97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
# 从人脸图像文件中提取人脸特征存入 CSV
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import os
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import dlib
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from skimage import io
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import csv
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import cv2
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import numpy as np
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# 要读取人脸图像文件的路径
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path_images_from_camera = "data/data_faces_from_camera/"
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# 1. Dlib 正向人脸检测器
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# detector = dlib.get_frontal_face_detector()
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# OpenCV DNN face detector
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detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt",
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"data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel")
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# 2. Dlib 人脸 landmark 特征点检测器
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predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
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# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
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face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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# 返回单张图像的 128D 特征
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def return_128d_features(path_img):
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img_rd = io.imread(path_img)
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(h, w) = img_rd.shape[:2]
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blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0,
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(300, 300), (104.0, 177.0, 123.0))
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detector.setInput(blob)
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faces = detector.forward()
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print("%-40s %-20s" % ("检测到人脸的图像 / Image with faces detected:", path_img), '\n')
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# 因为有可能截下来的人脸再去检测，检测不出来人脸了
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# 所以要确保是 检测到人脸的人脸图像 拿去算特征
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if faces.shape[2] != 0:
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box = faces[0, 0, 0, 3:7] * np.array([w, h, w, h])
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(startX, startY, endX, endY) = box.astype("int")
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rect = dlib.rectangle(startX, startY, endX, endY)
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shape = predictor(img_rd, rect)
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face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
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else:
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face_descriptor = 0
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print("no face")
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return face_descriptor
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# 将文件夹中照片特征提取出来, 写入 CSV
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def return_features_mean_personX(path_faces_personX):
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features_list_personX = []
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photos_list = os.listdir(path_faces_personX)
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if photos_list:
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for i in range(len(photos_list)):
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# 调用return_128d_features()得到128d特征
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print("%-40s %-20s" % ("正在读的人脸图像 / Image to read:", path_faces_personX + "/" + photos_list[i]))
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features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
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# print(features_128d)
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# 遇到没有检测出人脸的图片跳过
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if features_128d == 0:
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i += 1
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else:
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features_list_personX.append(features_128d)
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else:
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print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
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# 计算 128D 特征的均值
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# personX 的 N 张图像 x 128D -> 1 x 128D
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if features_list_personX:
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features_mean_personX = np.array(features_list_personX).mean(axis=0)
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else:
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features_mean_personX = np.zeros(128, dtype=int, order='C')
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return features_mean_personX
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# 获取已录入的最后一个人脸序号 / get the num of latest person
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person_list = os.listdir("data/data_faces_from_camera/")
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person_num_list = []
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for person in person_list:
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person_num_list.append(int(person.split('_')[-1]))
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person_cnt = max(person_num_list)
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with open("data/features_all.csv", "w", newline="") as csvfile:
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writer = csv.writer(csvfile)
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for person in range(person_cnt):
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# Get the mean/average features of face/personX, it will be a list with a length of 128D
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print(path_images_from_camera + "person_" + str(person + 1))
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features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
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writer.writerow(features_mean_personX)
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print("特征均值 / The mean of features:", list(features_mean_personX))
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print('\n')
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print("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv")
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