這篇文章主要介紹了如何通過python實(shí)現(xiàn)人臉識(shí)別驗(yàn)證,文中通過示例代碼介紹的非常詳細(xì)，對(duì)大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友可以參考下
直接上代碼，此案例是根據(jù)https://github.com/caibojian/face_login修改的,識(shí)別率不怎么好，有時(shí)擋了半個(gè)臉還是成功的
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# -*- coding: utf-8 -*-# __author__="maple"""" ┏┓ ┏┓ ┏┛┻━━━┛┻┓ ┃ ? ┃ ┃ ┳┛ ┗┳ ┃ ┃ ┻ ┃ ┗━┓ ┏━┛ ┃ ┗━━━┓ ┃ 神獸保佑 ┣┓ ┃ 永無BUG！ ┏┛ ┗┓┓┏━┳┓┏┛ ┃┫┫ ┃┫┫ ┗┻┛ ┗┻┛"""import base64import cv2import timefrom io import BytesIOfrom tensorflow import kerasfrom PIL import Imagefrom pymongo import MongoClientimport tensorflow as tfimport face_recognitionimport numpy as np#mongodb連接conn = MongoClient('mongodb://root:123@localhost:27017/')db = conn.myface #連接mydb數(shù)據(jù)庫，沒有則自動(dòng)創(chuàng)建user_face = db.user_face #使用test_set集合，沒有則自動(dòng)創(chuàng)建face_images = db.face_imageslables = []datas = []INPUT_NODE = 128LATER1_NODE = 200OUTPUT_NODE = 0TRAIN_DATA_SIZE = 0TEST_DATA_SIZE = 0def generateds(): get_out_put_node() train_x, train_y, test_x, test_y = np.array(datas),np.array(lables),np.array(datas),np.array(lables) return train_x, train_y, test_x, test_ydef get_out_put_node(): for item in face_images.find(): lables.append(item['user_id']) datas.append(item['face_encoding']) OUTPUT_NODE = len(set(lables)) TRAIN_DATA_SIZE = len(lables) TEST_DATA_SIZE = len(lables) return OUTPUT_NODE, TRAIN_DATA_SIZE, TEST_DATA_SIZE# 驗(yàn)證臉部信息def predict_image(image): model = tf.keras.models.load_model('face_model.h5',compile=False) face_encode = face_recognition.face_encodings(image) result = [] for j in range(len(face_encode)): predictions1 = model.predict(np.array(face_encode[j]).reshape(1, 128)) print(predictions1) if np.max(predictions1[0]) > 0.90: print(np.argmax(predictions1[0]).dtype) pred_user = user_face.find_one({'id': int(np.argmax(predictions1[0]))}) print('第%d張臉是%s' % (j+1, pred_user['user_name'])) result.append(pred_user['user_name']) return result# 保存臉部信息def save_face(pic_path,uid): image = face_recognition.load_image_file(pic_path) face_encode = face_recognition.face_encodings(image) print(face_encode[0].shape) if(len(face_encode) == 1): face_image = { 'user_id': uid, 'face_encoding':face_encode[0].tolist() } face_images.insert_one(face_image)# 訓(xùn)練臉部信息def train_face(): train_x, train_y, test_x, test_y = generateds() dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)) dataset = dataset.batch(32) dataset = dataset.repeat() OUTPUT_NODE, TRAIN_DATA_SIZE, TEST_DATA_SIZE = get_out_put_node() model = keras.Sequential([ keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(OUTPUT_NODE, activation=tf.nn.softmax) ]) model.compile(optimizer=tf.compat.v1.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy']) steps_per_epoch = 30 if steps_per_epoch > len(train_x): steps_per_epoch = len(train_x) model.fit(dataset, epochs=10, steps_per_epoch=steps_per_epoch) model.save('face_model.h5')def register_face(user): if user_face.find({"user_name": user}).count() > 0: print("用戶已存在") return video_capture=cv2.VideoCapture(0) # 在MongoDB中使用sort()方法對(duì)數(shù)據(jù)進(jìn)行排序，sort()方法可以通過參數(shù)指定排序的字段，并使用 1 和 -1 來指定排序的方式，其中 1 為升序，-1為降序。 finds = user_face.find().sort([("id", -1)]).limit(1) uid = 0 if finds.count() > 0: uid = finds[0]['id'] + 1 print(uid) user_info = { 'id': uid, 'user_name': user, 'create_time': time.time(), 'update_time': time.time() } user_face.insert_one(user_info) while 1: # 獲取一幀視頻 ret, frame = video_capture.read() # 窗口顯示 cv2.imshow('Video',frame) # 調(diào)整角度后連續(xù)拍5張圖片 if cv2.waitKey(1) & 0xFF == ord('q'): for i in range(1,6): cv2.imwrite('Myface{}.jpg'.format(i), frame) with open('Myface{}.jpg'.format(i),"rb")as f: img=f.read() img_data = BytesIO(img) im = Image.open(img_data) im = im.convert('RGB') imgArray = np.array(im) faces = face_recognition.face_locations(imgArray) save_face('Myface{}.jpg'.format(i),uid) break train_face() video_capture.release() cv2.destroyAllWindows()def rec_face(): video_capture = cv2.VideoCapture(0) while 1: # 獲取一幀視頻 ret, frame = video_capture.read() # 窗口顯示 cv2.imshow('Video',frame) # 驗(yàn)證人臉的5照片 if cv2.waitKey(1) & 0xFF == ord('q'): for i in range(1,6): cv2.imwrite('recface{}.jpg'.format(i), frame) break res = [] for i in range(1, 6): with open('recface{}.jpg'.format(i),"rb")as f: img=f.read() img_data = BytesIO(img) im = Image.open(img_data) im = im.convert('RGB') imgArray = np.array(im) predict = predict_image(imgArray) if predict: res.extend(predict) b = set(res) # {2, 3} if len(b) == 1 and len(res) >= 3: print(" 驗(yàn)證成功") else: print(" 驗(yàn)證失敗")if __name__ == '__main__': register_face("maple") rec_face() |
以上就是本文的全部?jī)?nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持服務(wù)器之家。
原文鏈接：https://www.cnblogs.com/angelyan/p/12113773.html
