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

    
def use_webcam(source, model):

    source = source
    imgsz = 640
    cudnn.benchmark = True
    dataset = LoadStreams(source, img_size=imgsz)
        

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

        results = model(img, size=640)
       

        # Loop through each detected object and count the people
        num_people = 0
        bgr = (0, 255, 0)

       
        for obj in results.xyxy[0]:
                # xmin, ymin, xmax, ymax = map(int, obj[:4])
                # accuracy = obj[4]
                # if (accuracy > 0.5):
                 
                #     cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
                #     cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin),
                #                     cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                
            if obj[-1] == 0:  # 0 is the class ID for 'person'

                    # Draw bounding boxes around people
                xmin, ymin, xmax, ymax = map(int, obj[:4])
                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)


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

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

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