def run_ensemble(train_filename, train_path, test_filename, test_path,
result_filename, output_filename, coords_filename,
batch_size, step_size):
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionRandom(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 0),
synapses=params['connection_rand']['synapses']),
ConnectionGaussian(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 1),
synapses=params['connection_gaus']['synapses'],
radius=params['connection_gaus']['radius'])
]
train_data, train_label, _, _ = LoadDataset(train_filename, train_path)
network.train(train_data, train_label)
test_data, test_label, x, y = LoadDataset(test_filename, test_path)
test_data, test_label = ClearFalseNegatives(train_label, test_label, test_data)
print 'Train size:', train_label.shape[0]
print 'Test size:', test_label.shape[0]
test_size = test_label.shape[0]
pred_label = -1 * np.ones((test_size,), dtype=int)
pred_buffer = -1 * np.ones((batch_size, batch_size), dtype=int)
for i in xrange(test_size):
test_data_i = test_data[i:i+batch_size,:]
predicted = network.testSequence(test_data_i, step_list(step_size))
'''
pred_label[i] = predicted[0]
'''
pred_buffer = np.roll(pred_buffer, -1, axis=0)
pred_buffer[batch_size-1,:] = np.pad(predicted, (0,batch_size-predicted.shape[0]), 'constant', constant_values=-1)
pred_label[i] = MajorityVoteWithinSecondaryDiagonal(pred_buffer)
print pred_label[i], test_label[i], predicted
pred_label[0:batch_size] = test_label[0:batch_size] #disregard first results
result_file = open(result_filename, 'w')
for frame_radius in xrange(19):
result = EvaluateOutput(pred_label, test_label.flatten(), max_number_of_frames=frame_radius)
print>>result_file, (result * 100.0)
np.savetxt(output_filename, [pred_label, test_label])
np.savetxt(coords_filename, [x, y])
def run_vgram(train_filename, train_path, test_filename, test_path,
result_filename, output_filename, coords_filename,
batch_size):
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionRandom(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 0),
synapses=params['connection_rand']['synapses']),
ConnectionGaussian(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 1),
synapses=params['connection_gaus']['synapses'],
radius=params['connection_gaus']['radius'])
]
train_data, train_label, _, _ = LoadDataset(train_filename, train_path)
network.train(train_data, train_label)
#network.unload()
#network.reload()
test_data, test_label, x, y = LoadDataset(test_filename, test_path)
test_data, test_label = ClearFalseNegatives(train_label, test_label, test_data)
print 'Train size:', train_label.shape[0]
print 'Test size:', test_label.shape[0]
output_data = network.test(test_data, test_label)
'''the output computed below takes the closest of top 3 most voted, which is closest to the previous'''
#pred_label = NetworkOutput.MajorityVoteClosestToPrevious(output_data, test_label.shape[0])
#pred_label = NetworkOutput.MajorityVoteClosestToPrevious2(output_data, test_label.shape[0], test_label[0])
'''the output computed below takes the closest of top 3 most voted, which is closest to ground truth'''
#pred_label = NetworkOutput.MajorityVoteClosestToExpected(output_data, test_label)
'''the output computed below takes the most voted and doesn't take into consideration the confidence'''
pred_label, confidence = NetworkOutput.MajorityVoteAndConfidence(output_data, test_label.shape[0])
#ClearLowConfidence(pred_label, confidence, 0.1)
pred_label[0:batch_size] = test_label[0:batch_size] #disregard first results
result_file = open(result_filename, 'w')
for frame_radius in xrange(19):
result = EvaluateOutput(pred_label, test_label.flatten(), max_number_of_frames=frame_radius)
print>>result_file, (result * 100.0)
np.savetxt(output_filename, [pred_label, test_label])
np.savetxt(coords_filename, [x, y])
def run_ensemble(train_filename, train_path, test_filename, test_path,
result_filename, output_filename, coords_filename, batch_size,
step_size):
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionRandom(input_layer=ConnectionInput(params['input']['width'],
params['input']['height'],
0),
synapses=params['connection_rand']['synapses']),
ConnectionGaussian(input_layer=ConnectionInput(
params['input']['width'], params['input']['height'], 1),
synapses=params['connection_gaus']['synapses'],
radius=params['connection_gaus']['radius'])
]
train_data, train_label, _, _ = LoadDataset(train_filename, train_path)
network.train(train_data, train_label)
test_data, test_label, x, y = LoadDataset(test_filename, test_path)
test_data, test_label = ClearFalseNegatives(train_label, test_label,
test_data)
print 'Train size:', train_label.shape[0]
print 'Test size:', test_label.shape[0]
test_size = test_label.shape[0]
pred_label = -1 * np.ones((test_size, ), dtype=int)
pred_buffer = -1 * np.ones((batch_size, batch_size), dtype=int)
for i in xrange(test_size):
test_data_i = test_data[i:i + batch_size, :]
predicted = network.testSequence(test_data_i, step_list(step_size))
'''
pred_label[i] = predicted[0]
'''
pred_buffer = np.roll(pred_buffer, -1, axis=0)
pred_buffer[batch_size - 1, :] = np.pad(
predicted, (0, batch_size - predicted.shape[0]),
'constant',
constant_values=-1)
pred_label[i] = MajorityVoteWithinSecondaryDiagonal(pred_buffer)
print pred_label[i], test_label[i], predicted
pred_label[0:batch_size] = test_label[0:
batch_size] #disregard first results
result_file = open(result_filename, 'w')
for frame_radius in xrange(19):
result = EvaluateOutput(pred_label,
test_label.flatten(),
max_number_of_frames=frame_radius)
print >> result_file, (result * 100.0)
np.savetxt(output_filename, [pred_label, test_label])
np.savetxt(coords_filename, [x, y])
def run_vgram(train_filename, train_path, test_filename, test_path,
result_filename, output_filename, coords_filename, batch_size):
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionRandom(input_layer=ConnectionInput(params['input']['width'],
params['input']['height'],
0),
synapses=params['connection_rand']['synapses']),
ConnectionGaussian(input_layer=ConnectionInput(
params['input']['width'], params['input']['height'], 1),
synapses=params['connection_gaus']['synapses'],
radius=params['connection_gaus']['radius'])
]
memory_size, input_size, num_samples = LoadDataset(train_filename)
print 'Train size:', memory_size, input_size, num_samples, train_filename
network.train(memory_size, input_size, num_samples, train_filename, True)
memory_size, input_size, num_samples = LoadDataset(test_filename)
print 'Test size:', memory_size, input_size, num_samples, test_filename
output_data, test_label = network.test(memory_size, input_size,
num_samples, test_filename, True)
'''the output computed below takes the closest of top 3 most voted, which is closest to the previous'''
#pred_label = NetworkOutput.MajorityVoteClosestToPrevious(output_data, test_label.shape[0])
#pred_label = NetworkOutput.MajorityVoteClosestToPrevious2(output_data, test_label.shape[0], test_label[0])
'''the output computed below takes the closest of top 3 most voted, which is closest to ground truth'''
#pred_label = NetworkOutput.MajorityVoteClosestToExpected(output_data, test_label)
'''the output computed below takes the most voted and doesn't take into consideration the confidence'''
pred_label, confidence = NetworkOutput.MajorityVoteAndConfidence(
output_data, num_samples)
#ClearLowConfidence(pred_label, confidence, 0.1)
#pred_label[0:batch_size] = test_label[0:batch_size] #disregard first results
result_file = open(result_filename, 'w')
for frame_radius in xrange(19):
result = EvaluateOutput(pred_label,
test_label.flatten(),
max_number_of_frames=frame_radius)
print(result * 100.0)
print >> result_file, (result * 100.0)
image_clahe[:,:,0] = ImageProcProxy.applyCLAHE(image_roi[:,:,0])
image_clahe[:,:,1] = ImageProcProxy.applyCLAHE(image_roi[:,:,1])
image_clahe[:,:,2] = ImageProcProxy.applyCLAHE(image_roi[:,:,2])
image_resized = ImageProcProxy.resizeInterLinear(image_clahe,
params['input']['width'],
params['input']['height'])
image_gaussian = ImageProcProxy.applyGaussian(image_resized,
params['filter']['gaussian_radius'],
params['filter']['gaussian_sigma'])
image_int = ImageProcProxy.convertBGR2INT(image_gaussian)
image_vector = ImageProcProxy.flattenImage(image_int)
input_images[sample]= image_vector
return input_images, file_list['classId']
if __name__ == '__main__':
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [ConnectionLogPolar(input_layer=ConnectionInput(params['input']['width'], params['input']['height']),
synapses=params['connection']['synapses'],
radius=params['connection']['radius'],
factor=params['connection']['factor'])]
input_data, class_data = LoadDataset(params['dataset']['train']['file'], params['dataset']['train']['path'])
network.train(input_data, class_data)
input_data, class_data = LoadDataset(params['dataset']['test']['file'], params['dataset']['train']['path'])
output_data = network.test(input_data, class_data)
output_value= NetworkOutput.MajorityVoteMean(output_data, class_data.flatten()) * 100.0
print "Percentage correct =", output_value, "%"
image_clahe[:, :, 1] = ImageProcProxy.applyCLAHE(image_roi[:, :, 1])
image_clahe[:, :, 2] = ImageProcProxy.applyCLAHE(image_roi[:, :, 2])
image_resized = ImageProcProxy.resizeInterLinear(
image_clahe, params['input']['width'], params['input']['height'])
image_gaussian = ImageProcProxy.applyGaussian(
image_resized, params['filter']['gaussian_radius'],
params['filter']['gaussian_sigma'])
image_int = ImageProcProxy.convertBGR2INT(image_gaussian)
image_vector = ImageProcProxy.flattenImage(image_int)
input_images[sample] = image_vector
return input_images, file_list['classId']
if __name__ == '__main__':
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionLogPolar(input_layer=ConnectionInput(
params['input']['width'], params['input']['height']),
synapses=params['connection']['synapses'],
radius=params['connection']['radius'],
factor=params['connection']['factor'])
]
input_data, class_data = LoadDataset(params['dataset']['train']['file'],
params['dataset']['train']['path'])
network.train(input_data, class_data)
input_data, class_data = LoadDataset(params['dataset']['test']['file'],
params['dataset']['train']['path'])
from pyram.mae.vgram.vgram_core import VGRAM
from pyram.mae.vgram.vgram_output import NetworkOutput
from pyram.mae.vgram.vgram_synapse import ConnectionGaussian, ConnectionRandom, ConnectionInput
from example_placerecog_utils import LoadDataset, ProcessImage, ClearFalseNegatives, EvaluateOutput
from example_placerecog_utils import LoadDatasetAndCropImages
from example_placerecog_config import params
import numpy as np
if __name__ == '__main__':
network = VGRAM(params['output']['width'], params['output']['height'])
network.connections = [
ConnectionRandom(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 0),
synapses=params['connection_rand']['synapses']),
ConnectionGaussian(input_layer=ConnectionInput(params['input']['width'], params['input']['height'], 1),
synapses=params['connection_gaus']['synapses'],
radius=params['connection_gaus']['radius'])
]
train_data, train_label, _, _ = LoadDataset(params['dataset']['train']['file'],
params['dataset']['train']['path'])
network.train(train_data, train_label)
#network.unload()
#network.reload()
test_data, test_label = LoadDatasetAndCropImages(params['dataset']['test']['file'],
params['dataset']['test']['path'])
test_data, test_label = ClearFalseNegatives(train_label, test_label, test_data)
confidence = np.zeros(test_label.shape[0], dtype=float)
predicted = np.zeros(test_label.shape[0], dtype=int)
for i in xrange(test_label.shape[0]):
