def run_model_on_dir():
data_location = os.path.join(DATA_DIR, PREFIX, 'set1')
output_location = os.path.join(RESULTS_DIR, PREFIX)
channel_names = ['phase']
image_size_x, image_size_y = get_image_sizes(data_location, channel_names)
model_name = '2018-06-13_ecoli_kc_polaris_channels_last_sample__0.h5'
weights = os.path.join(MODEL_DIR, PREFIX, model_name)
n_features = 3
window_size = (30, 30)
model_fn = bn_feature_net_61x61 if DATA_OUTPUT_MODE == 'sample' else bn_dense_feature_net
predictions = run_models_on_directory(data_location=data_location,
channel_names=channel_names,
output_location=output_location,
n_features=n_features,
model_fn=model_fn,
list_of_weights=[weights],
image_size_x=image_size_x,
image_size_y=image_size_y,
win_x=window_size[0],
win_y=window_size[1],
split=False)
nuclear_location = os.path.join(direc_name, 'Nuclear2') mask_location = os.path.join(direc_name, 'Masks') cyto_channel_names = ["Phase", "Far-red"] nuclear_channel_names = ['Far-red'] trained_network_cyto_directory = "/home/vanvalen/DeepCell/trained_networks/HeLa/" trained_network_nuclear_directory = "/home/vanvalen/DeepCell/trained_networks/Nuclear/" cyto_prefix = "2017-12-03_HeLa_joint_disc_same_61x61_bn_dense_feature_net_" nuclear_prefix = "2016-07-12_nuclei_all_61x61_bn_feature_net_61x61_" win_cyto = 30 win_nuclear = 30 image_size_x, image_size_y = get_image_sizes(data_location, cyto_channel_names) # image_size_x += 2*win_cyto # image_size_y += 2*win_cyto """ Define model """ list_of_cyto_weights = [] for j in xrange(1): cyto_weights = os.path.join(trained_network_cyto_directory, cyto_prefix + str(j) + ".h5") list_of_cyto_weights += [cyto_weights] # list_of_nuclear_weights = [] # for j in xrange(1): # nuclear_weights = os.path.join(trained_network_nuclear_directory, nuclear_prefix + str(j) + ".h5")
""" direc_name = '/data/testing_data/nuclei_broad/set1' data_location = os.path.join(direc_name, 'RawImages') nuclear_location = os.path.join(direc_name, 'Nuclear') mask_location = os.path.join(direc_name, 'Masks') nuclear_channel_names = ['nuclear'] trained_network_nuclear_directory = "/data/trained_networks/nuclei_broad/" nuclear_prefix = "2018-01-20_nuclei_broad_same_conv_61x61_bn_dense_feature_net_" win_nuclear = 30 image_size_x, image_size_y = get_image_sizes(data_location, nuclear_channel_names) """ Define model """ list_of_nuclear_weights = [] for j in xrange(1): nuclear_weights = os.path.join(trained_network_nuclear_directory, nuclear_prefix + str(j) + ".h5") list_of_nuclear_weights += [nuclear_weights] print list_of_nuclear_weights """ Run model on directory """