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face_rec_final.py
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# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
import os
from face_train_final import CNN
import cv2
import sys
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import dlib
detector = dlib.get_frontal_face_detector() #获取人脸分类器
if __name__ == '__main__':
#加载模型
model = CNN()
model.load_weights('./model/face1') #读取模型权重参数
#框住人脸的矩形边框颜色
color = (255, 0, 0)
#人脸识别分类器本地存储路径
cascade_path ="D:\Program Files\Python38\Lib\site-packages\cv2\data\haarcascade_frontalface_alt2.xml"
#"E:\python3\StarFaceRecognition\test_liu.jpg"
#"E:\python3\TensorFlow1\face_recognition\test_liu.jpg"
user=input("请选择图片（G）还是摄像头（V）:")
if user=="G":
path=input("请在此处输入图片路径：")
img = cv2.imread(path)
plt.imshow(img)
plt.show()
#timg=Image.open(path)
#timg.show()
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cascade = cv2.CascadeClassifier(cascade_path)
dets = detector(img, 1)
print("检测到: {}张人脸，已灰度化处理".format(len(dets)))
#利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(img_grey, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
#截取脸部图像提交给模型识别这是谁
image = img[y - 10: y + h + 10, x - 10: x + w + 10]
face_probe = model.face_predict(image) #获得预测值
cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10), color, thickness = 2)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同
pilimg = Image.fromarray(img)
print('识别结果：')
print('我:{:.2%}'.format(face_probe[0]))
print('刘畅:{:.2%}'.format(face_probe[1]))
print('刘德华:{:.2%}'.format(face_probe[2]))
print('吴彦祖:{:.2%}'.format(face_probe[3]))
print('郑爽:{:.2%}'.format(face_probe[4]))
#cv2.destroyAllWindows()
else:
#捕获指定摄像头的实时视频流
cap = cv2.VideoCapture(0)
#循环检测识别人脸
while True:
ret, frame = cap.read() #读取一帧视频
if ret is True:
#图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
continue
#使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(cascade_path)
#利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
#截取脸部图像提交给模型识别这是谁
image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
face_probe = model.face_predict(image) #获得预测值
cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, thickness = 2)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同
pilimg = Image.fromarray(frame)
draw = ImageDraw.Draw(pilimg) # 图片上打印 出所有人的预测值
font = ImageFont.truetype("simkai.ttf", 20, encoding="utf-8") # 参数1：字体文件路径，参数2：字体大小
draw.text((x+25,y-95), '我:{:.2%}'.format(face_probe[0]), (255, 0, 0), font=font)
draw.text((x+25,y-70), '刘畅:{:.2%}'.format(face_probe[1]), (255, 0, 0), font=font)
draw.text((x+25,y-45), '刘德华:{:.2%}'.format(face_probe[2]), (255, 0, 0), font=font)
draw.text((x+25,y-20), '吴彦祖:{:.2%}'.format(face_probe[3]), (255, 0, 0), font=font)
draw.text((x+25,y-120),'郑爽:{:.2%}'.format(face_probe[4]), (255, 0, 0), font=font)
frame = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR)
cv2.imshow("ShowTime", frame)
#等待10毫秒看是否有按键输入
k = cv2.waitKey(10)
#如果输入q则退出循环
if k & 0xFF == ord('q'):
break
#释放摄像头并销毁所有窗口
cap.release()
cv2.destroyAllWindows()