-
Notifications
You must be signed in to change notification settings - Fork 2
/
ssd_stimuli.py
237 lines (182 loc) · 10.5 KB
/
ssd_stimuli.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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
import ssd_predict
import argparse
import cv2
import numpy as np
from numpy import matlib
from psychopy import visual, core, event
import time
from PIL import Image
import torch
from torch.autograd import Variable
class SSVEP(object):
def __init__(self, mywin= visual.Window([800, 600], fullscr=True, monitor='testMonitor',units='deg', waitBlanking = False),trialdur = 3, numtrials=6, waitdur=2):
self.mywin = mywin
self.myStim = visual.GratingStim(win=self.mywin, pos=[0,0], units = 'norm')
# colour for psychopy
self.white = [1, 1, 1]
self.black = [-1, -1, -1]
self.red = [1, -1, -1]
self.fixation = visual.GratingStim(win=self.mywin, color = self.red, size = 10, sf=0, colorSpace='rgb', units='pix')
# frame array for 10Hz
self.frame_f0 = [1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1,1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1]
# frame array for 12Hz
self.frame_f1 = [1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1]
# frame array for 15Hz
self.frame_f2 = [1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1]
self.trialdur = trialdur
self.numtrials = numtrials
self.waitdur = waitdur
self.nBox = 3
self.numChan = 9
self.sample_rate = 500
self.capBox = int(self.numtrials/self.nBox)
self.aBox = np.arange(3)
self.unshuffled = np.matlib.repmat(self.aBox, self.capBox, 1)
self.randperm = np.random.permutation(self.numtrials)
self.Boxes = self.unshuffled.ravel()
self.Boxes = self.Boxes[self.randperm]
print (self.Boxes)
def initBoxes(self):
self.pattern1_f0 = visual.GratingStim(win=self.mywin, name='pattern1',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0.0,
color=self.white, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-1.0)
self.pattern2_f0 = visual.GratingStim(win=self.mywin, name='pattern2',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0,
color=self.black, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-2.0)
self.pattern1_f1 = visual.GratingStim(win=self.mywin, name='pattern1',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0.0,
color=self.white, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-1.0)
self.pattern2_f1 = visual.GratingStim(win=self.mywin, name='pattern2',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0,
color=self.black, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-2.0)
self.pattern1_f2 = visual.GratingStim(win=self.mywin, name='pattern1',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0.0,
color=self.white, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-1.0)
self.pattern2_f2 = visual.GratingStim(win=self.mywin, name='pattern2',units='pix',
tex=None, mask=None,
ori=0, sf=1, phase=0,
color=self.black, colorSpace='rgb', opacity=0.8,
texRes=256, interpolate=True, depth=-2.0)
def start (self):
cap = cv2.VideoCapture('/home/nikkhadijah/Videos/video.mp4')
pos_frame = cap.get(cv2.CAP_PROP_POS_FRAMES)
self.sframe = [(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))]
self.sframe = self.sframe[0]
self.fixCount = 0
self.count = 0
while(cap.isOpened()):
while self.count < self.numtrials:
SSVEP.initBoxes(self)
ret, img = cap.read()
if ret:
im = Image.frombytes("RGB", (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))), img.tostring(), "raw", "BGR", 0, 1)
self.myStim.setTex(im)
self.myStim.draw()
self.mywin.flip()
pos_frame = cap.get(cv2.CAP_PROP_POS_FRAMES)
retdet = ssd_predict.predict(img)
pt = retdet[0]
idxcls = retdet[1]
if len(pt) > 0:
pt = np.vstack(pt)
for ndet in range (0, (len(pt))):
# newpoint -- converting opencv format to psychopy
newPt = SSVEP.newPoint(self, pt[ndet])
# assign the positions and the boxes for stimuli based on new points calculated
if idxcls[ndet] == 0 or idxcls[ndet] == 3:
self.pattern1_f0.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern1_f0.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
self.pattern2_f0.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern2_f0.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
if idxcls[ndet] == 1 or idxcls[ndet] == 4:
self.pattern1_f1.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern1_f1.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
self.pattern2_f1.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern2_f1.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
if idxcls[ndet] == 2 or idxcls[ndet] == 5:
self.pattern1_f2.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern1_f2.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
self.pattern2_f2.pos = ((newPt[2]+newPt[0])/2), (newPt[1]+newPt[3])/2
self.pattern2_f2.size = (abs(newPt[2]-newPt[0])), (abs(newPt[3]-newPt[1]))
fixPos = [self.pattern1_f0.pos, self.pattern1_f1.pos, self.pattern1_f2.pos]
self.fixation.pos = (fixPos[self.Boxes[self.count]])
self.fixation.setAutoDraw(True)
ret, img = cap.read()
im = Image.frombytes("RGB", (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))), img.tostring(), "raw", "BGR", 0, 1)
self.myStim.setTex(im)
self.myStim.draw()
self.mywin.flip()
core.wait(2.0)
self.Trialclock = core.Clock()
#reset tagging
self.should_tag = False
while self.Trialclock.getTime() < self.trialdur:
self.fixation.setAutoDraw(True)
for frameN in range(len(self.frame_f0)):
self.myStim.draw()
if self.frame_f0[frameN] == 1 :
self.pattern1_f0.draw()
if self.frame_f0[frameN] == -1 :
self.pattern2_f0.draw()
if self.frame_f1[frameN] == 1 :
self.pattern1_f1.draw()
if self.frame_f1[frameN] == -1 :
self.pattern2_f1.draw()
if self.frame_f2[frameN] == 1 :
self.pattern1_f2.draw()
if self.frame_f2[frameN] == -1 :
self.pattern2_f2.draw()
self.mywin.flip()
self.myStim.draw()
self.fixation.setAutoDraw(False)
self.fixCount+=1
self.mywin.flip()
core.wait(self.waitdur)
self.Trialclock.reset()
print("Trial %d Complete" % self.count)
self.count+=1
else:
print ("End of video")
# It is better to wait for a while for the next frame to be ready
cv2.waitKey(1000)
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def stop(self):
self.mywin.close()
core.quit()
def newPoint(self, pt):
newPt = np.zeros(4)
if (pt[0]<(self.sframe[0]/2)):
newPt[0] = -((self.sframe[0]/2-pt[0]))
else:
newPt[0] = (pt[0] - (self.sframe[0]/2))
if (pt[1]<(self.sframe[1]/2)):
newPt[1] = ((self.sframe[1]/2)-pt[1])
else:
newPt[1] = -(pt[1]-(self.sframe[1]/2))
if (pt[2]<(self.sframe[0]/2)):
newPt[2] = -((self.sframe[0]/2)-pt[2])
else:
newPt[2] = (pt[2]-(self.sframe[0]/2))
if (pt[3]<(self.sframe[1]/2)):
newPt[3] = ((self.sframe[1]/2)-pt[3])
else:
newPt[3] = -(pt[3]-(self.sframe[1]/2))
return newPt
if __name__ == '__main__':
stimuli = SSVEP()
stimuli.start()
stimuli.stop()