import datetime
import os
import time
import ffmpeg
import torch
import cv2
import numpy as np
from multiprocessing import Process, Manager
from threading import Thread
from read_data import LoadImages, LoadStreams
import torch.backends.cudnn as cudnn

class FaceDetection():
    time_reference = datetime.datetime.now()
    counter_frame = 0
    processed_fps = 0
    global num_people
    num_people = 100
    global accuracy
    accuracy = 1

    def __init__(self,video_path=None, model=None):
        
        self.model = model
        self.classes = self.model.names
            
        self.frame = [None] 

        if video_path is not None:
            self.video_name = video_path
        else:
            self.video_name = 'vid2.mp4'  # A default video file
            
        
        self.dataset = LoadImages(self.video_name)
 
    def use_webcam(self, source):
        # self.dataset.release()  # Release any existing video capture
        # self.cap = cv2.VideoCapture(0)  # Open default webcam
        # print('use_webcam')
        source = source
        self.imgsz = 640
        cudnn.benchmark = True
        self.dataset = LoadStreams(source, img_size=self.imgsz)
 
    def class_to_label(self, x):
        return self.classes[int(x)]
    

           
    def get_frame(self):
 
        for im0s in self.dataset:
            # print(self.dataset.mode)
            # print(self.dataset)
            if self.dataset.mode == 'stream':
                img = im0s[0].copy()
            else:
                img = im0s.copy()

            results = self.model(img, size=640)
       
            num_people = 0
            bgr = (0, 255, 0)
       
            for obj in results.xyxy[0]:

                
                if obj[-1] == 0:  # 0 is the class ID for 'person'

                    # Draw bounding boxes around people
                    xmin, ymin, xmax, ymax = map(int, obj[:4])
                    global accuracy
                    accuracy = obj[4]
                    if (accuracy > 0.5):
                        num_people += 1
                        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
                        cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin),
                                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
           
                #cv2.putText(img, f'FPS: {int(self.cap.get(cv2.CAP_PROP_FPS))}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                # cv2.putText(img, f'People: {num_people}', (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                # cv2.putText(img, f'Processed FPS: {VideoPeopleDetection.processed_fps}', (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)


        # Draw the number of people on the frame and display it

   
            ret, jpeg = cv2.imencode(".jpg", img)
            # print(jpeg.shape)
            

            return jpeg.tobytes()

def time_synchronized():
    # pytorch-accurate time
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()