用tensorflow实现facenet人脸识别代码
时间: 2023-12-18 17:03:53 浏览: 36
以下是使用 TensorFlow 实现 FaceNet 人脸识别的代码:
```python
import tensorflow as tf
import numpy as np
import cv2
class FaceNet:
def __init__(self, model_path):
self.graph = tf.Graph()
with self.graph.as_default():
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
self.sess = tf.Session(config=sess_config)
self.sess.run(tf.global_variables_initializer())
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(self.sess, model_path)
self.images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
self.embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
self.phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
self.embedding_size = self.embeddings.get_shape()[1]
def prewhiten(self, x):
mean = np.mean(x)
std = np.std(x)
std_adj = np.maximum(std, 1.0 / np.sqrt(x.size))
y = np.multiply(np.subtract(x, mean), 1 / std_adj)
return y
def l2_normalize(self, x, axis=-1, epsilon=1e-10):
output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
return output
def calc_embeddings(self, images):
prewhiten_images = []
for image in images:
prewhiten_images.append(self.prewhiten(image))
feed_dict = {self.images_placeholder: prewhiten_images, self.phase_train_placeholder: False}
embeddings = self.sess.run(self.embeddings, feed_dict=feed_dict)
embeddings = self.l2_normalize(embeddings)
return embeddings
def calc_distance(self, feature1, feature2):
return np.sum(np.square(feature1 - feature2))
def compare(self, image1, image2):
feature1 = self.calc_embeddings([image1])[0]
feature2 = self.calc_embeddings([image2])[0]
distance = self.calc_distance(feature1, feature2)
return distance
if __name__ == '__main__':
model_path = 'model/20180402-114759/model-20180402-114759.ckpt-275'
facenet = FaceNet(model_path)
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
distance = facenet.compare(img1, img2)
print('Distance between img1 and img2:', distance)
```
在运行代码之前,需要下载 FaceNet 模型。可以从 [这里](https://github.com/davidsandberg/facenet/tree/master/src/models) 下载预训练模型。将下载的模型文件夹放到代码中 `model_path` 的位置即可。
代码中定义了一个 `FaceNet` 类,通过 `calc_embeddings` 方法可以计算图像的 embedding 特征向量。然后通过 `calc_distance` 方法计算两幅图像的距离,最后得到的值越小说明两幅图像越相似。