-
Notifications
You must be signed in to change notification settings - Fork 0
/
ImageRec_v4.py
223 lines (199 loc) · 5.52 KB
/
ImageRec_v4.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
from PIL import Image, ImageDraw
import numpy as np
from math import sqrt,pi,cos,sin
import datetime as dat
import os
#Approx. area of black pixels
def Area(pic):
w,h=pic.size
area=0.
for i in range(w):
for j in range(h):
area+=255.-pic.getpixel((i,j))
return area/(255.*w*h)
fg='BEAN_999.bmp' #'convex13.bmp'
IMR=os.path.join('./images/',fg)
im=Image.open(IMR)
wi,he=im.size
img=np.zeros((wi,he))
fx=lambda x: (x-float(wi)*0.5)/100.
fy=lambda y: (-y+float(he)*0.5)/100.
f_x=lambda x: x*100.+float(wi)*0.5
f_y=lambda y: -1.*(y*100.-float(he)*0.5)
for i in range(wi):
for j in range(wi):
pixv=im.getpixel((i,j))
img[i][j]=pixv
print "picture loaded..."
# ######################
#
# START AMCF
#
#
#Nvidia module
#
import pycuda.driver as cuda
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
tt=np.linspace(0,2*np.pi,31)
tt=list(tt)
tt.pop()
tt=np.array(tt)
global N
N=len(tt)
RR=9.0
radpix=5
# Import all (string) kernels for AMCF
import mod_amcf_14 as amcf
amcf.init_ker(N,wi,he)
#
#
# Create arrays
#
# Initial curve
M=np.empty((N,2))
for i in range(N):
M[i][0]=7.9*cos(tt[i])
M[i][1]=7.9*sin(tt[i])
#
#
A=np.zeros((N,N))
np.fill_diagonal(A,-1)
np.fill_diagonal(A[1:N,2:N],1)
A[0][:]=np.ones(N)
A[N-1][0]=1
A_inv=np.linalg.inv(A)
#
# Pass containers to GPU
M_gpu=gpuarray.to_gpu(M.astype(np.float64))
Tan_gpu=gpuarray.to_gpu(np.zeros((N,2)).astype(np.float64))
Nor_gpu=gpuarray.to_gpu(np.zeros((N,2)).astype(np.float64))
K_gpu=gpuarray.to_gpu(np.zeros((N,2)).astype(np.float64))
A_invgpu=gpuarray.to_gpu(A_inv.astype(np.float64))
b_gpu=gpuarray.to_gpu(np.zeros(N).astype(np.float64))
a_gpu=gpuarray.to_gpu(np.zeros(N).astype(np.float64))
d_gpu=gpuarray.to_gpu(np.zeros(N).astype(np.float64))
v_gpu=gpuarray.to_gpu(np.zeros(N).astype(np.float64))
im_gpu=gpuarray.to_gpu(img.astype(np.float64))
Pix_gpu=gpuarray.to_gpu(np.zeros(N).astype(np.float64))
Sol=gpuarray.to_gpu(np.zeros((N,N)).astype(np.float64))
bu_gpu=gpuarray.to_gpu(np.zeros(2*N).astype(np.float64))
Usys=gpuarray.to_gpu(np.zeros((2*N,2*N)).astype(np.float64))
#
# Load kernel strings as GPU functions
mod = SourceModule(amcf.TanNorCurv)
TNK=mod.get_function("TNK")
DistF = SourceModule(amcf.distanceV)
Dist=DistF.get_function("Dist")
Evolution=SourceModule(amcf.Evolution)
Evolve=Evolution.get_function("Evolution")
BL=SourceModule(amcf.BLin)
BLin=BL.get_function("BLinear")
MxV=SourceModule(amcf.MMxV)
MultiMxV=MxV.get_function("MultiMxV")
Upt=SourceModule(amcf.UPT)
UpTan=Upt.get_function("Up")
BSys=SourceModule(amcf.Bu,no_extern_c=True)
Bu=BSys.get_function("Bu")
ASys=SourceModule(amcf.Au,no_extern_c=True)
Au=ASys.get_function("Au")
PixV=SourceModule(amcf.pixval)
PixVal=PixV.get_function('pixv')
#Parameters
h=np.float64(1./N)
dt=np.float64(h**2)
mu=np.float64(0.15)
q_gpu=np.float64(1.0)
qpos_gpu=gpuarray.to_gpu(np.zeros(2).astype(np.float64)) #charge position
Tlim=45000 #Max iterations
if os.path.isdir('data')==0:
os.mkdir('data')
route='data'
print 'start...'
Ini=dat.datetime.now()
for tk in range(Tlim):
bu_gpu=gpuarray.to_gpu(np.zeros(2*N).astype(np.float64))
Usys=gpuarray.to_gpu(np.zeros((2*N,2*N)).astype(np.float64))
name='time_'+str(tk).zfill(15)+'.txt'
#save point coordinates each 1000 iterations (optional)
if (tk%1000)==0:
f=open(os.path.join(route,name),'w')
MM=M_gpu.get()
x=[MM[i][0] for i in range(N)]
y=[MM[i][1] for i in range(N)]
for i in range(N):
print>>f,MM[i][0],MM[i][1]
f.close()
Dist(M_gpu,d_gpu,block=(2,N,1))
C_lengpu=np.float64(gpuarray.sum(d_gpu).get())
BLin(d_gpu,h,C_lengpu,b_gpu,block=(N,1,1))
# Solve system for a's (tangential coponents)
MultiMxV(A_invgpu,b_gpu,a_gpu,block=(N,1,1))
# Compute Tangent, Normal and Curvature vectors (TNK)
TNK(M_gpu,Tan_gpu,K_gpu,Nor_gpu,mu,block=(2,N,1))
PixVal(M_gpu,Pix_gpu,im_gpu,block=(N,1,1))
# Set 2N linear system
Bu(M_gpu,K_gpu,Nor_gpu,q_gpu,qpos_gpu,Pix_gpu,bu_gpu,block=(2*N,1,1),grid=(1,1))
Au(M_gpu,Nor_gpu,Usys,q_gpu,qpos_gpu,Pix_gpu,block=(2*N,1,1),grid=(1,1))
bu=bu_gpu.get()
U=Usys.get()
ss=np.linalg.solve(U,bu)
sss=ss[:N]
Sol_gpu=gpuarray.to_gpu(sss.astype(np.float64))
# Compute a*T and v*N
UpTan(Tan_gpu,a_gpu,block=(2,N,1))
UpTan(Nor_gpu,Sol_gpu,block=(2,N,1))
#Update cordinates
Evolve(M_gpu,Tan_gpu,Nor_gpu,dt,block=(2,N,1))
Px=Pix_gpu.get()
# Stop if all points have reached the black area
if (len([Px[i] for i in range(N) if Px[i]<=190])==N) :
break
Fin=dat.datetime.now()
print 'time: ',dat.timedelta.total_seconds(Fin-Ini)
MM=M_gpu.get()
x=[MM[i][0] for i in range(N)]
y=[MM[i][1] for i in range(N)]
x.append(x[0])
y.append(y[0])
#
name='time_'+str(tk).zfill(15)+'.txt'
f=open(os.path.join(route,name),'w')
for i in range(N):
print>>f,MM[i][0],MM[i][1]
f.close()
# ######################
# Output
P=M_gpu.get()
pol=[(int(f_x(P[i][0])),int(f_y(P[i][1]))) for i in range(N) ]
im2=Image.new(mode='L',size=(wi,he),color=255)
draw=ImageDraw.Draw(im2)
draw.polygon(pol,fill=0)
del draw
fm='poly_detected.bmp'
im2.save(fm)
a1=Area(im)
a2=Area(Image.open(fm))
print 'Acc: ',(1-abs(a1-a2)/float(a1))*100,' %'
print 'iterations: ', tk
print 'matches: ', len([Px[i] for i in range(N) if Px[i]<=80]),'/'+str(N)
#
# Plot
import matplotlib.pyplot as plt
import pylab as pyl
im = plt.imread(IMR)
implot = plt.imshow(im,extent=[-9,9,-9,9] ,cmap='gray')
plt.grid(True)
dir='data'
FILES=os.listdir(dir)
FILES.sort()
for file in FILES:
x,y=pyl.loadtxt(os.path.join(dir,file),unpack=True)
X=list(x)
X.append(X[0])
Y=list(y)
Y.append(Y[0])
plt.plot(X,Y,'-o',ms=1.8,color='orange')
plt.savefig('REC_'+str(len(x)),fmt='png')
plt.show()