VerificationCode/border_recognize/bdr_interface.py

import base64
import json
import logging
import os
import time
import traceback
from glob import glob
import cv2
import numpy as np
from PIL import Image

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
from flask import Flask, request
from border_recognize.model import u_net_drag
from border_recognize.inference_drag import recognize
from utils import pil_resize, np2bytes, request_post, bytes2np

tf.compat.v1.disable_eager_execution()
sess = tf.compat.v1.Session(graph=tf.Graph())


# 接口配置
app = Flask(__name__)


@app.route('/bdr', methods=['POST'])
def bdr():
    start_time = time.time()
    logging.info("into bdr_interface bdr")
    try:
        # 接收网络数据
        if not request.form:
            logging.info("bdr no data!")
            return json.dumps({"data": "", "success": 0})
        data = request.form.get("data")
        logging.info("bdr_interface get data time" + str(time.time()-start_time))

        # 加载模型
        bdr_model = globals().get("global_bdr_model")
        if bdr_model is None:
            print("=========== init bdr model ===========")
            bdr_model = BdrModels().get_model()
            globals().update({"global_bdr_model": bdr_model})

        # 数据转换
        data = base64.b64decode(data)
        image_np = bytes2np(data)
        # 预测
        w = recognize(image_np, bdr_model, sess)
        return json.dumps({"data": w, "success": 1})
    except:
        traceback.print_exc()
        return json.dumps({"data": "", "success": 0})
    finally:
        logging.info("bdr interface finish time " + str(time.time()-start_time))


class BdrModels:
    def __init__(self):
        # python文件所在目录
        _dir = os.path.abspath(os.path.dirname(__file__))

        # detect
        model_path = _dir + "/models/drag_f1_0.42.h5"
        image_shape = (128, 256, 3)
        with sess.as_default():
            with sess.graph.as_default():
                self.model = u_net_drag(input_shape=image_shape)
                self.model.load_weights(model_path)

    def get_model(self):
        return self.model


def test_bdr_model(from_remote=True):
    paths = glob("D:/Project/captcha/data/test/yolo_18.jpg")
    for file_path in paths:
        img_np = cv2.imread(file_path)
        h, w = img_np.shape[:2]
        file_bytes = np2bytes(img_np)
        file_base64 = base64.b64encode(file_bytes)

        if from_remote:
            file_json = {"data": file_base64}
            # _url = "http://192.168.2.102:17000/ocr"
            _url = "http://127.0.0.1:17000/bdr"
            result = json.loads(request_post(_url, file_json))
            if result.get("success"):
                w = int(result.get("data"))
                print("w", w)
                img_new = np.concatenate([img_np[:, w:, :], img_np[:, :w, :]], axis=1)
                cv2.imshow("img_np", img_np)
                cv2.imshow("img_new", img_new)
                cv2.waitKey(0)
            else:
                print("failed!")


if __name__ == "__main__":
    # app.run(host='127.0.0.1', port=17000, debug=False)
    test_bdr_model()