245 lines
11 KiB
Python
245 lines
11 KiB
Python
# 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()
|
||
|
||
# OpenCV DNN face detector
|
||
detector = cv2.dnn.readNetFromCaffe("data/data_opencv/deploy.prototxt.txt",
|
||
"data/data_opencv/res10_300x300_ssd_iter_140000.caffemodel")
|
||
|
||
# 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
|
||
|
||
# TODO th
|
||
|
||
# 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, index=1)
|
||
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()
|
||
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 = []
|
||
|
||
(h, w) = img_rd.shape[:2]
|
||
blob = cv2.dnn.blobFromImage(cv2.resize(img_rd, (300, 300)), 1.0,
|
||
(300, 300), (104.0, 177.0, 123.0))
|
||
detector.setInput(blob)
|
||
faces = detector.forward()
|
||
|
||
# 2. 检测到人脸
|
||
if faces.shape[2] != 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(0, faces.shape[2]):
|
||
confidence = faces[0, 0, i, 2]
|
||
|
||
# filter out weak detections by ensuring the `confidence` is
|
||
# greater than the minimum confidence
|
||
if confidence < 0.5:
|
||
continue
|
||
box = faces[0, 0, i, 3:7] * np.array([w, h, w, h])
|
||
(startX, startY, endX, endY) = box.astype("int")
|
||
rect = dlib.rectangle(startX, startY, endX, endY)
|
||
shape = predictor(img_rd, rect)
|
||
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(0, faces.shape[2]):
|
||
# 计算矩形框大小
|
||
confidence = faces[0, 0, k, 2]
|
||
|
||
# filter out weak detections by ensuring the `confidence` is
|
||
# greater than the minimum confidence
|
||
if confidence < 0.5:
|
||
continue
|
||
print("##### camera person", k + 1, "#####")
|
||
# 让人名跟随在矩形框的上方
|
||
# 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
|
||
box = faces[0, 0, k, 3:7] * np.array([w, h, w, h])
|
||
(startX, startY, endX, endY) = box.astype("int")
|
||
self.pos_camera_list.append(tuple(
|
||
[int(startX+5), int(startY - 30)]))
|
||
|
||
# 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([startX, startY]), tuple([endX, endY]),
|
||
(0, 255, 0), 2)
|
||
cv2.rectangle(img_rd, tuple([startX, startY - 35]), tuple([endX, startY]),
|
||
(0, 255, 0), cv2.FILLED)
|
||
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()
|