camera_p2pnet.py

import cv2
      
                
import json

import argparse
import datetime
import random
import time
from pathlib import Path

import torch
import torchvision.transforms as standard_transforms
import numpy as np
import matplotlib.pyplot as plt
from threading import Thread, Event
from PIL import Image

from crowd_datasets import build_dataset
from engine import *
from models import build_model
import os
import warnings


def get_args_parser():
    parser = argparse.ArgumentParser('Set parameters for P2PNet evaluation', add_help=False)
    
    # * Backbone
    parser.add_argument('--backbone', default='vgg16_bn', type=str,
                        help="name of the convolutional backbone to use")

    parser.add_argument('--row', default=2, type=int,
                        help="row number of anchor points")
    parser.add_argument('--line', default=2, type=int,
                        help="line number of anchor points")

    parser.add_argument('--output_dir', default='',
                        help='path where to save')
    parser.add_argument('--weight_path', default='',
                        help='path where the trained weights saved')

    parser.add_argument('--gpu_id', default=0, type=int, help='the gpu used for evaluation')

    return parser


parser = argparse.ArgumentParser('P2PNet evaluation script', parents=[get_args_parser()])
args = parser.parse_args()

device = torch.device('cuda')
    # get the P2PNet
model = build_model(args)
    
    # move to GPU
model.to(device)
    
    
    # load trained model
    #using Args

    #Loading file directly
checkpoint = torch.load(Path('/home/zaki/Documents/Master/Code/image/P2PNet/CrowdCounting-P2PNet-main(mycode)/weights/SHTechA.pth'), map_location='cpu')
model.load_state_dict(checkpoint['model'])


    # convert to eval mode
model.eval()
    # create the pre-processing transform
transform = standard_transforms.Compose([standard_transforms.ToTensor(),standard_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])




class VideoCamera(object):
    def __init__(self,fileName):
        # Using OpenCV to capture from device 0. If you have trouble capturing
        # from a webcam, comment the line below out and use a video file
        # instead.
        if (fileName ==''):
            self.video = cv2.VideoCapture(0)
        else:  
            self.video = cv2.VideoCapture(fileName)   
#        self.video = cv2.resize(self.video,(840,640))
        # If you decide to use video.mp4, you must have this file in the folder
        # as the main.py.
        # self.video = cv2.VideoCapture('video.mp4')
    
    def __del__(self):
        self.video.release()
    
    def get_frame(self):
        
     
     
     
        cap =self.video 
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        #fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')



        ret, frame = cap.read()
        print(frame.shape)

        '''out video'''

        scale_factor = 0.4

        width = frame.shape[1] #output size
        height = frame.shape[0] #output size
    #out = cv2.VideoWriter('./demo.avi', fourcc, 30, (width, height))
    #out = cv2.VideoWriter('./demo.avi', fourcc, 30, (1280, 1280))

        while True:
            try:
                ret, frame = cap.read()


                new_width = width // 128 * 128
                new_height = height // 128 * 128
            
            #frame = imutils.resize(frame,width=int(new_width),height=int(new_height))


                scale_factor = 0.4
                frame = cv2.resize(frame, (0, 0), fx=scale_factor, fy=scale_factor)
                img_raw= frame.copy()
                ori_img = frame.copy()
            
        
            except:
                print("Test End")
                cap.release()
                break
            


            frame = frame.copy()

            # pre-proccessing
            img = transform(frame)
            samples = torch.Tensor(img).unsqueeze(0)
            samples = samples.to(device)

            with torch.no_grad():

            # run inference
                outputs = model(samples)
                outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]

                outputs_points = outputs['pred_points'][0]

                threshold = 0.5
            # filter the predictions
                points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
                predict_cnt = int((outputs_scores > threshold).sum())

                outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]


                outputs_points = outputs['pred_points'][0]
                print("Number of persons in the picture is: ",predict_cnt)

            # draw the predictions
                size = 2
            
                img_to_draw = img_raw
            
                for p in points:
                    img_to_draw = cv2.circle(img_raw , (int(p[0]), int(p[1])), size, (0, 0, 255), -1)
        
            
            
            #res = np.vstack((ori_img, img_to_draw))

            #cv2.putText(res, "Count:" + str(predict_cnt), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        
        
            # save the visualized image
            #cv2.imwrite('./demo.jpg', res)
                '''write in out_video'''
            #res = cv2.resize(res, (1280,1280))
            #out.write(res)

            
                img_to_draw = cv2.resize(img_to_draw, (1500,720))
                cv2.putText(img_to_draw, "Count:" + str(predict_cnt), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
         
       
       
       
       
                ret, jpeg = cv2.imencode('.jpg', img_to_draw)
        
        
        
        
                return jpeg.tobytes()