import json
import socket
import traceback

import cv2
import requests
from PIL import Image
import numpy as np


def request_post(url, param, time_out=1000, use_zlib=False):
    fails = 0
    text = json.dumps([-2])
    session_ocr = requests.Session()
    while True:
        try:
            if fails >= 1:
                break

            result = session_ocr.post(url, data=param, timeout=time_out)

            if result.status_code == 200:
                text = result.text
                break
            else:
                print('result.status_code', result.status_code)
                print('result.text', result.text)
                fails += 1
                continue
        except socket.timeout:
            fails += 1
            print('timeout! fail times:', fails)
        except:
            fails += 1
            print('fail! fail times:', fails)
            traceback.print_exc()
    return text


def line_iou(line1, line2, axis=0):
    inter = min(line1[1][axis], line2[1][axis]) - max(line1[0][axis], line2[0][axis])
    # union = max(line1[1][axis], line2[1][axis]) - min(line1[0][axis], line2[0][axis])
    union = min(abs(line1[0][axis]-line1[1][axis]), abs(line2[0][axis]-line2[1][axis]))
    if union in [0, 0.]:
        iou = 0.
    else:
        iou = inter / union
    return iou


def pil_resize(image_np, height, width):
    image_pil = Image.fromarray(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
    image_pil = image_pil.resize((int(width), int(height)), Image.BICUBIC)
    image_np = cv2.cvtColor(np.asarray(image_pil), cv2.COLOR_RGB2BGR)
    return image_np


def get_best_predict_size2(image_np, threshold=3000):
    h, w = image_np.shape[:2]
    scale = threshold / max(h, w)
    h = int(h * scale)
    w = int(w * scale)
    return h, w


def line_overlap(a1, a2, b1, b2):
    start = max(a1, b1)
    end = min(a2, b2)
    d = end - start
    if d < 0:
        return 0
    else:
        return d


def get_table_iou(x1_min, y1_min, x1_max, y1_max, x2_min, y2_min, x2_max, y2_max):
    # 计算矩形框1的宽度、高度和面积
    width1 = x1_max - x1_min
    height1 = y1_max - y1_min
    area1 = width1 * height1

    # 计算矩形框2的宽度、高度和面积
    width2 = x2_max - x2_min
    height2 = y2_max - y2_min
    area2 = width2 * height2

    # 计算相交矩形框的左上角和右下角坐标
    x_intersection_min = max(x1_min, x2_min)
    y_intersection_min = max(y1_min, y2_min)
    x_intersection_max = min(x1_max, x2_max)
    y_intersection_max = min(y1_max, y2_max)

    # 计算相交矩形框的宽度和高度
    intersection_width = max(0, x_intersection_max - x_intersection_min)
    intersection_height = max(0, y_intersection_max - y_intersection_min)

    # 计算相交矩形框的面积
    intersection_area = intersection_width * intersection_height

    # 判断包含关系并调整相交面积
    if (x1_min <= x2_min) and (y1_min <= y2_min) and (x1_max >= x2_max) and (y1_max >= y2_max):
        union_area = area2
    elif (x2_min <= x1_min) and (y2_min <= y1_min) and (x2_max >= x1_max) and (y2_max >= y1_max):
        union_area = area1
    else:
        # 计算并集矩形框的面积
        # union_area = area1 + area2 - intersection_area
        union_area = min(area1, area2)

    # 计算IoU
    if int(union_area) == 0:
        iou = 0
    else:
        iou = intersection_area / union_area

    return iou


if __name__ == '__main__':
    print(get_table_iou(1, 1, 4, 4, 0, 0, 3, 3))