HYTOS

from flask import Flask, jsonify, request

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__)

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(text_craft.get_text_box_batch, batch_size=64)

except ImportError as ex:

    ex

    pass

@app.route('/')

def index():

    return 'Hello Flask'

@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 = [[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[0]

        return jsonify({'text_box_list': boxes_list})

if __name__ == '__main__':

    app.run(debug=False)

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