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