def visualize(num_osc, steps, type_conn, repr_type, stimulus, height, width, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, None, None, ccore);
dynamic = net.simulate(steps, stimulus);
pcnn_visualizer.show_time_signal(dynamic);
pcnn_visualizer.show_output_dynamic(dynamic);
pcnn_visualizer.animate_spike_ensembles(dynamic, (height, width));
def templateGridRectangleDynamicLength(num_osc, steps, type_conn, repr_type, height, width, stimulus, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, height, width, ccore);
dynamic = net.simulate(steps, stimulus);
assert steps == len(dynamic);
assert num_osc == len(dynamic.output[0]);
assert steps == len(dynamic.allocate_time_signal());
def templateGridRectangleDynamicLength(num_osc, steps, type_conn, repr_type, height, width, stimulus, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, height, width, ccore);
dynamic = net.simulate(steps, stimulus);
assert steps == len(dynamic);
assert num_osc == len(dynamic.output[0]);
assert steps == len(dynamic.allocate_time_signal());
def templateAllocationInRectangleStructure(self, num_osc, height, width, steps, type_conn, repr_type, stimulus, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, height, width, ccore);
dynamic = net.simulate(steps, stimulus);
assert steps == len(dynamic);
assert num_osc == len(dynamic.output[0]);
assert steps == len(dynamic.allocate_time_signal());
def templateDynamicLength(self, num_osc, steps, type_conn, repr_type, stimulus, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, None, None, ccore)
dynamic = net.simulate(steps, stimulus)
assert steps == len(dynamic)
assert num_osc == len(dynamic.output[0])
assert steps == len(dynamic.allocate_time_signal())
def templateDynamicLength(self, num_osc, steps, type_conn, repr_type,
stimulus, ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, ccore)
dynamic = net.simulate(steps, stimulus)
assert steps == len(dynamic)
assert num_osc == len(dynamic.output[0])
assert steps == len(dynamic.allocate_time_signal())
def template_dynamic_pcnn(num_osc, steps, stimulus = None, params = None, conn_type = conn_type.NONE, separate_representation = True, ccore_flag = True):
net = pcnn_network(num_osc, params, conn_type, ccore = ccore_flag);
dynamic = net.simulate(steps, stimulus);
ensembles = dynamic.allocate_sync_ensembles();
print("Number of objects:", len(ensembles), "\nEnsembles:", ensembles);
pcnn_visualizer.show_output_dynamic(dynamic);
return ensembles;
def template_dynamic_pcnn(num_osc, steps, stimulus = None, params = None, conn_type = conn_type.NONE, separate_representation = True, ccore_flag = True):
net = pcnn_network(num_osc, params, conn_type, ccore = ccore_flag);
dynamic = net.simulate(steps, stimulus);
ensembles = dynamic.allocate_sync_ensembles();
print("Number of objects:", len(ensembles), "\nEnsembles:", ensembles);
draw_dynamics(dynamic.time, dynamic.output, x_title = "t", y_title = "y(t)", separate = separate_representation);
return ensembles;
def templateAllocationInRectangleStructure(num_osc, height, width, steps,
type_conn, repr_type, stimulus,
ccore):
net = pcnn_network(num_osc, None, type_conn, repr_type, height, width,
ccore)
dynamic = net.simulate(steps, stimulus)
assert steps == len(dynamic)
assert num_osc == len(dynamic.output[0])
assert steps == len(dynamic.allocate_time_signal())
def template_dynamic_pcnn(num_osc, steps, stimulus = None, params = None, conn_type = conn_type.NONE, separate_representation = True, ccore_flag = True):
net = pcnn_network(num_osc, params, conn_type, ccore = ccore_flag);
dynamic = net.simulate(steps, stimulus);
ensembles = dynamic.allocate_sync_ensembles();
print("Number of objects:", len(ensembles), "\nEnsembles:", ensembles);
pcnn_visualizer.show_output_dynamic(dynamic);
return ensembles;
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 templateDynamicLength(num_osc, steps, type_conn, repr_type, stimulus, ccore, **kwargs):
params = kwargs.get('params', None);
net = pcnn_network(num_osc, params, type_conn, repr_type, None, None, ccore);
dynamic = net.simulate(steps, stimulus);
assert steps == len(dynamic);
assert num_osc == len(dynamic.output[0]);
assert steps == len(dynamic.allocate_time_signal());
return net;
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 templateSyncEnsemblesAllocation(num_osc, type_conn, steps, stimulus,
ccore, ensembles):
net = pcnn_network(num_osc, None, type_conn, conn_represent.MATRIX,
None, None, ccore)
dynamic = net.simulate(steps, stimulus)
assert steps == len(dynamic)
sync_ensembles = dynamic.allocate_sync_ensembles()
if (ensembles is not None):
assert len(ensembles) == len(sync_ensembles)
for expected_ensemble in ensembles:
ensemble_correct = False
for index_ensemble in range(len(sync_ensembles)):
sorted_expected_ensemble = expected_ensemble.sort()
sorted_ensemble = sync_ensembles[index_ensemble].sort()
if (sorted_expected_ensemble == sorted_ensemble):
ensemble_correct = True
break
assert (True == ensemble_correct)
unique_indexes = set()
time_signal = dynamic.allocate_time_signal()
spike_ensembles = dynamic.allocate_spike_ensembles()
sync_ensembles = dynamic.allocate_sync_ensembles()
for ensemble in spike_ensembles:
assert len(ensemble) in time_signal
for ensemble in sync_ensembles:
spike_ensembles_exist = False
for index in range(len(spike_ensembles)):
if ensemble == spike_ensembles[index]:
spike_ensembles_exist = True
break
assert (True == spike_ensembles_exist)
for index_oscillator in ensemble:
assert index_oscillator not in unique_indexes
unique_indexes.add(index_oscillator)
def templateSyncEnsemblesAllocation(self, num_osc, type_conn, steps, stimulus, ccore, ensembles):
net = pcnn_network(num_osc, None, type_conn, conn_represent.MATRIX, None, None, ccore)
dynamic = net.simulate(steps, stimulus)
assert steps == len(dynamic)
sync_ensembles = dynamic.allocate_sync_ensembles()
if ensembles is not None:
assert len(ensembles) == len(sync_ensembles)
for expected_ensemble in ensembles:
ensemble_correct = False
for index_ensemble in range(len(sync_ensembles)):
sorted_expected_ensemble = expected_ensemble.sort()
sorted_ensemble = sync_ensembles[index_ensemble].sort()
if sorted_expected_ensemble == sorted_ensemble:
ensemble_correct = True
break
assert True == ensemble_correct
unique_indexes = set()
time_signal = dynamic.allocate_time_signal()
spike_ensembles = dynamic.allocate_spike_ensembles()
sync_ensembles = dynamic.allocate_sync_ensembles()
for ensemble in spike_ensembles:
assert len(ensemble) in time_signal
for ensemble in sync_ensembles:
spike_ensembles_exist = False
for index in range(len(spike_ensembles)):
if ensemble == spike_ensembles[index]:
spike_ensembles_exist = True
break
assert True == spike_ensembles_exist
for index_oscillator in ensemble:
assert index_oscillator not in unique_indexes
unique_indexes.add(index_oscillator)
def templateSyncEnsemblesAllocation(num_osc, type_conn, steps, stimulus, ccore, ensembles):
net = pcnn_network(num_osc, None, type_conn, conn_represent.MATRIX, None, None, ccore);
dynamic = net.simulate(steps, stimulus);
assert steps == len(dynamic);
sync_ensembles = dynamic.allocate_sync_ensembles();
if (ensembles is not None):
assert len(ensembles) == len(sync_ensembles);
for expected_ensemble in ensembles:
ensemble_correct = False;
for index_ensemble in range(len(sync_ensembles)):
sorted_expected_ensemble = expected_ensemble.sort();
sorted_ensemble = sync_ensembles[index_ensemble].sort();
if (sorted_expected_ensemble == sorted_ensemble):
ensemble_correct = True;
break;
assert (True == ensemble_correct);
unique_indexes = set();
time_signal = dynamic.allocate_time_signal();
spike_ensembles = dynamic.allocate_spike_ensembles();
sync_ensembles = dynamic.allocate_sync_ensembles();
for ensemble in spike_ensembles:
assert len(ensemble) in time_signal;
for ensemble in sync_ensembles:
spike_ensembles_exist = False;
for index in range(len(spike_ensembles)):
if ensemble == spike_ensembles[index]:
spike_ensembles_exist = True;
break;
assert (True == spike_ensembles_exist);
for index_oscillator in ensemble:
assert index_oscillator not in unique_indexes;
unique_indexes.add(index_oscillator);
def template_dynamic_pcnn(num_osc,
steps,
stimulus=None,
params=None,
conn_type=conn_type.NONE,
separate_representation=True,
ccore_flag=True):
net = pcnn_network(num_osc, params, conn_type, ccore=ccore_flag)
dynamic = net.simulate(steps, stimulus)
ensembles = dynamic.allocate_sync_ensembles()
print("Number of objects:", len(ensembles), "\nEnsembles:", ensembles)
draw_dynamics(dynamic.time,
dynamic.output,
x_title="t",
y_title="y(t)",
separate=separate_representation)
return ensembles
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)
