| |
| |
| |
|
|
| 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)) |
|
|
| |
| 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']) |
|
|
| |
| 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() |
|
|
