270 lines
8.9 KiB
Python
270 lines
8.9 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 4 18:33:01 2024
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@author: ym
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"""
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import numpy as np
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from sklearn.cluster import KMeans
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from .dotracks import MoveState, Track
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class frontTrack(Track):
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# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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# 0, 1, 2, 3, 4, 5, 6, 7, 8
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def __init__(self, boxes, features, imgshape=(1024, 1280)):
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super().__init__(boxes, features, imgshape)
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'''5个关键点（中心点、左上点、右上点、左下点、右下点 ）轨迹特征'''
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# self.compute_cornpts_feats()
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self.CART_HIGH_THRESH1 = imgshape[1]/2.98
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# if self.tid==10:
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# print(f"ID: {self.tid}")
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'''y1、y2静止状态区间，值是 boxes 中对 axis=0 的索引，不是帧索引'''
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det_y1 = np.diff(boxes[:, 1], axis=0)
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det_y2 = np.diff(boxes[:, 3], axis=0)
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self.static_y1, self.dynamic_y1 = self.pt_state_fids(det_y1)
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self.static_y2, self.dynamic_y2 = self.pt_state_fids(det_y2)
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self.isCornpoint = self.is_left_or_right_cornpoint()
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self.isBotmpoint = self.is_bottom_cornpoint()
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'''该函数依赖项: self.isCornpoint，不能在父类中初始化'''
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self.trajfeature()
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self.PositionState(camerType="front")
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'''手部状态分析'''
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self.HAND_STATIC_THRESH = 100
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self.CART_POSIT_0 = 430
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self.CART_POSIT_1 = 620
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def is_left_or_right_cornpoint(self):
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''' 基于 all(boxes)，
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boxes左下角点和图像左下角点重叠 或
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boxes右下角点和图像左下角点重叠
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'''
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x1, y1 = self.boxes[:, 0], self.boxes[:, 1]
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x2, y2 = self.boxes[:, 2], self.boxes[:, 3]
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# Left-Bottom cornpoint
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condt1 = all(x1 < 5) and all(y2 > self.imgshape[1]-5)
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# Right-Bottom cornpoint
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condt2 = all(x2 > self.imgshape[0]-5) and all(y2 > self.imgshape[1]-5)
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condt = condt1 or condt2
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return condt
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def is_edge_cornpoint(self):
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'''基于 all(boxes)，boxes是否和图像左右边缘重叠'''
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x1, x2 = self.boxes[:, 0], self.boxes[:, 2]
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condt = all(x1 < 3) or all(x2 > self.imgshape[0]-3)
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return condt
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def is_bottom_cornpoint(self):
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'''基于 all(boxes)，boxes是否和图像下边缘重叠'''
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condt = all(self.boxes[:, 3] > self.imgshape[1]-20)
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return condt
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# def is_OutTrack(self):
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# isout = False
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# if self.posState <= 1:
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# isout = True
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# return isout
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# =============================================================================
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# def compute_static_fids(self, det_y, STATIC_THRESH = 8):
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# '''
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# 前摄时，y一般选择为 box 的 y1 坐标，且需限定商品在购物车内。
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# inputs：
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# y：1D array，
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# parameters：
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# STATIC_THRESH：轨迹处于静止状态的阈值。
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# outputs：
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# 输出为差分值小于 STATIC_THRESH 的y中元素的（start, end）索引
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# ranges = [(x1, y1),
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# (x1, y1),
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# ...]
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# '''
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# # print(f"The ID is: {self.tid}")
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#
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# # det_y = np.diff(y, axis=0)
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# ranges, rangex = [], []
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#
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# static_indices = np.where(np.abs(det_y) < STATIC_THRESH)[0]
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#
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# if len(static_indices) == 0:
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# rangex.append((0, len(det_y)))
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# return ranges, rangex
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#
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# start_index = static_indices[0]
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#
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# for i in range(1, len(static_indices)):
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# if static_indices[i] != static_indices[i-1] + 1:
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# ranges.append((start_index, static_indices[i-1] + 1))
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# start_index = static_indices[i]
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# ranges.append((start_index, static_indices[-1] + 1))
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#
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# if len(ranges) == 0:
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# rangex.append((0, len(det_y)))
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# return ranges, rangex
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#
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# idx1, idx2 = ranges[0][0], ranges[-1][1]
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#
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# if idx1 != 0:
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# rangex.append((0, idx1))
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#
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# # 轨迹的最后阶段是运动状态
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# for k in range(1, len(ranges)):
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# index1 = ranges[k-1][1]
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# index2 = ranges[k][0]
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# rangex.append((index1, index2))
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#
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# if idx2 != len(det_y):
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# rangex.append((idx2, len(det_y)))
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#
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# return ranges, rangex
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#
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# =============================================================================
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def is_static(self):
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assert self.frnum > 1, "boxes number must greater than 1"
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# print(f"The ID is: {self.tid}")
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# 手部和小孩目标不考虑
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if self.cls == 0 or self.cls == 9:
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return False
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# boxes 全部 y2=1280
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if self.isBotmpoint:
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return True
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boxes = self.boxes
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y0 = (boxes[:, 1]+boxes[:, 3])/2
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## 纵轴矢量和
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sum_y0 = y0[-1] - y0[0]
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sum_y1 = boxes[-1, 1]-boxes[0, 1]
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sum_y2 = boxes[-1, 3]-boxes[0, 3]
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# 一些需要考虑的特殊情况
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isbottom = max(boxes[:, 3]) > 1280-3
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istop = min(boxes[:, 1]) < 3
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isincart = min(y0) > self.CART_HIGH_THRESH1
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uncert = abs(sum_y1)<100 and abs(sum_y2)<100
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'''初始条件：商品中心点始终在购物车内、'''
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condt0 = max((boxes[:, 1]+boxes[:, 3])/2) > self.CART_HIGH_THRESH1
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'''条件1：轨迹运动纵向和（y1 或 y2）描述商品轨迹长度，存在情况：
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(1). 检测框可能与图像上下边缘重合，
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(2). 上边或下边存在跳动
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'''
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if isbottom and istop:
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condt1 = abs(sum_y0) < 300
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elif isbottom: # y2在底部，用y1表征运动
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condt1 = sum_y1 > -120 and abs(sum_y0)<80 # 有底部点，方向向上阈值小于100
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elif istop: # y1在顶部，用y2表征运动
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condt1 = abs(sum_y2) < 100
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else:
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condt1 = (abs(sum_y1) < 30 or abs(sum_y2)<30)
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'''条件2：轨迹的开始和结束阶段均处于静止状态, 利用静止状态区间判断，用 y1
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a. 商品在购物车内，
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b. 检测框的起始阶段和结束阶段均为静止状态
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c. 静止帧长度 > 3'''
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condt2 = False
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if len(self.static_y1)>=2:
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condt_s0 = self.static_y1[0][0]==0 and self.static_y1[0][1] - self.static_y1[0][0] >= 3
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condt_s1 = self.static_y1[-1][1]==self.frnum-1 and self.static_y1[-1][1] - self.static_y1[-1][0] >= 3
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condt2 = condt_s0 and condt_s1 and isincart
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condt = condt0 and (condt1 or condt2)
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return condt
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def is_upward(self):
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'''判断商品是否取出，'''
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print(f"The ID is: {self.tid}")
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def is_free_move(self):
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if self.frnum == 1:
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return True
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# print(f"The ID is: {self.tid}")
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y0 = (self.boxes[:, 1] + self.boxes[:, 3]) / 2
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det_y0 = np.diff(y0, axis=0)
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sum_y0 = y0[-1] - y0[0]
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'''情况1：中心点向下 '''
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## 初始条件：商品第一次检测到在购物车内
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condt0 = y0[0] > self.CART_HIGH_THRESH1
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condt_a = False
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## 条件1：商品初始为静止状态，静止条件应严格一些
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condt11, condt12 = False, False
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if len(self.static_y1)>0:
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condt11 = self.static_y1[0][0]==0 and self.static_y1[0][1] - self.static_y1[0][0] >= 5
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if len(self.static_y2)>0:
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condt12 = self.static_y2[0][0]==0 and self.static_y2[0][1] - self.static_y2[0][0] >= 5
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# 条件2：商品中心发生向下移动
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condt2 = y0[-1] > y0[0]
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# 综合判断a
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condt_a = condt0 and (condt11 or condt12) and condt2
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'''情况2：中心点向上 '''
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## 商品中心点向上移动，但没有关联的Hand轨迹，也不是左右边界点
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condt_b = condt0 and len(self.Hands)==0 and y0[-1] < y0[0] and (not self.is_edge_cornpoint())
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'''情况3: 商品在购物车内，但运动方向无序'''
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## 中心点在购物车内，纵向轨迹和小于轨迹差中绝对值最大的两个值的和，说明运动没有主方向
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condt_c = False
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if self.frnum > 3:
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condt_c = all(y0>self.CART_HIGH_THRESH1) and \
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(abs(sum_y0) < sum(np.sort(np.abs(det_y0))[::-1][:2])-1)
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condt = (condt_a or condt_b or condt_c) and self.cls!=0
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return condt
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