File size: 2,456 Bytes
93aff39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #!/usr/bin/env python3
# Copyright 2022-2024,2026 NXP
# SPDX-License-Identifier: MIT
import argparse
import numpy as np
import cv2
try:
import tflite_runtime.interpreter as tflite
except ImportError:
import tensorflow as tf
tflite = tf.lite
def cosine_similarity(a, b):
return 1 - np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
def load_image(path, input_details):
img = cv2.imread(path, cv2.IMREAD_COLOR)
if img is None:
raise FileNotFoundError(f"Could not load image: {path}")
h, w = input_details[0]['shape'][1], input_details[0]['shape'][2]
img = cv2.resize(img, (w, h))
# Handle uint8 quantized input
if input_details[0]['dtype'] == np.uint8:
img = img.astype(np.uint8)
else:
img = (img / 255.0).astype(np.float32)
return img[None, ...]
def get_embedding(interpreter, input_details, output_details, img):
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
out = interpreter.get_tensor(output_details[0]['index'])
# Dequantize output if needed
if output_details[0]['dtype'] == np.uint8:
scale, zero_point = output_details[0]['quantization']
out = (out.astype(np.float32) - zero_point) * scale
return out[0]
def main():
parser = argparse.ArgumentParser(description="FaceNet512 face similarity example")
parser.add_argument('-m', '--model', default='original_model/facenet512_uint8_float32.tflite',
help='Path to the TFLite model file')
parser.add_argument('-i', '--input', default='face.jpg',
help='Path to the first face image')
parser.add_argument('-i2', '--input2', default='face2.jpg',
help='Path to the second face image')
args = parser.parse_args()
interpreter = tflite.Interpreter(model_path=args.model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
img1 = load_image(args.input, input_details)
img2 = load_image(args.input2, input_details)
emb1 = get_embedding(interpreter, input_details, output_details, img1)
emb2 = get_embedding(interpreter, input_details, output_details, img2)
dist = cosine_similarity(emb1, emb2)
print(f"Cosine similarity distance: {dist:.4f}")
print("Same face" if dist < 0.3 else "Different face")
if __name__ == '__main__':
main()
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