import numpy as np
import time,cv2
def ms(t1,t0):
    return (t1-t0)*1000.0
def center_coordinate(boundbxs):
    '''
    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):
    '''
    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 remove_simivalue(list1,list2):
    '''
    将list1中属于list2的元素都删除。
    输入：两个嵌套列表
    返回：嵌套列表
    '''
    list33=list1.copy()
    for i in range(len(list1)):
        for j in range(len(list2)):
            if list2[j] == list1[i]:
                # list33.pop(list1[i])
                list33.remove(list1[i])
    return list33

def remove_sameeleme_inalist(list3):
    '''
    将list3中重复嵌套列表元素删除。
    输入：嵌套列表
    返回：嵌套列表
    '''
    list3=list3
    list4=[]
    list4.append(list3[0])
    for dict in list3:
        k=0
        for item in list4:
            if dict!=item:
                k=k+1
            else:
                break
            if k==len(list4):
                list4.append(dict)
    return list4

def order_points(pts):
    ''' sort rectangle points by clockwise '''
    sort_x = pts[np.argsort(pts[:, 0]), :]

    Left = sort_x[:2, :]
    Right = sort_x[2:, :]
    # Left sort
    Left = Left[np.argsort(Left[:, 1])[::-1], :]
    # Right sort
    Right = Right[np.argsort(Right[:, 1]), :]
    return np.concatenate((Left, Right), axis=0)

def mixDrowing_water_postprocess(preds,_mask_cv,pars ):
    '''还未考虑船上人过滤'''
    '''输入：落水人员的结果（类别+坐标）、原图、mask图像
       过程：获得mask的轮廓，判断人员是否在轮廓内。
            在，则保留且绘制；不在，舍弃。
       返回：最终绘制的结果图、最终落水人员（坐标、类别、置信度），
    '''

    t0=time.time()
    '''1、最大分割水域作为判断依据'''
    img_gray = cv2.cvtColor(_mask_cv, cv2.COLOR_BGR2GRAY)  if len(_mask_cv.shape)==3 else _mask_cv
    contours, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    # 寻找轮廓(多边界)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2)
    contour_info = []
    for c in contours:
        contour_info.append((
            c,
            cv2.isContourConvex(c),
            cv2.contourArea(c),
        ))
    contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
    max_contour = contour_info[0]
    #print(max_contour)
    t1=time.time()
    '''2、preds中head+person取出，boat取出。'''
    init_head_person=[]
    init_boat = []
    for i in range(len(preds)):
        #if preds[i][4]=='head' or preds[i][4]=='person':
        if preds[i][0]==0 or preds[i][0]==1:
            init_head_person.append(preds[i])
        else:
            init_boat.append(preds[i])
    t2=time.time()
    '''3、preds中head+person，通过1中水域过滤'''
    init_head_person_filterwater=init_head_person
    final_head_person_filterwater=[]
    for i in range(len(init_head_person_filterwater)):
        center_x, center_y=center_coordinate(init_head_person_filterwater[i])
        flag = cv2.pointPolygonTest(max_contour[0], (center_x, center_y), False) #若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。
        if flag==1:
            final_head_person_filterwater.append(init_head_person_filterwater[i])
        else:
            pass
    t3=time.time()
    '''4、水域过滤后的head+person，再通过船舶范围过滤'''
    init_head_person_filterboat=final_head_person_filterwater
    # final_head_person_filterboat=[]
    #获取船舶范围
    boat_contour=[]
    for i in range(len(init_boat)):
        boundbxs1=[init_boat[i][0],init_boat[i][1],init_boat[i][2],init_boat[i][3]]
        contour_temp=fourcorner_coordinate(boundbxs1) #得到boat预测框的顺序contour
        contour_temp_=np.array(contour_temp)
        contour_temp_=np.float32(contour_temp_)
        boat_contour.append(np.array(contour_temp_))
    # 遍历船舶范围，取出在船舶范围内的head和person（可能有重复元素）
    list_headperson_inboat=[]
    for i in range(len(init_head_person_filterboat)):
        for j in range(len(boat_contour)):
            center_x, center_y=center_coordinate(init_head_person_filterboat[i])
            # yyyyyyyy=boat_contour[j]
            flag = cv2.pointPolygonTest(boat_contour[j], (center_x, center_y), False) #若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。
            if flag==1:
                list_headperson_inboat.append(init_head_person_filterboat[i])
            else:
                pass

    if len(list_headperson_inboat)==0:
        pass
    else:
        list_headperson_inboat=remove_sameeleme_inalist(list_headperson_inboat) #将重复嵌套列表元素删除
    # 过滤船舶范围内的head和person
    final_head_person_filterboat=remove_simivalue(init_head_person_filterboat,list_headperson_inboat)

    t4=time.time()
    timeInfos = '%.1f (step1:%.1f step2:%.2f step3:%.3f step4:%.1f) ' %( ms(t4,t0), ms(t1,t0),ms(t2,t1),ms(t3,t2),ms(t4,t3) )
    


    return final_head_person_filterboat,timeInfos  #返回最终绘制的结果图、最终落水人员（坐标、类别、置信度）