/
buildfeaturespolygon.py
607 lines (483 loc) · 28.9 KB
/
buildfeaturespolygon.py
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import numpy, pygame, math
pygame.surfarray.use_arraytype('numpy')
#-------------------------------------------------
def buildimage(rval_points, rval_nbpol, nb_poly_max, batchsize, rval_bg,rval_fg, img_shape, neg, **dic):
surface = pygame.Surface(img_shape, depth=8)
surface_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surface))
rval_image = numpy.ndarray((batchsize, img_shape[0], img_shape[1]), dtype='uint8')
rval_image_flat = rval_image.reshape(batchsize, img_shape[0] * img_shape[1])
for j in xrange(batchsize):
surface.fill(int(int(rval_bg[j])))
for i in range (rval_nbpol[j]):
pygame.draw.polygon(surface, int(int(rval_fg[j,i])), rval_points[nb_poly_max*j+i], 0)
rval_image[j] = surface_ndarray
return rval_image_flat / 255.0 if not neg else (rval_image_flat / 255.0)*2-1
#----------------------------------------------------
def buildsegmentation(rval_points, rval_nbpol, nb_poly_max, batchsize, img_shape, depthmap, neg, neighbor = 'V8', **dic):
if neighbor is 'V4':
nmult = 2
else:
nmult = 4
depth = depthmap.reshape(batchsize,img_shape[0],img_shape[1])
rval_segmentation = numpy.zeros((batchsize, img_shape[0]*nmult, img_shape[1]), dtype='bool')
rval_segmentation_flat = rval_segmentation.reshape(batchsize, img_shape[0] * img_shape[1] * nmult)
for j in xrange(batchsize):
maxi = numpy.zeros(2,'uint8')
mini = numpy.asarray((img_shape[0],img_shape[1]),'uint8')
for i in range(rval_nbpol[j]):
maxi = numpy.asarray((maxi, rval_points[nb_poly_max*j+i].max(0))).max(0)
mini = numpy.asarray((mini, rval_points[nb_poly_max*j+i].min(0))).min(0)
x1 = max(0,mini[0]-3)
x2 = min(img_shape[0],maxi[0]+3)
y1 = max(0,mini[1]-3)
y2 = min(img_shape[1],maxi[1]+3)
rval_segmentation[j,x1:x2-1,y1:y2] = (depth[j,x1:x2-1,y1:y2] != depth[j,x1+1:x2,y1:y2]) * 255
rval_segmentation[j,x1+img_shape[0]:x2+img_shape[0],y1:y2-1] = \
(depth[j,x1:x2,y1:y2-1] != depth[j,x1:x2,y1+1:y2])*255
if nmult != 2:
rval_segmentation[j,x1+2*img_shape[0]:x2+2*img_shape[0]-1,y1:y2-1] = \
(depth[j,x1:x2-1,y1:y2-1] !=depth[j,x1+1:x2,y1+1:y2]) * 255
rval_segmentation[j,x1+3*img_shape[0]+1:x2+3*img_shape[0],y1:y2-1] = \
(depth[j,x1+1:x2,y1:y2-1] != depth[j,x1:x2-1,y1+1:y2])*255
return rval_segmentation_flat*1.0 if not neg else ((rval_segmentation_flat)*2-1)*1.0
#--------------------------------------------------------
def buildidentity(rval_points, rval_nbpol, nb_poly_max, rval_poly_id, n_vertices, batchsize, img_shape, neg, **dic):
surfaceidentity = pygame.Surface(img_shape, depth=8)
surfaceidentity_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surfaceidentity))
rval_identity = numpy.ndarray((batchsize, len(n_vertices), img_shape[0], img_shape[1]), dtype='bool')
rval_identity_flat = rval_identity.reshape(batchsize,len(n_vertices) * img_shape[0] * img_shape[1])
for j in xrange(batchsize):
surfaceidentity.fill(255)
for i in range (rval_nbpol[j]):
pygame.draw.polygon(surfaceidentity,int(int(rval_poly_id[j,i])), rval_points[nb_poly_max*j+i], 0)
for i in range(len(n_vertices)):
rval_identity[j,i] = (surfaceidentity_ndarray == i)
return rval_identity_flat*1.0 if not neg else ((rval_identity_flat)*2-1)*1.0
#---------------------------------------------------------
def builddepthmap(rval_points, rval_nbpol, nb_poly_max,batchsize,img_shape, neg, **dic):
surfacedepth = pygame.Surface(img_shape, depth=8)
surfacedepth_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surfacedepth))
rval_depth = numpy.ndarray((batchsize, img_shape[0], img_shape[1]), dtype='uint8')
rval_depth_flat = rval_depth.reshape(batchsize, img_shape[0] * img_shape[1])
for j in xrange(batchsize):
surfacedepth.fill(0)
for i in range (rval_nbpol[j]):
pygame.draw.polygon(surfacedepth,int(((i+1.0)/(rval_nbpol[j]))*255),rval_points[nb_poly_max*j+i],0)
rval_depth[j] = surfacedepth_ndarray
return rval_depth_flat/255.0 if not neg else (rval_depth_flat/255.0)*2-1
# --------------------------------------
def output(rval_poly_id, n_vertices, nb_poly_max,batchsize, **dic):
rval_output = numpy.zeros((batchsize, len(n_vertices)), dtype='int')
tmp = numpy.ones(nb_poly_max, dtype='uint8')
for j in xrange(batchsize):
for i in range(len(n_vertices)):
rval_output[j,i] = ((rval_poly_id[j,:] == tmp*i).sum())
return rval_output*1.0
#----------------------------------------------------------
def buildedges(rval_points, rval_nbpol, nb_poly_max, batchsize, img_shape, segmentation, neg, **dic):
if segmentation.size == img_shape[0] * img_shape[1] * 2:
nmult = 2
else:
nmult = 4
seg=segmentation.reshape(batchsize,nmult,img_shape[0],img_shape[1])
fullseg=numpy.ones((batchsize,2*nmult,img_shape[0],img_shape[1]),dtype='bool')
fullseg[:,0,:,:]=1-seg[:,0,:,:]
fullseg[:,1,1:,:]=1-seg[:,0,:-1,:]
fullseg[:,2,:,:]=1-seg[:,1,:,:]
fullseg[:,3,:,1:]=1-seg[:,1,:,:-1]
if nmult == 4:
fullseg[:,4,:,:]=1-seg[:,2,:,:]
fullseg[:,5,1:,1:]=1-seg[:,2,:-1,:-1]
fullseg[:,6,:,:]=1-seg[:,3,:,:]
fullseg[:,7,:-1,1:]=1-seg[:,3,1:,:-1]
rval_edges = numpy.ones((batchsize, img_shape[0], img_shape[1]), dtype='bool')
rval_edges_flat = rval_edges.reshape(batchsize, img_shape[0] * img_shape[1])
for j in xrange(batchsize):
maxi = numpy.zeros(2,'uint8')
mini = numpy.asarray((img_shape[0],img_shape[1]),'uint8')
for i in range(rval_nbpol[j]):
maxi = numpy.asarray((maxi, rval_points[nb_poly_max*j+i].max(0))).max(0)
mini = numpy.asarray((mini, rval_points[nb_poly_max*j+i].min(0))).min(0)
x1 = max(0,mini[0]-3)
x2 = min(img_shape[0],maxi[0]+3)
y1 = max(0,mini[1]-3)
y2 = min(img_shape[1],maxi[1]+3)
if nmult == 2:
rval_edges[j,x1:x2,y1:y2] = ( ( fullseg[j,0,x1:x2,y1:y2] ) * ( fullseg[j,1,x1:x2,y1:y2] )\
* ( fullseg[j,2,x1:x2,y1:y2] ) * ( fullseg[j,3,x1:x2,y1:y2] ))
else:
rval_edges[j,x1:x2,y1:y2] = ( ( fullseg[j,0,x1:x2,y1:y2] ) * ( fullseg[j,1,x1:x2,y1:y2] )\
* ( fullseg[j,2,x1:x2,y1:y2] ) * ( fullseg[j,3,x1:x2,y1:y2] )\
* ( fullseg[j,4,x1:x2,y1:y2] ) * ( fullseg[j,5,x1:x2,y1:y2] )\
* ( fullseg[j,6,x1:x2,y1:y2] ) * ( fullseg[j,7,x1:x2,y1:y2] ))
return (1-rval_edges_flat)*1.0 if not neg else ((1-rval_edges_flat)*2-1)*1.0
#----------------------------------------------------------
def buildedgesangle(rval_points, rval_nbpol, nb_poly_max, batchsize, img_shape, segmentation, neg, **dic):
surfaceedges = pygame.Surface(img_shape, depth=8)
surfaceedges_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surfaceedges))
if segmentation.size == img_shape[0] * img_shape[1] * 2:
nmult = 2
else:
nmult = 4
seg=segmentation.reshape(batchsize,nmult,img_shape[0],img_shape[1])
fullseg=numpy.ones((batchsize,2*nmult,img_shape[0],img_shape[1]),dtype='bool')
fullseg[:,0,:,:] = 1-seg[:,0,:,:]
fullseg[:,1,1:,:] = 1-seg[:,0,:-1,:]
fullseg[:,2,:,:] = 1-seg[:,1,:,:]
fullseg[:,3,:,1:] = 1-seg[:,1,:,:-1]
if nmult == 4:
fullseg[:,4,:,:] = 1-seg[:,2,:,:]
fullseg[:,5,1:,1:] = 1-seg[:,2,:-1,:-1]
fullseg[:,6,:,:] = 1-seg[:,3,:,:]
fullseg[:,7,:-1,1:] = 1-seg[:,3,1:,:-1]
rval_angles = numpy.zeros((batchsize, 4, img_shape[0], img_shape[1]), dtype='bool')
rval_angles_flat = rval_angles.reshape(batchsize, 4 * img_shape[0] * img_shape[1])
if nmult == 2:
shifts = numpy.asarray([[1,0],[0,1],[-1,0],[0,-1]])
if nmult == 4:
shifts=numpy.asarray([[1,0],[1,1],[0,1],[-1,1],[-1,0],[-1,-1],[0,-1],[1,-1]])
for j in xrange(batchsize):
maxi = numpy.zeros(2,'uint8')
mini = numpy.asarray((img_shape[0],img_shape[1]),'uint8')
rval_edges = numpy.ones((img_shape[0], img_shape[1]), dtype='bool')
surfaceedges.fill(0)
for i in range(rval_nbpol[j]):
points = rval_points[nb_poly_max*j+i]
angl = numpy.zeros(len(points),'uint8')
center = points.mean(0)
pointsctr = points - center
for k1 in range(len(points)):
if k1 == len(points)-1:
k2 = 0
else:
k2 = k1+1
edge = points[k2,:] - points[k1,:]
edge = edge / math.sqrt(pow(edge[0],2)+pow(edge[1],2))
angltmp = math.atan2(-edge[1],edge[0]) % math.pi #because y is inverted in the image
if angltmp <= math.pi/8 or angltmp > 7*math.pi/8:
angl[k1] = 1
elif angltmp <= 3*math.pi/8 and angltmp > math.pi/8:
angl[k1] = 2
elif angltmp <= 5*math.pi/8 and angltmp > 3*math.pi/8:
angl[k1] = 3
elif angltmp <= 7*math.pi/8 and angltmp > 5*math.pi/8:
angl[k1] = 4
tmp1 = numpy.dot(shifts,pointsctr[k1,:])+numpy.dot(shifts,pointsctr[k2,:])
tmp2 = tmp1.argsort()
for l in tmp2[-nmult-1:]:
pygame.draw.polygon(surfaceedges,int(int(angl[k1])),\
[center+shifts[l,:],points[k1,:]+shifts[l,:],points[k2,:]+shifts[l,:]],0)
for k1 in range(len(points)):
if k1 == len(points)-1:
k2 = 0
else:
k2 = k1+1
pygame.draw.polygon(surfaceedges,int(int(angl[k1])),\
[(center+points[k2,:]+points[k1,:])/3.0,points[k1,:],points[k2,:]],0)
maxi = numpy.asarray((maxi, rval_points[nb_poly_max*j+i].max(0))).max(0)
mini = numpy.asarray((mini, rval_points[nb_poly_max*j+i].min(0))).min(0)
x1 = max(0,mini[0]-3)
x2 = min(img_shape[0],maxi[0]+3)
y1 = max(0,mini[1]-3)
y2 = min(img_shape[1],maxi[1]+3)
if nmult == 2:
rval_edges[x1:x2,y1:y2] = ( (fullseg[j,0,x1:x2,y1:y2] ) * (fullseg[j,1,x1:x2,y1:y2] )\
* (fullseg[j,2,x1:x2,y1:y2] ) * (fullseg[j,3,x1:x2,y1:y2] ))
else:
rval_edges[x1:x2,y1:y2] = ( (fullseg[j,0,x1:x2,y1:y2] ) * (fullseg[j,1,x1:x2,y1:y2] )\
* (fullseg[j,2,x1:x2,y1:y2] ) * (fullseg[j,3,x1:x2,y1:y2] )\
* (fullseg[j,4,x1:x2,y1:y2] ) * (fullseg[j,5,x1:x2,y1:y2] )\
* (fullseg[j,6,x1:x2,y1:y2] ) * (fullseg[j,7,x1:x2,y1:y2] ))
surfaceedges_ndarray1 = (surfaceedges_ndarray == 1)
surfaceedges_ndarray2 = (surfaceedges_ndarray == 2)
surfaceedges_ndarray3 = (surfaceedges_ndarray == 3)
surfaceedges_ndarray4 = (surfaceedges_ndarray == 4)
irval_edges = 1-rval_edges
rval_angles[j,0,:,:] = ((surfaceedges_ndarray1) + (surfaceedges_ndarray2))*(irval_edges)
rval_angles[j,1,:,:] = ((surfaceedges_ndarray2) + (surfaceedges_ndarray3))*(irval_edges)
rval_angles[j,2,:,:] = ((surfaceedges_ndarray3) + (surfaceedges_ndarray4))*(irval_edges)
rval_angles[j,3,:,:] = ((surfaceedges_ndarray4) + (surfaceedges_ndarray1))*(irval_edges)
return rval_angles_flat*1.0 if not neg else ((rval_angles_flat)*2-1)*1.0
#----------------------------------------------------------
class Edgefilter(object):
def __init__(self,img_shape = (64,64), gaussfiltbool = False, sigma = 0.5, size = 3):
self.gxprewitt = numpy.asarray([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
self.gyprewitt = numpy.asarray([[-1, -1, -1], [0, 0, 0], [1, 1, 1]])
self.gxsobel = numpy.asarray([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
self.gysobel = numpy.asarray([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
self.fftshape =(pow(2,self._nextpow2(img_shape[0]+2*(3+gaussfiltbool*size))),\
pow(2,self._nextpow2(img_shape[1]+2*(3+gaussfiltbool*size))))
self.fftgxprewitt = numpy.fft.rfft2(self.gxprewitt,self.fftshape)
self.fftgyprewitt = numpy.fft.rfft2(self.gyprewitt,self.fftshape)
self.fftgxsobel = numpy.fft.rfft2(self.gxsobel,self.fftshape)
self.fftgysobel = numpy.fft.rfft2(self.gysobel,self.fftshape)
self.sigma = sigma
self.size = size
self.gaussfilt = self._gaussian(sigma, size)
self.fftgaussfilt = numpy.fft.rfft2(self.gaussfilt,self.fftshape)
def _nextpow2(self,n,i=1):
if n/2.0 > 1:
i=self._nextpow2(n/2.0,i+1)
return i
def _gaussian(self,sigma=0.5, shape=None):
sigma = max(abs(sigma), 1e-10)
if shape is None:
shape = max(int(6*sigma+0.5), 1)
if not isinstance(shape, tuple):
shape = (shape, shape)
x = numpy.arange(-(shape[0]-1)/2.0, (shape[0]-1)/2.0+1e-8)
y = numpy.arange(-(shape[1]-1)/2.0, (shape[1]-1)/2.0+1e-8)
Kx = numpy.exp(-x**2/(2*sigma**2))
Ky = numpy.exp(-y**2/(2*sigma**2))
ans = numpy.outer(Kx, Ky) / (2.0*numpy.pi*sigma**2)
return ans/sum(sum(ans))
def buildedgesfilter(self,image, batchsize, img_shape, rval_bg, neg, edgetype='sobel', gaussfiltbool = False, \
sigma=0.5, size =3, **dic):
image=image.reshape(batchsize, img_shape[0], img_shape[1])
rval_angles = numpy.zeros((batchsize, 4, img_shape[0], img_shape[1]), dtype='bool')
rval_angles_flat = rval_angles.reshape(batchsize, 4 * img_shape[0] * img_shape[1])
fftshape =(pow(2,self._nextpow2(img_shape[0]+2*(3+gaussfiltbool*size))),\
pow(2,self._nextpow2(img_shape[1]+2*(3+gaussfiltbool*size))))
gaussupdate = False
if gaussfiltbool and (self.sigma != sigma or self.size != size):
self.gaussfilt = self._gaussian(sigma, size)
self.sigma = sigma
self.size = size
self.fftgaussfilt = numpy.fft.rfft2(self.gaussfilt,fftshape)
gaussupdate = True
if fftshape != self.fftshape:
self.fftshape = fftshape
self.fftgxprewitt = numpy.fft.rfft2(self.gxprewitt,self.fftshape)
self.fftgyprewitt = numpy.fft.rfft2(self.gyprewitt,self.fftshape)
self.fftgxsobel = numpy.fft.rfft2(self.gxsobel,self.fftshape)
self.fftgysobel = numpy.fft.rfft2(self.gysobel,self.fftshape)
if not gaussupdate:
self.fftgaussfilt = numpy.fft.rfft2(self.gaussfilt,self.fftshape)
if edgetype == 'prewitt':
fftgx = self.fftgxprewitt
fftgy = self.fftgyprewitt
else:
fftgx = self.fftgxsobel
fftgy = self.fftgysobel
for k in xrange(batchsize):
image2 = numpy.ones((img_shape[0]+2*(3+gaussfiltbool*size),img_shape[1]+2*(3+gaussfiltbool*size)),\
dtype='uint8')*rval_bg[k]
image2[3+gaussfiltbool*size:-3-gaussfiltbool*size,3+gaussfiltbool*size:-3-gaussfiltbool*size]=image[ k,:,:]
fftimage = numpy.fft.rfft2(image2[:,:],fftshape)
if gaussfiltbool:
igx = numpy.fft.irfft2(fftimage*self.fftgaussfilt*fftgx,fftshape)
igy = numpy.fft.irfft2(fftimage*self.fftgaussfilt*fftgy,fftshape)
else:
igx = numpy.fft.irfft2(fftimage*fftgx,fftshape)
igy = numpy.fft.irfft2(fftimage*fftgy,fftshape)
igx = igx[4+gaussfiltbool*(size+size/2):img_shape[0]+4+gaussfiltbool*(size+size/2),\
4+gaussfiltbool*(size+size/2):img_shape[1]+4+gaussfiltbool*(size+size/2)]
igy = igy[4+gaussfiltbool*(size+size/2):img_shape[0]+4+gaussfiltbool*(size+size/2),\
4+gaussfiltbool*(size+size/2):img_shape[1]+4+gaussfiltbool*(size+size/2)]
edgesdetec = (numpy.sqrt(igx*igx+igy*igy) >= 1.0)
angl = numpy.arctan2(igy,igx) % math.pi
tmp=numpy.ones(angl.shape)
angl1 = ((angl<=tmp*math.pi/8) + (angl>tmp*7*math.pi/8))
angl2 = ((angl<=tmp*3*math.pi/8) * (angl>tmp*math.pi/8))
angl3 = ((angl<=tmp*5*math.pi/8) * (angl>tmp*3*math.pi/8))
angl4 = ((angl<=tmp*7*math.pi/8) * (angl>tmp*5*math.pi/8))
rval_angles[k,0,:,:] = ((angl1) + (angl2))*edgesdetec
rval_angles[k,1,:,:] = ((angl2) + (angl3))*edgesdetec
rval_angles[k,2,:,:] = ((angl3) + (angl4))*edgesdetec
rval_angles[k,3,:,:] = ((angl4) + (angl1))*edgesdetec
return rval_angles_flat if not neg else (rval_angles_flat)*2-1
def buildedgesfilterc(self,image, batchsize, img_shape, rval_bg, neg, edgetype='sobel', gaussfiltbool = False,\
sigma = 0.5, size=3, **dic):
image=image.reshape(batchsize, img_shape[0], img_shape[1])
rval_angles = numpy.zeros((batchsize, 4, img_shape[0], img_shape[1]), dtype='double')
rval_angles_flat = rval_angles.reshape(batchsize, 4 * img_shape[0] * img_shape[1])
fftshape =(pow(2,self._nextpow2(img_shape[0]+2*(3+gaussfiltbool*size))),\
pow(2,self._nextpow2(img_shape[1]+2*(3+gaussfiltbool*size))))
gaussupdate = False
if gaussfiltbool and (self.sigma != sigma or self.size != size):
self.gaussfilt = self._gaussian(sigma, size)
self.sigma = sigma
self.size = size
self.fftgaussfilt = numpy.fft.rfft2(self.gaussfilt,fftshape)
gaussupdate = True
if fftshape != self.fftshape:
self.fftshape = fftshape
self.fftgxprewitt = numpy.fft.rfft2(self.gxprewitt,self.fftshape)
self.fftgyprewitt = numpy.fft.rfft2(self.gyprewitt,self.fftshape)
self.fftgxsobel = numpy.fft.rfft2(self.gxsobel,self.fftshape)
self.fftgysobel = numpy.fft.rfft2(self.gysobel,self.fftshape)
if not gaussupdate:
self.fftgaussfilt = numpy.fft.rfft2(self.gaussfilt,self.fftshape)
if edgetype == 'prewitt':
fftgx = self.fftgxprewitt
fftgy = self.fftgyprewitt
else:
fftgx = self.fftgxsobel
fftgy = self.fftgysobel
for k in xrange(batchsize):
image2 = numpy.ones((img_shape[0]+2*(3+gaussfiltbool*size),img_shape[1]+2*(3+gaussfiltbool*size)),\
dtype='uint8')*rval_bg[k]
image2[3+gaussfiltbool*size:-3-gaussfiltbool*size,3+gaussfiltbool*size:-3-gaussfiltbool*size]=image[ k,:,:]
fftimage = numpy.fft.rfft2(image2[:,:],fftshape)
if gaussfiltbool:
igx = numpy.fft.irfft2(fftimage*self.fftgaussfilt*fftgx,fftshape)
igy = numpy.fft.irfft2(fftimage*self.fftgaussfilt*fftgy,fftshape)
else:
igx = numpy.fft.irfft2(fftimage*fftgx,fftshape)
igy = numpy.fft.irfft2(fftimage*fftgy,fftshape)
igx = igx[4+gaussfiltbool*(size+size/2):img_shape[0]+4+gaussfiltbool*(size+size/2),\
4+gaussfiltbool*(size+size/2):img_shape[1]+4+gaussfiltbool*(size+size/2)]
igy = igy[4+gaussfiltbool*(size+size/2):img_shape[0]+4+gaussfiltbool*(size+size/2),\
4+gaussfiltbool*(size+size/2):img_shape[1]+4+gaussfiltbool*(size+size/2)]
edgesdetec = (numpy.sqrt(igx*igx+igy*igy) >= 1.0)
angl = numpy.arctan2(igy,igx) % math.pi
tmp=numpy.ones(angl.shape)
angl1 = (angl<tmp*math.pi/4)
angl2 = ((angl<tmp*math.pi/2) * (angl>=tmp*math.pi/4))
angl3 = ((angl<tmp*3*math.pi/4) * (angl>=tmp*math.pi/2))
angl4 = ((angl<tmp*math.pi) * (angl>=tmp*3*math.pi/4))
angl=(angl % (math.pi/4)) / (math.pi/4)
iangl=1-angl
rval_angles[k,0,:,:] = ((angl1) * (iangl) +\
(angl4) * angl)*edgesdetec
rval_angles[k,1,:,:] = ((angl2) * (iangl) +\
(angl1) * angl)*edgesdetec
rval_angles[k,2,:,:] = ((angl3) * (iangl) +\
(angl2) * angl)*edgesdetec
rval_angles[k,3,:,:] = ((angl4) * (iangl) +\
(angl3) * angl)*edgesdetec
return rval_angles_flat if not neg else (rval_angles_flat)*2-1
edgefilt = Edgefilter()
buildedgesfilter = edgefilt.buildedgesfilter
buildedgesfilterc = edgefilt.buildedgesfilterc
#----------------------------------------------------------
#other features versions:
#--------------------------------------------------------
def buildidentityc(rval_points, rval_nbpol, nb_poly_max, rval_poly_id, n_vertices, batchsize, img_shape, neg, **dic):
surfaceidentity = pygame.Surface(img_shape, depth=8)
surfaceidentity_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surfaceidentity))
rval_identity = numpy.ndarray((batchsize, img_shape[0], img_shape[1]), dtype='uint8')
rval_identity_flat = rval_identity.reshape(batchsize, img_shape[0] * img_shape[1])
for j in xrange(batchsize):
surfaceidentity.fill(0)
for i in range (rval_nbpol[j]):
pygame.draw.polygon(surfaceidentity,int(((rval_poly_id[j,i]+1.0)/(len(n_vertices)))*255),\
rval_points[nb_poly_max*j+i], 0)
rval_identity[j] = surfaceidentity_ndarray
return rval_identity_flat / 255.0 if not neg else (rval_identity_flat / 255.0)*2-1
#----------------------------------------------------------
def buildedgesanglec(rval_points, rval_nbpol, nb_poly_max, batchsize, img_shape, segmentation, neg, **dic):
surfaceedges = pygame.Surface(img_shape, depth=8)
surfaceedges_ndarray = numpy.asarray(pygame.surfarray.pixels2d(surfaceedges))
if segmentation.size == img_shape[0] * img_shape[1] * 2:
nmult = 2
else:
nmult = 4
seg=segmentation.reshape(batchsize,nmult,img_shape[0],img_shape[1])
fullseg=numpy.ones((batchsize,2*nmult,img_shape[0],img_shape[1]),dtype='bool')
fullseg[:,0,:,:] = 1-seg[:,0,:,:]
fullseg[:,1,1:,:] = 1-seg[:,0,:-1,:]
fullseg[:,2,:,:] = 1-seg[:,1,:,:]
fullseg[:,3,:,1:] = 1-seg[:,1,:,:-1]
if nmult == 4:
fullseg[:,4,:,:] = 1-seg[:,2,:,:]
fullseg[:,5,1:,1:] = 1-seg[:,2,:-1,:-1]
fullseg[:,6,:,:] = 1-seg[:,3,:,:]
fullseg[:,7,:-1,1:] = 1-seg[:,3,1:,:-1]
rval_angles = numpy.zeros((batchsize, 4, img_shape[0], img_shape[1]), dtype='double')
rval_angles_flat = rval_angles.reshape(batchsize, 4 * img_shape[0] * img_shape[1])
if nmult == 2:
shifts = numpy.asarray([[1,0],[0,1],[-1,0],[0,-1]])
if nmult == 4:
shifts=numpy.asarray([[1,0],[1,1],[0,1],[-1,1],[-1,0],[-1,-1],[0,-1],[1,-1]])
for j in xrange(batchsize):
maxi = numpy.zeros(2,'uint8')
mini = numpy.asarray((img_shape[0],img_shape[1]),'uint8')
rval_edges = numpy.ones((img_shape[0], img_shape[1]), dtype='bool')
surfaceedges.fill(0)
for i in range(rval_nbpol[j]):
points = rval_points[nb_poly_max*j+i]
angltmp = numpy.zeros(len(points),'uint8')
center = points.mean(0)
pointsctr = points - center
for k1 in range(len(points)):
if k1 == len(points)-1:
k2 = 0
else:
k2 = k1+1
edge = points[k2,:] - points[k1,:]
edge = edge[:] / math.sqrt(pow(edge[0],2)+pow(edge[1],2))
angltmp[k1] = round((math.atan2(-edge[1],edge[0]) % math.pi) / math.pi * 255)
#because y is inverted in the image drawing
tmp1 = numpy.dot(shifts,pointsctr[k1,:])+numpy.dot(shifts,pointsctr[k2,:])
tmp2 = tmp1.argsort()
for l in tmp2[-nmult-1:]:
pygame.draw.polygon(surfaceedges,int(int(angltmp[k1])),\
[center+shifts[l,:],points[k1,:]+shifts[l,:],points[k2,:]+shifts[l,:]],0)
for k1 in range(len(points)):
if k1 == len(points)-1:
k2 = 0
else:
k2 = k1+1
pygame.draw.polygon(surfaceedges,int(int(angltmp[k1])),\
[(center+points[k2,:]+points[k1,:])/3.0,points[k1,:],points[k2,:]],0)
maxi = numpy.asarray((maxi, rval_points[nb_poly_max*j+i].max(0))).max(0)
mini = numpy.asarray((mini, rval_points[nb_poly_max*j+i].min(0))).min(0)
x1 = max(0,mini[0]-3)
x2 = min(img_shape[0],maxi[0]+3)
y1 = max(0,mini[1]-3)
y2 = min(img_shape[1],maxi[1]+3)
if nmult == 2:
rval_edges[x1:x2,y1:y2] = ( (fullseg[j,0,x1:x2,y1:y2] ) * (fullseg[j,1,x1:x2,y1:y2] )\
* (fullseg[j,2,x1:x2,y1:y2] ) * (fullseg[j,3,x1:x2,y1:y2] ))
else:
rval_edges[x1:x2,y1:y2] = ( (fullseg[j,0,x1:x2,y1:y2] ) * (fullseg[j,1,x1:x2,y1:y2] )\
* (fullseg[j,2,x1:x2,y1:y2] ) * (fullseg[j,3,x1:x2,y1:y2] )\
* (fullseg[j,4,x1:x2,y1:y2] ) * (fullseg[j,5,x1:x2,y1:y2] )\
* (fullseg[j,6,x1:x2,y1:y2] ) * (fullseg[j,7,x1:x2,y1:y2] ))
tmp=numpy.ones(surfaceedges_ndarray.shape)*255
angl1 = (surfaceedges_ndarray<tmp/4.0)
angl2 = ((surfaceedges_ndarray<tmp/2.0) * (surfaceedges_ndarray>=tmp/4.0))
angl3 = ((surfaceedges_ndarray<tmp*3/4.0) * (surfaceedges_ndarray>=tmp/2.0))
angl4 = ((surfaceedges_ndarray<tmp) * (surfaceedges_ndarray>=tmp*3/4.0))
surfaceedgesc = (surfaceedges_ndarray % (255/4.0)) / (255/4.0)
isurfaceedgesc = 1 - surfaceedgesc
irval_edges = (1-rval_edges)
rval_angles[j,0,:,:] = ((angl1) * (isurfaceedgesc) +\
(angl4) * surfaceedgesc ) * (irval_edges)
rval_angles[j,1,:,:] = ((angl2) * (isurfaceedgesc) +\
(angl1) * surfaceedgesc ) * (irval_edges)
rval_angles[j,2,:,:] = ((angl3) * (isurfaceedgesc) +\
(angl2) * surfaceedgesc ) * (irval_edges)
rval_angles[j,3,:,:] = ((angl4) * (isurfaceedgesc) +\
(angl3) * surfaceedgesc ) * (irval_edges)
return rval_angles_flat*1.0 if not neg else ((rval_angles_flat)*2-1)*1.0
#def drawpolygon(oldimage,color,points):
#points = numpy.concatenate([points,numpy.asarray([points[0,:]])],0)
#mini = points.min(0)
#maxi = points.max(0)
#idx_x = [i for i in xrange(int(numpy.floor(mini[0])),int(numpy.ceil(maxi[0])),1)]
#idx_y = [i for i in xrange(int(numpy.floor(mini[1])),int(numpy.ceil(maxi[1])),1)]
#A = numpy.ndarray((4,len(idx_x)*len(idx_y),points.shape[0],points.shape[1]),dtype = 'float64')
#for i in range(len(idx_x)):
#for j in range(len(idx_y)):
#A[0,i*len(idx_y)+j,:,:] = points-\
#numpy.transpose(numpy.asarray([[idx_x[i]]*points.shape[0],[idx_y[j]]*points.shape[0]]))
#A[1,i*len(idx_y)+j,:,:] = points-\
#numpy.transpose(numpy.asarray([[idx_x[i]+1]*points.shape[0],[idx_y[j]]*points.shape[0]]))
#A[2,i*len(idx_y)+j,:,:] = points-\
#numpy.transpose(numpy.asarray([[idx_x[i]+1]*points.shape[0],[idx_y[j]+1]*points.shape[0]]))
#A[3,i*len(idx_y)+j,:,:] = points-\
#numpy.transpose(numpy.asarray([[idx_x[i]]*points.shape[0],[idx_y[j]+1]*points.shape[0]]))
#B=A[:,:,1:,0]*A[:,:,:-1,1] - A[:,:,:-1,0]*A[:,:,1:,1]
#b1 = (B > 0)
#b2 = (B < 0)
#c1 = b1.prod(2)
#c2 = b2.prod(2)
#d = (c1 + c2) != 0
#f = (d[0,:] + d[1,:] + d[2,:] + d[3,:]) != 0
#e = numpy.reshape(f,(len(idx_x),len(idx_y)))
#print e
#oldimage[idx_x[0]:idx_x[-1]+1,idx_y[0]:idx_y[-1]+1] = \
#oldimage[idx_x[0]:idx_x[-1]+1,idx_y[0]:idx_y[-1]+1]*(e==0)+(e==1)*color
#return oldimage