def segment_SLIP_pos(data_direc, mask_direc):
data_location = data_direc
phase_location = mask_direc
phase_channel_names = ['Phase'] #['channel000']
trained_network_phase_directory = "/home/nquach/DeepCell2/trained_networks/ecoli/ecoli_all/"
phase_prefix = "2016-07-20_ecoli_all_31x31_bn_feature_net_31x31_"
#"2017-02-12_ecoli_90x_31x31_ecoli_90x_feature_net_31x31_"
win_phase = 30
image_size_x, image_size_y = get_image_sizes(data_location,
phase_channel_names)
image_size_x /= 2
image_size_y /= 2
list_of_phase_weights = []
for j in xrange(5):
phase_weights = os.path.join(trained_network_phase_directory,
phase_prefix + str(j) + ".h5")
print(phase_weights)
list_of_phase_weights += [phase_weights]
phase_predictions = run_models_on_directory(
data_location,
phase_channel_names,
phase_location,
model_fn=fn,
list_of_weights=list_of_phase_weights,
image_size_x=image_size_x,
image_size_y=image_size_y,
win_x=win_phase,
win_y=win_phase,
std=False,
split=False)
#phase_masks = segment_nuclei(phase_predictions, mask_location = mask_location, threshold = 0.75, area_threshold = 100, solidity_threshold = 0.75, eccentricity_threshold = 0.95)
nuclear_location = os.path.join(direc_name, 'Nuclear')
mask_location = os.path.join(direc_name, 'Masks')
# cyto_channel_names = ['Pos3']
nuclear_channel_names = ['nucleus']
# trained_network_cyto_directory = "/home/vanvalen/DeepCell/trained_networks/"
trained_network_nuclear_directory = "/home/vanvalen/DeepCell/trained_networks/theriot/"
# cyto_prefix = "2016-08-02_ecoli_all_31x31_bn_feature_net_31x31_"
nuclear_prefix = "2017-04-13_theriot_61x61_bn_feature_net_61x61_"
# win_cyto = 15
win_nuclear = 30
image_size_x, image_size_y = get_image_sizes(data_location,
nuclear_channel_names)
"""
Define model
"""
# list_of_cyto_weights = []
# for j in xrange(5):
# 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")
list_of_nuclear_weights += [nuclear_weights]
for f in folders:
if not os.path.exists(os.path.join(mainfolder, f, 'Output')):
os.makedirs(os.path.join(mainfolder, f, 'Output'))
for f in folders:
if os.path.isdir(os.path.join(mainfolder, f,'Output')):
numfiles = len(os.listdir(os.path.join(mainfolder, f,'Output')));
if numfiles == 0:
print f
direc_name = os.path.join(mainfolder, f)
data_location = direc_name
cyto_location = os.path.join(direc_name, 'Output')
image_size_x, image_size_y = get_image_sizes(data_location,cyto_channel_names)
#image_size_x /= 4
#image_size_y /= 4
"""
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]
"""
Run model on directory