import cv2
import numpy as np
import torch
# from loguru import logger

def pannel_post_process(preds, pars):
    # pars={'solar':0}
    '''
    将光伏板上覆盖物、裂缝识别出来
    '''
    # print(preds[0])
    # logger.info('\n分类结果返回：%s'%preds)
    preds = torch.tensor(preds[0])
    preds = preds.tolist()
    preds = [[*sublist[:-1], int(sublist[-1])] for sublist in preds]  # 类别从浮点型转为整型
    # print(preds)
    # 设置空的列表
    # 1、判断类别中哪些有太阳能板？取出太阳能板检测结果，并取出覆盖物、裂缝检测结果。
    preds_solar = []
    preds_others = []
    for i in range(len(preds)):
        if preds[i][5] in pars['objs']:  # 识别为光伏板
            preds_solar.append(preds[i])
        else:  # 识别为裂缝、覆盖物
            preds_others.append(preds[i])

    return point_in_rectangle(preds_others, preds_solar)


def center_coordinate(boundbxs):
    '''
    根据检测矩形框，得到其矩形长度和宽度
    输入：两个对角坐标xyxy
    输出：矩形框重点坐标xy
    '''
    boundbxs_x1 = boundbxs[0]
    boundbxs_y1 = boundbxs[1]
    boundbxs_x2 = boundbxs[2]
    boundbxs_y2 = boundbxs[3]
    center_x = 0.5 * (boundbxs_x1 + boundbxs_x2)
    center_y = 0.5 * (boundbxs_y1 + boundbxs_y2)
    return center_x, center_y


def fourcorner_coordinate(boundbxs):
    '''
    通过矩形框对角xyxy坐标，得到矩形框轮廓
    输入：两个对角坐标xyxy
    输出：矩形框四个角点坐标，以contours顺序。
    '''
    boundbxs_x1 = boundbxs[0]
    boundbxs_y1 = boundbxs[1]
    boundbxs_x2 = boundbxs[2]
    boundbxs_y2 = boundbxs[3]
    wid = boundbxs_x2 - boundbxs_x1
    hei = boundbxs_y2 - boundbxs_y1
    boundbxs_x3 = boundbxs_x1 + wid
    boundbxs_y3 = boundbxs_y1
    boundbxs_x4 = boundbxs_x1
    boundbxs_y4 = boundbxs_y1 + hei
    contours_rec = [[boundbxs_x1, boundbxs_y1], [boundbxs_x3, boundbxs_y3], [boundbxs_x2, boundbxs_y2],
                    [boundbxs_x4, boundbxs_y4]]
    return contours_rec


def point_in_rectangle(preds_others, preds_solar):
    '''
    遍历所有光伏板异常目标，并输出
    '''
    if not preds_solar:
        return [[],'']

    preds = []
    for i in range(len(preds_others)):
        for solar in preds_solar:
            solar_contour = fourcorner_coordinate(solar)
            solar_contour = np.array(solar_contour, dtype=np.float32)
            center_x, center_y = center_coordinate(preds_others[i])
            # print(cv2.pointPolygonTest(solar_contour, (center_x, center_y), False))
            if cv2.pointPolygonTest(solar_contour, (center_x, center_y), False) == 1:
                preds.append(preds_others[i])

    # logger.info('\n分类结果返回：%s' % preds)
    return [preds,'']


if __name__ == "__main__":
    # 对应DJI_20230306140129_0001_Z_165.jpg检测结果
    # preds=[[6.49000e+02, 2.91000e+02, 1.07900e+03, 7.33000e+02, 9.08165e-01, 3.00000e+00],
    #  [8.11000e+02, 2.99000e+02, 1.31200e+03, 7.65000e+02, 8.61268e-01, 3.00000e+00],
    #  [7.05000e+02, 1.96000e+02, 7.19000e+02, 2.62000e+02, 5.66877e-01, 0.00000e+00]]

    # 对应DJI_20230306152702_0001_Z_562.jpg检测结果
    preds = [[7.62000e+02, 7.14000e+02, 1.82800e+03, 9.51000e+02, 9.00902e-01, 3.00000e+00],
             [2.00000e+01, 3.45000e+02, 1.51300e+03, 6.71000e+02, 8.81440e-01, 3.00000e+00],
             [8.35000e+02, 8.16000e+02, 8.53000e+02, 8.30000e+02, 7.07651e-01, 0.00000e+00],
             [1.35600e+03, 4.56000e+02, 1.42800e+03, 4.94000e+02, 6.70549e-01, 2.00000e+00]]
    print('before :\n ', preds)
    # preds=torch.tensor(preds)    #返回的预测结果
    imgwidth = 1920
    imgheight = 1680
    pars = {'imgSize': (imgwidth, imgheight), 'wRation': 1 / 6.0, 'hRation': 1 / 6.0, 'smallId': 0, 'bigId': 3,
            'newId': 4, 'recScale': 1.2}
    # 'smallId':0(国旗)，'bigId':3(船只),wRation和hRation表示判断的阈值条件，newId--新目标的id
    # yyy = channel2_post_process([preds], pars)  # 送入后处理函数
    #
    # print('after :\n ', yyy)