From 201133f7dac541fb5a6f52d727b994cc8799a6ee Mon Sep 17 00:00:00 2001
From: carl <cg@dispension.ca>
Date: Tue, 12 Sep 2023 08:38:22 -0300
Subject: [PATCH] some refactoring ahead of ukdi divorce proceedings...

---
 src/paravisionx.py | 135 ---------------------------------------------
 1 file changed, 135 deletions(-)
 delete mode 100644 src/paravisionx.py

diff --git a/src/paravisionx.py b/src/paravisionx.py
deleted file mode 100644
index b8db4a8d7..000000000
--- a/src/paravisionx.py
+++ /dev/null
@@ -1,135 +0,0 @@
- 
-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)
-