0                                            
__________________________________________________________________________________________________
conv1_1 (Conv2D)                (None, None, None, 6 1792        input_1[0][0]                    
__________________________________________________________________________________________________
activation_1 (Activation)       (None, None, None, 6 0           conv1_1[0][0]                    
__________________________________________________________________________________________________
conv1_2 (Conv2D)                (None, None, None, 6 36928       activation_1[0][0]               
__________________________________________________________________________________________________
activation_2 (Activation)       (None, None, None, 6 0           conv1_2[0][0]                    
__________________________________________________________________________________________________
pool1_1 (MaxPooling2D)          (None, None, None, 6 0           activation_2[0][0]               
__________________________________________________________________________________________________
conv2_1 (Conv2D)                (None, None, None, 1 73856       pool1_1[0][0]                    
__________________________________________________________________________________________________
activation_3 (Activation)       (None, None, None, 1 0           conv2_1[0][0]                    
__________________________________________________________________________________________________
conv2_2 (Conv2D)                (None, None, None, 1 147584      activation_3[0][0]               
__________________________________________________________________________________________________
activation_4 (Activation)       (None, None, None, 1 0           conv2_2[0][0]                    
__________________________________________________________________________________________________
pool2_1 (MaxPooling2D)          (None, None, None, 1 0           activation_4[0][0]               
__________________________________________________________________________________________________
conv3_1 (Conv2D)                (None, None, None, 2 295168      pool2_1[0][0]                    
__________________________________________________________________________________________________
activation_5 (Activation)       (None, None, None, 2 0           conv3_1[0][0]                    
__________________________________________________________________________________________________
conv3_2 (Conv2D)                (None, None, None, 2 590080      activation_5[0][0]               
__________________________________________________________________________________________________
activation_6 (Activation)       (None, None, None, 2 0           conv3_2[0][0]                    
__________________________________________________________________________________________________
conv3_3 (Conv2D)                (None, None, None, 2 590080      activation_6[0][0]               
__________________________________________________________________________________________________
activation_7 (Activation)       (None, None, None, 2 0           conv3_3[0][0]                    
__________________________________________________________________________________________________
conv3_4 (Conv2D)                (None, None, None, 2 590080      activation_7[0][0]               
__________________________________________________________________________________________________
activation_8 (Activation)       (None, None, None, 2 0           conv3_4[0][0]                    
__________________________________________________________________________________________________
pool3_1 (MaxPooling2D)          (None, None, None, 2 0           activation_8[0][0]               
__________________________________________________________________________________________________
conv4_1 (Conv2D)                (None, None, None, 5 1180160     pool3_1[0][0]                    
__________________________________________________________________________________________________
activation_9 (Activation)       (None, None, None, 5 0           conv4_1[0][0]                    
__________________________________________________________________________________________________
conv4_2 (Conv2D)                (None, None, None, 5 2359808     activation_9[0][0]               
__________________________________________________________________________________________________
activation_10 (Activation)      (None, None, None, 5 0           conv4_2[0][0]                    
__________________________________________________________________________________________________
conv4_3_CPM (Conv2D)            (None, None, None, 2 1179904     activation_10[0][0]              
__________________________________________________________________________________________________
activation_11 (Activation)      (None, None, None, 2 0           conv4_3_CPM[0][0]                
__________________________________________________________________________________________________
conv4_4_CPM (Conv2D)            (None, None, None, 1 295040      activation_11[0][0]              
__________________________________________________________________________________________________
activation_12 (Activation)      (None, None, None, 1 0           conv4_4_CPM[0][0]                
__________________________________________________________________________________________________
conv5_1_CPM_L1 (Conv2D)         (None, None, None, 1 147584      activation_12[0][0]              
__________________________________________________________________________________________________
conv5_1_CPM_L2 (Conv2D)         (None, None, None, 1 147584      activation_12[0][0]              
__________________________________________________________________________________________________
activation_13 (Activation)      (None, None, None, 1 0           conv5_1_CPM_L1[0][0]             
__________________________________________________________________________________________________
activation_17 (Activation)      (None, None, None, 1 0           conv5_1_CPM_L2[0][0]             
__________________________________________________________________________________________________
conv5_2_CPM_L1 (Conv2D)         (None, None, None, 1 147584      activation_13[0][0]              
__________________________________________________________________________________________________
conv5_2_CPM_L2 (Conv2D)         (None, None, None, 1 147584      activation_17[0][0]              
__________________________________________________________________________________________________
activation_14 (Activation)      (None, None, None, 1 0           conv5_2_CPM_L1[0][0]             
__________________________________________________________________________________________________
activation_18 (Activation)      (None, None, None, 1 0           conv5_2_CPM_L2[0][0]             
__________________________________________________________________________________________________
conv5_3_CPM_L1 (Conv2D)         (None, None, None, 1 147584      activation_14[0][0]              
__________________________________________________________________________________________________
conv5_3_CPM_L2 (Conv2D)         (None, None, None, 1 147584      activation_18[0][0]              
__________________________________________________________________________________________________
activation_15 (Activation)      (None, None, None, 1 0           conv5_3_CPM_L1[0][0]             
__________________________________________________________________________________________________
activation_19 (Activation)      (None, None, None, 1 0           conv5_3_CPM_L2[0][0]             
__________________________________________________________________________________________________
conv5_4_CPM_L1 (Conv2D)         (None, None, None, 5 66048       activation_15[0][0]              
__________________________________________________________________________________________________
conv5_4_CPM_L2 (Conv2D)         (None, None, None, 5 66048       activation_19[0][0]              
__________________________________________________________________________________________________
activation_16 (Activation)      (None, None, None, 5 0           conv5_4_CPM_L1[0][0]             
__________________________________________________________________________________________________
activation_20 (Activation)      (None, None, None, 5 0           conv5_4_CPM_L2[0][0]             
__________________________________________________________________________________________________
conv5_5_CPM_L1 (Conv2D)         (None, None, None, 3 19494       activation_16[0][0]              
__________________________________________________________________________________________________
conv5_5_CPM_L2 (Conv2D)         (None, None, None, 1 9747        activation_20[0][0]              
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, None, None, 1 0           conv5_5_CPM_L1[0][0]             
                                                                 conv5_5_CPM_L2[0][0]             
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
Mconv1_stage2_L1 (Conv2D)       (None, None, None, 1 1160448     concatenate_1[0][0]              
__________________________________________________________________________________________________
Mconv1_stage2_L2 (Conv2D)       (None, None, None, 1 1160448     concatenate_1[0][0]              
__________________________________________________________________________________________________
activation_21 (Activation)      (None, None, None, 1 0           Mconv1_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_27 (Activation)      (None, None, None, 1 0           Mconv1_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv2_stage2_L1 (Conv2D)       (None, None, None, 1 802944      activation_21[0][0]              
__________________________________________________________________________________________________
Mconv2_stage2_L2 (Conv2D)       (None, None, None, 1 802944      activation_27[0][0]              
__________________________________________________________________________________________________
activation_22 (Activation)      (None, None, None, 1 0           Mconv2_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_28 (Activation)      (None, None, None, 1 0           Mconv2_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv3_stage2_L1 (Conv2D)       (None, None, None, 1 802944      activation_22[0][0]              
__________________________________________________________________________________________________
Mconv3_stage2_L2 (Conv2D)       (None, None, None, 1 802944      activation_28[0][0]              
__________________________________________________________________________________________________
activation_23 (Activation)      (None, None, None, 1 0           Mconv3_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_29 (Activation)      (None, None, None, 1 0           Mconv3_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv4_stage2_L1 (Conv2D)       (None, None, None, 1 802944      activation_23[0][0]              
__________________________________________________________________________________________________
Mconv4_stage2_L2 (Conv2D)       (None, None, None, 1 802944      activation_29[0][0]              
__________________________________________________________________________________________________
activation_24 (Activation)      (None, None, None, 1 0           Mconv4_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_30 (Activation)      (None, None, None, 1 0           Mconv4_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv5_stage2_L1 (Conv2D)       (None, None, None, 1 802944      activation_24[0][0]              
__________________________________________________________________________________________________
Mconv5_stage2_L2 (Conv2D)       (None, None, None, 1 802944      activation_30[0][0]              
__________________________________________________________________________________________________
activation_25 (Activation)      (None, None, None, 1 0           Mconv5_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_31 (Activation)      (None, None, None, 1 0           Mconv5_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv6_stage2_L1 (Conv2D)       (None, None, None, 1 16512       activation_25[0][0]              
__________________________________________________________________________________________________
Mconv6_stage2_L2 (Conv2D)       (None, None, None, 1 16512       activation_31[0][0]              
__________________________________________________________________________________________________
activation_26 (Activation)      (None, None, None, 1 0           Mconv6_stage2_L1[0][0]           
__________________________________________________________________________________________________
activation_32 (Activation)      (None, None, None, 1 0           Mconv6_stage2_L2[0][0]           
__________________________________________________________________________________________________
Mconv7_stage2_L1 (Conv2D)       (None, None, None, 3 4902        activation_26[0][0]              
__________________________________________________________________________________________________
Mconv7_stage2_L2 (Conv2D)       (None, None, None, 1 2451        activation_32[0][0]              
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, None, None, 1 0           Mconv7_stage2_L1[0][0]           
                                                                 Mconv7_stage2_L2[0][0]           
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
Mconv1_stage3_L1 (Conv2D)       (None, None, None, 1 1160448     concatenate_2[0][0]              
__________________________________________________________________________________________________
Mconv1_stage3_L2 (Conv2D)       (None, None, None, 1 1160448     concatenate_2[0][0]              
__________________________________________________________________________________________________
activation_33 (Activation)      (None, None, None, 1 0           Mconv1_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_39 (Activation)      (None, None, None, 1 0           Mconv1_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv2_stage3_L1 (Conv2D)       (None, None, None, 1 802944      activation_33[0][0]              
__________________________________________________________________________________________________
Mconv2_stage3_L2 (Conv2D)       (None, None, None, 1 802944      activation_39[0][0]              
__________________________________________________________________________________________________
activation_34 (Activation)      (None, None, None, 1 0           Mconv2_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_40 (Activation)      (None, None, None, 1 0           Mconv2_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv3_stage3_L1 (Conv2D)       (None, None, None, 1 802944      activation_34[0][0]              
__________________________________________________________________________________________________
Mconv3_stage3_L2 (Conv2D)       (None, None, None, 1 802944      activation_40[0][0]              
__________________________________________________________________________________________________
activation_35 (Activation)      (None, None, None, 1 0           Mconv3_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_41 (Activation)      (None, None, None, 1 0           Mconv3_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv4_stage3_L1 (Conv2D)       (None, None, None, 1 802944      activation_35[0][0]              
__________________________________________________________________________________________________
Mconv4_stage3_L2 (Conv2D)       (None, None, None, 1 802944      activation_41[0][0]              
__________________________________________________________________________________________________
activation_36 (Activation)      (None, None, None, 1 0           Mconv4_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_42 (Activation)      (None, None, None, 1 0           Mconv4_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv5_stage3_L1 (Conv2D)       (None, None, None, 1 802944      activation_36[0][0]              
__________________________________________________________________________________________________
Mconv5_stage3_L2 (Conv2D)       (None, None, None, 1 802944      activation_42[0][0]              
__________________________________________________________________________________________________
activation_37 (Activation)      (None, None, None, 1 0           Mconv5_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_43 (Activation)      (None, None, None, 1 0           Mconv5_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv6_stage3_L1 (Conv2D)       (None, None, None, 1 16512       activation_37[0][0]              
__________________________________________________________________________________________________
Mconv6_stage3_L2 (Conv2D)       (None, None, None, 1 16512       activation_43[0][0]              
__________________________________________________________________________________________________
activation_38 (Activation)      (None, None, None, 1 0           Mconv6_stage3_L1[0][0]           
__________________________________________________________________________________________________
activation_44 (Activation)      (None, None, None, 1 0           Mconv6_stage3_L2[0][0]           
__________________________________________________________________________________________________
Mconv7_stage3_L1 (Conv2D)       (None, None, None, 3 4902        activation_38[0][0]              
__________________________________________________________________________________________________
Mconv7_stage3_L2 (Conv2D)       (None, None, None, 1 2451        activation_44[0][0]              
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, None, None, 1 0           Mconv7_stage3_L1[0][0]           
                                                                 Mconv7_stage3_L2[0][0]           
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
Mconv1_stage4_L1 (Conv2D)       (None, None, None, 1 1160448     concatenate_3[0][0]              
__________________________________________________________________________________________________
Mconv1_stage4_L2 (Conv2D)       (None, None, None, 1 1160448     concatenate_3[0][0]              
__________________________________________________________________________________________________
activation_45 (Activation)      (None, None, None, 1 0           Mconv1_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_51 (Activation)      (None, None, None, 1 0           Mconv1_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv2_stage4_L1 (Conv2D)       (None, None, None, 1 802944      activation_45[0][0]              
__________________________________________________________________________________________________
Mconv2_stage4_L2 (Conv2D)       (None, None, None, 1 802944      activation_51[0][0]              
__________________________________________________________________________________________________
activation_46 (Activation)      (None, None, None, 1 0           Mconv2_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_52 (Activation)      (None, None, None, 1 0           Mconv2_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv3_stage4_L1 (Conv2D)       (None, None, None, 1 802944      activation_46[0][0]              
__________________________________________________________________________________________________
Mconv3_stage4_L2 (Conv2D)       (None, None, None, 1 802944      activation_52[0][0]              
__________________________________________________________________________________________________
activation_47 (Activation)      (None, None, None, 1 0           Mconv3_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_53 (Activation)      (None, None, None, 1 0           Mconv3_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv4_stage4_L1 (Conv2D)       (None, None, None, 1 802944      activation_47[0][0]              
__________________________________________________________________________________________________
Mconv4_stage4_L2 (Conv2D)       (None, None, None, 1 802944      activation_53[0][0]              
__________________________________________________________________________________________________
activation_48 (Activation)      (None, None, None, 1 0           Mconv4_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_54 (Activation)      (None, None, None, 1 0           Mconv4_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv5_stage4_L1 (Conv2D)       (None, None, None, 1 802944      activation_48[0][0]              
__________________________________________________________________________________________________
Mconv5_stage4_L2 (Conv2D)       (None, None, None, 1 802944      activation_54[0][0]              
__________________________________________________________________________________________________
activation_49 (Activation)      (None, None, None, 1 0           Mconv5_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_55 (Activation)      (None, None, None, 1 0           Mconv5_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv6_stage4_L1 (Conv2D)       (None, None, None, 1 16512       activation_49[0][0]              
__________________________________________________________________________________________________
Mconv6_stage4_L2 (Conv2D)       (None, None, None, 1 16512       activation_55[0][0]              
__________________________________________________________________________________________________
activation_50 (Activation)      (None, None, None, 1 0           Mconv6_stage4_L1[0][0]           
__________________________________________________________________________________________________
activation_56 (Activation)      (None, None, None, 1 0           Mconv6_stage4_L2[0][0]           
__________________________________________________________________________________________________
Mconv7_stage4_L1 (Conv2D)       (None, None, None, 3 4902        activation_50[0][0]              
__________________________________________________________________________________________________
Mconv7_stage4_L2 (Conv2D)       (None, None, None, 1 2451        activation_56[0][0]              
__________________________________________________________________________________________________
concatenate_4 (Concatenate)     (None, None, None, 1 0           Mconv7_stage4_L1[0][0]           
                                                                 Mconv7_stage4_L2[0][0]           
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
Mconv1_stage5_L1 (Conv2D)       (None, None, None, 1 1160448     concatenate_4[0][0]              
__________________________________________________________________________________________________
Mconv1_stage5_L2 (Conv2D)       (None, None, None, 1 1160448     concatenate_4[0][0]              
__________________________________________________________________________________________________
activation_57 (Activation)      (None, None, None, 1 0           Mconv1_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_63 (Activation)      (None, None, None, 1 0           Mconv1_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv2_stage5_L1 (Conv2D)       (None, None, None, 1 802944      activation_57[0][0]              
__________________________________________________________________________________________________
Mconv2_stage5_L2 (Conv2D)       (None, None, None, 1 802944      activation_63[0][0]              
__________________________________________________________________________________________________
activation_58 (Activation)      (None, None, None, 1 0           Mconv2_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_64 (Activation)      (None, None, None, 1 0           Mconv2_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv3_stage5_L1 (Conv2D)       (None, None, None, 1 802944      activation_58[0][0]              
__________________________________________________________________________________________________
Mconv3_stage5_L2 (Conv2D)       (None, None, None, 1 802944      activation_64[0][0]              
__________________________________________________________________________________________________
activation_59 (Activation)      (None, None, None, 1 0           Mconv3_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_65 (Activation)      (None, None, None, 1 0           Mconv3_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv4_stage5_L1 (Conv2D)       (None, None, None, 1 802944      activation_59[0][0]              
__________________________________________________________________________________________________
Mconv4_stage5_L2 (Conv2D)       (None, None, None, 1 802944      activation_65[0][0]              
__________________________________________________________________________________________________
activation_60 (Activation)      (None, None, None, 1 0           Mconv4_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_66 (Activation)      (None, None, None, 1 0           Mconv4_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv5_stage5_L1 (Conv2D)       (None, None, None, 1 802944      activation_60[0][0]              
__________________________________________________________________________________________________
Mconv5_stage5_L2 (Conv2D)       (None, None, None, 1 802944      activation_66[0][0]              
__________________________________________________________________________________________________
activation_61 (Activation)      (None, None, None, 1 0           Mconv5_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_67 (Activation)      (None, None, None, 1 0           Mconv5_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv6_stage5_L1 (Conv2D)       (None, None, None, 1 16512       activation_61[0][0]              
__________________________________________________________________________________________________
Mconv6_stage5_L2 (Conv2D)       (None, None, None, 1 16512       activation_67[0][0]              
__________________________________________________________________________________________________
activation_62 (Activation)      (None, None, None, 1 0           Mconv6_stage5_L1[0][0]           
__________________________________________________________________________________________________
activation_68 (Activation)      (None, None, None, 1 0           Mconv6_stage5_L2[0][0]           
__________________________________________________________________________________________________
Mconv7_stage5_L1 (Conv2D)       (None, None, None, 3 4902        activation_62[0][0]              
__________________________________________________________________________________________________
Mconv7_stage5_L2 (Conv2D)       (None, None, None, 1 2451        activation_68[0][0]              
__________________________________________________________________________________________________
concatenate_5 (Concatenate)     (None, None, None, 1 0           Mconv7_stage5_L1[0][0]           
                                                                 Mconv7_stage5_L2[0][0]           
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
Mconv1_stage6_L1 (Conv2D)       (None, None, None, 1 1160448     concatenate_5[0][0]              
__________________________________________________________________________________________________
Mconv1_stage6_L2 (Conv2D)       (None, None, None, 1 1160448     concatenate_5[0][0]              
__________________________________________________________________________________________________
activation_69 (Activation)      (None, None, None, 1 0           Mconv1_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_75 (Activation)      (None, None, None, 1 0           Mconv1_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv2_stage6_L1 (Conv2D)       (None, None, None, 1 802944      activation_69[0][0]              
__________________________________________________________________________________________________
Mconv2_stage6_L2 (Conv2D)       (None, None, None, 1 802944      activation_75[0][0]              
__________________________________________________________________________________________________
activation_70 (Activation)      (None, None, None, 1 0           Mconv2_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_76 (Activation)      (None, None, None, 1 0           Mconv2_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv3_stage6_L1 (Conv2D)       (None, None, None, 1 802944      activation_70[0][0]              
__________________________________________________________________________________________________
Mconv3_stage6_L2 (Conv2D)       (None, None, None, 1 802944      activation_76[0][0]              
__________________________________________________________________________________________________
activation_71 (Activation)      (None, None, None, 1 0           Mconv3_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_77 (Activation)      (None, None, None, 1 0           Mconv3_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv4_stage6_L1 (Conv2D)       (None, None, None, 1 802944      activation_71[0][0]              
__________________________________________________________________________________________________
Mconv4_stage6_L2 (Conv2D)       (None, None, None, 1 802944      activation_77[0][0]              
__________________________________________________________________________________________________
activation_72 (Activation)      (None, None, None, 1 0           Mconv4_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_78 (Activation)      (None, None, None, 1 0           Mconv4_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv5_stage6_L1 (Conv2D)       (None, None, None, 1 802944      activation_72[0][0]              
__________________________________________________________________________________________________
Mconv5_stage6_L2 (Conv2D)       (None, None, None, 1 802944      activation_78[0][0]              
__________________________________________________________________________________________________
activation_73 (Activation)      (None, None, None, 1 0           Mconv5_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_79 (Activation)      (None, None, None, 1 0           Mconv5_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv6_stage6_L1 (Conv2D)       (None, None, None, 1 16512       activation_73[0][0]              
__________________________________________________________________________________________________
Mconv6_stage6_L2 (Conv2D)       (None, None, None, 1 16512       activation_79[0][0]              
__________________________________________________________________________________________________
activation_74 (Activation)      (None, None, None, 1 0           Mconv6_stage6_L1[0][0]           
__________________________________________________________________________________________________
activation_80 (Activation)      (None, None, None, 1 0           Mconv6_stage6_L2[0][0]           
__________________________________________________________________________________________________
Mconv7_stage6_L1 (Conv2D)       (None, None, None, 3 4902        activation_74[0][0]              
__________________________________________________________________________________________________
Mconv7_stage6_L2 (Conv2D)       (None, None, None, 1 2451        activation_80[0][0]              
==================================================================================================
Total params: 52,311,446
Trainable params: 52,311,446
Non-trainable params: 0
__________________________________________________________________________________________________

svg

开始加载图片,并做测试


%matplotlib inline
import cv2
import matplotlib
import pylab as plt
import numpy as np
import util

test_image = './sample_images/ski.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
plt.imshow(oriImg[:,:,[2,1,0]])

<matplotlib.image.AxesImage at 0x116303358>

output_7_1.png

output_11_0.png

加载配置文件,准备运算.
对关键点开始做热力图分析,找到关键点.
肘和手腕和髋,颈部等等关键节点的热力图.

param, model_params = config_reader()

multiplier = [x * model_params['boxsize'] / oriImg.shape[0] for x in param['scale_search']]

heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
# first figure shows padded images
f, axarr = plt.subplots(1, len(multiplier))
f.set_size_inches((20, 5))
# second figure shows heatmaps
f2, axarr2 = plt.subplots(1, len(multiplier))![output_7_1.png](http://upload-images.jianshu.io/upload_images/3160023-5cbf9798fc31baec.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

f2.set_size_inches((20, 5))
# third figure shows PAFs
f3, axarr3 = plt.subplots(2, len(multiplier))
f3.set_size_inches((20, 10))

for m in range(len(multiplier)):
    scale = multiplier[m]
    imageToTest = cv2.resize(oriImg, (0,0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
    imageToTest_padded, pad = util.padRightDownCorner(imageToTest, model_params['stride'], model_params['padValue'])        
    axarr[m].imshow(imageToTest_padded[:,:,[2,1,0]])
    axarr[m].set_title('Input image: scale %d' % m)

    input_img = np.transpose(np.float32(imageToTest_padded[:,:,:,np.newaxis]), (3,0,1,2))/256 - 0.5; # required shape (1, width, height, channels) 
    print("Input shape: " + str(input_img.shape))  

    output_blobs = model.predict(input_img)
    print("Output shape (heatmap): " + str(output_blobs[1].shape))
    
    # extract outputs, resize, and remove padding
    heatmap = np.squeeze(output_blobs[1]) # output 1 is heatmaps
    heatmap = cv2.resize(heatmap, (0,0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC)
    heatmap = heatmap[:imageToTest_padded.shape[0]-pad[2], :imageToTest_padded.shape[1]-pad[3], :]
    heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
    
    paf = np.squeeze(output_blobs[0]) # output 0 is PAFs
    paf = cv2.resize(paf, (0,0), fx=model_params['stride'], fy=model_params['stride'], interpolation=cv2.INTER_CUBIC)
    paf = paf[:imageToTest_padded.shape[0]-pad[2], :imageToTest_padded.shape[1]-pad[3], :]
    paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC)
    
    # visualization
    axarr2[m].imshow(oriImg[:,:,[2,1,0]])
    ax2 = axarr2[m].imshow(heatmap[:,:,3], alpha=.5) # right elbow
    axarr2[m].set_title('Heatmaps (Relb): scale %d' % m)
    
    axarr3.flat[m].imshow(oriImg[:,:,[2,1,0]])
    ax3x = axarr3.flat[m].imshow(paf[:,:,16], alpha=.5) # right elbow
    axarr3.flat[m].set_title('PAFs (x comp. of Rwri to Relb): scale %d' % m)
    axarr3.flat[len(multiplier) + m].imshow(oriImg[:,:,[2,1,0]])
    ax3y = axarr3.flat[len(multiplier) + m].imshow(paf[:,:,17], alpha=.5) # right wrist
    axarr3.flat[len(multiplier) + m].set_title('PAFs (y comp. of Relb to Rwri): scale %d' % m)
    
    heatmap_avg = heatmap_avg + heatmap / len(multiplier)
    paf_avg = paf_avg + paf / len(multiplier)

f2.subplots_adjust(right=0.93)
cbar_ax = f2.add_axes([0.95, 0.15, 0.01, 0.7])
_ = f2.colorbar(ax2, cax=cbar_ax)

f3.subplots_adjust(right=0.93)
cbar_axx = f3.add_axes([0.95, 0.57, 0.01, 0.3])
_ = f3.colorbar(ax3x, cax=cbar_axx)
cbar_axy = f3.add_axes([0.95, 0.15, 0.01, 0.3])
_ = f3.colorbar(ax3y, cax=cbar_axy)

Input shape: (1, 184, 200, 3)
Output shape (heatmap): (1, 23, 25, 19)
Input shape: (1, 368, 392, 3)
Output shape (heatmap): (1, 46, 49, 19)
Input shape: (1, 552, 584, 3)
Output shape (heatmap): (1, 69, 73, 19)
Input shape: (1, 736, 784, 3)
Output shape (heatmap): (1, 92, 98, 19)

output_9_1.png

output_9_2.png

output_9_3.png

我们看上图输出的热点图,能过看到,经过运算把关键点(肘和手腕和髋,颈部等等)表现出来.

plt.imshow(oriImg[:,:,[2,1,0]])
plt.imshow(heatmap_avg[:,:,9], alpha=.5)
fig = matplotlib.pyplot.gcf()
cax = matplotlib.pyplot.gca()
fig.set_size_inches(20, 20)
fig.subplots_adjust(right=0.93)
cbar_ax = fig.add_axes([0.95, 0.15, 0.01, 0.7])
_ = fig.colorbar(ax2, cax=cbar_ax)

output_11_0.png

右膝盖的热力图

from numpy import ma
U = paf_avg[:,:,16] * -1
V = paf_avg[:,:,17]
X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))
M = np.zeros(U.shape, dtype='bool')
M[U**2 + V**2 < 0.5 * 0.5] = True
U = ma.masked_array(U, mask=M)
V = ma.masked_array(V, mask=M)

# 1
plt.figure()
plt.imshow(oriImg[:,:,[2,1,0]], alpha = .5)
s = 5
Q = plt.quiver(X[::s,::s], Y[::s,::s], U[::s,::s], V[::s,::s], 
               scale=50, headaxislength=4, alpha=.5, width=0.001, color='r')

fig = matplotlib.pyplot.gcf()
fig.set_size_inches(20, 20)

output_13_0.png

右臂的热力图

from scipy.ndimage.filters import gaussian_filter
all_peaks = []
peak_counter = 0

for part in range(19-1):
    map_ori = heatmap_avg[:,:,part]
    map = gaussian_filter(map_ori, sigma=3)
    
    map_left = np.zeros(map.shape)
    map_left[1:,:] = map[:-1,:]
    map_right = np.zeros(map.shape)
    map_right[:-1,:] = map[1:,:]
    map_up = np.zeros(map.shape)
    map_up[:,1:] = map[:,:-1]
    map_down = np.zeros(map.shape)
    map_down[:,:-1] = map[:,1:]
    
    peaks_binary = np.logical_and.reduce((map>=map_left, map>=map_right, map>=map_up, map>=map_down, map > param['thre1']))
    peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse
    peaks_with_score = [x + (map_ori[x[1],x[0]],) for x in peaks]
    id = range(peak_counter, peak_counter + len(peaks))
    peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]

    all_peaks.append(peaks_with_score_and_id)
    peak_counter += len(peaks)
    
# find connection in the specified sequence, center 29 is in the position 15
limbSeq = [[2,3], [2,6], [3,4], [4,5], [6,7], [7,8], [2,9], [9,10], \
           [10,11], [2,12], [12,13], [13,14], [2,1], [1,15], [15,17], \
           [1,16], [16,18], [3,17], [6,18]]
# the middle joints heatmap correpondence
mapIdx = [[31,32], [39,40], [33,34], [35,36], [41,42], [43,44], [19,20], [21,22], \
          [23,24], [25,26], [27,28], [29,30], [47,48], [49,50], [53,54], [51,52], \
          [55,56], [37,38], [45,46]]

import math
connection_all = []
special_k = []
mid_num = 10

for k in range(len(mapIdx)):
    score_mid = paf_avg[:,:,[x-19 for x in mapIdx[k]]]
    candA = all_peaks[limbSeq[k][0]-1]
    candB = all_peaks[limbSeq[k][1]-1]
    nA = len(candA)
    nB = len(candB)
    indexA, indexB = limbSeq[k]
    if(nA != 0 and nB != 0):
        connection_candidate = []
        for i in range(nA):
            for j in range(nB):
                vec = np.subtract(candB[j][:2], candA[i][:2])
                norm = math.sqrt(vec[0]*vec[0] + vec[1]*vec[1])
                vec = np.divide(vec, norm)
                
                startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
                               np.linspace(candA[i][1], candB[j][1], num=mid_num)))
                
                vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
                                  for I in range(len(startend))])
                vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
                                  for I in range(len(startend))])

                score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
                score_with_dist_prior = sum(score_midpts)/len(score_midpts) + min(0.5*oriImg.shape[0]/norm-1, 0)
                criterion1 = len(np.nonzero(score_midpts > param['thre2'])[0]) > 0.8 * len(score_midpts)
                criterion2 = score_with_dist_prior > 0
                if criterion1 and criterion2:
                    connection_candidate.append([i, j, score_with_dist_prior, score_with_dist_prior+candA[i][2]+candB[j][2]])

        connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
        connection = np.zeros((0,5))
        for c in range(len(connection_candidate)):
            i,j,s = connection_candidate[c][0:3]
            if(i not in connection[:,3] and j not in connection[:,4]):
                connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
                if(len(connection) >= min(nA, nB)):
                    break

        connection_all.append(connection)
    else:
        special_k.append(k)
        connection_all.append([])
        
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.ones((0, 20))
candidate = np.array([item for sublist in all_peaks for item in sublist])

for k in range(len(mapIdx)):
    if k not in special_k:
        partAs = connection_all[k][:,0]
        partBs = connection_all[k][:,1]
        indexA, indexB = np.array(limbSeq[k]) - 1

        for i in range(len(connection_all[k])): #= 1:size(temp,1)
            found = 0
            subset_idx = [-1, -1]
            for j in range(len(subset)): #1:size(subset,1):
                if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
                    subset_idx[found] = j
                    found += 1
            
            if found == 1:
                j = subset_idx[0]
                if(subset[j][indexB] != partBs[i]):
                    subset[j][indexB] = partBs[i]
                    subset[j][-1] += 1
                    subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
            elif found == 2: # if found 2 and disjoint, merge them
                j1, j2 = subset_idx
                print ("found = 2")
                membership = ((subset[j1]>=0).astype(int) + (subset[j2]>=0).astype(int))[:-2]
                if len(np.nonzero(membership == 2)[0]) == 0: #merge
                    subset[j1][:-2] += (subset[j2][:-2] + 1)
                    subset[j1][-2:] += subset[j2][-2:]
                    subset[j1][-2] += connection_all[k][i][2]
                    subset = np.delete(subset, j2, 0)
                else: # as like found == 1
                    subset[j1][indexB] = partBs[i]
                    subset[j1][-1] += 1
                    subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]

            # if find no partA in the subset, create a new subset
            elif not found and k < 17:
                row = -1 * np.ones(20)
                row[indexA] = partAs[i]
                row[indexB] = partBs[i]
                row[-1] = 2
                row[-2] = sum(candidate[connection_all[k][i,:2].astype(int), 2]) + connection_all[k][i][2]
                subset = np.vstack([subset, row])

# delete some rows of subset which has few parts occur
deleteIdx = [];
for i in range(len(subset)):
    if subset[i][-1] < 4 or subset[i][-2]/subset[i][-1] < 0.4:
        deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)

我们把关键点全部可视化一下:

colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
          [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
          [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
cmap = matplotlib.cm.get_cmap('hsv')

canvas = cv2.imread(test_image) # B,G,R order

for i in range(18):
    rgba = np.array(cmap(1 - i/18. - 1./36))
    rgba[0:3] *= 255
    for j in range(len(all_peaks[i])):
        cv2.circle(canvas, all_peaks[i][j][0:2], 4, colors[i], thickness=-1)

to_plot = cv2.addWeighted(oriImg, 0.3, canvas, 0.7, 0)
plt.imshow(to_plot[:,:,[2,1,0]])
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(12, 12)

output_18_0.png

下一步我们把关键节点连起来就可以了

stickwidth = 4

for i in range(17):
    for n in range(len(subset)):
        index = subset[n][np.array(limbSeq[i])-1]
        if -1 in index:
            continue
        cur_canvas = canvas.copy()
        Y = candidate[index.astype(int), 0]
        X = candidate[index.astype(int), 1]
        mX = np.mean(X)
        mY = np.mean(Y)
        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
        polygon = cv2.ellipse2Poly((int(mY),int(mX)), (int(length/2), stickwidth), int(angle), 0, 360, 1)
        cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
        canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
        
plt.imshow(canvas[:,:,[2,1,0]])
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(12, 12)

output_20_0.png

以上过程可以学习到深度学习对人体姿态的算法过程,通过关键节点的识别和链接,完整形成人体姿态的描述.

下一步,我们可以通过人体姿态的识别可以实现如下功能:

人体姿态控制计算机或设备
通过姿态预测人的行为
实时姿态生成动画人物
等等吧

最后编辑于：2018.02.05 22:46:07

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万荣杀人案实录
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护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
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白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
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活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
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日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
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男人毒药：我在死后第九天来索命
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一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
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情欲美人皮
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代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
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