def conv():
a = np.random.rand(*(100, 100, 400))
b = np.random.rand(*(10, 10, 10))
conv1 = convolveim(a, b, mode='constant')
# conv2 = convolvesig(a,b, mode = 'same')
return conv1 # FLOPS / (t2-t1)
def compute_flops(loopcount):
a = np.random.rand(*(100, 100, 100))
b = np.random.rand(*(10, 10, 10))
conv1 = convolveim(a, b, mode='constant')
# conv2 = convolvesig(a,b, mode = 'same')
return conv1 # FLOPS / (t2-t1)
def convolve_imag(self, image):
"""
Returns a image convolved with the imaginary component of the kernel
"""
start_time = time.time()
conv = convolveim(image, np.imag(self.kernel), mode='wrap')
self.imag_time += time.time() - start_time
self.imag_cont += 1
return conv
def convolve_real(self, image):
"""
Returns a image convolved with the real component of the kernel
"""
start_time = time.time()
conv = convolveim(image, np.real(self.kernel), mode='wrap')
self.real_time += time.time() - start_time
self.real_cont += 1
return conv
def conv_oa(ias, weights, shift):
oc = weights.shape[0]
ic = ias.shape[0]
ia_size = ias.shape[1]
pad_size = (weights.shape[2] - 1)/2
#ia_pad = numpy.zeros((ic, ia_size+pad_size*2, ia_size+pad_size*2), dtype=numpy.int)
#ia_pad[:,pad_size:ia_size+pad_size,pad_size:ia_size+pad_size] = ias
oa_raw = numpy.zeros((oc, ia_size, ia_size), dtype=numpy.int)
for oc_cnt in range (0, oc):
for ic_cnt in range (0, ic):
oa_raw[oc_cnt] = numpy.add(oa_raw[oc_cnt], convolveim(ias[ic_cnt], weights[oc_cnt][ic_cnt], mode='constant'))
oa = numpy.right_shift(oa_raw, shift)
return oa, oa_raw
def Conv3d(self):
if self.origImg[0].shape[0] > 10:
paramText = self.conv3dLineEdit.text()
parameterList = paramText.split(',')
if len(parameterList) != 5:
QMessageBox.warning(self, 'warning',
'please input right parameter')
return
self.xBlurSize = int(parameterList[0])
self.yBlurSize = int(parameterList[1])
self.zBlurSize = int(parameterList[2])
self.sigma1 = float(parameterList[3])
self.sigma2 = float(parameterList[4])
center = [
np.round(self.zBlurSize / 2),
np.round(self.yBlurSize / 2),
np.round(self.xBlurSize / 2)
]
psf = np.zeros((self.zBlurSize, self.yBlurSize, self.xBlurSize))
for i in range(self.xBlurSize):
for j in range(self.yBlurSize):
for k in range(self.zBlurSize):
psf[k, j,
i] = np.exp(-((i - center[2])**2 +
(j - center[1])**2) / self.sigma1 -
((k - center[0])**2) / self.sigma2)
psf /= np.sum(psf)
#img = self.origImg[0].astype(np.float)
self.origImg[1] = convolveim(self.origImg[0],
psf) # np.ones((11,1,1))/11
#self.origImg[1] = self.origImg[1].astype(np.uint8)
#psfImg = psf.copy()
#psfImg /= np.max(psfImg)
#psfImg *= 255.
#psfImg = np.uint8(psfImg)
self._RenderImage(self.origImg[1], 1)
else:
pass
def convolve_imag(self, image):
"""
Returns a image convolved with the imaginary component of the kernel
"""
return convolveim(image, np.imag(self.kernel), mode='wrap')
def convolve_real(self, image):
"""
Returns a image convolved with the real component of the kernel
"""
return convolveim(image, np.real(self.kernel), mode='wrap')
