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47 lines
1.5 KiB
Plaintext
47 lines
1.5 KiB
Plaintext
import cv2
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import insightface
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import numpy as np
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from insightface.app import FaceAnalysis
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# Initialize face analysis model
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app = FaceAnalysis(name='buffalo_l', providers=['CPUExecutionProvider']) # Use 'CUDAExecutionProvider' for GPU
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app.prepare(ctx_id=-1) # ctx_id=-1 for CPU, 0 for GPU
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def get_face_embedding(image_path):
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"""Extract face embedding from an image"""
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"Could not read image: {image_path}")
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faces = app.get(img)
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if len(faces) < 1:
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raise ValueError("No faces detected in the image")
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if len(faces) > 1:
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print("Warning: Multiple faces detected. Using first detected face")
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return faces[0].embedding
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def compare_faces(emb1, emb2, threshold=0.65): # Adjust this threshold according to your usecase.
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"""Compare two embeddings using cosine similarity"""
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similarity = np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2))
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return similarity, similarity > threshold
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# Paths to your Indian face images
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image1_path = "dataset/img1.jpg"
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image2_path = "dataset/img2.jpg"
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try:
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# Get embeddings
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emb1 = get_face_embedding(image1_path)
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emb2 = get_face_embedding(image2_path)
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# Compare faces
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similarity_score, is_same_person = compare_faces(emb1, emb2)
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print(f"Similarity Score: {similarity_score:.4f}")
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print(f"Same person? {'YES' if is_same_person else 'NO'}")
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except Exception as e:
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print(f"Error: {str(e)}")
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