test.py

import argparse
import cv2
import os
import torch
import numpy as np
from lprnet import build_lprnet
from load_data import CHARS
import time

def infer(args, image, model):
    img = cv2.imread(image)
    height, width, _ = img.shape
    if height != 24 or width != 94:
        img = cv2.resize(img, (94, 24))
    img = img.astype('float32')
    img -= 127.5
    img *= 0.0078125
    img = np.transpose(img, (2, 0, 1))

    with torch.no_grad():
        img = torch.from_numpy(img).unsqueeze(0).to(args.device)
        preb = model(img)
        preb = preb.detach().cpu().numpy().squeeze()
    preb_label = []
    for j in range(preb.shape[1]):
        preb_label.append(np.argmax(preb[:, j], axis=0))
    no_repeat_blank_label = []
    pre_c = preb_label[0]
    if pre_c != len(CHARS) - 1:
        no_repeat_blank_label.append(pre_c)
    for c in preb_label:
        if (pre_c == c) or (c == len(CHARS) - 1):
            if c == len(CHARS) - 1:
                pre_c = c
            continue
        no_repeat_blank_label.append(c)
        pre_c = c
    return ''.join(list(map(lambda x: CHARS[x], no_repeat_blank_label)))

def validation(args):
    model = build_lprnet(len(CHARS))
    model.load_state_dict(torch.load(args.model, map_location=args.device))
    model.to(args.device)

    if os.path.isdir(args.imgpath):
        images = os.listdir(args.imgpath)
        count = 0
        time1 = time.perf_counter()
        for image in images:
            result = infer(args, os.path.join(args.imgpath, image), model)
            if result == image[:-4]:
                count += 1
            print('Image: ' + image + ' recongise result: '+ result)
        time2 = time.perf_counter()
        print('accuracy rate:', count / len(images))
        print('average time', (time2 - time1)/count*1000)
    else:
        result = infer(args, args.imgpath, model)
        print('Image: ' + args.imgpath + ' recongise result: '+ result)
    
    if args.export_onnx:
        print('export pytroch model to onnx model...')
        onnx_input = torch.randn(1, 3, 24, 94, device=args.device)
        torch.onnx.export(
            model,
            onnx_input,
            'LPRNet.onnx',
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={'input': {0: 'batch'}, 'output': {0: 'batch'}} if args.dynamic else None,
            opset_version=12,
            )
    return res
    

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='parameters to vaildate net')
    parser.add_argument('--model', default='model/lprnet.pth', help='model path to vaildate')
    # parser.add_argument('--model', default='weights/Final_LPRNet_model.pth', help='model path to vaildate')
    # parser.add_argument('--imgpath', default='imgs/川JK0707.jpg', help='the image path')
    parser.add_argument('--imgpath', default='/code/lpr_ori/data/test', help='the image path')
    parser.add_argument('--device', default='cuda', help='Use cuda to vaildate model')
    parser.add_argument('--export_onnx', default=False, help='export model to onnx')
    parser.add_argument('--dynamic', default=False, help='use dynamic batch size')
    args = parser.parse_args()

    result = validation(args)