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some refactoring ahead of ukdi divorce proceedings...

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carl 2023-09-12 08:38:22 -03:00
parent a927da97db
commit 201133f7da
1 changed files with 0 additions and 135 deletions
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src/paravisionx.py
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from paravision.recognition.exceptions import ParavisionException
from paravision.recognition.engine import Engine
from paravision.recognition.sdk import SDK
from openvino.inference_engine import Engineq
#from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
class Paravisionx(object):
def init(self):
print("@@@ initialising paravision")
try:
self.sdk = SDK(engine=Engine.AUTO)
except ParavisionException:
pass
def read(self, imgpath):
if not os.path.exists(imgpath):
print("File not found ",imgpath)
return False
self.imgpath = imgpath
self.image = pru.load_image(imgpath)
print(self.image)
return True
def process(self):
# Get all faces metadata
print("Finding faces in %s" %(self.imgpath))
faces = self.sdk.get_faces([self.image], qualities=True, landmarks=True, embeddings=True)
print("Getting metadata")
inferences = faces.image_inferences
print("Getting best face")
ix = inferences[0].most_prominent_face_index()
print("Getting a mathematical mode of that best face")
self.model = inferences[0].faces[ix].embedding
print("Getting image quality scores..")
self.score = round(1000*inferences[0].faces[ix].quality)
print("Score was %d" %(self.score))
return self.score
def compare(self,other):
# Get face match score
return self.sdk.get_match_score(self.model, other.model)
#mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm
def load(self, dev1, dev2, id_image_filepath, photo_image_filepath):
self.dev1 = dev1
self.dev2 = dev2
try:
# Load images
self.id_image = load_image(id_image_filepath)
self.photo_image = load_image(photo_image_filepath)
print("++++++++++++++++ ",self.id_image)
return True
except:
return None
def get_faces(self):
try:
# Get all faces from images with qualities, landmarks, and embeddings
self.inference_result = self.sdk.get_faces([self.id_image, self.photo_image], qualities=True, landmarks=True, embeddings=True)
self.image_inference_result = self.inference_result.image_inferences
if len(self.image_inference_result)==0:
return "no inferences found"
# Get most prominent face
self.id_face = self.image_inference_result[0].most_prominent_face_index()
self.photo_face = self.image_inference_result[1].most_prominent_face_index()
if self.id_face<0:
return "no id face found"
if self.photo_face<0:
return "no live face found"
# Get numerical representation of faces (required for face match)
if (len(self.image_inference_result)<2):
return "ID or human face could not be recognised"
self.id_emb = self.image_inference_result[0].faces[self.id_face].embedding
self.photo_emb = self.image_inference_result[1].faces[self.photo_face].embedding
except Exception as ex:
return "image processing exception "+str(ex)
return None
# return " id=%d photo=%d result=%d " % (self.id_face, self.photo_face, len(self.image_inference_result))
def compute_scores(self):
try:
# Get image quality scores (how 'good' a face is)
self.id_qual = self.image_inference_result[0].faces[self.id_face].quality
self.photo_qual = self.image_inference_result[1].faces[self.photo_face].quality
self.id_qual = round(self.id_qual, 3)
self.photo_qual = round(self.photo_qual, 3)
# Get face match score
self.match_score = self.sdk.get_match_score(self.id_emb, self.photo_emb)
# Create .json
self.face_match_json = {"device1":self.dev1,
"device2":self.dev2,
"passmark":500,
"device1_qual":self.id_qual,
"device2_qual":self.photo_qual,
"match_score":self.match_score}
#return json.dumps(self.face_match_json)
#print(self.face_match_json)
# Send to core
#url = "%s/notify/%s/%s" % (self.conf["core"], self.conf["identity"], face_match_json)
#url = url.replace(" ", "%20") # Remove spaces
#buf = []
#req = urllib.request.Request( url )
#with urllib.request.urlopen(req) as response:
#print(response.read())
except Exception as ex:
return str(ex)
def get_scores(self):
return json.dumps(self.face_match_json)