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 VideoPeopleDetection():
    time_reference = datetime.datetime.now()
    counter_frame = 0
    processed_fps = 0


    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 transpic(self, img):
    #     return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
   
           
    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)
       

        # Loop through each detected object and count the people
            accuracy = 0
            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)
            resText=f'检测到{num_people}人'
            # print(num_people)
            return jpeg.tobytes(), resText

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