def templateDynamicLengthFastLinking(num_osc, steps, type_conn, repr_type,
stimulus, ccore):
params = pcnn_parameters()
params.FAST_LINKING = True
return PcnnTestTemplates.templateDynamicLength(num_osc,
steps,
type_conn,
repr_type,
stimulus,
ccore,
params=params)
def template_segmentation_image(image, parameters, simulation_time, brightness, scale_color = True, fastlinking = False, show_spikes = False, ccore_flag = True):
image_source = Image.open(image);
image_size = image_source.size;
width = image_size[0];
height = image_size[1];
stimulus = read_image(image);
stimulus = rgb2gray(stimulus);
if (brightness != None):
for pixel_index in range(len(stimulus)):
if (stimulus[pixel_index] < brightness): stimulus[pixel_index] = 1;
else: stimulus[pixel_index] = 0;
else:
maximum_stimulus = float(max(stimulus));
minimum_stimulus = float(min(stimulus));
delta = maximum_stimulus - minimum_stimulus;
for pixel_index in range(len(stimulus)):
if (scale_color is True):
stimulus[pixel_index] = 1.0 - ((float(stimulus[pixel_index]) - minimum_stimulus) / delta);
else:
stimulus[pixel_index] = float(stimulus[pixel_index]) / 255;
if (parameters is None):
parameters = pcnn_parameters();
parameters.AF = 0.1;
parameters.AL = 0.1;
parameters.AT = 0.8;
parameters.VF = 1.0;
parameters.VL = 1.0;
parameters.VT = 30.0;
parameters.W = 1.0;
parameters.M = 1.0;
parameters.FAST_LINKING = fastlinking;
net = pcnn_network(len(stimulus), parameters, conn_type.GRID_EIGHT, height = height, width = width, ccore = ccore_flag);
output_dynamic = net.simulate(simulation_time, stimulus);
pcnn_visualizer.show_output_dynamic(output_dynamic);
ensembles = output_dynamic.allocate_sync_ensembles();
draw_image_mask_segments(image, ensembles);
pcnn_visualizer.show_time_signal(output_dynamic);
if (show_spikes is True):
spikes = output_dynamic.allocate_spike_ensembles();
draw_image_mask_segments(image, spikes);
pcnn_visualizer.animate_spike_ensembles(output_dynamic, image_size);
def template_segmentation_image(image, parameters, simulation_time, brightness, scale_color = True, fastlinking = False, show_spikes = False, ccore_flag = True):
image_source = Image.open(image);
image_size = image_source.size;
width = image_size[0];
height = image_size[1];
stimulus = read_image(image);
stimulus = rgb2gray(stimulus);
if (brightness != None):
for pixel_index in range(len(stimulus)):
if (stimulus[pixel_index] < brightness): stimulus[pixel_index] = 1;
else: stimulus[pixel_index] = 0;
else:
maximum_stimulus = float(max(stimulus));
minimum_stimulus = float(min(stimulus));
delta = maximum_stimulus - minimum_stimulus;
for pixel_index in range(len(stimulus)):
if (scale_color is True):
stimulus[pixel_index] = 1.0 - ((float(stimulus[pixel_index]) - minimum_stimulus) / delta);
else:
stimulus[pixel_index] = float(stimulus[pixel_index]) / 255;
if (parameters is None):
parameters = pcnn_parameters();
parameters.AF = 0.1;
parameters.AL = 0.1;
parameters.AT = 0.8;
parameters.VF = 1.0;
parameters.VL = 1.0;
parameters.VT = 30.0;
parameters.W = 1.0;
parameters.M = 1.0;
parameters.FAST_LINKING = fastlinking;
net = pcnn_network(len(stimulus), parameters, conn_type.GRID_EIGHT, height = height, width = width, ccore = ccore_flag);
output_dynamic = net.simulate(simulation_time, stimulus);
pcnn_visualizer.show_output_dynamic(output_dynamic);
ensembles = output_dynamic.allocate_sync_ensembles();
draw_image_mask_segments(image, ensembles);
pcnn_visualizer.show_time_signal(output_dynamic);
if (show_spikes is True):
spikes = output_dynamic.allocate_spike_ensembles();
draw_image_mask_segments(image, spikes);
pcnn_visualizer.animate_spike_ensembles(output_dynamic, image_size);
def segmentation_fast_linking_field_flowers():
parameters = pcnn_parameters();
parameters.AF = 0.1;
parameters.AL = 0.1;
parameters.AT = 0.8;
parameters.VF = 1.0;
parameters.VL = 1.0;
parameters.VT = 80.0;
parameters.W = 1.0;
parameters.M = 1.0;
parameters.FAST_LINKING = True;
template_segmentation_image(IMAGE_REAL_SAMPLES.IMAGE_FIELD_FLOWER, parameters, 80, None, False, True, True);
def twenty_five_neurons_mix_stimulated():
"Object allocation"
"If M = 0 then only object will be allocated"
params = pcnn_parameters()
params.AF = 0.1
params.AL = 0.0
params.AT = 0.7
params.VF = 1.0
params.VL = 1.0
params.VT = 10.0
params.M = 0.0
template_dynamic_pcnn(25, 100, [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0
], params, conn_type.GRID_FOUR, False)
def twenty_five_neurons_mix_stimulated():
"Object allocation"
"If M = 0 then only object will be allocated"
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.0;
params.AT = 0.7;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 10.0;
params.M = 0.0;
template_dynamic_pcnn(25, 100, [0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 1, 1, 0, 0,
0, 1, 1, 0, 0,
0, 0, 0, 0, 0], params, conn_type.GRID_FOUR, False);
def hundred_neurons_mix_stimulated():
"Allocate several clusters: the first contains borders (indexes of oscillators) and the second objects (indexes of oscillators)"
params = pcnn_parameters()
params.AF = 0.1
params.AL = 0.1
params.AT = 0.8
params.VF = 1.0
params.VL = 1.0
params.VT = 20.0
params.W = 1.0
params.M = 1.0
template_dynamic_pcnn(100, 50, [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0
], params, conn_type.GRID_EIGHT, False)
def segmentation_double_t():
image = read_image(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10);
image = rgb2gray(image);
for pixel_index in range(len(image)):
if (image[pixel_index] < 128):
image[pixel_index] = 1;
else:
image[pixel_index] = 0;
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.1;
params.AT = 0.8;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 20.0;
params.W = 1.0;
params.M = 1.0;
ensembles = template_dynamic_pcnn(32 * 32, 28, image, params, conn_type.GRID_EIGHT, False);
draw_image_mask_segments(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10, ensembles);
def hundred_neurons_mix_stimulated():
"Allocate several clusters: the first contains borders (indexes of oscillators) and the second objects (indexes of oscillators)"
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.1;
params.AT = 0.8;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 20.0;
params.W = 1.0;
params.M = 1.0;
template_dynamic_pcnn(100, 50, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0], params, conn_type.GRID_EIGHT, False);
def segmentation_double_t():
image = read_image(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10);
image = rgb2gray(image);
for pixel_index in range(len(image)):
if (image[pixel_index] < 128):
image[pixel_index] = 1;
else:
image[pixel_index] = 0;
params = pcnn_parameters();
params.AF = 0.1;
params.AL = 0.1;
params.AT = 0.8;
params.VF = 1.0;
params.VL = 1.0;
params.VT = 20.0;
params.W = 1.0;
params.M = 1.0;
ensembles = template_dynamic_pcnn(32 * 32, 28, image, params, conn_type.GRID_EIGHT, False);
draw_image_mask_segments(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE10, ensembles);
def templateDynamicLengthFastLinking(num_osc, steps, type_conn, repr_type, stimulus, ccore):
params = pcnn_parameters();
params.FAST_LINKING = True;
return PcnnTestTemplates.templateDynamicLength(num_osc, steps, type_conn, repr_type, stimulus, ccore, params=params);
def nine_neurons_stimulated_one_sync():
"Just dynamic demonstration"
params = pcnn_parameters()
template_dynamic_pcnn(9, 100, [1.0] * 9, params, conn_type.GRID_FOUR)
def template_segmentation_image(image,
parameters,
simulation_time,
brightness,
scale_color=True,
fastlinking=False,
show_spikes=False,
ccore_flag=True):
image_source = Image.open(image)
image_size = image_source.size
width = image_size[0]
height = image_size[1]
stimulus = read_image(image)
stimulus = rgb2gray(stimulus)
if brightness is not None:
for pixel_index in range(len(stimulus)):
if stimulus[pixel_index] < brightness:
stimulus[pixel_index] = 1
else:
stimulus[pixel_index] = 0
else:
maximum_stimulus = float(max(stimulus))
minimum_stimulus = float(min(stimulus))
delta = maximum_stimulus - minimum_stimulus
for pixel_index in range(len(stimulus)):
if scale_color is True:
stimulus[pixel_index] = 1.0 - (
(float(stimulus[pixel_index]) - minimum_stimulus) / delta)
else:
stimulus[pixel_index] = float(stimulus[pixel_index]) / 255
if parameters is None:
parameters = pcnn_parameters()
parameters.AF = 0.1
parameters.AL = 0.1
parameters.AT = 0.8
parameters.VF = 1.0
parameters.VL = 1.0
parameters.VT = 30.0
parameters.W = 1.0
parameters.M = 1.0
parameters.FAST_LINKING = fastlinking
net = pcnn_network(len(stimulus),
parameters,
conn_type.GRID_EIGHT,
height=height,
width=width,
ccore=ccore_flag)
output_dynamic = net.simulate(simulation_time, stimulus)
pcnn_visualizer.show_output_dynamic(output_dynamic)
ensembles = output_dynamic.allocate_sync_ensembles()
draw_image_mask_segments(image, ensembles)
pcnn_visualizer.show_time_signal(output_dynamic)
if show_spikes is True:
spikes = output_dynamic.allocate_spike_ensembles()
draw_image_mask_segments(image, spikes)
pcnn_visualizer.animate_spike_ensembles(output_dynamic, image_size)
def nine_neurons_stimulated_one_sync():
"Just dynamic demonstration"
params = pcnn_parameters();
template_dynamic_pcnn(9, 100, [1.0] * 9, params, conn_type.GRID_FOUR);