PytorchOCR/tools/rec_infer_att_test.py

# -*- coding: utf-8 -*-
# @Time    : 2020/6/16 10:57
# @Author  : zhoujun
import os
import sys
import pathlib

# 将 torchocr路径加到python陆经里
__dir__ = pathlib.Path(os.path.abspath(__file__))

import numpy as np

sys.path.append(str(__dir__))
sys.path.append(str(__dir__.parent.parent))

import torch
from torch import nn
from torchocr.networks import build_model
from torchocr.datasets.RecDataSet import RecDataProcess
from torchocr.utils import CTCLabelConverter


class RecInfer:
    def __init__(self, model_path, batch_size=16):
        ckpt = torch.load(model_path, map_location='cpu')
        cfg = ckpt['cfg']
        self.model = build_model(cfg['model'])
        state_dict = {}
        for k, v in ckpt['state_dict'].items():
            state_dict[k.replace('module.', '')] = v
        self.model.load_state_dict(state_dict)

        self.device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()

        self.process = RecDataProcess(cfg['dataset']['train']['dataset'])
        self.converter = CTCLabelConverter(cfg['dataset']['alphabet'])
        # self.converter = CTCLabelConverter("C:\\Users\\Administrator\\Desktop\\OCR_pytorch\\PytorchOCR1\\char_std_7782.txt")
        self.batch_size = batch_size

    def predict(self, imgs):
        # 预处理根据训练来
        if not isinstance(imgs,list):
            imgs = [imgs]
        imgs = [self.process.normalize_img(self.process.resize_with_specific_height(img)) for img in imgs]
        widths = np.array([img.shape[1] for img in imgs])
        idxs = np.argsort(widths)
        txts = []
        for idx in range(0, len(imgs), self.batch_size):
            batch_idxs = idxs[idx:min(len(imgs), idx+self.batch_size)]
            batch_imgs = [self.process.width_pad_img(imgs[idx], imgs[batch_idxs[-1]].shape[1]) for idx in batch_idxs]
            batch_imgs = np.stack(batch_imgs)
            tensor = torch.from_numpy(batch_imgs.transpose([0,3, 1, 2])).float()
            tensor = tensor.to(self.device)
            with torch.no_grad():
                out = self.model(tensor)
                # print(out)
                # out[1] 为最后输出
                out = out[1]
                out = out.softmax(dim=2)
            out = out.cpu().numpy()
            txts.extend([self.converter.decode(np.expand_dims(txt, 0)) for txt in out])
        #按输入图像的顺序排序
        idxs = np.argsort(idxs)
        out_txts = [txts[idx] for idx in idxs]
        return out_txts


def init_args():
    import argparse
    parser = argparse.ArgumentParser(description='PytorchOCR infer')
    # parser.add_argument('--model_path', required=True, type=str, help='rec model path')
    parser.add_argument('--model_path', required=False, type=str,
        default="C:\\Users\\Administrator\\Desktop\\OCR_pytorch\\PytorchOCR1\\best.pth", help='rec model path')
    # parser.add_argument('--img_path', required=True, type=str, help='img path for predict')
    parser.add_argument('--img_path', required=False, type=str,
        default="C:\\Users\\Administrator\\Desktop\\OCR_pytorch\\PytorchOCR1\\test_image/Snipaste_2023-08-21_17-07-58.jpg", help='img path for predict')
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    import cv2
    import re
    from unicodedata import normalize
    # args = init_args()
    # img = cv2.imread(args.img_path)
    model_path = '/data2/znj/PytorchOCR/tools/output/CRNN/checkpoint_resnet3/best.pth'
    model = RecInfer(model_path)
    cnt = 0
    right_cnt = 0
    error_cnt = 0
    with open("/data2/znj/ocr_data/image2.txt",mode='r') as f:
        for line in f.readlines():
            line = line.strip()
            # line_split = line.split(" ")
            # line_split = re.split(" ",line,maxsplit=2)
            iamge_path,line_split2 = re.split(" ",line,maxsplit=1)
            text, box = re.split(" \[\[",line_split2,maxsplit=1)
            box = '[['+box
            # if len(line_split)==3 :
            try:
                if True :
                    # iamge_path,text,box = line_split
                    img = cv2.imread(iamge_path)
                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

                    bbox = eval(box)
                    x1 = int(min([i[0] for i in bbox]))
                    x2 = int(max([i[0] for i in bbox]))
                    y1 = int(min([i[1] for i in bbox]))
                    y2 = int(max([i[1] for i in bbox]))
                    img = img[y1:y2, x1:x2]

                    out = model.predict(img)
                    out = out[0][0][0]
                    cnt += 1
                    # if out==text:
                    if normalize('NFKD', out)==normalize('NFKD', text):
                        right_cnt += 1
                    else:
                        if not re.search("\s",out) and len(out)>0:
                            print(iamge_path+" "+text+" "+box+" rec_res:"+out)
                        # print(out+" -> "+ text)
            except:
                error_cnt += 1
                pass
            # if cnt>= 500000:
            #     break
            if cnt-right_cnt>= 400000:
                break

    print('count_num:',cnt)
    print('right_num:',right_cnt)
    print('right_%:',right_cnt/cnt)
    print('process_error_cnt:',error_cnt)



    pass