使用fer数据集，卷积神经网络实现人脸表情识别
python与CNN实现，有GUI界面，支持摄像头实时识别和手动选取图片识别，GUI界面选取图片进行识别实现效果如下图
摄像头实时读取并识别表情结果如下图：
GUI界面代码如下：
import tkinter as tkfrom tkinter.filedialog import *from tkinter import ttkimport cv2from PIL import Image, ImageTkimport timefrom Picture_Expression import demoimport numpy as npimport tensorflow as tfclass Surface(ttk.Frame):pic_path = ""viewhigh = 300viewwide = 300update_time = 0thread = Nonethread_run = Falsecamera = Nonedef __init__(self, win):ttk.Frame.__init__(self, win)frame_left = ttk.Frame(self)frame_right1 = ttk.Frame(self)frame_right2 = ttk.Frame(self)win.title("表情识别")win.state("zoomed")self.pack(fill=tk.BOTH, expand=tk.YES, padx="5", pady="5")frame_left.pack(side=LEFT,expand=1,fill=BOTH)frame_right1.pack(side=TOP,expand=1,fill=tk.Y)frame_right2.pack(side=RIGHT,expand=0)ttk.Label(frame_left, text='原图：').pack(anchor="nw")from_pic_ctl = ttk.Button(frame_right2, text="打开表情图片", width=20, command=self.from_pic)self.image_ctl = ttk.Label(frame_left)self.image_ctl.pack(anchor="nw")self.roi_ctl = ttk.Label(frame_right1)self.roi_ctl.grid(column=0, row=1, sticky=tk.W)ttk.Label(frame_right1, text='识别结果：').grid(column=0, row=2, sticky=tk.W)self.r_ctl = ttk.Label(frame_right1, text="")self.r_ctl.grid(column=0, row=3, sticky=tk.W)from_pic_ctl.pack(anchor="se", pady="5")def get_imgtk(self, img_bgr):img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)im = Image.fromarray(img)imgtk = ImageTk.PhotoImage(image=im) # 文本框里插入图片wide = imgtk.width()high = imgtk.height()if wide > self.viewwide or high > self.viewhigh:wide_factor = self.viewwide / widehigh_factor = self.viewhigh / highfactor = min(wide_factor, high_factor)wide = int(wide * factor)if wide <= 0:wide = 1high = int(high * factor)if high <= 0:high = 1imgtk = ImageTk.PhotoImage(image=im)return imgtkdef show_roi(self, text):if text :self.r_ctl.configure(text=str(text))self.update_time = time.time()elif self.update_time + 8 < time.time():self.roi_ctl.configure(state='disabled')self.r_ctl.configure(text="")def from_pic(self):self.thread_run = Falseself.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg")])img = cv2.imread(self.pic_path )img_1 = cv2.imdecode(np.fromfile(self.pic_path, dtype=np.uint8), cv2.IMREAD_COLOR)print(self.pic_path)if self.pic_path:self.imgtk = self.get_imgtk(img_1) # 将读取的图片插入调整大小并显示self.image_ctl.configure(image=self.imgtk)text = demo(img_1,FLAGS.checkpoint_dir, FLAGS.show_box)self.show_roi(text)print("run end")def close_window():print("destroy")if surface.thread_run :surface.thread_run = Falsesurface.thread.join(2.0)win.destroy()if __name__ == '__main__':win=tk.Tk()surface = Surface(win)win.protocol('WM_DELETE_WINDOW', close_window)win.mainloop()
整个系统实现了从训练，到识别的整体流程，想要代码，可加v：mql13148
