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from __future__ import division

from __future__ import absolute_import

from __future__ import print_function





import os

import argparse

import torch

import torch.nn as nn

import torch.backends.cudnn as cudnn

from torch.autograd import Variable



import torch.npu

import cv2

import time

import numpy as np

from PIL import Image



from data.config import cfg

from pyramidbox import build_net

from utils.augmentations import to_chw_bgr



parser = argparse.ArgumentParser(description='pyramidbox demo')

parser.add_argument('--save_dir',

                    type=str, default='tmp/',

                    help='Directory for detect result')

parser.add_argument('--model',

                    type=str, default='/home/wch/Pyramidbox.pytorch-master/weights/pyramidbox_120000_99.02.pth',

                    help='trained model')

parser.add_argument('--thresh',

                    default=0.4, type=float,

                    help='Final confidence threshold')

args = parser.parse_args()





if not os.path.exists(args.save_dir):

    os.makedirs(args.save_dir)



use_npu = torch.npu.is_available()



if use_npu:

    device=f'npu:0'

    torch.npu.set_device(device)

    



def detect(net, img_path, thresh):

    img = Image.open(img_path)

    if img.mode == 'L':

        img = img.convert('RGB')



    img = np.array(img)

    height, width, _ = img.shape

    max_im_shrink = np.sqrt(

        1200 * 1100 / (img.shape[0] * img.shape[1]))

    image = cv2.resize(img, None, None, fx=max_im_shrink,

                       fy=max_im_shrink, interpolation=cv2.INTER_LINEAR)



    x = to_chw_bgr(image)

    x = x.astype('float32')

    x -= cfg.img_mean

    x = x[[2, 1, 0], :, :]

    #x = x * cfg.scale



    x = Variable(torch.from_numpy(x).unsqueeze(0))

    if use_npu:

        x = x.npu()

    t1 = time.time()

    y = net(x)

    detections = y.data

    scale = torch.Tensor([img.shape[1], img.shape[0],

                          img.shape[1], img.shape[0]])



    for i in range(detections.size(1)):

        j = 0

        while detections[0, i, j, 0] >= thresh:

            score = detections[0, i, j, 0]

            pt = (detections[0, i, j, 1:] * scale).cpu().numpy().astype(int)

            left_up, right_bottom = (pt[0], pt[1]), (pt[2], pt[3])

            j += 1

            cv2.rectangle(img, left_up, right_bottom, (0, 0, 255), 2)

            conf = "{:.2f}".format(score)

            text_size, baseline = cv2.getTextSize(

                conf, cv2.FONT_HERSHEY_SIMPLEX, 0.3, 1)

            p1 = (left_up[0], left_up[1] - text_size[1])

            cv2.rectangle(img, (p1[0] - 2 // 2, p1[1] - 2 - baseline),

                          (p1[0] + text_size[0], p1[1] + text_size[1]), [255, 0, 0], -1)

            cv2.putText(img, conf, (p1[0], p1[

                        1] + baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1, 8)



    t2 = time.time()

    print('detect:{} timer:{}'.format(img_path, t2 - t1))



    cv2.imwrite(os.path.join(args.save_dir, os.path.basename(img_path)), img)





if __name__ == '__main__':

    net = build_net('test', cfg.NUM_CLASSES)

    net.load_state_dict(torch.load(args.model,map_location=lambda storage, loc: storage))

    net.eval()



    if use_npu:

        net.npu()

        cudnn.benckmark = True



    img_path = './img'

    img_list = [os.path.join(img_path, x)

                for x in os.listdir(img_path) if x.endswith('jpg')]

    for path in img_list:

        detect(net, path, args.thresh)