import base64
import multiprocessing as mp
import os
# os.environ['TF_XLA_FLAGS'] = '--tf_xla_cpu_global_jit'
import sys
import time
import traceback
from multiprocessing.context import Process
import multiprocessing
from flask import Flask, jsonify
from flask import request
import logging
# from table_line import *
import cv2
import numpy as np
import tensorflow as tf

from table_line import get_best_predict_size, table_line, get_points, get_split_line, get_points_row, get_points_col, \
    delete_close_points, fix_outline, get_bbox, get_outline_point, table_net

app = Flask(__name__)
app.config['JSON_AS_ASCII'] = False


logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def log(msg):
    """
    @summary:打印信息
    """
    logger.info(msg)


# @app.before_first_request
# def init():


@app.route('/otr', methods=['POST'])
def otr():
    start_time = time.time()
    if request.method == "POST":
        # 检测是否有数据
        if not request.data:
            return 'no data'

        print("child process ", os.getpid(), request.data[-6:])

        img_data = base64.b64decode(request.data)
        # points_and_lines = pool.apply(table_detect, (img_data,))
        points_and_lines = table_detect(img_data)
        return points_and_lines


flag = 0
model_path = "models/table-line.h5"
def table_detect(img_data):
    start_time = time.time()
    try:
        # 二进制数据流转np.ndarray [np.uint8: 8位像素]
        img = cv2.imdecode(np.frombuffer(img_data, np.uint8), cv2.IMREAD_COLOR)
        # cv2.imwrite("111111.jpg", img)
        # 将bgr转为rbg
        image_np = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

        # 选择与图片最接近分辨率，以防失真
        best_h, best_w = get_best_predict_size(img)

        # 调用模型
        # rows, cols = table_line(image_np, otr_model)
        rows, cols, image_np = table_line(image_np, otr_model, size=(best_w, best_h), hprob=0.5, vprob=0.5)

        if not rows or not cols:
            print("points", 0, "split_lines", 0, "bboxes", 0)
            return {"points": str([]), "split_lines": str([]),
                    "bboxes": str([]), "outline_points": str([])}

        # 处理结果
        # 计算交点、分割线
        points = get_points(rows, cols, (image_np.shape[0], image_np.shape[1]))
        if not points:
            print("points", 0, "split_lines", 0, "bboxes", 0)
            return {"points": str([]), "split_lines": str([]),
                    "bboxes": str([]), "outline_points": str([])}

        split_lines, split_y = get_split_line(points, cols, image_np)

        # 计算交点所在行列，剔除相近交点
        row_point_list = get_points_row(points, split_y, 0)
        col_point_list = get_points_col(points, split_y, 0)
        points = delete_close_points(points, row_point_list, col_point_list)

        # 修复边框
        new_rows, new_cols, long_rows, long_cols = fix_outline(image_np, rows, cols, points,
                                                               split_y)
        if new_rows or new_cols:
            # 连接至补线的延长线
            if long_rows:
                rows = long_rows
            if long_cols:
                cols = long_cols
            # 新的补线
            if new_rows:
                rows += new_rows
            if new_cols:
                cols += new_cols

            # 修复边框后重新计算交点、分割线
            points = get_points(rows, cols, (image_np.shape[0], image_np.shape[1]))
            split_lines, split_y = get_split_line(points, cols, image_np)

            # 计算交点所在行列，剔除相近交点
            row_point_list = get_points_row(points, split_y, 0)
            col_point_list = get_points_col(points, split_y, 0)
            points = delete_close_points(points, row_point_list, col_point_list)
            row_point_list = get_points_row(points, split_y)
            col_point_list = get_points_col(points, split_y)

        # 获取bbox 单元格
        bboxes = get_bbox(image_np, row_point_list, col_point_list, split_y, rows, cols)

        # 获取每个表格的左上右下两个点
        outline_points = get_outline_point(points, split_y)

        if bboxes:
            print("bboxes number", len(bboxes))
            # print("bboxes", bboxes)
        else:
            print("bboxes number", "None")
        print("use time: ", time.time()-start_time)
        return {"points": str(points), "split_lines": str(split_lines),
                "bboxes": str(bboxes), "outline_points": str(outline_points)}
    except Exception as e:
        print("otr_interface cannot detected table!", traceback.print_exc())
        print("points", 0, "split_lines", 0, "bboxes", 0)
        return {"points": str([]), "split_lines": str([]), "bboxes": str([]),
                "outline_points": str([])}


otr_model = table_net((None, None, 3), 2)
otr_model.load_weights(model_path)
if __name__ == '__main__':
    if len(sys.argv) == 2:
        port = int(sys.argv[1])
    else:
        port = 15017
    app.run(host='0.0.0.0', port=port, threaded=False, debug=False)
    log("OTR running "+str(port))

    # print("init model...")
    # g1 = tf.Graph()
    # tf.compat.v1.disable_eager_execution()
    # sess1 = tf.compat.v1.Session(graph=g1)
    # with sess1.as_default():
    #     with g1.as_default():
    #         _model = table_net((None, None, 3), 2)
    #         _model.load_weights(model_path)
    #         otr_model_list[0] = _model
    #
    # g2 = tf.Graph()
    # tf.compat.v1.disable_eager_execution()
    # sess2 = tf.compat.v1.Session(graph=g2)
    # with sess2.as_default():
    #     with g2.as_default():
    #         _model = table_net((None, None, 3), 2)
    #         _model.load_weights(model_path)
    #         otr_model_list[1] = _model
    #
    # otr_graph_list[0] = g1
    # otr_graph_list[1] = g2
    # print("init finish")
    #
    # p = MyProcess(15017)
    # p.start()
    #
    # p1 = MyProcess(15018)
    # p1.start()

    # p.join()
    # p1.join()

    # otr_model = table_net((None, None, 3), 2)
    # otr_model.load_weights(model_path)
    #
    # start_interface(15017)