def getRiMappedImage(case_id, index_1, index_2):
#if username is in session then the call is web call, so use that username
if 'username' in session and len(session['username']) > 0:
username = session['username']
elif 'key' not in request.args or len(request.args['key']) == 0:
abort(403)
return ""
else:
auth_result = Auth.checkKeyValidity(request.args['key'])
if not auth_result['valid']:
abort(403)
return ""
username = auth_result['username']
if not PatientCases.userHasAuthentication(case_id, username):
abort(403)
return ""
#TODO get the ri-buffer from a controller module and send it to the Response
#for testing only
f = PatientCases.findImage(case_id, index_1, index_2)
if f == None:
abort(404)
buffer = Conversions(f['disk_filename']).dicomToRIColoredImage()
return Response(buffer, mimetype='image/jpeg')
def analysisImage(case_id, index_1, index_2, analysis_id):
res = AnalysisResult.find(analysis_id, case_id, index_1, index_2)
if res == None:
return abort(404)
res.pop('_id', None)
f = PatientCases.findImage(case_id, index_1, index_2)
if f == None:
return abort(404)
filename = f['disk_filename']
boxes = []
for infected_area in res['infected_areas']:
coordinate = infected_area['coordinate']
tumor_size = (infected_area['box_height'], infected_area['box_width'])
points = infected_area['points']
rgb = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
boxes.append((coordinate, tumor_size, rgb, [(150, 150), [150, 151]]))
buffer = Segment(filename, boxes).drawBox()
return Response(buffer, mimetype='image/jpeg')
def getDataSet(cls):
mlData = AnalysisResult.getMLData()
res = []
for case in mlData:
case.pop('_id')
i = 0
case_id = case['case_id']
index_1 = case['index_1']
index_2 = case['index_2']
case.pop('case_id')
case.pop('index_1')
case.pop('index_2')
image = PatientCases.findImage(case_id, index_1,
index_2)['disk_filename']
pix = Converter(DicomDecoder(UPLOAD_DIR,
image)).convertToPixelList()
for infected_area in case['infected_areas']:
infected_area.pop('points')
infected_area.pop('detected_result')
infected_area['average_chemical_composition'] = infected_area[
'average_chemical_composition']['y']
#infected_area['image'] = pix
if 'actual_result' in infected_area:
res.append(infected_area)
def analyseSingleImage(case_id, index_1, index_2):
result = {'error': {'error': False}}
#if username is in session then the call is web call, so use that username
if 'username' in session and len(session['username']) > 0:
username = session['username']
elif 'key' not in request.args or len(request.args['key']) == 0:
abort(403)
return ""
else:
auth_result = Auth.checkKeyValidity(request.args['key'])
if not auth_result['valid']:
abort(403)
return ""
username = auth_result['username']
if not PatientCases.userHasAuthentication(case_id, username):
abort(403)
return ""
f = PatientCases.findImage(case_id, index_1, index_2)
if f == None:
return abort(404)
result['error']['error'] = False
if 're_analysis' not in request.args or request.args[
're_analysis'] == 'false':
analysis = AnalysisResult.findByCase(case_id, index_1, index_2)
if analysis == None:
flag = True
else:
analysis['error'] = result['error']
analysis['analysis_image'] = BASE_URL + url_for(
'patient_cases_api.analysisImage',
case_id=case_id,
index_1=index_1,
index_2=index_2,
analysis_id=str(analysis['_id']))
#pop fields that are not required as response to api
analysis_index = 0
for infected_area in analysis['infected_areas']:
#pop fields that are not required as response to api
infected_area.pop('points')
infected_area.pop('box_width')
infected_area.pop('box_height')
#add the update url
infected_area['update_actual_result_url'] = BASE_URL + url_for(
'patient_cases_api.uploadAnlysis',
case_id=case_id,
index_1=index_1,
index_2=index_2,
analysis_id=str(analysis['_id']),
analysis_index=analysis_index)
analysis_index += 1
#pop fields that are not required as response to api
analysis.pop('_id')
analysis.pop('case_id')
analysis.pop('index_1')
analysis.pop('index_2')
return jsonify(analysis)
else:
flag = True
if flag:
dicom = Conversions(f['disk_filename'])
cluster = SingleClusterer(dicom)
result['infected_areas'] = []
infected_areas = []
AnalysisResult.delete(case_id, index_1, index_2)
for _disease_key in cluster.diseases.keys():
disease_clusters = cluster.diseases_clusters[_disease_key]
if len(disease_clusters.keys()) > 0:
for disease_cluster_key in disease_clusters.keys():
c = disease_clusters[disease_cluster_key]
c.compute()
#c.searchForDiseases()
i_area = {}
i_area['detected_result'] = _disease_key
i_area['coordinate'] = c.p1
i_area['area'] = c.getTightArea()
i_area['tumor_width'] = c.tumorwidth
i_area['tumor_height'] = c.tumorheight
i_area['average_chemical_composition'] = {
'x': (0, 0.5, 0.8, 1.3, 1.4, 1.5, 1.6, 1.8, 2, 2.4,
2.6, 2.8, 3, 3.2, 3.4, 3.6, 3.7, 3.9, 4, 4.4),
'y':
c.getAverageChemicalComposition(),
'labels':
('', '', 'LIP', '', 'LAC', '', '', '', 'NAA', 'GLM',
'', '', 'CR', '', 'CHO', '', 'MI', '', 'CR2', '')
}
infected_areas.append(
InfectedAreas((c.p1[0], c.p1[1]), c.getWidth(),
c.getHeight(), c.tumorwidth,
c.tumorheight,
i_area['average_chemical_composition'],
c.getArea(), _disease_key, c.elements))
result['infected_areas'].append(i_area)
#result['diseases'].append(disease)
if len(infected_areas) != 0:
analysisResult = AnalysisResult(case_id, index_1, index_2,
infected_areas)
analysis_id = analysisResult.save()
result['analysis_image'] = BASE_URL + url_for(
'patient_cases_api.analysisImage',
case_id=case_id,
index_1=index_1,
index_2=index_2,
analysis_id=analysis_id)
analysis_index = 0
for infected_area in result['infected_areas']:
infected_area['update_actual_result_url'] = BASE_URL + url_for(
'patient_cases_api.uploadAnlysis',
case_id=case_id,
index_1=index_1,
index_2=index_2,
analysis_id=analysis_id,
analysis_index=analysis_index)
analysis_index += 1
return jsonify(result)
