def computeFaceDetectionAmazon(self,face_image_url, file_name, gender):
table_name = 'FFS3'
partition_name = 'Amazon'
urllib.request.urlretrieve(face_image_url, file_name)
with open(file_name, 'rb') as image:
response = self.amazon_client.detect_faces(Image={'Bytes': image.read()}, Attributes=['ALL'])
faces = response['FaceDetails']
success = False
faceDetected = False
genderPrediction = 'None'
if len(faces) == 0:
success = False
faceDetected = False
else:
faceDetected = True
genderPrediction = faces[0]["Gender"]["Value"]
if gender.lower()==genderPrediction.lower():
success = True
else:
success = False
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(faces)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()
def computeFaceDetectionIBM(self,face_image_url, file_name, gender):
table_name = 'IBM'
partition_name = 'IBM'
urllib.request.urlretrieve(face_image_url, file_name)
with open(file_name, 'rb') as image_file:
response = self.IBM_visual_recognition.detect_faces(image_file)
faces = response['images'][0]['faces']
success = False
faceDetected = False
genderPrediction = 'None'
if len(faces) == 0:
success = False
faceDetected = False
elif len(faces)>0:
faceDetected = True
genderPrediction = faces[0]["gender"]["gender"]
if gender.lower()==genderPrediction.lower():
success = True
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(response)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()
def computeFaceDetectionMicrosoft(self,face_image_url, file_name, gender):
table_name = 'Microsoft'
partition_name = 'Microsoft'
data = {'url': face_image_url}
response = requests.post(self.msft_face_detection_url, params=self.msft_params, headers=self.msft_headers, json=data)
faces = response.json()
success = False
genderPrediction = 'None'
faceDetected = False
if len(faces) == 0:
success = False
faceDetected = False
elif len(faces)>0:
faceDetected = True
genderPrediction = faces[0]["faceAttributes"]["gender"]
if gender==genderPrediction:
success = True
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(faces)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()
def computeFaceDetectionFacePlusPlus(self,face_image_url, file_name, gender):
table_name = 'FacePlusPlus'
partition_name = 'FacePlusPlus'
boundary = '----------%s' % hex(int(time.time() * 1000))
data = []
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'api_key')
data.append(self.faceplusplus_key)
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'api_secret')
data.append(self.faceplusplus_secret)
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'return_attributes')
data.append('gender')
data.append('--%s' % boundary)
data.append('Content-Disposition: form-data; name="%s"\r\n' % 'image_url')
data.append(face_image_url)
data.append('--%s--\r\n' % boundary)
http_body='\r\n'.join(data)
req=urllib.request.Request(self.faceplusplus_http_url)
req.add_header('Content-Type', 'multipart/form-data; boundary=%s' % boundary)
req.data = str.encode(http_body)
try:
resp = urllib.request.urlopen(req, timeout=5)
qrcont=resp.read().decode("utf-8")
faces = json.loads(qrcont)
success = False
faceDetected = False
genderPrediction = 'None'
if 'faces' in faces.keys():
faceDetected = True
genderPrediction = faces["faces"][0]["attributes"]["gender"]["value"]
if gender.lower()==genderPrediction.lower():
success = True
else:
success = None
faceDetected = None
time.sleep(2)
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(faces)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()
except urllib.request.HTTPError as e:
return None, None, None, None
def computeFaceDetectionGoogle(self,face_image_url, file_name, gender):
table_name = 'Google'
partition_name = 'Google'
self.clientImgAnnotator = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = face_image_url
try:
response = self.clientImgAnnotator.face_detection(image=image)
r = MessageToDict(response, preserving_proto_field_name = True)
success = False
faceDetected = False
genderPrediction = 'None'
if len(r) == 0:
success = False
faceDetected = False
faces=[]
elif len(r)>0:
faceDetected = True
success = False
faces = r['face_annotations']
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(faces)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()
except WatsonApiException as ex:
print("Method failed with status code " + str(ex.code) + ": " + ex.message)
def computeFaceDetectionSightEngine(self,face_image_url, file_name, gender):
table_name = 'SightEngine'
partition_name = 'SightEngine'
output = self.SEclient.check('face-attributes').set_url(face_image_url)
r = response.json()
faces = r["faces"]
success = False
genderPrediction = 'None'
faceDetected = False
if len(faces) == 0:
success = False
faceDetected = False
elif len(faces)>0:
faceDetected = True
femaleProb = faces[0]["attributes"]["female"]
maleProb = faces[0]["attributes"]["male"]
if femaleProb>maleProb:
genderPrediction = "female"
else:
genderPrediction = "male"
if gender==genderPrediction:
success = True
face_entry = Entity()
face_entry.PartitionKey = partition_name
face_entry.RowKey = file_name
face_entry.Result = json.dumps(faces)
face_entry.DetectionSuccess = success
self.table_service.insert_entity(table_name, face_entry)
return success, faces, faceDetected, genderPrediction.lower()