def main(author):
print('OpenCV version {} '.format(cv2.__version__))
current_dir = os.path.dirname(__file__)
training_folder = os.path.join(current_dir, 'data/training/', author)
test_folder = os.path.join(current_dir, 'data/test/', author)
training_data = []
training_labels = []
for filename in os.listdir(training_folder):
img = cv2.imread(os.path.join(training_folder, filename), 0)
if img is not None:
data = preprocessor.prepare(img)
training_data.append(data)
training_labels.append([0, 1] if "genuine" in filename else [1, 0])
test_data = []
test_labels = []
for filename in os.listdir(test_folder):
img = cv2.imread(os.path.join(test_folder, filename), 0)
if img is not None:
data = preprocessor.prepare(img)
test_data.append(data)
test_labels.append([0, 1] if "genuine" in filename else [1, 0])
return sgd(training_data, training_labels, test_data, test_labels)
def main():
print('OpenCV version {} '.format(cv2.__version__))
current_dir = os.path.dirname(__file__)
author = '021'
training_folder = os.path.join(current_dir, 'data/training/', author)
test_folder = os.path.join(current_dir, 'data/test/', author)
training_data = []
for filename in os.listdir(training_folder):
img = cv2.imread(os.path.join(training_folder, filename), 0)
if img is not None:
data = np.array(preprocessor.prepare(img))
data = np.reshape(data, (901, 1))
result = [[0], [1]] if "genuine" in filename else [[1], [0]]
result = np.array(result)
result = np.reshape(result, (2, 1))
training_data.append((data, result))
test_data = []
for filename in os.listdir(test_folder):
img = cv2.imread(os.path.join(test_folder, filename), 0)
if img is not None:
data = np.array(preprocessor.prepare(img))
data = np.reshape(data, (901, 1))
result = 1 if "genuine" in filename else 0
test_data.append((data, result))
net = network.NeuralNetwork([901, 500, 500, 2])
net.sgd(training_data, 10, 50, 0.01, test_data)
def run(args):
text = args['text'] or main.read_whole_file(args['file'])
p_params, l_params = main.separate_params(args)
prep_text = preprocessor.prepare(text, args['mode'], **p_params)
min_d = (args['min_dictionary_absolute']
or 0) / len(prep_text) or args['min_dictionary_relative']
max_d = (args['max_dictionary_absolute']
or 0) / len(prep_text) or args['max_dictionary_relative']
freq = stats.relative_frequency(prep_text, args['ngram'])
vocab = [k for k, v in freq.items() if v >= min_d and v <= max_d]
res = []
output_file = args['output_file']
for v in vocab:
args['template_string'] = v
args['output_file'] = None
args['aprox_output_file'] = None
_, gamma = main.run(args, False)
res.append((v, gamma))
if output_file is not None and len(output_file) > 0:
main.write_to_file(output_file, res, False)
def run(args, visualize=True):
# print(args)
text = args['text'] or read_whole_file(args['file'])
p_params, l_params = separate_params(args)
prep_text = preprocessor.prepare(text, args['mode'], **p_params)
print('text legth: ',len(prep_text))
print()
encoded_text = prep_text
if args['mode'] != 'prep':
if args['template_string'] is None:
encoded_text = fluctuation.encode_vocab(prep_text)
else:
template = ( args['template_string'] if args['case_sensitive'] else args['template_string'].lower() ).split()
encoded_text = fluctuation.encode(prep_text, template)
if args['only_encode']:
if args['output_file']:
write_to_file(args['output_file'], encoded_text, True)
return encoded_text, None
result, gamma = analyse(encoded_text, args['mode'], l_params, visualize)
if args['output_file']:
write_to_file(args['output_file'], result, False)
if args['aprox_output_file']:
write_to_file(args['aprox_output_file'], [gamma], False)
return result, gamma
#This takes the center of the stack as a center frame(s)
center = np.int(n_total_frames) // 2
#Check this in case we are in double exposure
if center % 2 == 1:
center -= 1
if metadata["double_exposure"]:
metadata["double_exp_time_ratio"] = metadata["dwell1"] // metadata[
"dwell2"] # time ratio between long and short exposure
center_frames = np.array([raw_frames[center], raw_frames[center + 1]])
else:
center_frames = raw_frames[center]
#print('energy (eV)',metadata['energy'])
metadata, background_avg = preprocessor.prepare(metadata, center_frames,
dark_frames)
#print('energy (J)',metadata['energy'])
#we take the center of mass from rank 0
#metadata["center_of_mass"] = mpi_Bcast(metadata["center_of_mass"], metadata["center_of_mass"], 0, mode = "cpu")
io = ptycommon.IO()
output_filename = os.path.splitext(json_file)[:-1][0][:-4] + "cosmic2.cxi"
if rank == 0:
#output_filename = os.path.splitext(json_file)[:-1][0] + "_cosmic2.cxi"
printv("\nSaving cxi file metadata: " + output_filename + "\n")
#data_dictionary["data"] = np.concatenate(data_dictionary["data"], axis=0)
