Visual_Tracking_api/DSSTtracker.py at master · vtddggg/Visual_Tracking_api · 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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import sys
import cv2
import numpy as np
import utils
import vot
from pyhog import pyhog

class padding:
    def __init__(self):
        self.generic = 1.8
        self.large = 1
        self.height = 0.4


class DSSTtracker:
    def __init__(self, image, region):
        output_sigma_factor = 1 / float(16)
        scale_sigma_factor = 1 / float(4)
        self.lamda = 1e-2
        self.lamda_scale = 1e-2
        self.interp_factor = 0.025
        nScales = 33  # number of scale levels
        scale_model_factor = 1.0
        scale_step = 1.02  # step of one scale level
        scale_model_max_area = 32 * 16
        self.currentScaleFactor = 1.0

        self.target_size = np.array([region.height, region.width])
        self.pos = [region.y + region.height / 2, region.x + region.width / 2]
        init_target_size = self.target_size
        self.base_target_size = self.target_size / self.currentScaleFactor
        self.sz = utils.get_window_size(self.target_size, image.shape[:2],padding())

        output_sigma = np.sqrt(np.prod(self.target_size)) * output_sigma_factor
        scale_sigma = np.sqrt(nScales) * scale_sigma_factor
        grid_y = np.arange(np.floor(self.sz[0])) - np.floor(self.sz[0] / 2)
        grid_x = np.arange(np.floor(self.sz[1])) - np.floor(self.sz[1] / 2)
        rs, cs = np.meshgrid(grid_x, grid_y)
        y = np.exp(-0.5 / output_sigma ** 2 * (rs ** 2 + cs ** 2))

        # Gaussian shaped label for scale estimation
        ss = np.arange(nScales) - np.ceil(nScales / 2)
        ys = np.exp(-0.5 * (ss ** 2) / scale_sigma ** 2)
        self.scaleFactors = np.power(scale_step, -ss)
        self.yf = np.fft.fft2(y, axes=(0, 1))
        self.ysf = np.fft.fft(ys)

        feature_map = utils.get_subwindow(image, self.pos, self.sz, feature='hog')

        self.cos_window = np.outer(np.hanning(y.shape[0]), np.hanning(y.shape[1]))
        x_hog = np.multiply(feature_map, self.cos_window[:, :, None])
        xf = np.fft.fft2(x_hog, axes=(0, 1))

        # scale search preprocess
        if nScales % 2 == 0:
            self.scale_window = np.hanning(nScales + 1)
            self.scale_window = self.scale_window[1:]
        else:
            self.scale_window = np.hanning(nScales)

        self.scaleSizeFactors = self.scaleFactors
        self.min_scale_factor = np.power(scale_step,
                                    np.ceil(np.log(5. / np.min(self.sz)) / np.log(scale_step)))

        self.max_scale_factor = np.power(scale_step,
                                    np.floor(np.log(np.min(np.divide(image.shape[:2],
                                                                     self.base_target_size)))
                                             / np.log(scale_step)))

        if scale_model_factor * scale_model_factor * np.prod(init_target_size) > scale_model_max_area:
            scale_model_factor = np.sqrt(scale_model_max_area / np.prod(init_target_size))

        self.scale_model_sz = np.floor(init_target_size * scale_model_factor)

        s = utils.get_scale_subwindow(image, self.pos, self.base_target_size,
                                self.currentScaleFactor * self.scaleSizeFactors, self.scale_window,
                                self.scale_model_sz)

        sf = np.fft.fftn(s, axes=[0])

        self.x_num = np.multiply(self.yf[:, :, None], np.conj(xf))
        self.x_den = np.real(np.sum(np.multiply(xf, np.conj(xf)), axis=2))

        self.s_num = np.multiply(self.ysf[:, None], np.conj(sf))
        self.s_den = np.real(np.sum(np.multiply(sf, np.conj(sf)), axis=1))

    def track(self, image):
        test_patch = utils.get_subwindow(image, self.pos, self.sz, scale_factor=self.currentScaleFactor)
        hog_feature_t = pyhog.features_pedro(test_patch / 255., 1)
        hog_feature_t = np.lib.pad(hog_feature_t, ((1, 1), (1, 1), (0, 0)), 'edge')
        xt = np.multiply(hog_feature_t, self.cos_window[:, :, None])
        xtf = np.fft.fft2(xt, axes=(0, 1))
        response = np.real(np.fft.ifft2(np.divide(np.sum(np.multiply(self.x_num, xtf),
                                                         axis=2), (self.x_den + self.lamda))))

        v_centre, h_centre = np.unravel_index(response.argmax(), response.shape)
        vert_delta, horiz_delta = \
            [(v_centre - response.shape[0] / 2) * self.currentScaleFactor,
             (h_centre - response.shape[1] / 2) * self.currentScaleFactor]

        self.pos = [self.pos[0] + vert_delta, self.pos[1] + horiz_delta]

        st = utils.get_scale_subwindow(image, self.pos, self.base_target_size,
                                 self.currentScaleFactor * self.scaleSizeFactors, self.scale_window,
                                 self.scale_model_sz)
        stf = np.fft.fftn(st, axes=[0])

        scale_reponse = np.real(np.fft.ifftn(np.sum(np.divide(np.multiply(self.s_num, stf),
                                                              (self.s_den[:, None] + self.lamda_scale)), axis=1)))
        recovered_scale = np.argmax(scale_reponse)
        self.currentScaleFactor = self.currentScaleFactor * self.scaleFactors[recovered_scale]

        if self.currentScaleFactor < self.min_scale_factor:
            self.currentScaleFactor = self.min_scale_factor
        elif self.currentScaleFactor > self.max_scale_factor:
            self.currentScaleFactor = self.max_scale_factor

        # update
        update_patch = utils.get_subwindow(image, self.pos, self.sz, scale_factor=self.currentScaleFactor)
        hog_feature_l = pyhog.features_pedro(update_patch / 255., 1)
        hog_feature_l = np.lib.pad(hog_feature_l, ((1, 1), (1, 1), (0, 0)), 'edge')
        xl = np.multiply(hog_feature_l, self.cos_window[:, :, None])
        xlf = np.fft.fft2(xl, axes=(0, 1))
        new_x_num = np.multiply(self.yf[:, :, None], np.conj(xlf))
        new_x_den = np.real(np.sum(np.multiply(xlf, np.conj(xlf)), axis=2))

        sl = utils.get_scale_subwindow(image, self.pos, self.base_target_size,
                                       self.currentScaleFactor * self.scaleSizeFactors, self.scale_window,
                                       self.scale_model_sz)
        slf = np.fft.fftn(sl, axes=[0])
        new_s_num = np.multiply(self.ysf[:, None], np.conj(slf))
        new_s_den = np.real(np.sum(np.multiply(slf, np.conj(slf)), axis=1))

        self.x_num = (1 - self.interp_factor) * self.x_num + self.interp_factor * new_x_num
        self.x_den = (1 - self.interp_factor) * self.x_den + self.interp_factor * new_x_den
        self.s_num = (1 - self.interp_factor) * self.s_num + self.interp_factor * new_s_num
        self.s_den = (1 - self.interp_factor) * self.s_den + self.interp_factor * new_s_den

        self.target_size = self.base_target_size * self.currentScaleFactor

        return vot.Rectangle(self.pos[1] - self.target_size[1] / 2,
                             self.pos[0] - self.target_size[0] / 2,
                             self.target_size[1],
                             self.target_size[0]
                             )

handle = vot.VOT("rectangle")
selection = handle.region()

imagefile = handle.frame()
if not imagefile:
    sys.exit(0)

image = cv2.imread(imagefile)
tracker = DSSTtracker(image, selection)
while True:
    imagefile = handle.frame()
    if not imagefile:
        break
    image = cv2.imread(imagefile)
    region = tracker.track(image)
    handle.report(region)
handle.quit()