def smooth_resize(img, n_rows, n_cols, smooth_type): ##----CAUTION--- ## THIS FUNCTION DOES NOT UPSCALE ON ONE AXIS AND DOWNSCALE IN THE OTHER # smooth_type= gaussian, bilinear interpolation, median or whatever # flag: 0 down else up if n_rows==img.shape[0]: return img if n_cols==img.shape[1]: return img flag = n_rows > img.shape[0] ratio_rows = (n_rows)*1.0 / img.shape[0] ratio_cols = (n_cols)*1.0 / img.shape[1] if flag: kernel_size = (math.ceil((ratio_rows + ratio_cols) / 2))*2-1 else: kernel_size = (math.ceil(math.log(math.ceil((math.pow(ratio_rows,-1) + math.pow(ratio_cols,-1)) / 2),2)))* 2 + 1 resized_img = Sampling.simple_resize(img, n_rows, n_cols) if flag else Sampling.simple_resize(img, n_rows, n_cols) if smooth_type == Sampling.FILTER_MEDIAN: return conv.median_filter_nd(resized_img, kernel_size) elif smooth_type == Sampling.FILTER_BILINEAR_INTERPOLATION: return conv.convolution_nd(resized_img, ker.bilinear_interpolation(kernel_size)) elif smooth_type == Sampling.FILTER_GAUSSIAN: return conv.convolution_nd(resized_img, ker.gaussian(kernel_size))
def __init__( self, shape, **kwargs ):
self.shape = shape
self.posecells = zeros(shape)
#NOTE: might not need if gaussian_filter works
self.kernel_3d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=3 )
self.kernel_2d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=2 )
self.kernel_1d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=1 )
self.kernel_1d_sep = self.diff_gaussian_separable( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA )
self.global_inhibition = PC_GLOBAL_INHIB
self.pc_vtrans_scale = PC_CELL_X_SIZE
self.pc_vrot_scale = 2.0*pi/shape[2]#PC_C_SIZE_TH
#TODO: maybe change everything to float32 to make it go faster?
#self.conv = Convolution( im=self.posecells, fil=self.kernel_1d, sep=True, type=float64 )
#self.conv = Convolution( im=self.posecells, fil=self.kernel_1d_sep, sep=True, type=float64 )
self.conv = Convolution( im=self.posecells, fil=self.kernel_3d, sep=False, type=float64 )
filter = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), origin=(0,0) )
#self.conv.new_filter( self.kernel_2d, dim=2 )
self.conv.new_filter( filter, dim=2 )
self.filter_dict_2d = self.build_diff_gaussian_set_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), precision=1 )
self.filter_dict_2d_precision = 10 # multiple the decimal by this number to get the right key
def convoluteWavs(self,wav): BLOCKSIZE = 1024 def preprocess(): if self.audioData.shape[0] < wav.audioData.shape[0]: audioSize = self.audioData.shape[0] if audioSize%BLOCKSIZE != 0: lastblock = self.audioData[(self.audioData.shape[0]-(self.audioData.shape[0]%BLOCKSIZE)):] temp = self.audioData[:(self.audioData.shape[0]-(self.audioData.shape[0]%BLOCKSIZE))] self.audioData = np.concatenate((temp,WavProcessing.pad(len(lastblock),lastblock,BLOCKSIZE))) resultConv = np.zeros((self.audioData.shape[0],2),dtype=np.int16) elif self.audioData.shape[0] >= wav.audioData.shape[0]: audioSize = wav.audioData.shape[0] if audioSize%BLOCKSIZE != 0: lastblock = wav.audioData[(wav.audioData.shape[0]-(wav.audioData.shape[0]%BLOCKSIZE)):] temp = wav.audioData[:(wav.audioData.shape[0]-(wav.audioData.shape[0]%BLOCKSIZE))] wav.audioData = np.concatenate((temp,WavProcessing.pad(len(lastblock),lastblock,BLOCKSIZE))) resultConv = np.zeros((wav.audioData.shape[0],2),dtype=np.int16) return resultConv,Matrix.ceil(audioSize,BLOCKSIZE) resultConv, nblocks = preprocess() for i in xrange(nblocks): start = BLOCKSIZE*i end = BLOCKSIZE*(i+1) conv = Convolution(Matrix(self.audioData[start:end,0].tolist(),0),Matrix(wav.audioData[start:end,0].tolist(),0),Convolution.CIRCULAR) resultConv[start:end] = conv.convolute() self.audioData = resultConv[:] conv._plot()
class Conv(Layer):
def __init__(self, n_filters, filter_shape,weight_scale,weight_decay):
self.n_filters = n_filters
self.filter_shape = filter_shape
self.weight_scale = weight_scale
self.weight_decay = weight_decay
self.conv_ob = Convolution()
def setup(self, input_shape, rng):
n_featuremaps = input_shape[1]
W_shape = (n_featuremaps, self.n_filters) + self.filter_shape # this appends the filter shape eg : (16,4,3,3)
self.W = rng.normal( size=W_shape, scale=self.weight_scale) # generate random filters with gaussian(normal) distribution
self.b = numpy.zeros(self.n_filters) # initialize bias weights equal to zero
def params(self):
return self.W, self.b
def param_incs(self):
return self.dW, self.db
def set_params(self,W,b):
self.W = W
self.b = b
def output_shape(self, input_shape):
# Zero padding is considered
height = input_shape[2] #- self.filter_shape[0] + 1
width = input_shape[3] #- self.filter_shape[1] + 1
shape = (input_shape[0],
self.n_filters,
height,
width)
return shape
def forward_propogation(self, input): # forward propogation
print("Forward Propogation : Convolution")
self.last_input = input # activation this layer
self.last_input_shape = input.shape # shape of activation
conv_shape = self.output_shape(input.shape)
convout = self.conv_ob.convolve(input,self.W,conv_shape)
z=self.W
return convout + self.b[numpy.newaxis, :, numpy.newaxis, numpy.newaxis]
def backward_propogation(self, output_grad): # backward propogation of gradients
print("Backward Propogation : Convolution")
input_grad = numpy.empty(self.last_input_shape)
self.dW = numpy.zeros(self.W.shape)
# Convolve
input_grad, self.dW = self.conv_ob.convolve_backprop(self.last_input, output_grad, self.W, self.dW)
n_imgs = output_grad.shape[0]
self.db = numpy.sum(output_grad, axis=(0, 2, 3)) / (n_imgs)
self.dW =self.dW - self.weight_decay * self.W
return input_grad
def resol_spectrum (self,E,a,b,ordering,plot_this=False):
if (ordering < -1) or (ordering > 1):
print('Error: set ordering = 1 for NO, -1 for IO, 0 for both')
return -1
Reactor_Spectrum.unosc_spectrum(self,E)
Oscillation_Prob.eval_prob(self,E,0)
self.norm_osc_spect_N = self.norm_spectrum_un * self.prob_E_N
self.norm_osc_spect_I = self.norm_spectrum_un * self.prob_E_I
#Evis = E - 0.8
### class Convolution
conv = Convolution()
print('\n...adding experimental resolution via numerical convolution...')
print('\nIt might take a while')
self.resol_N = conv.numerical_conv(self.norm_osc_spect_N,E,a=a,b=b)
self.resol_I = conv.numerical_conv(self.norm_osc_spect_I,E,a=a,b=b)
if plot_this:
fig = plt.figure()
#fig.suptitle(r'Antineutrino spectrum')
ax = fig.add_subplot(111)
ax.legend()
ax.grid()
ax.set_xlabel(r'$\text{E}_{\text{vis}}$ [\si{MeV}]')
ax.set_ylabel(r'N($\bar{\nu}$) [arb. unit]')
ax.set_ylim(-0.005,0.095)
ax.set_title(r'Antineutrino spectrum' + '\nwith finite energy resolution')
if ordering == 1: # NO
ax.plot(E-0.8,self.resol_N,'b',linewidth=1,label='NO')
ax.legend()
fig.savefig('AntineutrinoSpectrum/resol_spectrum_N.pdf',format='pdf',transparent=True)
print('\nThe plot has been saved in AntineutrinoSpectrum/resol_spectrum_N.pdf')
if ordering == -1: # IO
ax.plot(E-0.8,self.resol_I,'r',linewidth=1,label='IO')
ax.legend()
fig.savefig('AntineutrinoSpectrum/resol_spectrum_I.pdf',format='pdf',transparent=True)
print('\nThe plot has been saved in AntineutrinoSpectrum/resol_spectrum_I.pdf')
if ordering == 0: # both NO and IO
ax.plot(E-0.8,self.resol_N,'b',linewidth=1,label='NO')
ax.plot(E-0.8,self.resol_I,'r--',linewidth=1,label='IO')
ax.legend()
fig.savefig('AntineutrinoSpectrum/resol_spectrum.pdf', format='pdf', transparent=True)
print('\nThe plot has been saved in AntineutrinoSpectrum/resol_spectrum.pdf')
return self.resol_N, self.resol_I
def __init__(self):
'''Get the four layer's kernels, create the correlations and
a convolution wrapper.
:const MIN_IMG_WIDTH: Minimum image width for which to use
NumPy/SciPy for convolution
'''
self.kernels = DifferenceOfGaussians()
self.correlations = Correlation(self.kernels.full_kernels)
self.convolver = Convolution()
self.MIN_IMG_WIDTH = 256
class MapDNF(FieldMap):
def __init__(self,
name,
size,
dt=0.1,
wrap=True,
tau=0.64,
h=0,
model='cnft',
th=0.75,
iExc=1.25,
iInh=0.7,
wExc=0.1,
wInh=10,
alpha=10,
mapSize=1.,
**kwargs):
super(MapDNF, self).__init__(name,
size,
dt=dt,
wrap=wrap,
tau=tau,
h=h,
model=model,
th=th,
**kwargs)
self.act = ActivationMap(name + "_activation",
size,
dt=dt,
model=model,
th=th)
self.lat = Convolution(name + "_lateral", size, dt=dt, wrap=wrap)
self.kernel = LateralWeightsMap(name + "_kernel",
mapSize=mapSize,
globalSize=size,
wrap=wrap,
iExc=iExc,
iInh=iInh,
wExc=wExc,
wInh=wInh,
alpha=alpha)
self.act.addChildren(field=self)
self.addChildren(lat=self.lat)
self.lat.addChildren(source=self.act, kernel=self.kernel)
self.kernel.compute()
def getActivation(self):
return self.act
def getArrays(self):
return [
self.act,
self.lat,
]
def plotDefaults(self):
self.convolution = Convolution()
self.plotUpdate()
self.updateSlider()
self.tminXInput.setText('{}'.format(self.convolution.getMinRangeX()))
self.tminHInput.setText('{}'.format(self.convolution.getMinRangeH()))
self.tmaxXInput.setText('{}'.format(self.convolution.getMaxRangeX()))
self.tmaxHInput.setText('{}'.format(self.convolution.getMaxRangeH()))
self.XFunctionInput.setText('{}'.format(self.convolution.getXFunctionString()))
self.HFunctionInput.setText('{}'.format(self.convolution.getHFunctionString()))
def __init__(self):
super().__init__()
self.title = 'La construction de la convolution'
self.left = 50
self.top = 50
self.width = 1600
self.height = 900
self.sliderTimeFactor = 10
self.modeEcho = True
self.convolution = Convolution()
self.initUI()
self.plotDefaults()
def convoluteWavs(self, wav):
BLOCKSIZE = 1024
def preprocess():
if self.audioData.shape[0] < wav.audioData.shape[0]:
audioSize = self.audioData.shape[0]
if audioSize % BLOCKSIZE != 0:
lastblock = self.audioData[(
self.audioData.shape[0] -
(self.audioData.shape[0] % BLOCKSIZE)):]
temp = self.audioData[:(
self.audioData.shape[0] -
(self.audioData.shape[0] % BLOCKSIZE))]
self.audioData = np.concatenate(
(temp,
WavProcessing.pad(len(lastblock), lastblock,
BLOCKSIZE)))
resultConv = np.zeros((self.audioData.shape[0], 2),
dtype=np.int16)
elif self.audioData.shape[0] >= wav.audioData.shape[0]:
audioSize = wav.audioData.shape[0]
if audioSize % BLOCKSIZE != 0:
lastblock = wav.audioData[(
wav.audioData.shape[0] -
(wav.audioData.shape[0] % BLOCKSIZE)):]
temp = wav.audioData[:(
wav.audioData.shape[0] -
(wav.audioData.shape[0] % BLOCKSIZE))]
wav.audioData = np.concatenate(
(temp,
WavProcessing.pad(len(lastblock), lastblock,
BLOCKSIZE)))
resultConv = np.zeros((wav.audioData.shape[0], 2),
dtype=np.int16)
return resultConv, Matrix.ceil(audioSize, BLOCKSIZE)
resultConv, nblocks = preprocess()
for i in xrange(nblocks):
start = BLOCKSIZE * i
end = BLOCKSIZE * (i + 1)
conv = Convolution(
Matrix(self.audioData[start:end, 0].tolist(), 0),
Matrix(wav.audioData[start:end, 0].tolist(), 0),
Convolution.CIRCULAR)
resultConv[start:end] = conv.convolute()
self.audioData = resultConv[:]
conv._plot()
def __init__(
self,
input_dim=(1, 28, 28),
conv_param={
'filter_num': 30,
'filter_size': 5,
'pad': 0,
'stride': 1
},
hidden_size=100,
output_size=10,
weight_init_std=0.01
):
# 畳み込み層のハイパーパラメータ
filter_num = conv_param['filter_num']
filter_size = conv_param['filter_size']
filter_pad = conv_param['pad']
filter_stride = conv_param['stride']
input_size = input_dim[1]
conv_output_size = (input_size - filter_size + 2 *
filter_pad) / filter_stride + 1
pool_output_size = int(
filter_num * (conv_output_size / 2) * (conv_output_size / 2)
)
# 重みパラメータ
self.params = {}
self.params['W1'] = weight_init_std * \
np.random.randn(filter_num, input_dim[0], filter_size, filter_size)
self.params['b1'] = np.zeros(filter_num)
self.params['W2'] = weight_init_std * \
np.random.randn(pool_output_size, hidden_size)
self.params['b2'] = np.zeros(hidden_size)
self.params['W3'] = weight_init_std * \
np.random.randn(hidden_size, output_size)
self.params['b3'] = np.zeros(output_size)
# レイヤー
self.layers = OrderedDict()
self.layers['Conv1'] = Convolution(
self.params['W1'],
self.params['b1'],
conv_param['stride'],
conv_param['pad']
)
self.layers['Relu1'] = Relu()
self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2)
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
self.layers['Relu2'] = Relu()
self.layers['Affine3'] = Affine(self.params['W3'], self.params['b3'])
self.last_layer = Softmax()
def createConvolution(self):
width = 1280
height = 720
window_width = 50
window_height = 40 # Break image into 18 vertical layers since image height is 720
margin = 100 # How much to slide left and right for searching print("warped shape: ", warped.shape)
rough_lane_width = 897
return Convolution(width = width, height = height, window_width = window_width,
window_height = window_height, margin = margin,
rough_lane_width = rough_lane_width)
def Convolution(x, args, name):
"""
Create a convolutional layer with
:param x: the placeholder for the tensor
:param args: the arguments for the convolution (filter, strides, padding)
:param name: a label for the operation
:return: a convolved tensor
"""
from convolution import Convolution
return Convolution(x, args, name)
def __init__(self):
output_size = 10
hidden_size = 12
self.modules = [
Reshape((28, 28), (28, 28, 1)),
Convolution((28, 28, 1), 5, 8), # to 24
MaxPool(2), # to 12
Pad(1), # to 14
Convolution((14, 14, 8), 3, 16), #to 12
MaxPool(2), # to 6
Convolution((6, 6, 16), 3, 24), # to 4
MaxPool(2), # to 2
Reshape((2,2,24),(2 * 2 * 24,)),
MatrixMult([2 * 2 * 24, hidden_size]),
MatrixAdd([hidden_size]),
Relu(),
MatrixMult([hidden_size, output_size]),
Softmax(),
]
def __init__(self, n_filters, filter_shape,weight_scale,weight_decay):
self.n_filters = n_filters
self.filter_shape = filter_shape
self.weight_scale = weight_scale
self.weight_decay = weight_decay
self.conv_ob = Convolution()
class Focal():
'''Filter Overlap Correction ALgorithm, simulates the foveal pit
region of the human retina.
Created by Basabdatta Sen Bhattacharya.
See DOI: 10.1109/TNN.2010.2048339
'''
def __init__(self):
self.kernels = DifferenceOfGaussians()
self.correlations = Correlation(self.kernels.full_kernels)
self.convolver = Convolution()
self.MIN_IMG_WIDTH = 256
def apply(self, image, spikes_per_unit=0.3):
spike_images = self.filter_image(image)
focal_spikes = self.focal(spike_images, spikes_per_unit=spikes_per_unit)
return focal_spikes
def focal(self, spike_images, spikes_per_unit=0.3):
'''Filter Overlap Correction ALgorithm, simulates the foveal pit
region of the human retina.
Created by Basabdatta Sen Bhattacharya.
See DOI: 10.1109/TNN.2010.2048339
spike_images => A list of the values generated by the convolution
procedure, stored as four 2D arrays, each with
the same size/shape of the original image.
spikes_per_unit => Percentage of the total spikes to be processed,
specified in a per unit [0, 1] range.
returns: an ordered list of
[spike index, sorting value, cell layer/type] tuples.
'''
ordered_spikes = []
img_size = spike_images[0].size
img_shape = spike_images[0].shape
height, width = img_shape
num_images = len(spike_images)
#how many non-zero spikes are in the images
max_cycles = 0
for i in range(num_images):
max_cycles += numpy.sum(img[i] != 0)
total_spikes = max_cycles.copy()
#reduce to desired number
max_cycles = numpy.int(spikes_per_unit*max_cycles)
#copy images from list to a large image to make it a single search space
big_shape = (height*2, width*2)
big_image = numpy.zeros(big_shape)
big_coords = [(0, 0), (0, width), (height, 0), (height, width)]
for cell_type in range(num_images):
row, col = big_coords[cell_type]
tmp_img = original_img[cell_type].copy()
tmp_img[numpy.where(tmp_img == 0)] = -numpy.inf
big_image[row:row+height, col:col+width] = tmp_img
# Main FoCal loop
for count in xrange(max_cycles):
# print out completion percentage
percent = (count*100.)/float(total_spikes-1)
sys.stdout.write("\rFocal %d%%"%(percent))
# Get maximum value's index
max_idx = numpy.argmax(img_copies)
# and its coordinates
max_coords = numpy.unravel_index(max_idx, big_shape)
# and the value
max_val = big_image[max_coords]
if max_val == numpy.inf or max_val == -numpy.inf or max_val == numpy.nan:
sys.stderr.write("\nWrong max value in FoCal!")
break
# translate coordinates from the big image's to a single image coordinates
if max_coords[0] < height and max_coords[1] < width:
single_coords = max_coords
else:
single_coords = self.global_to_single_coords(max_coords, img_shape)
# calculate a local index, to store per ganglion cell layer info
local_idx = single_coords[0]*width + single_coords[1]
# calculate the type of cell from the index
cell_type = self.cell_type_from_global_coords(max_coords, img_shape)
# append max spike info to return list
ordered_spikes.append([local_idx, max_val, cell_type])
# correct surrounding pixels for overlapping kernel influence
for overlap_cell_type in range(len(original_img)):
# get equivalent coordinates for each layer
overlap_idx = self.local_coords_to_global_idx(single_coords,
overlap_cell_type,
img_shape, big_shape)
is_max_val_layer = overlap_cell_type == cell_type
# c_i = c_i - c_{max}<K_i, K_{max}>
self.adjust_with_correlation_g(big_image,
self.correlations[cell_type]\
[overlap_cell_type],
overlap_idx, max_val,
is_max_val_layer=is_max_val_layer)
return ordered_spikes
def filter_image(self, img, force_homebrew=False):
'''Perform convolution with calculated kernels
img => the image to convolve
force_hombrew => if True: use my separated convolution code
else: use SciPy sepfir2d
'''
num_kernels = len(self.kernels.full_kernels)
img_width, img_height = img.shape
convolved_img = {}
for cell_type in range(num_kernels):
if img_width < self.MIN_IMG_WIDTH or img_height < self.MIN_IMG_WIDTH:
force_homebrew = True
else:
force_homebrew = False
c = self.convolver.dog_sep_convolution(img, self.kernels[cell_type],
cell_type,
originating_function="filter",
force_homebrew=force_homebrew)
convolved_img[cell_type] = c
return convolved_img
def adjust_with_correlation(self, img, correlation, max_idx, max_val,
is_max_val_layer=True):
img_height, img_width = img.shape
correlation_width = correlation.shape[0]
half_correlation_width = correlation_width/2
half_img_width = img_width/2
half_img_height = img_height/2
# Get max value's coordinates
row, col = idx2coord(max_idx, img_width)
row_idx = row/half_img_height
col_idx = col/half_img_width
# Calculate the zone to affect with the correlation
up_lim = (row_idx)*half_img_height
left_lim = (col_idx)*half_img_width
down_lim = (row_idx + 1)*half_img_height
right_lim = (col_idx + 1)*half_img_width
max_img_row = numpy.min([down_lim - 1, row + half_correlation_width + 1])
max_img_col = numpy.min([right_lim - 1, col + half_correlation_width + 1])
min_img_row = numpy.max([up_lim, row - half_correlation_width])
min_img_col = numpy.max([left_lim, col - half_correlation_width])
max_img_row_diff = max_img_row - row
max_img_col_diff = max_img_col - col
min_img_row_diff = row - min_img_row
min_img_col_diff = col - min_img_col
min_knl_row = half_correlation_width - min_img_row_diff
min_knl_col = half_correlation_width - min_img_col_diff
max_knl_row = half_correlation_width + max_img_row_diff
max_knl_col = half_correlation_width + max_img_col_diff
img_r = [r for r in range(min_img_row, max_img_row)]
img_c = [c for c in range(min_img_col, max_img_col)]
knl_r = [r for r in range(min_knl_row, max_knl_row)]
knl_c = [c for c in range(min_knl_col, max_knl_col)]
# c_i = c_i - c_{max}<K_i, K_{max}>
img[img_r, img_c] -= max_val*correlation[knl_r, knl_c]
# mark any weird pixels as -inf so they don't matter in the search
inf_indices = numpy.where(img[img_r, img_c] == numpy.inf)
img[inf_indices] = 0
img[inf_indices] -= numpy.inf
nan_indices = numpy.where(img[img_r, img_c] == numpy.nan)
img[nan_indices] = 0
img[nan_indices] -= numpy.inf
# mark max value's coordinate to -inf to get it out of the search
if is_max_val_layer:
img[row, col] -= numpy.inf
# No need to return, values are affected because I'm doing = and -= ops
def local_coords_to_global_idx(self, coords, cell_type,
local_img_shape, global_img_shape):
row_add = cell_type/2
col_add = cell_type%2
global_coords = (coords[0] + row_add*local_img_shape[0],
coords[1] + col_add*local_img_shape[1])
global_idx = global_coords[0]*global_img_shape[1] + global_coords[1]
return global_idx
def global_to_single_coords(self, coords, single_shape):
row_count = coords[0]/single_shape[0]
col_count = coords[1]/single_shape[1]
new_row = coords[0] - single_shape[0]*row_count
new_col = coords[1] - single_shape[1]*col_count
return (new_row, new_col)
def cell_type_from_global_coords(self, coords, single_shape):
row_type = coords[0]/single_shape[0]
col_type = coords[1]/single_shape[1]
cell_type = row_type*2 + col_type
return cell_type
class App(QWidget):
def __init__(self):
super().__init__()
self.title = 'La construction de la convolution'
self.left = 50
self.top = 50
self.width = 1600
self.height = 900
self.sliderTimeFactor = 10
self.modeEcho = True
self.convolution = Convolution()
self.initUI()
self.plotDefaults()
def initUI(self):
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
self.show()
self.createCanvas()
self.createWidgets()
self.createGridLayout()
def createCanvas(self):
# Figure and canvas instances - canvas takes the `figure` instance as a parameter to __init__
self.figureX = plt.figure()
self.figureH = plt.figure()
self.figureRelative = plt.figure()
self.figureResult = plt.figure()
self.figureProducts = plt.figure()
self.canvasX = FigureCanvas(self.figureX)
self.canvasH = FigureCanvas(self.figureH)
self.canvasRelative = FigureCanvas(self.figureRelative)
self.canvasResult = FigureCanvas(self.figureResult )
self.canvasProducts = FigureCanvas(self.figureProducts)
# this is the Navigation widget
# it takes the Canvas widget and a parent
# self.toolbarX = NavigationToolbar(self.canvasX, self)
def createWidgets(self):
#Button Widgets
self.buttonReset = QPushButton('Réinitialisation')
self.buttonReset.clicked.connect(self.plotDefaults)
self.buttonUpdate = QPushButton('Mettre à jour les graphiques')
self.buttonUpdate.clicked.connect(self.plotUpdate)
self.buttonEcho = QRadioButton('Mode echo')
self.buttonEcho.setChecked(True)
self.buttonEcho.clicked.connect(self.setModeEcho)
self.buttonReverse = QRadioButton('Mode retourné')
self.buttonReverse.clicked.connect(self.setModeReverse)
self.sliderTimeLabel = QLabel("Selection du point t à évaluer")
self.sliderTimeLabel.setAlignment(Qt.AlignCenter)
self.sliderEchoLabel = QLabel("Nombre de points contribuant à l'évaluation du point t")
self.sliderEchoLabel.setAlignment(Qt.AlignCenter)
self.tminXLabel = QLabel("Valeur minimum de t pout x(t)")
self.tminXLabel.setAlignment(Qt.AlignRight)
self.tmaxXLabel = QLabel("Valeur maximum de t pour x(t)")
self.tmaxXLabel.setAlignment(Qt.AlignRight)
self.tminHLabel = QLabel("Valeur minimum de t pour h(t)")
self.tminHLabel.setAlignment(Qt.AlignRight)
self.tmaxHLabel = QLabel("Valeur maximum de t pour h(t)")
self.tmaxHLabel.setAlignment(Qt.AlignRight)
self.XFunctionLabel = QLabel("Fonction x(t)")
self.XFunctionLabel.setAlignment(Qt.AlignRight)
self.HFunctionLabel = QLabel("Fonction h(t)")
self.HFunctionLabel.setAlignment(Qt.AlignRight)
self.MessageLabel = QLabel("Les fonctions doivent être entrées avec" +
" la syntaxe Python et la nomenclature Numpy. " +
"Il y a 1000 points dans le vecteur \"t\".")
self.MessageLabel.setAlignment(Qt.AlignCenter)
self.sliderTime = QSlider(Qt.Horizontal)
self.sliderTime.valueChanged.connect(self.moveTau)
self.sliderTime.setTickPosition(QSlider.TicksBelow)
self.sliderEcho = QSlider(Qt.Horizontal)
self.sliderEcho.valueChanged.connect(self.changeNumberOfEchos)
self.sliderEcho.setTickPosition(QSlider.TicksBelow)
self.sliderEcho.setMinimum(1)
self.sliderEcho.setMaximum(300)
self.tminXInput = QLineEdit()
self.tminXInput.setValidator(QDoubleValidator())
self.tminXInput.textChanged.connect(self.updateMinRangeX)
self.tmaxXInput = QLineEdit()
self.tmaxXInput.setValidator(QDoubleValidator())
self.tmaxXInput.textChanged.connect(self.updateMaxRangeX)
self.tminHInput = QLineEdit()
self.tminHInput.setValidator(QDoubleValidator())
self.tminHInput.textChanged.connect(self.updateMinRangeH)
self.tmaxHInput = QLineEdit()
self.tmaxHInput.setValidator(QDoubleValidator())
self.tmaxHInput.textChanged.connect(self.updateMaxRangeH)
self.XFunctionInput = QLineEdit()
self.XFunctionInput.returnPressed.connect(self.updateXFunction)
self.HFunctionInput = QLineEdit()
self.HFunctionInput.returnPressed.connect(self.updateHFunction)
def createGridLayout(self):
# set the layout
self.horizontalGroupBox = QGroupBox("Grid")
layout = QGridLayout()
layout.setColumnStretch(0, 50)
layout.setColumnStretch(2, 50)
layout.setColumnStretch(4, 1000)
layout.addWidget(self.canvasX, 0, 0, 1, 2)
layout.addWidget(self.canvasH, 0, 2, 1, 2)
layout.addWidget(self.canvasRelative, 1, 0, 1, 4)
layout.addWidget(self.canvasProducts, 0, 4, 2, 1)
layout.addWidget(self.canvasResult, 2, 4, 10, 1)
layout.addWidget(self.sliderTimeLabel, 2, 0, 1, 4)
layout.addWidget(self.sliderTime, 3, 0, 1, 4)
layout.addWidget(self.sliderEchoLabel, 4, 0, 1, 4)
layout.addWidget(self.sliderEcho, 5, 0, 1, 4)
layout.addWidget(self.buttonReset, 6, 0)
layout.addWidget(self.buttonUpdate, 6, 1)
layout.addWidget(self.buttonEcho, 6, 2)
layout.addWidget(self.buttonReverse, 6, 3)
layout.addWidget(self.tminXLabel, 7,0)
layout.addWidget(self.tmaxXLabel, 8,0)
layout.addWidget(self.tminXInput, 7,1)
layout.addWidget(self.tmaxXInput, 8,1)
layout.addWidget(self.tminHLabel, 7,2)
layout.addWidget(self.tmaxHLabel, 8,2)
layout.addWidget(self.tminHInput, 7,3)
layout.addWidget(self.tmaxHInput, 8,3)
layout.addWidget(self.XFunctionInput, 9,1,1,3)
layout.addWidget(self.HFunctionInput, 10,1,1,3)
layout.addWidget(self.XFunctionLabel, 9,0,1,1)
layout.addWidget(self.HFunctionLabel, 10,0, 1,1)
layout.addWidget(self.MessageLabel, 11,0, 1, 4)
self.setLayout(layout)
def plotDefaults(self):
self.convolution = Convolution()
self.plotUpdate()
self.updateSlider()
self.tminXInput.setText('{}'.format(self.convolution.getMinRangeX()))
self.tminHInput.setText('{}'.format(self.convolution.getMinRangeH()))
self.tmaxXInput.setText('{}'.format(self.convolution.getMaxRangeX()))
self.tmaxHInput.setText('{}'.format(self.convolution.getMaxRangeH()))
self.XFunctionInput.setText('{}'.format(self.convolution.getXFunctionString()))
self.HFunctionInput.setText('{}'.format(self.convolution.getHFunctionString()))
def updateSlider(self):
self.sliderTime.setMinimum(self.convolution.getMinRangeX()*self.sliderTimeFactor)
self.sliderTime.setMaximum(self.convolution.getMaxRangeX()*self.sliderTimeFactor)
#self.sliderTime.setTickInterval(self.convolution.getStep())
self.sliderTime.setSingleStep((self.convolution.getMaxRangeX()-self.convolution.getMinRangeX())/100)
def plotUpdate(self):
self.figureX.clear()
ax = self.figureX.add_subplot(111)
dataX = self.convolution.getXfunction()
ax.plot(dataX[0], dataX[1])
ax.set_title("x(t)")
self.canvasX.draw()
self.figureH.clear()
ax = self.figureH.add_subplot(111)
dataH = self.convolution.getHfunction()
ax.plot(dataH[0], dataH[1])
ax.set_title("h(t)")
self.canvasH.draw()
self.figureRelative.clear()
ax = self.figureRelative.add_subplot(111)
tau = self.convolution.getTau()
ax.plot(dataX[0], dataX[1], color='blue')
ax.axvline(x=tau, color='red')
ax.plot((dataH[0]-dataH[0][0]+tau), dataH[1], color='green')
ax.text(0.9, 0.9, 't = {}'.format(tau), transform=ax.transAxes, fontsize='large')
ax.set_title("Positions relatives de x(t) et h(t)")
self.canvasRelative.draw()
tauindex = int(((tau-self.convolution.getMinRangeX())/self.convolution.getStep()))
self.figureResult.clear()
ax = self.figureResult.add_subplot(111)
ax.set_title("Convolution x(t) et h(t)")
data = self.convolution.getConvolution()
ax.plot(data[0], data[1])
ax.axvline(x=tau, color='red')
ax.scatter(tau, data[1][tauindex])
ax.text(0.8, 0.9, 'x(t) * h(t) = {0:5.4f}'.format(data[1][tauindex]), transform=ax.transAxes, fontsize='large')
self.canvasResult.draw()
if self.modeEcho == True:
self.productPlotWithEchos()
else:
self.productPlotReverse()
def productPlotWithEchos(self):
self.figureProducts.clear()
tau = self.convolution.getTau()
dataX = self.convolution.getXfunction()
dataH = self.convolution.getHfunction()
ax = self.figureProducts.add_subplot(111)
ax.axvline(x=tau, color='red')
ax.plot(dataX[0], dataX[1], color='blue')
ax.plot((dataH[0]-dataH[0][0]+tau), dataH[1], color='green')
tauindex = int(((tau-self.convolution.getMinRangeX())/self.convolution.getStep()))
ax.text(0.7, 0.95, r'$\Delta\tau=$ {0:5.4f}'.format(self.convolution.getEchoPoints()*self.convolution.getStep()), transform=ax.transAxes, fontsize='large')
textincrement = 0
total=dataH[1][0]*dataX[1][tauindex]*self.convolution.getEchoPoints()*self.convolution.getStep()
ax.scatter(tau, dataX[1][tauindex], marker='x')
for i, echo in enumerate(self.convolution.createEchos()):
try:
intersection = (dataX[1][i*(self.convolution.getEchoPoints())]*
dataH[1][self.convolution.getHindex(echo-self.convolution.getMinRangeX()+self.convolution.getMinRangeH())])
except IndexError:
intersection = 0
if intersection>0:
tOffset = dataH[0][0]-tau+echo-self.convolution.getMinRangeX()
line, = ax.plot((dataH[0]-tOffset), dataX[1][i*(self.convolution.getEchoPoints())] * dataH[1], alpha = 0.5)
ax.scatter(tau, intersection, color=line.get_color())
if textincrement < 16:
ax.text(0.7, 0.9-textincrement*0.05,
r'$x(t-\tau_{{{0}}})h(\tau_{{{0}}}) =$ {1:5.4f}'.format(textincrement+1, intersection),
transform=ax.transAxes, fontsize='large', color=line.get_color())
textincrement+=1
total += intersection*self.convolution.getEchoPoints()*self.convolution.getStep()
ax.text(0.95, 0.1, r"$\sum{{x(t-\tau_{{n}})\cdot h(\tau_{{n}})\cdot\Delta\tau}}=${0:5.2f}".format(total), transform=ax.transAxes, fontsize='x-large', ha='right')
ax.set_title("Translation de h(t) en un point")
minVert = npmin([dataX[1], dataH[1]])
maxVert = npmax([dataX[1], dataH[1]])
adjust = (maxVert-minVert)*0.1
plt.axis([dataX[0][0], dataX[0][-1], minVert-adjust, maxVert+adjust])
self.canvasProducts.draw()
def productPlotReverse(self):
self.figureProducts.clear()
tau = self.convolution.getTau()
dataX = self.convolution.getXfunction()
dataH = self.convolution.getHfunction()
ax = self.figureProducts.add_subplot(111)
ax.axvline(x=tau, color='red')
ax.plot(dataX[0], dataX[1], color='blue')
ax.plot(-(dataH[0]-dataH[0][0])+tau, dataH[1], color='green')
tauindex = int(((tau-self.convolution.getMinRangeX())/self.convolution.getStep()))
ax.text(0.7, 0.95, r'$\Delta\tau=$ {0:5.4f}'.format(self.convolution.getEchoPoints()*self.convolution.getStep()), transform=ax.transAxes, fontsize='large')
textincrement = 0
total=dataH[1][0]*dataX[1][tauindex]*self.convolution.getEchoPoints()*self.convolution.getStep()
ax.scatter(tau, dataX[1][tauindex], marker='x')
for i, echo in enumerate(self.convolution.createEchos()):
try:
intersection = (dataX[1][i*(self.convolution.getEchoPoints())]*
dataH[1][self.convolution.getHindex(echo-self.convolution.getMinRangeX()+
self.convolution.getMinRangeH())])
except IndexError:
intersection = 0
if intersection>0:
xposition = tau-echo+self.convolution.getMinRangeX()
line, = ax.plot([xposition, xposition], [0, intersection])
#Find color and apply to scatter and text
ax.scatter(xposition, intersection, color=line.get_color())
if textincrement < 16:
ax.text(0.7, 0.9-textincrement*0.05,
r'$x(t-\tau_{{{0}}})h(\tau_{{{0}}}) =$ {1:5.4f}'.format(textincrement+1, intersection),
transform=ax.transAxes, fontsize='large', color=line.get_color())
textincrement+=1
total += intersection*self.convolution.getEchoPoints()*self.convolution.getStep()
ax.text(0.95, 0.1, r"$\sum{{x(\tau_{{n}})\cdot h(t-\tau_{{n}})\cdot\Delta\tau}}=${0:5.2f}".format(total), transform=ax.transAxes, fontsize='x-large', ha='right')
ax.set_title("Translation de h(t) en un point")
#SET Axes to fix X
minVert = npmin([dataX[1], dataH[1]])
maxVert = npmax([dataX[1], dataH[1]])
adjust = (maxVert-minVert)*0.1
plt.axis([dataX[0][0], dataX[0][-1], minVert-adjust, maxVert+adjust])
self.canvasProducts.draw()
def updateMinRangeX(self, minvalue):
self.convolution.setMinRangeX(float(minvalue))
self.plotUpdate()
self.updateSlider()
def updateMaxRangeX(self, maxvalue):
self.convolution.setMaxRangeX(float(maxvalue))
self.plotUpdate()
self.updateSlider()
def updateMinRangeH(self, minvalue):
self.convolution.setMinRangeH(float(minvalue))
self.plotUpdate()
self.updateSlider()
def updateMaxRangeH(self, maxvalue):
self.convolution.setMaxRangeH(float(maxvalue))
self.plotUpdate()
self.updateSlider()
def updateXFunction(self):
self.convolution.setXFunction(self.XFunctionInput.text())
self.plotUpdate()
def updateHFunction(self):
self.convolution.setHFunction(self.HFunctionInput.text())
self.plotUpdate()
def moveTau(self):
tau = self.sliderTime.value()/float(self.sliderTimeFactor)
self.convolution.setTau(tau)
self.plotUpdate()
def changeNumberOfEchos(self):
self.convolution.setEchoRate(self.sliderEcho.value())
self.plotUpdate()
def setModeEcho(self):
self.modeEcho = True
self.productPlotWithEchos()
def setModeReverse(self):
self.modeEcho = False
self.productPlotReverse()
names = ['i-3-a', 'i-3-b', 'i-3-c']
char = 97
for name in names:
path = '../input/convolution/' + name + '.jpg'
img = cv2.imread(path)
print(path)
i = 0
for n in [3, 7, 15]:
kernel = np.ones((n, n), dtype=np.float32) / (n * n)
size = str(n) + 'x' + str(n)
print(size)
start_time = time.time()
img_out_1 = conv.convolution_nd(img,
kernel,
conv_type=conv.CONV_ITERATIVE)
print("--- classical algorithm: %s seconds ---" %
(time.time() - start_time))
name = '../output/convolution/o-3-' + chr(char) + '-' + str(
i) + '-' + size + 'ca.png'
cv2.imwrite(name, img_out_1)
i += 1
start_time = time.time()
img_out_2 = conv.convolution_nd(img,
kernel,
conv_type=conv.CONV_KERNEL_SIZE)
print("--- vectorized element-wise multiplication %s seconds ---" %
(time.time() - start_time))
name = '../output/convolution/o-3-' + chr(char) + '-' + str(
def __init__(self,num_filt=64): Neural_Network.__init__(self) self.conv = Convolution(num_filt)
class CNN(Neural_Network) :
def __init__(self,num_filt=64):
Neural_Network.__init__(self)
self.conv = Convolution(num_filt)
def train(self,X=None,y=None,nodesNumLayer=[16],nodes_outputLayer=1,learning_rate=0.1,epochs=100,hidden_activation=None,output_activation = None,save=False):
if X is None or y is None :
print('No Dataset or labels given')
exit()
if hidden_activation is None or output_activation is None :
print("Activation function not given")
exit()
self.nodesNumLayer=nodesNumLayer #list of number of nodes in each layer
self.nodes_outputLayer=nodes_outputLayer
self.lr = learning_rate
self.init_weights_bias(self.conv.feed_through_layer(X[0,:]).flatten().shape[0])
self.hidden_activation = hidden_activation
self.output_activation = output_activation
cost_function=self.MSE
for i in range(epochs) :
cost_sum = 0
print("epoch",i)
for j in range(X.shape[0]) :
convoluted_op = self.conv.feed_through_layer(X[j,:])
cop_shape = convoluted_op.shape
#print("cop_shape",cop_shape)
convoluted_op = convoluted_op.flatten()
y_pred = self.feedForward(convoluted_op,hidden_activation,output_activation)
cost_sum += np.sum(cost_function(y_pred,y[j]))
self.backProp(cost_function,y[j],y_pred,X[j,:],cop_shape)
print("cost:",cost_sum)
print()
def backProp(self,cost_function,actual,predicted,inp,conv_layer_shape):
errors = []
derivative_cost = scp.derivative(self.MSE,actual,dx=1e-6,args=(predicted,))
output_layer = self.layer_vals[-1]
output_error = derivative_cost*np.vectorize(scp.derivative)(self.output_activation,output_layer,dx=1e-6)
current_err = output_error.reshape(1,-1)
i=1
while i<=(len(self.weights)) :
errors.append(current_err)
current_layer = self.layer_vals[-(i+1)]
current_err = np.matmul(current_err,self.weights[-i].T)*np.vectorize(scp.derivative)(self.hidden_activation,current_layer,dx=1e-6)
i+=1
self.conv.conv_backProp(current_err.reshape(conv_layer_shape[0],conv_layer_shape[1],conv_layer_shape[2]),inp)
for i in range(len(self.weights)-1) :
x,y = np.meshgrid(errors[i],np.vectorize(self.hidden_activation)(self.layer_vals[-(i+2)]))
delta_w = x*y*self.lr
self.weights[-(i+1)] += delta_w
self.bias[-(i+1)] += (self.lr*errors[i].flatten())
x,y = np.meshgrid(errors[len(self.weights)-1],self.layer_vals[0])
delta_w=x*y*self.lr
self.weights[0] += delta_w
self.bias[0] += (self.lr*errors[len(self.weights)-1].flatten())
def test(self,X,y):
count=0
for i in range(X.shape[0]) :
convoluted_op = self.conv.feed_through_layer(X[i,:])
cop_shape = convoluted_op.shape
#print("cop_shape",cop_shape)
convoluted_op = convoluted_op.flatten()
y_pred = self.feedForward(convoluted_op,self.hidden_activation,self.output_activation)
if np.argmax(y_pred) == np.argmax(y[i]) :
count+=1
return count/y.shape[0]
h3 = self.i(h3)
h4 = self.i(h4)
h3_square = h3 * h3
h4_square = h4 * h4
summed = numpy.add(h3_square, h4_square)
img = numpy.sqrt(summed)
img = self.f(summed)
self.set(img)
self.set(self.normalize(self.get()))
def i(self, img):
return img / 255
def f(self, img):
return img * 255
if __name__ == "__main__":
import sys
first = Convolution(sys.argv[1])
second = Convolution(sys.argv[1])
first.apply_def('h3')
second.apply_def('h4')
i = ConvolutionSquare(sys.argv[1])
i.apply_multiplication(first, second)
i.save_to_file("output.png")
from MLP import MLP
from convolution import Convolution, Pooling, Model
from mnist import loadData
if __name__ == '__main__':
trainX, trainY, testX, testY, valX, valY = loadData()
model = Model()
model.add_layer(Convolution(16, 1, 3, 1))
model.add_layer(Pooling(2, 2))
model.add_layer(MLP(784, 128, 10, activation='sigmoid'))
model.feed_forward(trainX[0])
class Focal():
'''Filter Overlap Correction ALgorithm, simulates the foveal pit
region of the human retina.
Created by Basabdatta Sen Bhattacharya.
See DOI: 10.1109/TNN.2010.2048339
'''
def __init__(self):
'''Get the four layer's kernels, create the correlations and
a convolution wrapper.
:const MIN_IMG_WIDTH: Minimum image width for which to use
NumPy/SciPy for convolution
'''
self.kernels = DifferenceOfGaussians()
self.correlations = Correlation(self.kernels.full_kernels)
self.convolver = Convolution()
self.MIN_IMG_WIDTH = 256
def apply(self, image, spikes_per_unit=0.3, overlapcorr=True):
'''Wrapper function to convert an image into a FoCal representation
:param image: The image to convert
:param spikes_per_unit: How many spikes to return, specified in per-unit
(i.e. multiply by 100 to get percentage)
'''
spike_images = self.filter_image(image)
focal_spikes = self.focal(spike_images,
spikes_per_unit=spikes_per_unit,
overlapcorr=overlapcorr)
return focal_spikes
def focal(self, spike_images, spikes_per_unit=0.3, overlapcorr=True):
'''Filter Overlap Correction ALgorithm, simulates the foveal pit
region of the human retina.
Created by Basabdatta Sen Bhattacharya.
See DOI: 10.1109/TNN.2010.2048339
spike_images => A list of the values generated by the convolution
procedure, stored as four 2D arrays, each with
the same size/shape of the original image.
spikes_per_unit => Percentage of the total spikes to be processed,
specified in a per unit [0, 1] range.
returns: an ordered list of
[spike index, sorting value, cell layer/type] tuples.
'''
ordered_spikes = []
img_size = spike_images[0].size
img_shape = spike_images[0].shape
height, width = img_shape
num_images = len(spike_images)
#how many non-zero spikes are in the images
max_cycles = 0
for i in range(num_images):
max_cycles += numpy.sum(spike_images[i] != 0)
total_spikes = max_cycles.copy()
#reduce to desired number
max_cycles = numpy.int(spikes_per_unit * max_cycles)
#copy images from list to a large image to make it a single search space
big_shape = (height * 2, width * 2)
big_image = numpy.zeros(big_shape)
big_coords = [(0, 0), (0, width), (height, 0), (height, width)]
for cell_type in range(num_images):
row, col = big_coords[cell_type]
tmp_img = spike_images[cell_type].copy()
tmp_img[numpy.where(tmp_img == 0)] = -numpy.inf
big_image[row:row + height, col:col + width] = tmp_img
# Main FoCal loop
for count in range(max_cycles):
# print out completion percentage
percent = (count * 100.) / float(total_spikes - 1)
sys.stdout.write("\rFocal %d%%" % (percent))
# Get maximum value's index,
max_idx = numpy.argmax(big_image)
# its coordinates
max_coords = numpy.unravel_index(max_idx, big_shape)
# and the value
max_val = big_image[max_coords]
if max_val == numpy.inf or max_val == -numpy.inf or max_val == numpy.nan:
sys.stderr.write("\nWrong max value in FoCal!")
break
# translate coordinates from the big image's to a single image coordinates
if max_coords[0] < height and max_coords[1] < width:
single_coords = max_coords
else:
single_coords = self.global_to_single_coords(
max_coords, img_shape)
# calculate a local index, to store per ganglion cell layer info
local_idx = single_coords[0] * width + single_coords[1]
# calculate the type of cell from the index
cell_type = self.cell_type_from_global_coords(
max_coords, img_shape)
# append max spike info to return list
ordered_spikes.append([local_idx, max_val, cell_type])
if overlapcorr:
# correct surrounding pixels for overlapping kernel influence
for overlap_cell_type in range(len(spike_images)):
# get equivalent coordinates for each layer
overlap_idx = self.local_coords_to_global_idx(
single_coords, overlap_cell_type, img_shape, big_shape)
is_max_val_layer = overlap_cell_type == cell_type
# c_i = c_i - c_{max}<K_i, K_{max}>
self.adjust_with_correlation(
big_image,
self.correlations[cell_type][overlap_cell_type],
overlap_idx,
max_val,
is_max_val_layer=is_max_val_layer)
return ordered_spikes
def filter_image(self, img, force_homebrew=False):
'''Perform convolution with calculated kernels
img => the image to convolve
force_hombrew => if True: use my separated convolution code
else: use SciPy sepfir2d
'''
num_kernels = len(self.kernels.full_kernels)
img_width, img_height = img.shape
convolved_img = {}
for cell_type in range(num_kernels):
if img_width < self.MIN_IMG_WIDTH or img_height < self.MIN_IMG_WIDTH:
force_homebrew = True
else:
force_homebrew = False
c = self.convolver.dog_sep_convolution(
img,
self.kernels[cell_type],
cell_type,
originating_function="filter",
force_homebrew=force_homebrew)
convolved_img[cell_type] = c
return convolved_img
def adjust_with_correlation(self,
img,
correlation,
max_idx,
max_val,
is_max_val_layer=True):
'''Modify surrounding pixels by the correlation of kernels.
Example:
If max_val is in layer 1, we overlap all layers and then multiply
surrounding pixels by the correlation between layer 1 and layerX (1,2,3, or 4).
:param img: An image representing the spikes generated by the previous step
or simulation of a ganglion cell layer
:param max_idx: index of the pixel/neuron that has the maximum value from
the previous step or simulation
:param max_val: value of the max pixel/neuron
:param is_max_val_layer: Required to mark max_idx as visited
:returns: img with the proper adjustment
'''
img_height, img_width = img.shape
correlation_width = correlation.shape[0]
half_correlation_width = int(correlation_width / 2)
half_img_width = int(img_width / 2)
half_img_height = int(img_height / 2)
# Get max value's coordinates
row, col = idx2coord(max_idx, img_width)
row_idx = int(row / half_img_height)
col_idx = int(col / half_img_width)
# Calculate the zone to affect with the correlation
up_lim = (row_idx) * half_img_height
left_lim = (col_idx) * half_img_width
down_lim = (row_idx + 1) * half_img_height
right_lim = (col_idx + 1) * half_img_width
max_img_row = numpy.min(
[down_lim - 1, row + half_correlation_width + 1])
max_img_col = numpy.min(
[right_lim - 1, col + half_correlation_width + 1])
min_img_row = numpy.max([up_lim, row - half_correlation_width])
min_img_col = numpy.max([left_lim, col - half_correlation_width])
max_img_row_diff = max_img_row - row
max_img_col_diff = max_img_col - col
min_img_row_diff = row - min_img_row
min_img_col_diff = col - min_img_col
min_knl_row = half_correlation_width - min_img_row_diff
min_knl_col = half_correlation_width - min_img_col_diff
max_knl_row = half_correlation_width + max_img_row_diff
max_knl_col = half_correlation_width + max_img_col_diff
# c_i = c_i - c_{max}<K_i, K_{max}>
img[min_img_row:max_img_row,
min_img_col:max_img_col] -= max_val * correlation[
min_knl_row:max_knl_row, min_knl_col:max_knl_col]
# mark any weird pixels as -inf so they don't matter in the search
inf_indices = numpy.where(img[min_img_row:max_img_row,
min_img_col:max_img_col] == numpy.inf)
img[inf_indices] = 0
img[inf_indices] -= numpy.inf
nan_indices = numpy.where(img[min_img_row:max_img_row,
min_img_col:max_img_col] == numpy.nan)
img[nan_indices] = 0
img[nan_indices] -= numpy.inf
# mark max value's coordinate to -inf to get it out of the search
if is_max_val_layer:
img[row, col] -= numpy.inf
# No need to return, variables are passed as reference because we're doing = and -= ops
def local_coords_to_global_idx(self, coords, cell_type, local_img_shape,
global_img_shape):
'''Utility to transform from local (width*height) coordinates to
global (2*width, 2*height) coords. ---------
Layers are represented in the global image as: [ 0 | 1 ]
-------
[ 2 | 3 ]
---------
'''
row_add = int(cell_type / 2)
col_add = int(cell_type % 2)
global_coords = (coords[0] + row_add * local_img_shape[0],
coords[1] + col_add * local_img_shape[1])
global_idx = global_coords[0] * global_img_shape[1] + global_coords[1]
return global_idx
def global_to_single_coords(self, coords, single_shape):
'''Utility to transform from global (2*width, 2*height) coordinates to
local (width*height) coords . ---------
Layers are represented in the global image as: [ 0 | 1 ]
-------
[ 2 | 3 ]
---------
'''
row_count = int(coords[0] / single_shape[0])
col_count = int(coords[1] / single_shape[1])
new_row = coords[0] - single_shape[0] * row_count
new_col = coords[1] - single_shape[1] * col_count
return (new_row, new_col)
def cell_type_from_global_coords(self, coords, single_shape):
'''Utility to compute which layer does a coordinate belong to'''
row_type = int(coords[0] / single_shape[0])
col_type = int(coords[1] / single_shape[1])
cell_type = row_type * 2 + col_type
return cell_type
class PoseCellNetwork:
def __init__( self, shape, **kwargs ):
self.shape = shape
self.posecells = zeros(shape)
#NOTE: might not need if gaussian_filter works
self.kernel_3d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=3 )
self.kernel_2d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=2 )
self.kernel_1d = self.diff_gaussian( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA, order=1 )
self.kernel_1d_sep = self.diff_gaussian_separable( PC_E_DIM, PC_I_DIM, PC_E_SIGMA, PC_I_SIGMA )
self.global_inhibition = PC_GLOBAL_INHIB
self.pc_vtrans_scale = PC_CELL_X_SIZE
self.pc_vrot_scale = 2.0*pi/shape[2]#PC_C_SIZE_TH
#TODO: maybe change everything to float32 to make it go faster?
#self.conv = Convolution( im=self.posecells, fil=self.kernel_1d, sep=True, type=float64 )
#self.conv = Convolution( im=self.posecells, fil=self.kernel_1d_sep, sep=True, type=float64 )
self.conv = Convolution( im=self.posecells, fil=self.kernel_3d, sep=False, type=float64 )
filter = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), origin=(0,0) )
#self.conv.new_filter( self.kernel_2d, dim=2 )
self.conv.new_filter( filter, dim=2 )
self.filter_dict_2d = self.build_diff_gaussian_set_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), precision=1 )
self.filter_dict_2d_precision = 10 # multiple the decimal by this number to get the right key
def build_diff_gaussian_set_2d( self, sigma_e, sigma_i, shape=(7,7), precision=1 ):
"""Builds a set of difference of gaussian kernels with origins within a unit square.
These will be used as a look up table of approximate kernels to improve speed"""
dict = {}
# larger precision values denote greater precision. Must be integer to prevent floating point errors
for x in xrange( -5 * precision, 5 * precision ):
for y in xrange( -5 * precision, 5 * precision ):
dict[ ( x, y ) ] = self.diff_gaussian_offset_2d( sigma_e, sigma_i, shape=shape,
origin=( x / ( precision * 10 ), y / ( precision * 10 ) ) )
return dict
# builds a 3D gaussian filter kernel with lengths of 'dim'
def build_kernel( self, dim, sigma, order=3 ):
if order==3:
f = empty( ( dim, dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
for z in xrange( dim ):
f[x,y,z] = 1.0 / (sigma*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 - (z-center)**2 ) / (2*sigma**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==2:
f = empty( ( dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
f[x,y] = 1.0 / (sigma*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 ) / (2*sigma**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==1:
f = empty( ( dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
f[x] = 1.0 / (sigma*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma**2))
f /= abs(sum(f.ravel())) # normalize
return f
else:
pass #TODO: put an error statement here
# builds a 3D difference of gaussians filter kernel with lengths of 'dim'
def diff_gaussian( self, dim_e, dim_i, sigma_e, sigma_i, order=3 ):
dim = max(dim_e, dim_i)
if order==3:
f = empty( ( dim, dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
for z in xrange( dim ):
f[x,y,z] = ( 1 if max(x,y,z) <= center + dim_e and min(x,y,z) >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi))**3 * \
math.exp( (-(x-center)**2 - (y-center)**2 - (z-center)**2 ) / (2*sigma_e**2)) - \
( 1 if max(x,y,z) <= center + dim_i and min(x,y,z) >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi))**3 * \
math.exp( (-(x-center)**2 - (y-center)**2 - (z-center)**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==2:
f = empty( ( dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
f[x,y] = ( 1 if max(x,y) <= center + dim_e and min(x,y) >= center - dim_e else 0 ) * \
1.0 / (sigma_e * sigma_e * 2 * pi) * \
math.exp( (-(x-center)**2 - (y-center)**2 ) / (2*sigma_e**2)) - \
( 1 if max(x,y) <= center + dim_i and min(x,y) >= center - dim_i else 0 ) * \
1.0 / (sigma_i * sigma_i * 2 * pi) * \
math.exp( (-(x-center)**2 - (y-center)**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==1:
f = empty( ( dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
f[x] = ( 1 if x <= center + dim_e and x >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_e**2)) - \
( 1 if x <= center + dim_i and x >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
else:
pass #TODO: put an error statement here
""" This one uses the Ratslam C++ gaussian formulas, which are not technically correct
# builds a 3D difference of gaussians filter kernel with lengths of 'dim'
def diff_gaussian( self, dim_e, dim_i, sigma_e, sigma_i, order=3 ):
dim = max(dim_e, dim_i)
if order==3:
f = zeros( ( dim, dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
for z in xrange( dim ):
f[x,y,z] = ( 1 if max(x,y,z) <= center + dim_e and min(x,y,z) >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 - (z-center)**2 ) / (2*sigma_e**2)) - \
( 1 if max(x,y,z) <= center + dim_i and min(x,y,z) >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 - (z-center)**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==2:
f = zeros( ( dim, dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
for y in xrange( dim ):
f[x,y] = ( 1 if max(x,y) <= center + dim_e and min(x,y) >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 ) / (2*sigma_e**2)) - \
( 1 if max(x,y) <= center + dim_i and min(x,y) >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( (-(x-center)**2 - (y-center)**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
elif order==1:
f = zeros( ( dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
f[x] = ( 1 if x <= center + dim_e and x >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_e**2)) - \
( 1 if x <= center + dim_i and x >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
return f
else:
pass #TODO: put an error statement here
"""
# FIXME: this doesn't actually work at all, because DoG filters are not separable
# builds a 1D difference of gaussians filter kernel that can be used successively to create a 3D kernel
def diff_gaussian_separable( self, dim_e, dim_i, sigma_e, sigma_i ):
dim = max(dim_e, dim_i)
f = empty( ( dim ) )
center = math.floor( dim / 2 )
for x in xrange( dim ):
f[x] = ( 1 if x <= center + dim_e and x >= center - dim_e else 0 ) * \
1.0 / (sigma_e*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_e**2)) - \
( 1 if x <= center + dim_i and x >= center - dim_i else 0 ) * \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
math.exp( -(x-center)**2 / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
f = cbrt( f ) # Take the cubed root, so the filter applied 3 times will be normalized
return f
def diff_gaussian_offset_2d( self, sigma_e, sigma_i, shape=( 7, 7 ), origin=( 0, 0 ) ):
"""Builds a 2D difference of gaussian kernel centered at the origin given"""
f = empty( shape )
x, y = meshgrid( arange( shape[0] ) - origin[0], arange( shape[1] ) - origin[1] )
center = ( math.floor( shape[0] / 2 ), math.floor( shape[1] / 2 ) )
f = 1.0 / ( 2* sigma_e**2 * pi ) * \
exp( (-( x - center[0])**2 - (y - center[1])**2 ) / (2*sigma_e**2)) - \
1.0 / ( 2 * sigma_i**2 * pi ) * \
exp( (-( x - center[0])**2 - (y - center[1])**2 ) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
#f = square( cbrt( f ) ) # The result of this filter and a 1D filter should be normalized
f = cbrt( f ) # TEMP
return f
def diff_gaussian_offset_1d( self, sigma_e, sigma_i, size=7, origin=0 ):
"""Builds a 1D difference of gaussian kernel centered at the origin given"""
f = empty( size )
x = arange( size ) - origin
center = math.floor( size / 2 )
f = 1.0 / (sigma_e*math.sqrt(2*pi)) * \
exp( -square(x-center) / (2*sigma_e**2)) - \
1.0 / (sigma_i*math.sqrt(2*pi)) * \
exp( -square(x-center) / (2*sigma_i**2))
f /= abs(sum(f.ravel())) # normalize
f = cbrt( f ) # The result of this filter and a 2D filter should be normalized
return f
def filters_from_origins( self, origins, shape=( 7, 7 ) ):
num = origins.shape[1]
filters = empty( ( shape[0], shape[1], num ) )
for z in xrange(num):
filters[:,:,z] = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=shape, origin=origins[:,z] )
return filters
def filters_from_origins_approx( self, origins, shape=( 7, 7 ) ):
num = origins.shape[1]
filters = empty( ( shape[0], shape[1], num ) )
prec = self.filter_dict_2d_precision
for z in xrange(num):
filters[:,:,z] = self.filter_dict_2d[ ( int( origins[:,z][0] * prec ), int( origins[:,z][0] * prec ) ) ]
return filters
def path_integration( self, vtrans, vrot ):
vtrans /= self.pc_vtrans_scale
vrot /= self.pc_vrot_scale #TODO ?? is this right?
#TODO - this can be optimized better in the future
mid = math.floor( self.shape[2] / 2 )
#"""
dir_pc = arange( self.shape[2] ).reshape( (1, self.shape[2] ) )
origins_exact = concatenate( ( vtrans*cos( (dir_pc - mid)*self.pc_vrot_scale ),
vtrans*sin( (dir_pc - mid)*self.pc_vrot_scale ) ), axis=0 )
origins = concatenate( ( around( vtrans*cos( (dir_pc - mid)*self.pc_vrot_scale ) ),
around( vtrans*sin( (dir_pc - mid)*self.pc_vrot_scale ) ) ), axis=0 )
origins_diff = origins_exact - origins
#filters = self.filters_from_origins( origins_diff )
# Use a lookup table to get the filter set quickly
filters = self.filters_from_origins_approx( origins_diff )
self.posecells = self.conv.conv_im( self.posecells, axes=[0,1], radius=ceil( abs( vtrans ) ),
origins=origins, filters=filters )
#"""
"""
for dir_pc in xrange( self.shape[2] ):
# use a 2D gaussian filter across every theta (direction) layer, with the origin offset based on vtrans and vrot
origin = ( vtrans*cos( (dir_pc - mid)*self.pc_vrot_scale ), vtrans*sin( (dir_pc - mid)*self.pc_vrot_scale ) )
#origin = ( vtrans*cos( (dir_pc)*self.pc_vrot_scale ), vtrans*sin( (dir_pc)*self.pc_vrot_scale ) )
#origin = (4,4)
####print origin
#filter = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), origin=origin )
filter = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(12,12), origin=origin )
#filter = self.diff_gaussian_offset_2d( PC_E_SIGMA, PC_I_SIGMA, shape=(7,7), origin=(0,0) )
#self.conv.new_filter( filter, dim=2 )
#self.posecells = self.conv.conv_im( self.posecells, axes=[0,1] )
# Using origin shifted filter
self.posecells[:,:,dir_pc] = \
ndimage.correlate( self.posecells[:,:,dir_pc], filter, mode='wrap' )
#self.posecells[:,:,dir_pc] = \
# ndimage.correlate( self.posecells[:,:,dir_pc], filter, mode='wrap', origin=origin )
#self.posecells[:,:,dir_pc] = \
# ndimage.correlate( self.posecells[:,:,dir_pc], self.kernel_2d, mode='wrap', origin=origin )
"""
# Remove any negative values
self.posecells[self.posecells < 0] = 0
# and then use a 1D gaussian across the theta layers
#TODO - this can be optimized better in the future
origin = math.floor(vrot+.5)
#for x in xrange( self.shape[0] ):
# for y in xrange( self.shape[1] ):
# self.posecells[x,y,:] = ndimage.correlate( self.posecells[x,y,:], self.kernel_1d, mode='wrap', origin=origin )
filter = self.diff_gaussian_offset_1d( PC_E_SIGMA, PC_I_SIGMA, size=7, origin=origin )
self.conv.new_filter( filter, dim=1 )
self.posecells = self.conv.conv_im( self.posecells, axes=[2] )
#self.posecells = ndimage.correlate1d(input=self.posecells, weights=filter.tolist(), axis=2, mode='wrap')
# Remove any negative values
self.posecells[self.posecells < 0] = 0
# Not entirely sure what this should do yet ##???????????????????
def get_pc_max( self ):
(x,y,th) = unravel_index(self.posecells.argmax(), self.posecells.shape)
return (x,y,th)
#pass # TODO - just copy it from ratslam-python for now
def inject( self, energy, loc ): # loc is of the form [ x, y, z ]
self.posecells[ loc ] += energy
def update( self, v=(0.0, 0.0) ):
vtrans = v[0]
vrot = v[1]
# 1. Internal X-Y Layer Update
# 2. Inter-Layer Update
#input and output the same might not work
#self.posecells = ndimage.correlate(self.posecells, self.kernel_3d, mode='wrap')
self.posecells = self.conv.conv_im( self.posecells )
# 3. Global Inhibition
self.posecells[self.posecells < self.global_inhibition] = 0
self.posecells[self.posecells >= self.global_inhibition] -= self.global_inhibition
# 4. Normalization
total = sum(self.posecells.ravel())
if total != 0:
self.posecells /= total
# Path Integration
self.path_integration( vtrans, vrot )
# get the maximum pose cell
self.max_pc = self.get_pc_max()
return self.max_pc
def __init__(self): self.kernels = DifferenceOfGaussians() self.correlations = Correlation(self.kernels.full_kernels) self.convolver = Convolution() self.MIN_IMG_WIDTH = 256