face detection by one-shot simple version

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")