def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: validate-treshold.py project_file model_name best_epoch_number [models_dir]'
sys.exit(1)
project_file = sys.argv[1]
model_name = sys.argv[2]
best_epoch_number = int(sys.argv[3])
if len(sys.argv) >= 5:
models_dir = sys.argv[4]
else:
models_dir = '.'
# Load the project file and the model
project = Project(project_file)
model_filename = os.path.join(models_dir, model_name + '.model')
model = get_model_from_name(project, model_name)
# Load the weight corresponding to the right epoch
load_weights(model, model_name, model_filename, best_epoch_number)
# Generate validation data
validation_dataset = project.get_validation_dataset()
validation_genconfig = project.get_validation_genconfig() #using the same validation dataset for threshold fine tuning
input_data = validation_dataset.generate_input_data(validation_genconfig)
output_data = validation_dataset.generate_ground_truth_data(validation_genconfig)
validate_threshold(model,input_data,output_data)
def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: binarize_image.py project_file model_name best_epoch_number test_image_file [models_dir]'
sys.exit(1)
project_file = sys.argv[1]
model_name = sys.argv[2]
best_epoch_number = int(sys.argv[3])
test_image_file = sys.argv[4]
if len(sys.argv) >= 6:
models_dir = sys.argv[5]
else:
models_dir = '.'
# Load the project file and the model
project = Project(project_file)
model_filename = os.path.join(models_dir, model_name + '.model')
model = get_model_from_name(project, model_name)
# Load the weight corresponding to the right epoch
load_weights(model, model_name, model_filename, best_epoch_number)
# Load the image
dataset = DataSet('.')
dataset.imagespaths[test_image_file] = test_image_file
data = TrainingData(dataset, dataset)
patch_size = project.get_training_dataset().config['patch_size']
data.set_config({'patch_size': patch_size})
genconfig = data.exhaustive_gen_config([patch_size, patch_size])
patches = data.generate_input_data(genconfig)
# Binarize the image
binarized_image = binarize(patches, patch_size, genconfig[test_image_file], model, dataset.open_image(test_image_file).size)
# Draw the image using PIL for easier image saving method
im = Image.fromarray(np.uint8(binarized_image*255))
im.show()
def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: train.py project_file.json [output_dir]'
sys.exit(1)
project_file = sys.argv[1]
if len(sys.argv) >= 3:
output_dir = sys.argv[2]
else:
output_dir = '.'
# Load project
project = Project(project_file)
# Generate training data
training_dataset = project.get_training_dataset()
training_genconfig = project.get_training_genconfig()
input_data = training_dataset.generate_input_data(training_genconfig)
output_data = training_dataset.generate_ground_truth_data(training_genconfig)
print str(input_data.shape[0]) + " training samples were generated."
print "Each sample contains " + str(input_data.shape[1]) + " pixels."
# Create output directory if necessary
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Train each model
for i in range(project.get_models_count()):
model = project.get_model(i)
model_name = project.get_model_name(i)
print 'Training model ' + model_name + '...'
model_path = os.path.join(output_dir, model_name + '.model')
model.train(input_data, output_data, open(model_path, 'wb+'))
def main():
# Parse command line args
if len(sys.argv) < 2:
print 'USAGE: validate.py project_file.json [models_dir]'
sys.exit(1)
project_file = sys.argv[1]
if len(sys.argv) >= 3:
models_dir = sys.argv[2]
else:
models_dir = '.'
# Check that the models directory exists
if not os.path.exists(models_dir):
raise IOError('The models directory does not exist.')
# Load project
project = Project(project_file)
# Generate validation data
validation_dataset = project.get_validation_dataset()
validation_genconfig = project.get_validation_genconfig()
input_data = validation_dataset.generate_input_data(validation_genconfig)
output_data = validation_dataset.generate_ground_truth_data(validation_genconfig)
print str(input_data.shape[0]) + ' validation samples were generated.'
print 'Each sample contains ' + str(input_data.shape[1]) + ' pixels.'
# Validate each model
for i in range(project.get_models_count()):
#TODO : implement a validation method that doesn't need training to be finished, so that validation error could be visualized while the app is still running
model = project.get_model(i)
model_name = project.get_model_name(i)
print 'Validating model ' + model_name + '...'
model_filename = os.path.join(models_dir, model_name + '.model')
validate(model, model_filename, model_name, input_data, output_data)
# Show all plots
plt.show()
