##################################################
#1. webcam에서 얼굴을 인식합니다.
#2. 얼굴일 확률이 97% 이상이고 영역이 15000 이상인 이미지를 서버에 전송
##################################################
import tkinter as tk
import tkinter.font
import tkinter.messagebox
import threading
import torch
import numpy as np
import cv2
import asyncio
import websockets
import json
import os
import timeit
import base64
import time

from PIL import Image, ImageTk
from io import BytesIO
import requests

from models.mtcnn import MTCNN

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Running on device: {}'.format(device))

mtcnn = MTCNN(keep_all=True, post_process=True, device=device)

uri = 'ws://169.56.95.131:8765'

class Client(tk.Frame):
    def __init__(self, parent, *args, **kwargs):
        tk.Frame.__init__(self, parent, *args, **kwargs)

        # URI
        self.uri = 'ws://169.56.95.131:8765'

        # Pytorch Model
        self.device = device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        self.mtcnn = MTCNN(keep_all=True, device=device)

        # OpenCV
        self.cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
        self.cam_width = 640
        self.cam_height = 480
        self.cap.set(3, self.cam_width)
        self.cap.set(4, self.cam_height)

        # tkinter GUI
        self.parent.title("출석시스템")
        self.width = 740
        self.height = 640
        self.parent = parent
        self.parent.geometry("%dx%d+100+100" % (self.width, self.height))
        self.pack()
        self.create_widgets()
        
        # Event loop and Thread
        self.event_loop = asyncio.new_event_loop()
        self.thread = threading.Thread(target=self.mainthread)
        self.thread.start()

    def create_widgets(self):
        image = np.zeros([self.cam_height, self.cam_width, 3], dtype=np.uint8)
        image = Image.fromarray(image)
        image = ImageTk.PhotoImage(image)

        font = tk.font.Font(family="맑은 고딕", size=15)
        
        self.alert = tk.Label(self, text="출석시스템", font=font)
        self.alert.grid(row=0, column=0, columnspan=20)
        self.label = tk.Label(self, image=image)
        self.label.grid(row=1, column=0, columnspan=20)

        self.log = tk.Text(self)
        self.log.grid(row=2, column=0, columnspan=20)

        
        self.quit = tk.Button(self, text="나가기", fg="red", command=self.stop)
        self.quit.grid(row=5, column=10)

    
    def detect_face(self, frame):
        results = self.mtcnn.detect(frame)
        faces = self.mtcnn(frame, return_prob = False)
        image_list = []
        face_list = []
        if results[1][0] == None:
            return [], []
        for box, face, prob in zip(results[0], faces, results[1]):
            if prob < 0.97:
                continue
            # for debug
            # print('face detected. prob:', prob)
            x1, y1, x2, y2 = box
            if (x2-x1) * (y2-y1) < 15000:
                # 얼굴 해상도가 너무 낮으면 무시
                self.alert.config(text= "카메라에 더 가까이 접근해주세요.", fg="red")
                self.alert.update()
                continue
            image = frame
            image_list.append(image)
            # MTCNN 데이터 저장
            face_list.append(face.numpy())
        return face_list, image_list

    def mainthread(self):
        t = threading.currentThread()
        asyncio.set_event_loop(self.event_loop)
        while getattr(t, "do_run", True):
            ret, frame = self.cap.read()

            # model에 이용하기 위해 convert
            converted = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            face_list, image_list = self.detect_face(converted)

            # show image
            image = Image.fromarray(converted)
            image = ImageTk.PhotoImage(image)
            self.label.configure(image=image)
            self.label.image = image # kind of double buffering

            # 얼굴이 인식되면 요청
            if face_list:
                self.event_loop.run_until_complete(self.send_face(face_list, image_list))

                
    async def wait(self, n):
        await asyncio.sleep(n)
        
    async def send_face(self, face_list, image_list):
        try:
            async with websockets.connect(uri) as websocket:
                for face, image in zip(face_list, image_list):
                    #type: np.float32
                    send = json.dumps({'action': 'verify', 'MTCNN': face.tolist()})
                    await websocket.send(send)
                    recv = await websocket.recv()
                    data = json.loads(recv)
                    if data['status'] == 'success':
                        # 성공
                        self.log.insert(tkinter.CURRENT, data['student_id'] + 'is attend')
                        self.log.insert(tkinter.CURRENT, '\n')
                    else:
                        self.log.insert(tkinter.CURRENT, 'verification failed:' + data['status'])
                        self.log.insert(tkinter.CURRENT, '\n')
                        if data['status'] == 'failed':
                            send = json.dumps({'action': 'save_image', 'image': image.tolist()})
        except Exception as e:
            self.log.insert(tkinter.CURRENT, e)
            self.log.insert(tkinter.CURRENT, '\n')

    def stop(self):
        self.thread.do_run = False
        # self.thread.join() # there is a freeze problem
        self.event_loop.close()
        self.cap.release()
        self.parent.destroy()
        

if __name__ == '__main__':
    root = tk.Tk()
    Client(root)
    root.mainloop()