def plotclick(clickdata):
if (clickdata.inaxes is None):
return
else:
z = axes.index(clickdata.inaxes)
y = int(clickdata.ydata / TN_size)
x = int(clickdata.xdata / TN_size)
if (ShowXinsteadOfZ): x, z = z, x # swap
#print(str([y,x,z]))
patch = [p for p in region['patches']
if p['patchstep'] == [y, x, z]][0]
plotting.print_dict(patch)
if (clickdata.button is 1):
set_to_core(patch['id'])
elif (clickdata.button is 2):
set_to_boundary(patch['id'])
else:
set_to_outside(patch['id'])
region = filehandling.pload(BASEP + 'data/F15/region.pickledump')
segmentation = filehandling.pload(BASEP + 'data/F15/segmentation.pickledump')
segmentation_patch_515 = filehandling.pload(
BASEP + 'data/F15/segmentations/segmentation_patch_515.pickledump')
tumor_volume_515 = filehandling.readNifti(
ROOTP + 'LocalData/F15/C02_tumorBoost/patchvolume_515.nii'
) # we're reading in a .nii file, so it's real image data, not just metadata
#%% Main code
# print dictionary in structured format to console
print("--- Region (meta data for patch locations):")
plotting.print_dict(region)
print("\n\n")
print("--- Segmentation (aggregated list of metastases across all patches):")
plotting.print_dict(segmentation)
print("\n\n")
print("--- Single segmentation (list of metastases for one patch):")
plotting.print_dict(segmentation_patch_515)
# Print metastases of patch 515 (probably we don't need this)
print(
"\nHere are all the metastases of patch 515, described by ID and Volume:")
metastases_in_patch515 = segmentation_patch_515['metastases']
for metastasis in metastases_in_patch515:
print('ID: ' + str(metastasis['id']) + '\tVolume: ' +
#%% Main code
# print dictionary in structured format to console
'''
print("--- Region (meta data for patch locations):")
plotting.print_dict(region)
print("\n\n")
print("--- Segmentation (aggregated list of metastases across all patches):")
plotting.print_dict(segmentation)
'''
print("\n\n")
print("--- Single segmentation (list of metastases for one patch):")
plotting.print_dict(segmentation_patch_515)
'''
# Print metastases of patch 515 (probably we don't need this)
print("\nHere are all the metastases of patch 515, described by ID and Volume")
metastases_in_patch515 = segmentation_patch_515['metastases']
for metastasis in metastases_in_patch515:
print('ID:',str(metastasis['id']),'Volume:',str(metastasis['volume']))
print("There are " + str(len(metastases_in_patch515)) + " metastases in patch 515")
'''
# ALTERNATIVE: Print metastases of patch 515
print(
"Here are all metastases of patch 515, described by their global ID and volume:"
)
all_metastases = segmentation['metastases']
metastases_in_p515 = {}
model = load_model('model_1/KERAS_check_best_model.h5', custom_objects=co)
import plotting
from sklearn.metrics import accuracy_score
y_keras = model.predict(X_test)
print("Accuracy: {}".format(
accuracy_score(np.argmax(y_test, axis=1), np.argmax(y_keras, axis=1))))
import hls4ml
hls4ml.model.optimizer.OutputRoundingSaturationMode.layers = ['Activation']
hls4ml.model.optimizer.OutputRoundingSaturationMode.rounding_mode = 'AP_RND'
hls4ml.model.optimizer.OutputRoundingSaturationMode.saturation_mode = 'AP_SAT'
config = hls4ml.utils.config_from_keras_model(model, granularity='name')
config['LayerName']['softmax']['exp_table_t'] = 'ap_fixed<18,8>'
config['LayerName']['softmax']['inv_table_t'] = 'ap_fixed<18,4>'
print("-----------------------------------")
plotting.print_dict(config)
print("-----------------------------------")
hls_model = hls4ml.converters.convert_from_keras_model(
model,
hls_config=config,
output_dir='model_1/hls4ml_prj',
fpga_part='xc7z020clg400-1',
backend='Pynq')
hls_model.compile()
y_hls = hls_model.predict(X_test)
hls_model.build(csim=False, synth=True, export=True)
hls4ml.report.read_vivado_report('model_1/hls4ml_prj/')
hls4ml.templates.PynqBackend.make_bitfile(hls_model)
def yaml_load(config):
with open(config, 'r') as stream:
param = yaml.safe_load(stream)
return param
model_file = 'keras_model.h5'
model = keras_model.load_model(model_file)
model.summary()
hls_config = yaml_load('hls4ml_config.yml')
hls_config = hls_config['HLSConfig']
backend_config = hls4ml.converters.create_vivado_config(
fpga_part='xczu7ev-ffvc1156-2-e')
backend_config['ProjectName'] = 'anomaly_detector'
backend_config['KerasModel'] = model
backend_config['HLSConfig'] = hls_config
backend_config['OutputDir'] = 'anomaly_detector_prj'
backend_config['Backend'] = 'Vivado'
backend_config['Implementation'] = 'serial'
backend_config['ClockPeriod'] = 10
plotting.print_dict(backend_config)
hls_model = hls4ml.converters.keras_to_hls(backend_config)
_ = hls_model.compile()
def inspect_update_plot():
global metastasis, CoM_abs, inspect_fig, inspect_ax_plt_list, inspect_addremove_STATUS, inspect_maskvis
plt.figure(num=inspect_fig.number)
try:
for ax in inspect_ax_plt_list:
inspect_fig.delaxes(ax)
except:
pass
plt.draw()
if(OoI is None):
return
min_padding = segmentation['autosegmentation']['min_padding']
if(type(OoI)==int):
metastasis = next((m for m in metastases if m["id"] == OoI))
if(metastasis['characterization']['NofSTDs'] is None): metastasis['characterization']['NofSTDs'] = 4
padding = np.max([np.round(1*metastasis['max_dist']),min_padding]) # diameter of seedblob, but at least 5 voxel
CoM_abs = metastasis['offset'] + metastasis['CoM']
inspect_addremove_STATUS = 'remove'
else:
metastasis = None
inspect_addremove_STATUS = 'add'
inspect_maskvis = False
padding = min_padding
CoM_abs = OoI
inspect_maskvis_updaterender()
inspect_masksize_updaterender()
inspect_flag_updaterender()
inspect_addremove_updaterender()
miny = np.clip(int(CoM_abs[0] - padding), 0, volume_vis.shape[0])
maxy = np.clip(int(CoM_abs[0] + padding), 0, volume_vis.shape[0])
minx = np.clip(int(CoM_abs[1] - padding), 0, volume_vis.shape[1])
maxx = np.clip(int(CoM_abs[1] + padding), 0, volume_vis.shape[1])
minz = np.clip(int(CoM_abs[2] - padding), 0, volume_vis.shape[2])
maxz = np.clip(int(CoM_abs[2] + padding), 0, volume_vis.shape[2])
mybox = volume_vis[miny:maxy,minx:maxx,minz:maxz]
mask = np.zeros(mybox.shape).astype(np.bool)
ax2 = None
if(type(OoI)==int):
abs_points = metastasis['points']
pointarray = np.asarray(abs_points) - np.asarray([miny,minx,minz])
mask[pointarray[:,0],pointarray[:,1],pointarray[:,2]] = 1
plotting.print_dict(metastasis)
#if(inspect_view is not '3D view'):
#ax2 = inspect_fig.add_subplot(2,2,2)
#ax2.text(0,0.3,plotting.print_dict(metastasis,filterlist='volume,max_dist,characterization,medianFG,maxFG,maxBG,avgSTDabovelocalmean,NofSTDs,evaluation,last_inspection,manually_added',printit=False),family='monospace',fontsize=fs[1])
#ax2.axis('off')
############## Autofluo instead of metastasis info (which is the code above within the if statement)
if(inspect_view is not '3D view'):
ax2 = inspect_fig.add_subplot(2,2,2)
autofluo_projection = np.max(volume_autofluo[miny:maxy,minx:maxx,minz:maxz],2)
plotting.intensity(autofluo_projection,color='white',cap=np.max(autofluo_projection),ahandle=ax2)
plt.title('Autofluorescence (z-Projection)')
##############
if(inspect_view=='Max. Projections'):
ax1 = inspect_fig.add_subplot(2,2,1)
ax3 = inspect_fig.add_subplot(2,2,3)
ax4 = inspect_fig.add_subplot(2,2,4)
inspect_ax_plt_list = [ax1, ax3, ax4, ax2]
plotting.projections(mybox, mask=mask, ahandles=inspect_ax_plt_list[0:3], outline=inspect_maskvis)
if(inspect_view=='Cum. Projections'):
ax1 = inspect_fig.add_subplot(2,2,1)
ax3 = inspect_fig.add_subplot(2,2,3)
ax4 = inspect_fig.add_subplot(2,2,4)
inspect_ax_plt_list = [ax1, ax3, ax4, ax2]
plotting.projections(mybox, mask=mask, ahandles=inspect_ax_plt_list[0:3], outline=inspect_maskvis, mode='cum')
elif(inspect_view=='Slices through center'):
ax1 = inspect_fig.add_subplot(2,2,1)
ax3 = inspect_fig.add_subplot(2,2,3)
ax4 = inspect_fig.add_subplot(2,2,4)
inspect_ax_plt_list = [ax1, ax3, ax4, ax2]
plotting.orthoslices(mybox, mask=mask, ahandles=inspect_ax_plt_list[0:3], outline=inspect_maskvis)
elif(inspect_view=='3D view'):
inspect_ax_plt_list = [inspect_fig.gca(projection='3d')]
plotting.voxelintensity(mybox, mask=mask, ahandle=inspect_ax_plt_list[0],threshold=0.08)
inspect_rotate_3dplot_to('YX')
# Render updated plot
plt.draw()
plt.subplots_adjust(top=0.8)
inspect_update_title()
# Update last inspection
if(type(OoI)==int):
metastasis['evaluation']['last_inspection'] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
save_updated_metastasis()