-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathDateLoader.py
213 lines (155 loc) · 6.6 KB
/
DateLoader.py
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
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
import glob
import cv2
import numpy as np
import mxnet as mx
from mxnet import gluon, image
from mxnet.gluon import nn
from mxnet.gluon import data as gdata
class VOT_DataLoader(gluon.data.Dataset):
def __init__(self,rootPath='VOT/',numofSeq=1,seqrange=100,test=True):
self.rootPath = rootPath
self.numofSeq = numofSeq
self.seqrange = seqrange
self.data, self.GTinfo = self.getDataset()
self.test = test
# self.cropSize = 128
def getDataset(self):
rootPath = self.rootPath
with open(rootPath + 'list2.txt', 'r') as f:
FolderList = f.readlines()
for folderName in FolderList[:self.numofSeq ]:
folder_Imgs = glob.glob(rootPath + folderName[:-1] + "/*.jpg")
with open(rootPath + folderName[:-1] + "/groundtruth.txt", 'r') as f:
GTinfo = f.readlines()
data = []
for idx in range(len(folder_Imgs)):
img = mx.image.imread(folder_Imgs[idx])
data.append(img)
#print(GTinfo)
return data, GTinfo
def cropImg(self,img,bbox,Det=False):
bboxes = bbox.strip('\n')
bboxes = bboxes.split(',')
bboxes = [int(float(x)) for x in bboxes]
coord = np.array(bboxes).reshape(-1, 2)
xy_max = np.max(coord, axis=0)
xy_min = np.min(coord, axis=0)
w = xy_max[0] - xy_min[0]
h = xy_max[1] - xy_min[1]
p = ((w + h) / 2)
if Det:
A = int((np.sqrt((w + p) * (h + p)))) * 2
else:
A = int((np.sqrt((w + p) * (h + p))))
center = xy_max / 2 + xy_min / 2
center = center.astype('int32')
luy = np.clip((center[1] - (A // 2)), 0, img.shape[0])
rdy = np.clip((center[1] + (A // 2)), 0, img.shape[0])
lux = np.clip((center[0] - (A // 2)), 0, img.shape[1])
rdx = np.clip((center[0] + (A // 2)), 0, img.shape[1])
img = img[luy:rdy, lux:rdx, :]
if Det:
# print(img.shape)
scale_w= 255/img.shape[1]
scale_h=255/img.shape[0]
img = image.imresize(img, 255, 255)
bboxInDet = mx.ndarray.array([(((center[0]) - lux)*scale_w) / 255, (((center[1] - luy )*scale_h)) / 255, ((w*scale_w) / 255), ((h*scale_h) / 255)])
#coord = mx.ndarray.array([(center[0] ) /255, center[1] /255, w/255, h/255])
return img,bboxInDet
else:
img = image.imresize(img,127,127)
return img
def cropBoundingbox_center(self, tempImg, detImg, tempbbox, detbbox):
cropTempImg = self.cropImg(tempImg, tempbbox)
cropDetImg,coord = self.cropImg(detImg, detbbox,Det=True)
return cropTempImg, cropDetImg,coord
def normalize_image(self, data):
data = data.astype('float32') / 255
normalized = mx.image.color_normalize(data,
mean=mx.nd.array([0.485, 0.456, 0.406]),
std=mx.nd.array([0.229, 0.224, 0.225]))
return normalized
def __getitem__(self, item):
endidx = item + self.seqrange if item + self.seqrange < len(self.data) else len(self.data)
if not self.test:
detIdx = np.random.randint(item, endidx)
if self.test:
detIdx = item+1
tempImg = self.data[item]
detImg = self.data[detIdx]
cropTempImg, cropDetImg,Gtbbox = self.cropBoundingbox_center(tempImg, detImg, self.GTinfo[item], self.GTinfo[detIdx])
#cropTempImg = self.normalize_image(cropTempImg)
#cropDetImg = self.normalize_image(cropDetImg)
x, y, w, h = Gtbbox[0], Gtbbox[1], Gtbbox[2], Gtbbox[3]
#print(w,h)
lux = x-w/2
luy = y-h/2
rdx = x+w/2
rdy = y+h/2
Gtbbox[0] =lux
Gtbbox[1] =luy
Gtbbox[2] =rdx
Gtbbox[3] =rdy
#x, y, w, h = Gtbbox[0], Gtbbox[1], Gtbbox[2], Gtbbox[3]
# lux = (Gtbbox[0] - Gtbbox[2]) /2
# luy = (Gtbbox[1] - Gtbbox[3]) /2
# rdx = (Gtbbox[0] + Gtbbox[2]) /2
# rdy = (Gtbbox[1] + Gtbbox[3]) /2
# Gtbbox[0] = lux
# Gtbbox[1] = luy
# Gtbbox[2] = rdx
# Gtbbox[3] = rdy
return cropTempImg.transpose((2, 0, 1)).astype('float32'), cropDetImg.transpose((2, 0, 1)).astype('float32'), Gtbbox
def __len__(self):
return len(self.data)
# def show(self):
# tempIdx = np.random.randint(len(self.folder_Imgs)-100)
# detIdx = np.random.randint(self.seqrange) +tempIdx
# print(tempIdx,detIdx)
# tempImg = mx.image.imread(self.folder_Imgs[tempIdx])
# detImg = mx.image.imread(self.folder_Imgs[detIdx])
#
# cropTempImg, cropDetImg = self.cropBoundingbox_center(tempImg,detImg , self.GTinfo[tempIdx], self.GTinfo[detIdx])
# print(cropTempImg.shape,cropDetImg.shape)
# # for idx, f_img in enumerate(self.folder_Imgs):
# # # img = cv2.imread(f_img)
# # img = mx.image.imread(f_img )
# # tempImg, detImg =self.cropBoundingbox_center(img, self.GTinfo[idx],crop_size=128) # ori or center
def LoadDataset(batchsize,shuffle=False,test=True):
dataset = VOT_DataLoader(test=test)
data_iter = gdata.DataLoader(dataset, batchsize, shuffle=shuffle)
return data_iter
def showndimg(img):
img = mx.ndarray.transpose(img, (0, 2, 3, 1)).astype('uint8')
img = img.asnumpy()
return img
def center2cornr(bbox):
bbox = [int(x * 255) for x in bbox]
lux, luy, rdx, rdy = bbox[0], bbox[1], bbox[2], bbox[3]
#x, y, w, h = bbox[0], bbox[1], bbox[2], bbox[3]
# lux = x-w//2
# luy = y-h//2
# rdx = x+w//2
# rdy = y+h//2
return lux, luy, rdx, rdy
def plotImg(Tempimgs, Detimgs, bbox):
bbox = bbox.asnumpy()
Detimgs = showndimg(Detimgs)
Tempimgs = showndimg(Tempimgs)
for idx, detimg in enumerate(Detimgs):
lux, luy, rdx, rdy = center2cornr(bbox[idx])
cv2.rectangle(detimg, (lux, luy), (rdx, rdy), (0, 255, 0), 2)
print(Tempimgs.shape)
cv2.imshow('Tempimg', Tempimgs[idx, :, :, ::-1])
cv2.imshow('Detimg', detimg[:, :, ::-1])
cv2.waitKey(0)
if __name__ == "__main__":
ctx = mx.gpu()
batch_size = 30
train_iter = LoadDataset(batch_size)
for TempImgs, DetImgs, bboxes in train_iter:
break
print(TempImgs.shape)
print(DetImgs.shape)
print(bboxes.shape)
plotImg(TempImgs, DetImgs, bboxes)