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recognition_by_features.py
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# 基于已经存储的特征向量进行人脸识别
import dlib
import numpy as np
import cv2
from config import threshold, model_dir, label_path, features_path # 导入设置的阈值 模型 label标签路径 特征向量路径
import pandas as pd
import csv
# 人脸识别模型，提取128D的特征矢量
# face recognition model, the object maps human faces into 128D vectors
recognition_model = dlib.face_recognition_model_v1(model_dir["recognition_model"])
cnn_face_detector = dlib.cnn_face_detection_model_v1(model_dir["cnn"])
predictor = dlib.shape_predictor(model_dir["predictor"])
def get_labels(csv_path):
'''
从csv文件中获取标签(学生姓名)
:param csv_path: label.csv路径
:return: 列表存储学生姓名
'''
with open(csv_path, "r") as f:
reader = csv.reader(f)
labels = list(reader)
return labels
def get_local_features(path):
'''
获取本地存储的特征向量，加载到列表中
:param path: 所有人的特征向量
:return: 向量列表
'''
with open(path, "r") as f:
reader = csv.reader(f)
local_fea = (list(reader))
local_features = []
for item in local_fea:
local_features.append(list(map(lambda x: float(x), item)))
return local_features
def return_euclidean_distance(feature_1, feature_2):
'''
计算两个128D向量间的欧式距离
:param feature_1: 特征向量1
:param feature_2: 特征向量2
:return:
'''
feature_1 = np.array(feature_1)
feature_2 = np.array(feature_2)
a = np.square(feature_1 - feature_2)
dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
print("求得的欧式距离为: ", dist)
if dist > threshold:
return False
else:
return True
def get_128d_features(img_gray):
'''
返回一张图片中的所检测到的每个人脸的特征向量
:param img_gray: 图片
:return: 检测
'''
faces = cnn_face_detector(img_gray, 1)
if len(faces) != 0:
face_feas = []
for i in range(len(faces)):
shape = predictor(img_gray, faces[i])
face_feas.append(recognition_model.compute_face_descriptor(img_gray, shape))
else:
face_feas = []
return face_feas
def chage_opencv_to_Pil():
'''
opencv无法显示中文，将其转换为PIL格式
暂时不显示
:return:
'''
pass
if __name__ == '__main__':
labelss = get_labels(label_path)[0]
local_features = get_local_features(features_path)
print(local_features)
# video = "http://admin:[email protected]:8081/"
# cap = cv2.VideoCapture(video)
#
# # cap.set(propId, value)
# # 设置视频参数，propId 设置的视频参数，value 设置的参数值
# cap.set(3, 480)
#
# while cap.isOpened():
#
# flag, img_rd = cap.read()
# kk = cv2.waitKey(1)
#
# # 取灰度
# img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)
#
# # 人脸数 faces
# faces = cnn_face_detector(img_gray, 0)
#
# # 待会要写的字体
# font = cv2.FONT_HERSHEY_COMPLEX
#
# # 存储当前摄像头中捕获到的所有人脸的坐标/名字
# pos_namelist = []
# name_namelist = []
#
# # 按下 q 键退出
# if kk == ord('q'):
# break
# else:
# # 检测到人脸
# if len(faces) != 0:
# # 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
# features_cap_arr = []
# for i, d in enumerate(faces):
# s = dlib.rectangle(d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom())
# shape = predictor(img_rd, s)
# features_cap_arr.append(recognition_model.compute_face_descriptor(img_rd, shape))
#
# # 遍历捕获到的图像中所有的人脸
# for k in range(len(faces)):
# # 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
# name_namelist.append("unknown")
#
# # 每个捕获人脸的名字坐标
# pos_namelist.append(
# tuple([faces[k].rect.left(),
# int(faces[k].rect.bottom() + (faces[k].rect.bottom() - faces[k].rect.top()) / 4)]))
#
# # 对于某张人脸，遍历所有存储的人脸特征
# for i in range(len(labelss)):
# print("with person_", str(i + 1), "the ", end='')
# # 将某张人脸与存储的所有人脸数据进行比对
# compare = return_euclidean_distance(features_cap_arr[k], local_features[i])
# if compare: # 找到了相似脸
# name_namelist[k] = labelss[i]
#
# # 矩形框
# for kk, d in enumerate(faces):
# # 绘制矩形框
# cv2.rectangle(img_rd, tuple([d.rect.left(), d.rect.top()]), tuple([d.rect.right(), d.rect.bottom()]), (0, 255, 255),
# 2)
#
# # 在人脸框下面写人脸名字
# for i in range(len(faces)):
# cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
#
# print("Name list now:", name_namelist, "\n")
#
# cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
# cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
# cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
#
# # 窗口显示
# cv2.imshow("camera", img_rd)
#
# # 释放摄像头
# cap.release()
#
# # 删除建立的窗口
# cv2.destroyAllWindows()