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133 lines
4.1 KiB
133 lines
4.1 KiB
from dataclasses import dataclass, field
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from numbers import Number
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from typing import Optional, Dict, Deque
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from collections import deque
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import numpy as np
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from dataclasses_json import dataclass_json, config
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import utils
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@dataclass_json
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@dataclass
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class Point:
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x:float
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y:float
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def __iter__(self): # 使对象可迭代，可直接转为元组
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yield self.x
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yield self.y
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@dataclass
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class RectangleArea:
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x: int
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y: int
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w: int
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h: int
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@classmethod
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def from_dict(cls, data: dict):
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return cls(
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x=data['x'],
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y=data['y'],
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w=data['w'],
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h = data['h'])
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@dataclass
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class Threshold:
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binary: int
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gauss: int
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@dataclass_json
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@dataclass
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class TargetInfo:
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# 标靶方形区域
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rectangle_area:RectangleArea
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threshold:Threshold
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# 标靶物理半径
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radius:float=0.0
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id:int =-1
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desc:str=""
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base:bool=False
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def __init__(self,id,desc,rectangle_area:RectangleArea,radius,threshold:Threshold,base:bool,**kwargs):
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self.id = id
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self.desc = desc
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self.rectangle_area=rectangle_area
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self.radius=radius
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self.threshold=threshold
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self.base=base
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@classmethod
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def from_dict(cls,data: dict):
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return cls(data['id'],data['rectangle_area'],data['radius'])
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@dataclass_json
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@dataclass
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class HandlerInfo:
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# 初始话
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is_init=True
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radius_pix:float= 1.0
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pix_length:float=0.0
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# 标靶中心
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center_point_queue:Deque[Point] = field(default_factory=lambda: deque(maxlen=10))
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center_point: Optional[Point]= field(default=None, metadata=config(exclude=lambda x: x is None))
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center_init : Optional[Point]= field(default=None, metadata=config(exclude=lambda x: x is None))
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# 标靶位移(像素)
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displacement_pix: Optional[Point]= field(default=None, metadata=config(exclude=lambda x: x is None))
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displacement_phy: Optional[Point]= field(default=None, metadata=config(exclude=lambda x: x is None))
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def calculate_mean(self)-> Point:
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"""计算队列中所有数据的均值"""
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if self.center_point_queue:
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length=len(self.center_point_queue)
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mean_x = sum(p.x for p in self.center_point_queue) / length
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mean_y = sum(p.y for p in self.center_point_queue) / length
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return Point(mean_x, mean_y)
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else:
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return None
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def enqueue_center_point(self, data) -> Point:
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"""入队操作"""
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self.center_point_queue.append(data)
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return self.calculate_mean()
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@dataclass_json
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@dataclass
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class CircleTarget:
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# 标靶方形区域
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info:TargetInfo
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# 标靶透视矩阵
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perspective: np.ndarray = field(
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metadata=config(
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encoder=utils.encode_perspective,
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decoder=utils.decode_perspective
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)
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)
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handler_info: Optional[HandlerInfo]=None
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# def __init__(self,info:TargetInfo,center_point,center_init,displacement_pix,displacement_phy):
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# self.info=info
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# self.center_point=center_point
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# self.center_init=center_init
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# self. displacement_pix=displacement_pix
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# self.displacement_phy=displacement_phy
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@classmethod
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def init_by_info(cls,t:TargetInfo):
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return CircleTarget(t,None,None,None,None)
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def circle_displacement_pix(self):
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previous = self.handler_info.center_init
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if previous != ():
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self.handler_info.displacement_pix = Point(self.handler_info.center_point.x - previous.x,
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self.handler_info.center_point.y - previous.y)
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return self
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def circle_displacement_phy(self):
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if self.info.radius != 0 and self.handler_info.displacement_pix is not None:
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# 单位像素->距离
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self.handler_info.pix_length = self.info.radius / self.handler_info.radius_pix
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offset_x = round(float(self.handler_info.displacement_pix.x * self.handler_info.pix_length), 5)
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offset_y = round(float(self.handler_info.displacement_pix.y * self.handler_info.pix_length), 5)
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self.handler_info.displacement_phy = Point(offset_x, offset_y)
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return self
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