face detection by one-shot simple version

Class/collect_data/face_reco_from_camera.py

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+# 摄像头实时人脸识别
+
+import dlib
+import numpy as np
+import cv2
+import pandas as pd
+import os
+import time
+from PIL import Image, ImageDraw, ImageFont
+
+# 1. Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+# 2. Dlib 人脸 landmark 特征点检测器
+predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
+
+# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
+face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
+
+
+class Face_Recognizer:
+    def __init__(self):
+        # 用来存放所有录入人脸特征的数组
+        self.features_known_list = []
+
+        # 存储录入人脸名字
+        self.name_known_cnt = 0
+        self.name_known_list = []
+
+        # 存储当前摄像头中捕获到的所有人脸的坐标名字
+        self.pos_camera_list = []
+        self.name_camera_list = []
+        # 存储当前摄像头中捕获到的人脸数
+        self.faces_cnt = 0
+        # 存储当前摄像头中捕获到的人脸特征
+        self.features_camera_list = []
+
+        # Update FPS
+        self.fps = 0
+        self.frame_start_time = 0
+
+    # 从 "features_all.csv" 读取录入人脸特征
+    def get_face_database(self):
+        if os.path.exists("data/features_all.csv"):
+            path_features_known_csv = "data/features_all.csv"
+            csv_rd = pd.read_csv(path_features_known_csv, header=None)
+            # 2. 读取已知人脸数据
+            for i in range(csv_rd.shape[0]):
+                features_someone_arr = []
+                for j in range(0, 128):
+                    if csv_rd.iloc[i][j] == '':
+                        features_someone_arr.append('0')
+                    else:
+                        features_someone_arr.append(csv_rd.iloc[i][j])
+                self.features_known_list.append(features_someone_arr)
+                self.name_known_list.append("Person_"+str(i+1))
+            self.name_known_cnt = len(self.name_known_list)
+            print("Faces in Database：", len(self.features_known_list))
+            return 1
+        else:
+            print('##### Warning #####', '\n')
+            print("'features_all.csv' not found!")
+            print(
+                "Please run 'get_faces_from_camera.py' and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'",
+                '\n')
+            print('##### End Warning #####')
+            return 0
+
+    # 计算两个128D向量间的欧式距离
+    @staticmethod
+    def return_euclidean_distance(feature_1, feature_2):
+        feature_1 = np.array(feature_1)
+        feature_2 = np.array(feature_2)
+        dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
+        return dist
+
+    # 更新 FPS
+    def update_fps(self):
+        now = time.time()
+        self.frame_time = now - self.frame_start_time
+        self.fps = 1.0 / self.frame_time
+        self.frame_start_time = now
+
+    def draw_note(self, img_rd):
+        font = cv2.FONT_ITALIC
+
+        cv2.putText(img_rd, "Face Recognizer", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "FPS:   " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "Faces: " + str(self.faces_cnt), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
+        cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
+
+    def draw_name(self, img_rd):
+        # 在人脸框下面写人脸名字
+        font = ImageFont.truetype("simsun.ttc", 30)
+        img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
+        draw = ImageDraw.Draw(img)
+        for i in range(self.faces_cnt):
+            # cv2.putText(img_rd, self.name_camera_list[i], self.pos_camera_list[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
+            draw.text(xy=self.pos_camera_list[i], text=self.name_camera_list[i], font=font)
+            img_with_name = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+        return img_with_name
+
+    # 修改显示人名
+    def modify_name_camera_list(self):
+        # TODO 数据库 ID
+        # Default known name: person_1, person_2, person_3
+        self.name_known_list[0] ='唐麒'.encode('utf-8').decode()
+        # self.name_known_list[1] ='李四'.encode('utf-8').decode()
+        # self.name_known_list[2] ='xx'.encode('utf-8').decode()
+        # self.name_known_list[3] ='xx'.encode('utf-8').decode()
+        # self.name_known_list[4] ='xx'.encode('utf-8').decode()
+
+    # 处理获取的视频流，进行人脸识别
+    def process(self, stream):
+        # 1. 读取存放所有人脸特征的 csv
+        if self.get_face_database():
+            while stream.isOpened():
+                flag, img_rd = stream.read()
+                faces = detector(img_rd, 0)
+                kk = cv2.waitKey(1)
+                # 按下 q 键退出
+                if kk == ord('q'):
+                    break
+                else:
+                    self.draw_note(img_rd)
+                    self.features_camera_list = []
+                    self.faces_cnt = 0
+                    self.pos_camera_list = []
+                    self.name_camera_list = []
+
+                    # 2. 检测到人脸
+                    if len(faces) != 0:
+                        # 3. 获取当前捕获到的图像的所有人脸的特征，存储到 self.features_camera_list
+                        for i in range(len(faces)):
+                            shape = predictor(img_rd, faces[i])
+                            self.features_camera_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
+
+                        # 4. 遍历捕获到的图像中所有的人脸
+                        for k in range(len(faces)):
+                            print("##### camera person", k + 1, "#####")
+                            # 让人名跟随在矩形框的下方
+                            # 确定人名的位置坐标
+                            # 先默认所有人不认识，是 unknown
+                            # Set the default names of faces with "unknown"
+                            self.name_camera_list.append("unknown")
+
+                            # 每个捕获人脸的名字坐标
+                            self.pos_camera_list.append(tuple(
+                                [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
+
+                            # 5. 对于某张人脸，遍历所有存储的人脸特征
+                            e_distance_list = []
+                            for i in range(len(self.features_known_list)):
+                                # 如果 person_X 数据不为空
+                                if str(self.features_known_list[i][0]) != '0.0':
+                                    print("with person", str(i + 1), "the e distance: ", end='')
+                                    e_distance_tmp = self.return_euclidean_distance(self.features_camera_list[k],
+                                                                                    self.features_known_list[i])
+                                    print(e_distance_tmp)
+                                    e_distance_list.append(e_distance_tmp)
+                                else:
+                                    # 空数据 person_X
+                                    e_distance_list.append(999999999)
+                            # 6. 寻找出最小的欧式距离匹配
+                            similar_person_num = e_distance_list.index(min(e_distance_list))
+                            print("Minimum e distance with person", self.name_known_list[similar_person_num])
+
+                            if min(e_distance_list) < 0.4:
+                                self.name_camera_list[k] = self.name_known_list[similar_person_num]
+                                print("May be person " + str(self.name_known_list[similar_person_num]))
+                            else:
+                                print("Unknown person")
+
+                            # 矩形框
+                            for kk, d in enumerate(faces):
+                                # 绘制矩形框
+                                cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
+                                              (0, 255, 255), 2)
+                            print('\n')
+
+                        self.faces_cnt = len(faces)
+                        # 7. 在这里更改显示的人名
+                        self.modify_name_camera_list()
+                        # 8. 写名字
+                        # self.draw_name(img_rd)
+                        img_with_name = self.draw_name(img_rd)
+                    else:
+                        img_with_name = img_rd
+
+                print("Faces in camera now:", self.name_camera_list, "\n")
+
+                cv2.imshow("camera", img_with_name)
+
+                # 9. 更新 FPS / Update stream FPS
+                self.update_fps()
+
+    # OpenCV 调用摄像头并进行 process
+    def run(self):
+        cap = cv2.VideoCapture(0)
+        cap.set(3, 480)
+        self.process(cap)
+
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+def main():
+    Face_Recognizer_con = Face_Recognizer()
+    Face_Recognizer_con.run()
+
+
+if __name__ == '__main__':
+    main()

Class/collect_data/features_extraction_to_csv.py

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+# 从人脸图像文件中提取人脸特征存入 CSV
+
+import os
+import dlib
+from skimage import io
+import csv
+import numpy as np
+
+# 要读取人脸图像文件的路径
+path_images_from_camera = "data/data_faces_from_camera/"
+
+# 1. Dlib 正向人脸检测器
+detector = dlib.get_frontal_face_detector()
+
+# 2. Dlib 人脸 landmark 特征点检测器
+predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
+
+# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
+face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
+
+
+# 返回单张图像的 128D 特征
+def return_128d_features(path_img):
+    img_rd = io.imread(path_img)
+    faces = detector(img_rd, 1)
+
+    print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
+
+    # 因为有可能截下来的人脸再去检测，检测不出来人脸了
+    # 所以要确保是 检测到人脸的人脸图像 拿去算特征
+    if len(faces) != 0:
+        shape = predictor(img_rd, faces[0])
+        face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
+    else:
+        face_descriptor = 0
+        print("no face")
+
+    return face_descriptor
+
+
+# 将文件夹中照片特征提取出来, 写入 CSV
+def return_features_mean_personX(path_faces_personX):
+    features_list_personX = []
+    photos_list = os.listdir(path_faces_personX)
+    if photos_list:
+        for i in range(len(photos_list)):
+            # 调用return_128d_features()得到128d特征
+            print("%-40s %-20s" % ("正在读的人脸图像 / Image to read:", path_faces_personX + "/" + photos_list[i]))
+            features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
+            #  print(features_128d)
+            # 遇到没有检测出人脸的图片跳过
+            if features_128d == 0:
+                i += 1
+            else:
+                features_list_personX.append(features_128d)
+    else:
+        print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
+
+    # 计算 128D 特征的均值
+    # personX 的 N 张图像 x 128D -> 1 x 128D
+    if features_list_personX:
+        features_mean_personX = np.array(features_list_personX).mean(axis=0)
+    else:
+        features_mean_personX = np.zeros(128, dtype=int, order='C')
+
+    return features_mean_personX
+
+
+# 获取已录入的最后一个人脸序号 / get the num of latest person
+person_list = os.listdir("data/data_faces_from_camera/")
+person_num_list = []
+for person in person_list:
+    person_num_list.append(int(person.split('_')[-1]))
+person_cnt = max(person_num_list)
+
+with open("data/features_all.csv", "w", newline="") as csvfile:
+    writer = csv.writer(csvfile)
+    for person in range(person_cnt):
+        # Get the mean/average features of face/personX, it will be a list with a length of 128D
+        print(path_images_from_camera + "person_" + str(person + 1))
+        features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
+        writer.writerow(features_mean_personX)
+        print("特征均值 / The mean of features:", list(features_mean_personX))
+        print('\n')
+    print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")