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+*.pyc
 __pycache__/
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+face_db/

README.md

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+# 人脸和年龄性别识别
+ - `camera_demo.py` 调用摄像头进行人脸识别和年龄性别识别，并通过视频展示
+ - `camera_main.py` 通过HTTP接口调用启动摄像头，并进行识别，把识别结果返回
+ - `server_main.py` 人脸识别和年龄性别识别，通过HTTP调用，接收传过来的图片进行识别并返回结果

camera_infer.py

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+import configparser
+import cv2
+import numpy as np
+import sklearn
+from utils import face_preprocess
+from utils.utils import feature_compare, load_mtcnn, load_faces, load_mobilefacenet, add_faces
+
+conf = configparser.ConfigParser()
+conf.read("config/main.cfg")
+# 加载人脸检测模型
+VERIFICATION_THRESHOLD = float(conf.get("MOBILEFACENET", "VERIFICATION_THRESHOLD"))
+
+# 检测人脸检测模型
+mtcnn_detector = load_mtcnn(conf)
+# 加载人脸识别模型
+face_sess, inputs_placeholder, embeddings = load_mobilefacenet(conf)
+# 添加人脸
+add_faces(conf, mtcnn_detector)
+# 加载已经注册的人脸
+faces_db = load_faces(conf, face_sess, inputs_placeholder, embeddings)
+
+
+# 注册人脸
+def register_face():
+    print("点击y确认拍照！")
+    cap = cv2.VideoCapture(0)
+    while True:
+        ret, frame = cap.read()
+        if ret:
+            cv2.imshow('image', frame)
+            if cv2.waitKey(1) & 0xFF == ord('y'):
+                faces, landmarks = mtcnn_detector.detect(frame)
+                if faces.shape[0] is not 0:
+                    faces_sum = 0
+                    bbox = []
+                    points = []
+                    for i, face in enumerate(faces):
+                        if round(faces[i, 4], 6) > 0.95:
+                            bbox = faces[i, 0:4]
+                            points = landmarks[i, :].reshape((5, 2))
+                            faces_sum += 1
+                    if faces_sum == 1:
+                        nimg = face_preprocess.preprocess(frame, bbox, points, image_size='112,112')
+                        user_name = input("请输入注册名（不支持中文）：")
+                        cv2.imwrite('face_db/%s.png' % user_name, nimg)
+                        print("注册成功！")
+                    else:
+                        print('注册图片有错，图片中有且只有一个人脸')
+                else:
+                    print('注册图片有错，图片中有且只有一个人脸')
+                break
+
+
+def infer_face():
+    cap = cv2.VideoCapture(0)
+    while True:
+        ret, frame = cap.read()
+        if ret:
+            faces, landmarks = mtcnn_detector.detect(frame)
+            if faces.shape[0] is not 0:
+                faces_sum = 0
+                for i, face in enumerate(faces):
+                    if round(faces[i, 4], 6) > 0.95:
+                        faces_sum += 1
+                if faces_sum == 0:
+                    continue
+                # 人脸信息
+                info_location = np.zeros(faces_sum)
+                info_location[0] = 1
+                info_name = []
+                probs = []
+                # 提取图像中的人脸
+                input_images = np.zeros((faces.shape[0], 112, 112, 3))
+                for i, face in enumerate(faces):
+                    if round(faces[i, 4], 6) > 0.95:
+                        bbox = faces[i, 0:4]
+                        points = landmarks[i, :].reshape((5, 2))
+                        nimg = face_preprocess.preprocess(frame, bbox, points, image_size='112,112')
+                        nimg = nimg - 127.5
+                        nimg = nimg * 0.0078125
+                        input_images[i, :] = nimg
+
+                # 进行人脸识别
+                feed_dict = {inputs_placeholder: input_images}
+                emb_arrays = face_sess.run(embeddings, feed_dict=feed_dict)
+                emb_arrays = sklearn.preprocessing.normalize(emb_arrays)
+                for i, embedding in enumerate(emb_arrays):
+                    embedding = embedding.flatten()
+                    temp_dict = {}
+                    # 比较已经存在的人脸数据库
+                    for com_face in faces_db:
+                        ret, sim = feature_compare(embedding, com_face["feature"], 0.70)
+                        temp_dict[com_face["name"]] = sim
+                    dict = sorted(temp_dict.items(), key=lambda d: d[1], reverse=True)
+                    if dict[0][1] > VERIFICATION_THRESHOLD:
+                        name = dict[0][0]
+                        probs.append(dict[0][1])
+                        info_name.append(name)
+                    else:
+                        probs.append(dict[0][1])
+                        info_name.append("unknown")
+
+                for k in range(faces_sum):
+                    # 写上人脸信息
+                    x1, y1, x2, y2 = faces[k][0], faces[k][1], faces[k][2], faces[k][3]
+                    x1 = max(int(x1), 0)
+                    y1 = max(int(y1), 0)
+                    x2 = min(int(x2), frame.shape[1])
+                    y2 = min(int(y2), frame.shape[0])
+                    prob = '%.2f' % probs[k]
+                    label = "{}, {}".format(info_name[k], prob)
+                    size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)[0]
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                    cv2.rectangle(frame, (x1, y1 - size[1]), (x1 + size[0], y1), (255, 0, 0), cv2.FILLED)
+                    cv2.putText(frame, label, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+        cv2.imshow('image', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+
+if __name__ == '__main__':
+    i = int(input("请选择功能，1为注册人脸，2为识别人脸："))
+    if i == 1:
+        register_face()
+    elif i == 2:
+        infer_face()
+    else:
+        print("功能选择错误")

config/main.cfg

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+[MTCNN]
+MODEL_PATH = ./models/mtcnn_model
+MIN_FACE_SIZE = 120
+STEPS_THRESHOLD = 0.9,0.6,0.7
+
+[MOBILEFACENET]
+MODEL_PATH = ./models/mobilefacenet_model/MobileFaceNet_9925_9680.pb
+VERIFICATION_THRESHOLD = 0.6
+FACE_DB_PATH = ./face_db/
+TEMP_FACE_PATH = ./temp/
+
+[SERVER]
+HOST = 192.168.89.175
+POST = 5000