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/DTI_PID/DTI_PID/res/Result/*.txt

/DTI_PID/PID.db

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            ocr_image = imgGray.copy()  # np.ones(imgGray.shape, np.uint8) * 255

            # get text box original way

            # using craft

            rects = []

        img_path = os.path.join(project.getTempPath(), 'OCR_CRAFT_{}.png'.format(app_doc_data.imgName))

        if not web:

        else:

            app_web_service = AppWebService()

            boxes = app_web_service.request_text_box(ocr_image, img_path, score_path)

import torch.nn as nn

import torch.nn.functional as F

#from .basenet.vgg16_bn import vgg16_bn, init_weights

class double_conv(nn.Module):

import os

import numpy as np

import cv2

#import imgproc

# borrowed from https://github.com/lengstrom/fast-style-transfer/blob/master/src/utils.py

import cv2

from skimage import io

import numpy as np

import json

import zipfile

#from CRAFT_pytorch_master.craft import CRAFT

from collections import OrderedDict

def copyStateDict(state_dict):

    return boxes, polys, ret_score_text

    if img.shape[0] == 2: img = img[0]

    if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)

    if img.shape[2] == 4:   img = img[:,:,:3]

    #image = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)

    parser = argparse.ArgumentParser(description='CRAFT Text Detection')

    parser.add_argument('--text_threshold', default=0.7, type=float, help='text confidence threshold')

    parser.add_argument('--low_text', default=0.4, type=float, help='text low-bound score')

    parser.add_argument('--link_threshold', default=0.4, type=float, help='link confidence threshold')

import numpy as np

import sys, os

app = Flask(__name__)

@app.route('/')

@app.route('/text_box', methods=['POST'])

def text_box():

    r = request

    nparr = np.fromstring(r.data, np.uint8)

    return jsonify({'text_box': boxes})