ShuttlecockHittingEventDetection-RallyAnalysis-in-Badminton-Using-ComputerVision-MachineLearning/TrackNet.py at main · FooHou111/ShuttlecockHittingEventDetection-RallyAnalysis-in-Badminton-Using-ComputerVision-MachineLearning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
from keras.models import *
from keras.layers import *
from keras.activations import *

def TrackNet( input_height, input_width ): #input_height = 288, input_width = 512

	imgs_input = Input(shape=(9,input_height,input_width))

	#Layer1
	x = Conv2D(64, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(imgs_input)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer2
	x = Conv2D(64, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x1 = ( BatchNormalization())(x)

	#Layer3
	x = MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first' )(x1)

	#Layer4
	x = Conv2D(128, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer5
	x = Conv2D(128, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x2 = ( BatchNormalization())(x)

	#Layer6
	x = MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first' )(x2)

	#Layer7
	x = Conv2D(256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer8
	x = Conv2D(256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer9
	x = Conv2D(256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('relu'))(x)
	x3 = ( BatchNormalization())(x)

	#Layer10
	x = MaxPooling2D((2, 2), strides=(2, 2), data_format='channels_first' )(x3)

	#Layer11
	x = ( Conv2D(512, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer12
	x = ( Conv2D(512, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer13
	x = ( Conv2D(512, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer14
	x = concatenate( [UpSampling2D( (2,2), data_format='channels_first')(x), x3], axis=1)

	#Layer15
	x = ( Conv2D( 256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer16
	x = ( Conv2D( 256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer17
	x = ( Conv2D( 256, (3, 3), kernel_initializer='random_uniform', padding='same', data_format='channels_first'))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer18
	x = concatenate( [UpSampling2D( (2,2), data_format='channels_first')(x), x2], axis=1)

	#Layer19
	x = ( Conv2D( 128 , (3, 3), kernel_initializer='random_uniform', padding='same' , data_format='channels_first' ))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer20
	x = ( Conv2D( 128 , (3, 3), kernel_initializer='random_uniform', padding='same' , data_format='channels_first' ))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer21
	x = concatenate( [UpSampling2D( (2,2), data_format='channels_first')(x), x1], axis=1)

	#Layer22
	x = ( Conv2D( 64 , (3, 3), kernel_initializer='random_uniform', padding='same'  , data_format='channels_first' ))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer23
	x = ( Conv2D( 64 , (3, 3), kernel_initializer='random_uniform', padding='same'  , data_format='channels_first' ))(x)
	x = ( Activation('relu'))(x)
	x = ( BatchNormalization())(x)

	#Layer24
	x =  Conv2D( 1 , (1, 1) , kernel_initializer='random_uniform', padding='same', data_format='channels_first' )(x)
	x = ( Activation('sigmoid'))(x)


	o_shape = Model(imgs_input , x ).output_shape

	#print ("layer24 output shape:", o_shape[1],o_shape[2],o_shape[3])
	#Layer24 output shape: (1, 288, 512)

	OutputHeight = o_shape[2]
	OutputWidth = o_shape[3]

	#Reshape the size to (288, 512)
	output = (Reshape((OutputHeight, OutputWidth)))(x)

	model = Model( imgs_input , output)
	#model input unit:9*288*512, output unit:288*512
	model.outputWidth = OutputWidth
	model.outputHeight = OutputHeight

	#Show model's details
	#model.summary()

	return model