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face_reco_from_camera.py
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face_reco_from_camera.py
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# 摄像头实时人脸识别
# Real-time face recognition
# Author: coneypo
# Blog: http://www.cnblogs.com/AdaminXie
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
# Created at 2018-05-11
# Updated at 2020-04-19
import dlib # 人脸处理的库 Dlib
import numpy as np # 数据处理的库 Numpy
import cv2 # 图像处理的库 OpenCV
import pandas as pd # 数据处理的库 Pandas
import os
import time
from PIL import Image, ImageDraw, ImageFont # ImageDraw 润饰已存在的图像 # ImageFont 可以使用Truetype的字体
# 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 / Save the features of faces in the database
self.features_known_list = []
# 存储录入人脸名字 / Save the name of faces known
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 / Save the positions and names of current faces captured
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 / Print known faces
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 / Compute the e-distance between two 128D features
@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 / Update FPS of Video stream
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 / Write names under rectangle
image = Image.fromarray(cv2.cvtColor(img_rd,cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(image)
# 在/usr/share/fonts/手动添加了simsun.ttc
# font = ImageFont.truetype("/usr/share/fonts/simsun.ttc", 40, encoding="utf-8")
# 下面改成Ubuntu自带字体
font = ImageFont.truetype('LiberationSans-Regular.ttf', 60)
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(self.pos_camera_list[i],self.name_camera_list[i],font=font,fill=(0,255,255))
cvt_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
return cvt_image
# 修改显示人名
def modify_name_camera_list(self):
# Default known name: person_1, person_2, person_3
self.name_known_list[0] ='张三' # 支持中文
# self.name_known_list[1] ='TOM'
# self.name_known_list[2] ='TOM'
# self.name_known_list[3] ='TOM'
# 处理获取的视频流，进行人脸识别 / Input video stream and face reco process
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 / Press 'q' to quit
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 / when face detected
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
# 3. Get the features captured and save into 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 / Traversal all the faces in the database
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 / Positions of faces captured
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
# For every faces detected, compare the faces in the database
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 / Find the one with minimum e distance
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 " + self.name_known_list[similar_person_num])
else:
print("Unknown person")
# 矩形框 / Draw rectangle
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 / Modify name if needed
self.modify_name_camera_list()
# 8. 写名字 / Draw name
# self.draw_name(img_rd)
cvt_image = self.draw_name(img_rd) # 将生成的带中文标签的图片输出 / Output Chinese title in picture
print("Faces in camera now:", self.name_camera_list, "\n")
cv2.imshow("camera", cvt_image) # 将赋值后的cvt_image输出 / Output cvt_image assaigned new value
# 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()