add_new_id.py
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import os
import numpy as np
import cv2
from tqdm import tqdm
from face_id import Face_Recognizer
def get_embeddings(id_dir):
embedding_names = os.listdir(id_dir)
embeddings = []
for embedding_name in embedding_names:
embedding_path = os.path.join(id_dir, embedding_name)
embedding = np.load(embedding_path)
embeddings.append(embedding)
return embeddings
def get_high_similarity():
add_file_txt = open('add_id.txt', 'w')
id_names = os.listdir(embeddings_dir)
id_names_set = set(id_names)
new_id_names = os.listdir(add_image_dir)
for new_id_name in tqdm(new_id_names):
new_id_dir = os.path.join(add_image_dir, new_id_name)
add_image_names = os.listdir(new_id_dir)
add_norm_images = []
for add_image_name in add_image_names:
add_image_path = os.path.join(new_id_dir, add_image_name)
add_image = cv2.imread(add_image_path)
add_image = cv2.resize(add_image, (112, 112))
add_norm_images.append(add_image)
add_embeddings = face_recognizer.recognize(add_norm_images)
is_add = 1
for id_name in id_names_set:
id_dir = os.path.join(embeddings_dir, id_name)
embeddings = get_embeddings(id_dir)
for embedding in embeddings:
embedding = np.mat(embedding)
for add_embedding in add_embeddings:
add_embedding = np.mat(add_embedding)
dot = np.sum(np.multiply(embedding, add_embedding), axis=1)
norm = np.linalg.norm(embedding, axis=1) * np.linalg.norm(add_embedding, axis=1)
dist_1 = dot / norm
sim = dist_1.tolist()
sim = sim[0][0]
if sim > 0.4:
print('same file:{}'.format(sim))
is_add = 0
add_file_txt.write(new_id_name)
add_file_txt.write(',')
add_file_txt.write(str(is_add))
add_file_txt.write('\n')
add_file_txt.close()
reg_face_id_model_path = r'models/cls_face_mnn_1.0.0_v0.0.3.mnn'
add_image_dir = r'/data2/face_id/situ_other/add'
embeddings_dir = r'/data2/face_id/situ_other/train_norm_embeddings'
face_recognizer = Face_Recognizer(reg_face_id_model_path)
get_high_similarity()