def detection_evaluation():
annotation_file = "/data/fyl/datasets/LUNA16/csvfiles/annotations_corrected.csv"
#result_file = "pytorch_project/experiments_dt/evaluations_test2/result.csv"
evaluation_path = 'pytorch_project/experiments_dt/evaluations_sliccand_densecropnet_e11_fold4'
#eva.csv_evaluation(annotation_file, result_file, evaluation_path, exclude_file=None)
#results = np.load('pytorch_project/experiments_dt/evaluations_sliccand_densecropnet_e11_fold4/result.npy')
results_csv = pd.read_csv(
'pytorch_project/experiments_dt/evaluations_sliccand_densecropnet_e11_fold4/result.csv'
)
results = []
for r in range(len(results_csv["seriesuid"].values)):
uid = results_csv["seriesuid"].values[r]
coordX = results_csv["coordX"].values[r]
coordY = results_csv["coordY"].values[r]
coordZ = results_csv["coordZ"].values[r]
prob = results_csv["probability"].values[r]
results.append([uid, coordX, coordY, coordZ, prob])
#result_order = results[:,-1].argsort()[::-1]
#for oi in result_order:
# print(results[oi])
assessment = eva.detection_assessment(results, annotation_file, label=1)
eva.evaluation_vision([],
assessment['num_scans'],
assessment['FROC'][0],
assessment['FROC'][1],
assessment['CPM'],
assessment['detection_cites'],
output_path=evaluation_path)
origin[1],
(nodule_center_predictions[nc][0] * new_spacing[0]) +
origin[0], nodule_center_predictions[nc][3]
])
print('{} {} {} {}'.format(nodule_center_predictions[nc][0],
nodule_center_predictions[nc][1],
nodule_center_predictions[nc][2],
nodule_center_predictions[nc][3]))
output_frame = pd.DataFrame(
data=results,
columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'])
output_frame.to_csv(result_file, index=False, float_format='%.4f')
#if len(results)<=0:
# print("No results to evaluate, continue")
# continue
assessment = eva.detection_assessment(results, annotation_file,
candidate_file)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, nodules_detected = assessment
if len(FPsperscan) <= 0 or len(sensitivities) <= 0:
print("No results to evaluate, continue")
else:
eva.evaluation_vision(CPMs,
num_scans,
FPsperscan,
sensitivities,
CPMscore,
nodules_detected,
for nc in range(10):
nodule_centers.append([random.randint(0,image.shape[0]-1), random.randint(0,image.shape[1]-1), random.randint(0,image.shape[2]-1), random.random()])
'''
print('Nodule coordinations:')
if len(nodule_centers)<=0:
print('none')
for nc in range(len(nodule_centers)):
#the output coordination order is [x,y,z], while the order for volume image should be [z,y,x]
results.append([uid, (nodule_centers[nc][2]*new_spacing[2])+origin[2], (nodule_centers[nc][1]*new_spacing[1])+origin[1], (nodule_centers[nc][0]*new_spacing[0])+origin[0], nodule_centers[nc][3]])
print('%d %d %d %f' %(nodule_centers[nc][0], nodule_centers[nc][1], nodule_centers[nc][2], nodule_centers[nc][3]))
output_frame = pd.DataFrame(data=results, columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'])
output_frame.to_csv(result_file, index=False, float_format='%.4f')
#if len(results)<=0:
# print("No results to evaluate, continue")
# continue
assessment = eva.detection_assessment(results, annotation_file)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, nodules_detected = assessment
if len(FPsperscan)<=0 or len(sensitivities)<=0:
print("No results to evaluate, continue")
else:
CPMs.append([CPMscore, num_scans])
CPMoutput = open(evaluation_path + "/CPMscores.log", "w")
for CPM, num_scan in CPMs:
CPMoutput.write("CPM {} of {} scans\n" .format(CPM, num_scan))
CPMoutput.write("detection order:\n{}" .format(nodules_detected))
CPMoutput.close()
if label is not None: result.append(label)
result.append(nodule_center_predictions[nc][3])
results.append(result)
#results.append([uid, (nodule_center_predictions[nc][2]*new_spacing[2])+origin[2], (nodule_center_predictions[nc][1]*new_spacing[1])+origin[1], (nodule_center_predictions[nc][0]*new_spacing[0])+origin[0], nodule_center_predictions[nc][3]])
#if len(nodule_center_predictions)<1000:
#print('{} {} {} {}' .format(nodule_center_predictions[nc][0], nodule_center_predictions[nc][1], nodule_center_predictions[nc][2], nodule_center_predictions[nc][3]))
columns = ['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability']
if label is not None:
columns.insert(4, 'class')
result_frame = pd.DataFrame(data=results, columns=columns)
result_frame.to_csv(result_file, index=False, float_format='%f')
np.save(evaluation_path + '/result.npy', np.array(results))
if 'annotation_file' in dir():
assessment = eva.detection_assessment(results,
annotation_file,
label=label)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
#num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, nodules_detected = assessment
num_scans = assessment['num_scans']
FPsperscan, sensitivities = assessment['FROC']
CPMscore = assessment['CPM']
prediction_order = assessment['prediction_order']
nodules_detected = assessment['detection_cites']
if len(FPsperscan) <= 0 or len(sensitivities) <= 0:
print("No results to evaluate, continue")
else:
eva.evaluation_vision(CPMs,
def detection(**kwargs):
opt.parse(kwargs)
test_paths = ["/data/fyl/datasets/Tianchi_Lung_Disease/train"]
#test_sample_filelist = "/data/fyl/models_pytorch/DensecropNet_detection_test_rfold1/filelist_val_fold0.log"
#net_file = "/data/fyl/models_pytorch/DensecropNet_stripe_detection_rfold1/DensecropNet_stripe_detection_rfold1_epoch27"
annotation_file = "/data/fyl/datasets/Tianchi_Lung_Disease/chestCT_round1_annotation.csv"
#candidate_file = "/data/fyl/datasets/Tianchi_Lung_Disease/candidate.csv"
evaluation_path = "./experiments_dt/evaluations_tianchild_densecropnet_nodule_rfold1"
#evaluation_path = "experiments_dt/evaluations_test"
#vision_path = evaluation_path
result_file = evaluation_path + "/result.csv"
hard_negatives_file = evaluation_path + "/hard_negatives.csv"
region_size = opt.input_size
batch_size = opt.batch_size
label_dict = {
'noduleclass': 1,
'stripeclass': 5,
'arterioclass': 31,
'lymphnodecalclass': 32
}
label = label_dict[opt.label_mode]
use_gpu = opt.use_gpu
net_file = opt.load_model_path
if 'vision_path' in dir() and vision_path is not None and not os.access(
vision_path, os.F_OK):
os.makedirs(vision_path)
#if os.access(evaluation_path, os.F_OK): shutil.rmtree(evaluation_path)
if not os.access(evaluation_path, os.F_OK): os.makedirs(evaluation_path)
if "test_paths" in dir():
all_patients = []
for path in test_paths:
all_patients += glob(path + "/*.mhd")
if len(all_patients) <= 0:
print("No patient found")
exit()
else:
print("No test data")
exit()
if hasattr(opt, 'filelists') and 'test' in opt.filelists.keys():
test_samples = bt.filelist_load(opt.filelists['test'])
test_uids = []
for test_sample in test_samples:
sample_uid = os.path.basename(test_sample).split('_')[0]
if sample_uid not in test_uids:
test_uids.append(sample_uid)
pd.DataFrame(data=test_uids,
columns=['series_uid'
]).to_csv(result_path + '/patients_uid.csv',
index=False)
#else:
# for path in opt.filelists['test']:
# test_samples = glob(path + '/*.mhd')
#model = models.DensecropNet(input_size=region_size, drop_rate=0, growth_rate=64, num_blocks=4, num_fin_growth=3).eval()
model = getattr(models, opt.model)(input_size=region_size,
**opt.model_setup).eval()
if net_file is not None:
model.load(net_file)
print('model loaded from %s' % (net_file))
shutil.copyfile(net_file,
evaluation_path + '/' + net_file.split('/')[-1])
#model.eval()
if use_gpu: model.cuda()
start_time = time.time()
#patient_evaluations = open(evaluation_path + "/patient_evaluations.log", "w")
results = []
CPMs = []
CPMs2 = []
hard_negatives = []
test_patients = all_patients
#random.shuffle(test_patients)
bt.filelist_store(test_patients, evaluation_path + "/patientfilelist.log")
for p in range(len(test_patients)):
patient = test_patients[p]
#patient = "./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.212608679077007918190529579976.mhd"
#patient = "./LUNA16/subset9/1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408431413578140925249.mhd"
#patient = "./TIANCHI_examples/LKDS-00005.mhd"
uid = mt.get_mhd_uid(patient)
if 'test_uids' in dir() and uid not in test_uids:
print("%d/%d patient %s not belong to test set" %
(p + 1, len(test_patients), uid))
continue
#if uid!='656867':
# continue
print('%d/%d processing patient:%s' % (p + 1, len(test_patients), uid))
full_image_info = sitk.ReadImage(patient)
full_scan = sitk.GetArrayFromImage(full_image_info)
origin = np.array(full_image_info.GetOrigin(
))[::-1] #the order of origin and old_spacing is initially [z,y,x]
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
image, new_spacing = mt.resample(full_scan, old_spacing,
np.array([1, 1, 1]))
#image = np.load(patient)
#new_spacing = np.array([1, 1, 1])
#origin = np.array([0, 0, 0])
print('Resample Done. time:{}s'.format(time.time() - start_time))
if 'annotation_file' in dir():
annotations = mt.get_challenge_annotations(uid, annotation_file,
label)
if len(annotations) == 0:
print("%d/%d patient %s has no annotations, ignore it." %
(p + 1, len(test_patients), uid))
#patient_evaluations.write('%d/%d patient %s has no annotations, ignore it\n' %(p+1, len(test_patients), uid))
continue
#make a real lesion visualization
if 'vision_path' in dir() and vision_path is not None:
real_lesions = []
for annotation in annotations:
#real_lesion = np.int_([abs(annotation[2]-origin[0])/new_spacing[0], abs(annotation[1]-origin[1])/new_spacing[1], abs(annotation[0]-origin[2])/new_spacing[2]])
real_lesion = mt.coord_conversion(annotation[:3][::-1],
origin,
old_spacing,
full_scan.shape,
image.shape,
dir_array=True)
real_lesions.append(real_lesion)
annotation_vision = cvm.view_coordinates(image,
real_lesions,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_annotations.npy",
annotation_vision)
if 'candidate_file' in dir():
print('Detection with given candidates:{}'.format(candidate_file))
candidate_coords = nd.luna_candidate(image,
uid,
origin,
new_spacing,
candidate_file,
lung_segment=True,
vision_path=vision_path)
if 'vision_path' in dir() and vision_path is not None:
volume_candidate = cvm.view_coordinates(image,
candidate_coords,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_candidate.npy",
volume_candidate)
print('Candidate Done. time:{}s'.format(time.time() - start_time))
print('candidate number:%d' % (len(candidate_coords)))
candidate_predictions = nd.precise_detection_pt(
image,
region_size,
candidate_coords,
model,
None,
batch_size,
use_gpu=use_gpu,
prediction_threshold=0.4)
positive_predictions = candidate_predictions > 0
predicted_coords = np.delete(
candidate_coords,
np.logical_not(positive_predictions).nonzero()[0],
axis=0)
predictions = candidate_predictions[positive_predictions]
lesion_center_predictions = nd.prediction_combine(
predicted_coords, predictions)
if 'vision_path' in dir() and vision_path is not None:
volume_predicted = cvm.view_coordinates(image,
predicted_coords,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_predicted.npy",
volume_predicted)
lesions = []
for nc in range(len(lesion_center_predictions)):
lesions.append(np.int_(lesion_center_predictions[nc][0:3]))
volume_prediction = cvm.view_coordinates(image,
lesions,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_prediction.npy",
volume_prediction)
else:
print('Detection with slic candidates')
candidate_results = nd.slic_candidate(image, 30, focus_area='lung')
if candidate_results is None:
continue
candidate_coords, candidate_labels, cluster_labels = candidate_results
if 'vision_path' in dir() and vision_path is not None:
np.save(vision_path + "/" + uid + "_segmask.npy",
cluster_labels)
#segresult = lc.segment_vision(image, cluster_labels)
#np.save(vision_path + "/" + uid + "_segresult.npy", segresult)
print('Candidate Done. time:{}s'.format(time.time() - start_time))
print('candidate number:%d' % (len(candidate_coords)))
candidate_predictions = nd.precise_detection_pt(
image,
region_size,
candidate_coords,
model,
None,
batch_size,
use_gpu=use_gpu,
prediction_threshold=0.4)
positive_predictions = candidate_predictions > 0
result_predictions, result_labels = nd.predictions_map_fast(
cluster_labels, candidate_predictions[positive_predictions],
candidate_labels[positive_predictions])
if 'vision_path' in dir() and vision_path is not None:
np.save(vision_path + "/" + uid + "_detlabels.npy",
result_labels)
np.save(vision_path + "/" + uid + "_detpredictions.npy",
result_predictions)
#detresult = lc.segment_vision(image, result_labels)
#np.save(vision_path+"/"+uid+"_detresult.npy", detresult)
lesion_center_predictions = nd.prediction_centering_fast(
result_predictions)
#lesion_center_predictions, prediction_labels = nd.prediction_cluster(result_predictions)
if 'vision_path' in dir() and vision_path is not None:
lesions = []
for nc in range(len(lesion_center_predictions)):
lesions.append(np.int_(lesion_center_predictions[nc][0:3]))
volume_predicted = cvm.view_coordinates(result_predictions *
1000,
lesions,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(vision_path + "/" + uid + "_prediction.npy",
volume_predicted)
if 'prediction_labels' in dir():
prediction_cluster_vision = lc.segment_color_vision(
prediction_labels)
np.save(
vision_path + "/" + uid + "_prediction_clusters.npy",
prediction_cluster_vision)
print('Detection Done. time:{}s'.format(time.time() - start_time))
'''
#randomly create a result for testing
lesion_center_predictions = []
for nc in range(10):
lesion_center_predictions.append([random.randint(0,image.shape[0]-1), random.randint(0,image.shape[1]-1), random.randint(0,image.shape[2]-1), random.random()])
'''
if len(lesion_center_predictions) < 1000:
print('Nodule coordinations:')
if len(lesion_center_predictions) <= 0:
print('none')
for nc in range(len(lesion_center_predictions)):
print('{} {} {} {}'.format(lesion_center_predictions[nc][0],
lesion_center_predictions[nc][1],
lesion_center_predictions[nc][2],
lesion_center_predictions[nc][3]))
for nc in range(len(lesion_center_predictions)):
#the output coordination order is [x,y,z], while the order for volume image should be [z,y,x]
result = [uid]
result.extend(
mt.coord_conversion(lesion_center_predictions[nc][:3],
origin,
old_spacing,
full_scan.shape,
image.shape,
dir_array=False)[::-1])
if label is not None: result.append(label)
result.append(lesion_center_predictions[nc][3])
results.append(result)
#results.append([uid, (lesion_center_predictions[nc][2]*new_spacing[2])+origin[2], (lesion_center_predictions[nc][1]*new_spacing[1])+origin[1], (lesion_center_predictions[nc][0]*new_spacing[0])+origin[0], lesion_center_predictions[nc][3]])
#if len(lesion_center_predictions)<1000:
#print('{} {} {} {}' .format(lesion_center_predictions[nc][0], lesion_center_predictions[nc][1], lesion_center_predictions[nc][2], lesion_center_predictions[nc][3]))
columns = ['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability']
if label is not None:
columns.insert(4, 'class')
result_frame = pd.DataFrame(data=results, columns=columns)
result_frame.to_csv(result_file, index=False, float_format='%.4f')
np.save(evaluation_path + '/result.npy', np.array(results))
if 'annotation_file' in dir():
assessment = eva.detection_assessment(results,
annotation_file,
label=label)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
#num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, lesions_detected = assessment
num_scans = assessment['num_scans']
FPsperscan, sensitivities = assessment['FROC']
CPMscore = assessment['CPM']
prediction_order = assessment['prediction_order']
lesions_detected = assessment['detection_cites']
if len(FPsperscan) <= 0 or len(sensitivities) <= 0:
print("No results to evaluate, continue")
else:
eva.evaluation_vision(CPMs,
num_scans,
FPsperscan,
sensitivities,
CPMscore,
lesions_detected,
output_path=evaluation_path)
#patient_evaluations.write('%d/%d patient %s CPM score:%f\n' %(p+1, len(test_patients), uid, single_assessment[6]))
print('Evaluation Done. time:{}s'.format(time.time() - start_time))
num_positive = (lesions_detected >= 0).nonzero()[0].size
for ndi in range(len(lesions_detected)):
if results[prediction_order[ndi]][-1] <= 0.5 or (
lesions_detected[:ndi] >=
0).nonzero()[0].size == num_positive:
break
if lesions_detected[ndi] == -1:
hard_negatives.append(results[prediction_order[ndi]])
hard_negatives_frame = pd.DataFrame(data=hard_negatives,
columns=columns)
hard_negatives_frame.to_csv(hard_negatives_file,
index=False,
float_format='%.4f')
print('Hard Negatives Extracted. time:{}s'.format(time.time() -
start_time))
print('Overall Detection Done')
def detection_fusion(
test_path=None,
result_path="./experiments_dt/evaluations_tianchild_densecropnet_31,32",
**kwargs):
opt.parse(kwargs)
if test_path is None:
test_paths = ["/data/fyl/datasets/Tianchi_Lung_Disease/train"]
else:
test_paths = [test_path]
#test_sample_filelist = "/data/fyl/models_pytorch/DensecropNet_detection_test_rfold1/filelist_val_fold0.log"
net_files = [
"/data/fyl/models_pytorch/DensecropNet_arterio_detection_rfold1/DensecropNet_arterio_detection_rfold1_epoch2",
"/data/fyl/models_pytorch/DensecropNet_lymphnodecal_detection_rfold1/DensecropNet_lymphnodecal_detection_rfold1_epoch2"
]
annotation_file = "/data/fyl/datasets/Tianchi_Lung_Disease/chestCT_round1_annotation.csv"
#candidate_file = "/data/fyl/datasets/Tianchi_Lung_Disease/candidate.csv"
labels = [31, 32]
#result_path = "./experiments_dt/evaluations_tianchild_densecropnet_fusion"
#vision_path = result_path
#result_file = result_path + "/result.csv"
hard_negatives_file = result_path + "/hard_negatives.csv"
region_size = opt.input_size
batch_size = opt.batch_size
use_gpu = opt.use_gpu
if 'vision_path' in dir() and vision_path is not None and not os.access(
vision_path, os.F_OK):
os.makedirs(vision_path)
#if os.access(result_path, os.F_OK): shutil.rmtree(result_path)
if not os.access(result_path, os.F_OK): os.makedirs(result_path)
if "test_paths" in dir():
all_patients = []
for path in test_paths:
all_patients += glob(path + "/*.mhd")
if len(all_patients) <= 0:
print("No patient found")
exit()
else:
print("No test data")
exit()
if hasattr(opt, 'filelists') and 'test' in opt.filelists.keys():
test_samples = bt.filelist_load(opt.filelists['test'])
test_uids = []
for test_sample in test_samples:
sample_uid = os.path.basename(test_sample).split('_')[0]
if sample_uid not in test_uids:
test_uids.append(sample_uid)
pd.DataFrame(data=test_uids,
columns=['series_uid'
]).to_csv(result_path + '/patients_uid.csv',
index=False)
#else:
# for path in opt.filelists['test']:
# test_samples = glob(path + '/*.mhd')
#model = models.DensecropNet(input_size=region_size, drop_rate=0, growth_rate=64, num_blocks=4, num_fin_growth=3).eval()
networks = [
getattr(models, opt.model)(input_size=region_size,
**opt.model_setup).eval()
for m in range(len(net_files))
]
for n in range(len(net_files)):
networks[n].load(net_files[n])
print('model loaded from %s' % (net_files[n]))
shutil.copyfile(net_files[n],
result_path + '/' + net_files[n].split('/')[-1])
if use_gpu: networks[n].cuda()
start_time = time.time()
#patient_evaluations = open(result_path + "/patient_evaluations.log", "w")
results = []
labeled_results = [[] for l in range(len(labels))]
CPMs = [[] for l in range(len(labels))]
#hard_negatives = []
test_patients = all_patients
#random.shuffle(test_patients)
bt.filelist_store(test_patients, result_path + "/patientfilelist.log")
for p in range(len(test_patients)):
patient = test_patients[p]
uid = mt.get_mhd_uid(patient)
if 'test_uids' in dir() and uid not in test_uids:
print("%d/%d patient %s not belong to test set" %
(p + 1, len(test_patients), uid))
continue
print('%d/%d processing patient:%s' % (p + 1, len(test_patients), uid))
full_image_info = sitk.ReadImage(patient)
full_scan = sitk.GetArrayFromImage(full_image_info)
origin = np.array(full_image_info.GetOrigin(
))[::-1] #the order of origin and old_spacing is initially [z,y,x]
old_spacing = np.array(full_image_info.GetSpacing())[::-1]
image, new_spacing = mt.resample(full_scan, old_spacing,
np.array([1, 1, 1]))
#image = np.load(patient)
#new_spacing = np.array([1, 1, 1])
#origin = np.array([0, 0, 0])
print('Resample Done. time:{}s'.format(time.time() - start_time))
candidate_results = nd.slic_candidate(image, 20, focus_area='body')
if candidate_results is None:
continue
candidate_coords, candidate_labels, cluster_labels = candidate_results
if 'vision_path' in dir() and vision_path is not None:
np.save(vision_path + "/" + uid + "_segmask.npy", cluster_labels)
#segresult = lc.segment_vision(image, cluster_labels)
#np.save(vision_path + "/" + uid + "_segresult.npy", segresult)
print('Candidate Done. time:{}s'.format(time.time() - start_time))
print('candidate number:%d' % (len(candidate_coords)))
candidate_predictions = nd.precise_detection_pt(
image,
region_size,
candidate_coords,
networks,
None,
batch_size,
use_gpu=use_gpu,
prediction_threshold=0.4)
labeled_predictions = []
for l in range(len(labels)):
label = labels[l]
print('label: %d' % (label))
evaluation_path = result_path + '/' + str(label)
if not os.access(evaluation_path, os.F_OK):
os.makedirs(evaluation_path)
if 'annotation_file' in dir():
annotations = mt.get_challenge_annotations(uid,
annotation_file,
label=label)
if len(annotations) == 0:
print("%d/%d patient %s has no annotations, ignore it." %
(p + 1, len(test_patients), uid))
#patient_evaluations.write('%d/%d patient %s has no annotations, ignore it\n' %(p+1, len(test_patients), uid))
continue
#make a real lesion visualization
if 'vision_path' in dir() and vision_path is not None:
real_lesions = []
for annotation in annotations:
#real_lesion = np.int_([abs(annotation[2]-origin[0])/new_spacing[0], abs(annotation[1]-origin[1])/new_spacing[1], abs(annotation[0]-origin[2])/new_spacing[2]])
real_lesion = mt.coord_conversion(annotation[:3][::-1],
origin,
old_spacing,
full_scan.shape,
image.shape,
dir_array=True)
real_lesions.append(real_lesion)
annotation_vision = cvm.view_coordinates(image,
real_lesions,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(evaluation_path + "/" + uid + "_annotations.npy",
annotation_vision)
positive_predictions = candidate_predictions[l] > 0
result_predictions, result_labels = nd.predictions_map_fast(
cluster_labels, candidate_predictions[l][positive_predictions],
candidate_labels[positive_predictions])
labeled_predictions.append(result_predictions)
if 'vision_path' in dir() and vision_path is not None:
np.save(evaluation_path + "/" + uid + "_detlabels.npy",
result_labels)
np.save(evaluation_path + "/" + uid + "_detpredictions.npy",
result_predictions)
#detresult = lc.segment_vision(image, result_labels)
#np.save(evaluation_path+"/"+uid+"_detresult.npy", detresult)
lesion_center_predictions = nd.prediction_centering_fast(
result_predictions)
#lesion_center_predictions, prediction_labels = nd.prediction_cluster(result_predictions)
if 'vision_path' in dir() and vision_path is not None:
lesions = []
for nc in range(len(lesion_center_predictions)):
lesions.append(np.int_(lesion_center_predictions[nc][0:3]))
volume_predicted = cvm.view_coordinates(result_predictions *
1000,
lesions,
window_size=10,
reverse=False,
slicewise=False,
show=False)
np.save(evaluation_path + "/" + uid + "_prediction.npy",
volume_predicted)
if 'prediction_labels' in dir():
prediction_cluster_vision = lc.segment_color_vision(
prediction_labels)
np.save(
evaluation_path + "/" + uid +
"_prediction_clusters.npy", prediction_cluster_vision)
print('Detection Done. time:{}s'.format(time.time() - start_time))
'''
#randomly create a result for testing
lesion_center_predictions = []
for nc in range(10):
lesion_center_predictions.append([random.randint(0,image.shape[0]-1), random.randint(0,image.shape[1]-1), random.randint(0,image.shape[2]-1), random.random()])
'''
for nc in range(len(lesion_center_predictions)):
#the output coordination order is [x,y,z], while the order for volume image should be [z,y,x]
result = [uid]
result.extend(
mt.coord_conversion(lesion_center_predictions[nc][:3],
origin,
old_spacing,
full_scan.shape,
image.shape,
dir_array=False)[::-1])
if label is not None: result.append(label)
result.append(lesion_center_predictions[nc][3])
#results.append(result)
labeled_results[l].append(result)
columns = [
'seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'
]
if label is not None:
columns.insert(4, 'class')
result_frame = pd.DataFrame(data=labeled_results[l],
columns=columns)
result_frame.to_csv("{}/result_{}.csv".format(
evaluation_path, label),
index=False,
float_format='%f')
#np.save("{}/result_{}.npy"%(evaluation_path, label), np.array(results))
if 'annotation_file' in dir():
assessment = eva.detection_assessment(labeled_results[l],
annotation_file,
label=label)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
#num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, lesions_detected = assessment
num_scans = assessment['num_scans']
FPsperscan, sensitivities = assessment['FROC']
CPMscore = assessment['CPM']
prediction_order = assessment['prediction_order']
lesions_detected = assessment['detection_cites']
if len(FPsperscan) <= 0 or len(sensitivities) <= 0:
print("No results to evaluate, continue")
else:
eva.evaluation_vision(CPMs[l],
num_scans,
FPsperscan,
sensitivities,
CPMscore,
lesions_detected,
output_path=evaluation_path)
#patient_evaluations.write('%d/%d patient %s CPM score:%f\n' %(p+1, len(test_patients), uid, single_assessment[6]))
print('Evaluation Done. time:{}s'.format(time.time() -
start_time))
labeled_predictions = np.array(labeled_predictions)
prediction_labels = np.argmax(labeled_predictions, axis=0)
predictions_fusion = labeled_predictions.sum(axis=0) / 4.0
fused_center_predictions = nd.prediction_centering_fast(
predictions_fusion)
if 'vision_path' in dir() and vision_path is not None:
np.save(vision_path + "/" + uid + "_classlabels.npy",
prediction_labels)
for lcp in range(len(fused_center_predictions)):
#the output coordination order is [x,y,z], while the order for volume image should be [z,y,x]
center = fused_center_predictions[lcp]
result = [uid]
result.extend(
mt.coord_conversion(center[:3],
origin,
old_spacing,
full_scan.shape,
image.shape,
dir_array=False)[::-1])
result.append(labels[prediction_labels[center[0], center[1],
center[2]]])
result.append(center[3])
results.append(result)
columns = ['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability']
if label is not None:
columns.insert(4, 'class')
result_frame = pd.DataFrame(data=results, columns=columns)
result_frame.to_csv(result_path + '/result.csv',
index=False,
float_format='%f')
np.save(result_path + '/result.npy', np.array(results))
print('Overall Detection Done')
#test_files = glob(test_path + '/*.npy')
shuffle(train_files)
shuffle(test_files)
train_features, train_labels = data4svm(train_files, 'malignancy')
test_features, test_labels = data4svm(test_files, 'malignancy')
train_features /= 1000.0
test_features /= 1000.0
svm_clf = LinearSVC()
svm_clf.fit(train_features, train_labels)
decisions = svm_clf.decision_function(test_features)
predictions = 1 / (1 + np.exp(-decisions))
np.save('experiments/ipris_fold1_svm_retrieval.npy',
np.array([predictions, test_labels]))
eva.binclass_evaluation(test_labels, predictions,
"experiments/ipris_fold1_svm_roc")
'''
acc = len(np.where((predictions>0.5)==test_labels)[0]) / float(len(test_labels))
fps, tps, thresholds = metrics.roc_curve(test_labels, predictions)
np.save('experiments/ipris_fold5_svm_roc.npy', np.array([tps, fps]))
#plt.plot(fpr, tpr)
#plt.grid(True)
#plt.xlabel("True Positive Rate")
#plt.ylabel("False Positive Rate")
#plt.savefig('ipris_svm_roc.png')
auc = metrics.auc(fps, tps)
conf = metrics.confusion_matrix(test_labels, predictions>0.5)
sens = conf[1][1] / float(conf[1][0] + conf[1][1])
spec = conf[0][0] / float(conf[0][0] + conf[0][1])
print("accuracy:{} auc:{} sensitivity:{} specificity:{}" .format(acc, auc, sens, spec))
'''
'''
if len(nodule_center_predictions)<1000:
print('Nodule coordinations:')
if len(nodule_center_predictions)<=0:
print('none')
for nc in range(len(nodule_center_predictions)):
print('{} {} {} {}' .format(nodule_center_predictions[nc][0], nodule_center_predictions[nc][1], nodule_center_predictions[nc][2], nodule_center_predictions[nc][3]))
for nc in range(len(nodule_center_predictions)):
#the output coordination order is [x,y,z], while the order for volume image should be [z,y,x]
results.append([uid, (nodule_center_predictions[nc][2]*new_spacing[2])+origin[2], (nodule_center_predictions[nc][1]*new_spacing[1])+origin[1], (nodule_center_predictions[nc][0]*new_spacing[0])+origin[0], nodule_center_predictions[nc][3]])
#if len(nodule_center_predictions)<1000:
#print('{} {} {} {}' .format(nodule_center_predictions[nc][0], nodule_center_predictions[nc][1], nodule_center_predictions[nc][2], nodule_center_predictions[nc][3]))
result_frame = pd.DataFrame(data=results, columns=['seriesuid', 'coordX', 'coordY', 'coordZ', 'probability'])
result_frame.to_csv(result_file, index=False, float_format='%.4f')
assessment = eva.detection_assessment(results, annotation_file, exclude_file)
if assessment is None:
print('assessment failed')
#patient_evaluations.write('%d/%d patient %s assessment failed\n' %(p+1, len(test_patients), uid))
continue
#num_scans, FPsperscan, sensitivities, CPMscore, FPsperscan2, sensitivities2, CPMscore2, nodules_detected = assessment
num_scans = assessment['num_scans']
FPsperscan, sensitivities = assessment['FROC']
CPMscore = assessment['CPM']
prediction_order = assessment['prediction_order']
nodules_detected = assessment['detection_cites']
if len(FPsperscan)<=0 or len(sensitivities)<=0:
print("No results to evaluate, continue")
else:
eva.evaluation_vision(CPMs, num_scans, FPsperscan, sensitivities, CPMscore, nodules_detected, evaluation_path)
#patient_evaluations.write('%d/%d patient %s CPM score:%f\n' %(p+1, len(test_patients), uid, single_assessment[6]))