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