Keras之CNN：基于Keras利用cv2建立训练存储卷积神经网络模型(2+1)并调用摄像头进行实时人脸识别

 

 

目录

输出结果

设计思路

核心代码

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输出结果

 

设计思路

 

核心代码

 

# -*- coding:utf-8 -*-
import cv2
from train_model import Model
from read_data import read_name_list
from timeit import default_timer as timer  ### to calculate FPS

class Camera_reader(object):
    def __init__(self):
        self.model = Model()
        self.model.load()
        self.img_size = 128


    def build_camera(self):
        face_cascade = cv2.CascadeClassifier('F:\\Program Files\\Python\\Python36\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt.xml')
#         print(face_cascade) #输出<CascadeClassifier 000002240244CC70>
        name_list = read_name_list('F:\\File_Python\\Python_example\\face_recognition_name\\After_cut_picture')
#         print(name_list)

        cameraCapture = cv2.VideoCapture(0)  
        
        success, frame = cameraCapture.read()  

        while success and cv2.waitKey(1) == -1:  
             success, frame = cameraCapture.read()
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
             faces = face_cascade.detectMultiScale(gray, 1.3, 5) 
                 ROI = gray[x:x + w, y:y + h]
                 ROI = cv2.resize(ROI, (self.img_size, self.img_size), interpolation=cv2.INTER_LINEAR) 
                 label,prob = self.model.predict(ROI)  
                 if prob >0.7:   
                     show_name = name_list[label]
                 else:
                     show_name = 'Stranger'
                 cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)  
                 frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)  
             cv2.imshow("Camera", frame) 

        cameraCapture.release()  
        cv2.destroyAllWindows()  
    def detect_video(self): 
        face_cascade = cv2.CascadeClassifier('F:\\Program Files\\Python\\Python36\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt.xml')
        
        name_list = read_name_list('F:\\File_Python\\Python_example\\face_recognition_name\\After_cut_picture')
        video = cv2.VideoCapture(video_path)  ### TODO: will video path other than 0 be used?
        success, frame = video.read() 
        accum_time = 0
        curr_fps = 0
        fps = "FPS: ??"  #fps = "FPS: ??"
        prev_time = timer()
        
        while success and cv2.waitKey(1) == -1: 
            success, frame = video.read() 
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
            
            curr_time = timer()
            exec_time = curr_time - prev_time
            prev_time = curr_time
            accum_time = accum_time + exec_time
            curr_fps = curr_fps + 1    #1   
            if accum_time > 1:
                accum_time = accum_time - 1   #1
                fps = "FPS: " + str(curr_fps)
                curr_fps = 0   #0     
                
            for (x, y, w, h) in faces:  
                 ROI = gray[x:x + w, y:y + h]
                 ROI = cv2.resize(ROI, (self.img_size, self.img_size), interpolation=cv2.INTER_LINEAR) #cv2.INTER_LINEAR图像尺寸变换的方法，默认的双线性插值
                 label,prob = self.model.predict(ROI) 
                 
                 if prob >0.7:    
                     show_name = name_list[label]
                 else:
                     show_name = 'Stranger'
                 cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)  
                 frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)  
            cv2.namedWindow("result", cv2.WINDOW_NORMAL)
            cv2.imshow("result",frame)
        
if __name__ == '__main__':
    camera = Camera_reader()
    camera.build_camera()
#     video_path='F:/File_Python/Python_example/YOLOv3_use_TF/RunMan1.mp4'
#     camera.detect_video()