Upload FaceAuth.py

FaceAuth utilizing the Insight-face Model

FaceAuth.py

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+import os
+import cv2
+from insightface.app import FaceAnalysis
+import torch
+
+
+
+# prompt: compare face embediggs
+
+
+
+
+class FaceRec:
+    def __init__(self):
+        self.foldername = '/home/emmanuel/Pictures/Webcam'
+        self.files = []
+        self.embeds = []
+        self.diff = []
+        self.ground_mathches = []
+        self.sampling = None
+        
+            
+
+    def folder(self, attempt=True, folder='/home/emmanuel/Pictures/Webcam'):
+        if attempt:
+            for file in os.listdir(folder):
+                self.files.append(file)
+
+            self.image_pair = list(zip(self.files[0:len(self.files)//2], self.files[len(self.files)//2:]))
+            print(self.image_pair)
+
+
+        else:
+            self.foldername = '/home/emmanuel/Pictures/webcam'
+            self.files = []
+            self.folder(attempt=True, folder=self.foldername)
+            
+
+        
+    
+    def embeddings(self, image):
+        app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        app.prepare(ctx_id=0, det_size=(640, 640))
+        image1 = cv2.imread(image)
+        faces = app.get(image1)
+
+        faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
+        return(torch.Tensor(faceid_embeds))
+
+
+
+    def face_embed(self, face, face1):
+        # Load the two images and get their face embeddings.
+        face_encodings = self.embeddings(face)
+        face_encodings1 = self.embeddings(face1)
+        return(torch.nn.functional.cosine_similarity(face_encodings, face_encodings1))
+    
+
+
+    def closeness(self):
+        self.embeds = []
+        for faces in self.image_pair:
+            self.embeds.append(self.face_embed(self.foldername+'/'+faces[0], self.foldername+'/'+faces[1]))
+
+        return(0)
+    
+
+    def compare(self, attempt=True):
+        self.diff = []
+        for diffs in list(zip(self.embeds[0:len(self.embeds)//2], self.embeds[len(self.embeds)//2:])):
+            self.diff.append(torch.nn.functional.pairwise_distance(diffs[0], diffs[1]))
+
+
+
+    
+    def expectation(self):
+        mean, std = torch.mean(torch.Tensor(self.diff[0:])), torch.std(torch.Tensor(self.diff[0:]))
+        distribute = torch.distributions.Normal(mean, std)
+        self.sampling = distribute.sample(sample_shape=(10,))
+
+
+
+    def model(self):       
+        self.closeness()
+        return(self.compare())
+
+
+
+    def verify(self):
+        self.folder()
+        self.model()
+        self.expectation()
+        self.folder(attempt=False)
+        self.model()
+
+        fails = 0
+        success = 0
+        max_itter = 10
+        while max_itter >= 0:
+            for samples in self.sampling:
+                if self.diff[0] <= samples:
+                    success = success+1
+                
+                else:
+                    fails = fails+1
+                
+            max_itter = max_itter-1
+
+
+        if fails > success:
+            return(False)
+
+        else:
+            return(True)
+
+
+    
+
+
+
+Recognition = FaceRec()
+print(Recognition.verify())
+