from enum import Enum
import numpy as np
# 根据标签修改
# class tlabel(Enum):
#     BBLOCK   = 5    # 蓝色方块
#     RBLOCK   = 2    # 红色方块
#     HOSPITAL = 3    # 医院
#     BBALL    = 4    # 蓝球
#     YBALL    = 5    # 黄球
#     TOWER    = 6    # 通信塔
#     RBALL    = 7    # 红球
#     BASKET   = 8    # 球筐
#     MARKL    = 9    # 指向标
#     MARKR    = 10   # 指向标
#     SPILLAR  = 11   # 小柱体 (红色)
#     MPILLAR  = 12   # 中柱体 (蓝色)
#     LPILLAR  = 13   # 大柱体 (红色)
#     SIGN     = 14   # 文字标牌
#     TARGET   = 15   # 目标靶
#     SHELTER  = 16   # 停车区
#     BASE     = 17   # 基地
class tlabel(Enum):
    TOWER = 0
    SIGN = 1
    SHELTER = 2
    HOSPITAL = 3
    BASKET = 4
    BASE = 5
    YBALL = 6
    SPILLER = 7
    RMARK = 8
    RBLOCK = 9
    RBALL = 10
    MPILLER = 11
    LPILLER = 12
    LMARK = 13
    BBLOCK = 14
    BBALL = 15
test_resp = {
    'code': 0,
    'data': np.array([
        [4., 0.97192055, 26.64415, 228.26755, 170.16872, 357.6216],
        [4., 0.97049206, 474.0152, 251.2854, 612.91644, 381.6831],
        [5., 0.972649, 250.84174, 238.43622, 378.115, 367.34906]
    ])
}
test1_resp = {
    'code': 0,
    'data': np.array([])
}
class label_filter:
    def __init__(self, socket, threshold=0.6):
        self.num = 0
        self.pos = []
        self.socket = socket
        self.threshold = threshold
        
        self.img_size = (320, 240)
    def get_resp(self):
        self.socket.send_string('')
        response = self.socket.recv_pyobj()
        return response
    def switch_camera(self,camera_id):
        if camera_id == 1 or camera_id == 2:
            self.socket.send_string(f'{camera_id}')
            response = self.socket.recv_pyobj()
            return response
    def filter_box(self,data):
        if len(data) > 0:
            expect_boxes = (data[:, 1] > self.threshold) & (data[:, 0] > -1)
            np_boxes = data[expect_boxes, :]
            results = [
                [
                    item[0], # 'label': 
                    item[1], # 'score': 
                    item[2], # 'xmin':
                    item[3], # 'ymin':
                    item[4], # 'xmax':
                    item[5]  # 'ymax':
                ]
                for item in np_boxes
            ]
            if len(results) > 0:
                return True, np.array(results)
        return False, None
    def get(self, tlabel):
        # 循环查找匹配的标签值
        # 返回对应标签的个数，以及坐标列表
        # TODO  self.filter_box none judge
        response = self.get_resp()
        if response['code'] == 0:
            ret, results = self.filter_box(response['data'])
            if ret:
                expect_boxes = (results[:, 0] == tlabel.value)
                boxes = results[expect_boxes, :]
                self.num = len(boxes)
                self.pos = boxes[:, 2:] # [[x1 y1 x2 y2]]
                return self.num, self.pos
        return 0, []
    def find(self, tlabel):
        # 遍历返回的列表，有对应标签则返回 True
        response = self.get_resp()
        if response['code'] == 0:
            ret, results = self.filter_box(response['data'])
            if ret:
                expect_boxes = (results[:, 0] == tlabel.value)
                boxes = results[expect_boxes, :]
                if len(boxes) != 0:
                    return True 
        return False
    def aim_left(self, tlabel):
        # 如果标签存在，则返回列表中位置最靠左的目标框和中心的偏移值
        response = self.get_resp()
        if response['code'] == 0:
            ret, results = self.filter_box(response['data'])
            if ret:
                expect_boxes = (results[:, 0] == tlabel.value)
                boxes = results[expect_boxes, :]
                if len(boxes) == 0:
                    return (False, )
                xmin_values = boxes[:, 2] # xmin
                xmin_index = np.argmin(xmin_values)
                error = (boxes[xmin_index][4] + boxes[xmin_index][2] - self.img_size[0]) / 2
                return (True, error)
        return (False, )
    def aim_right(self, tlabel):
        # 如果标签存在，则返回列表中位置最靠右的目标框和中心的偏移值
        response = self.get_resp()
        if response['code'] == 0:
            ret, results = self.filter_box(response['data'])
            if ret:
                expect_boxes = (results[:, 0] == tlabel.value)
                boxes = results[expect_boxes, :]
                if len(boxes) == 0:
                    return (False, )
                xmax_values = boxes[:, 4] # xmax
                xmax_index = np.argmax(xmax_values)
                error = (boxes[xmax_index][4] + boxes[xmax_index][2] - self.img_size[0]) / 2
                return (True, error)
        return (False, )
    def aim_near(self, tlabel):
        # 如果标签存在，则返回列表中位置最近的目标框和中心的偏移值
        response = self.get_resp()
        if response['code'] == 0:
            ret, results = self.filter_box(response['data'])
            if ret:
                expect_boxes = (results[:, 0] == tlabel.value)
                boxes = results[expect_boxes, :]
                if len(boxes) == 0:
                    return (False, 0)
                center_x_values = np.abs(boxes[:, 2] + boxes[:, 4] - self.img_size[0])
                center_x_index = np.argmin(center_x_values)
                error = (boxes[center_x_index][4] + boxes[center_x_index][2] - self.img_size[0]) / 2
                return (True, error+15)
        return (False, 0)

# class Calibrate:
#     def __init__(self,by_cmd):
#         # 车控制对象初始化
#         self.by_cmd = by_cmd
#     def aim(self,error):
#         self.by_cmd.send_distance_x(error,)


if __name__ == '__main__':
    obj = label_filter(None)
    # results = obj.filter_box(resp['data'])
    # expect_boxes = (results[:, 0] == tlabel.SPILLAR.value)
    # np_boxes = results[expect_boxes, :]
    # print(np_boxes[:, 2:])
    # print(len(np_boxes))
    print(obj.find(tlabel.BBALL))
    print(obj.aim_left(tlabel.BBALL))
    print(obj.aim_right(tlabel.BBALL))
    print(obj.aim_near(tlabel.BBALL))
    print(obj.get(tlabel.HOSPITAL))