HYTOS

from flask import Flask, jsonify, request, render_template

import cv2

import numpy as np

import sys, os

import json, base64

# craft

sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master')

# service streamer

sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + '\\service_streamer_master')

# deep ocr

#sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + '\\deep_text_recognition_benchmark_master')

app = Flask(__name__)

def recognition(params=None):

    if params[0] == 'Text Area':

        res = text_craft.get_text_box_batch(params[1:])

        return ['Text Area', res]

    elif params[0] == 'Symbol':

        pass

try:

    #from model import get_prediction, batch_prediction

    import text_craft

    from service_streamer import ThreadedStreamer

    # for error at 3.8

    #import ctypes

    #ctypes.cdll.LoadLibrary('caffe2_nvrtc.dll')

    streamer = ThreadedStreamer(recognition, batch_size=64)

except ImportError as ex:

    ex

    pass

@app.route('/')

def index():

    return 'Hello ID2'

@app.route('/requset_license_key/')

def requset_license_key():

    return render_template('license.html')

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

def gen_key():

    import base64

    if request.method == 'POST':

        r = request

        key = 'Image Drawing to Intelligent Drawing'

        pw = r.form['Authorization']

        clear = r.form['Computer_Name']

        if pw != 'admin':

            return 'Invalid Authorization'

        enc = []

        for i in range(len(clear)):

            key_c = key[i % len(key)]

            enc_c = (ord(clear[i]) + ord(key_c)) % 256

            enc.append(enc_c)

        new_key = base64.urlsafe_b64encode(bytes(enc))

        return 'License Key for ' + clear + ' : ' + new_key.decode('utf-8')

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

def symbol_box():

    if request.method == 'POST':

        r = request

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

        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        boxes = text_craft.get_text_box(img, img_path=None, score_path=None, trained_model=os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth')

        return jsonify({'symbol_box': boxes})

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

def text_box():

    if request.method == 'POST':

        r = request

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

        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        #img = img.reshape(1, -1)

        boxes = text_craft.get_text_box(img, img_path=None, score_path=None, trained_model=os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth')

        return jsonify({'text_box': boxes})

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

def stream_text_box():

    if request.method == 'POST':

        r = request

        str_imgs = json.loads(r.data)

        imgs = []

        for str_img in str_imgs:

            str_img = base64.b64decode(str_img)

            nparr = np.fromstring(str_img, np.uint8)

            img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

            imgs.append(img)

        boxes_list = []

        '''

        for img in imgs:

            # faster

            #boxes = streamer.predict([[img, None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_ic15_20k.pth']])

            # More accurate

            boxes = streamer.predict([[img, None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth']])

            boxes_list.append(boxes[0])

        '''

        '''

        infos = []

        for img in imgs:

            infos.append([img, None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth'])

        boxes = streamer.predict(infos)

        boxes_list = boxes

        '''

        infos = ['Text Area', [None, None, os.path.dirname(os.path.realpath(__file__)) + '\\CRAFT_pytorch_master\\weights\\craft_mlt_25k.pth', imgs]]

        boxes = streamer.predict(infos)

        boxes_list = boxes[1][0]

        return jsonify({'text_box_list': boxes_list})

if __name__ == '__main__':

    app.run(debug=False)

    #app.run(host='0,0,0,0')