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initial project version!

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王庆刚
2024-05-20 20:01:06 +08:00
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 4 18:33:01 2024
@author: ym
"""
import numpy as np
from sklearn.cluster import KMeans
from .dotracks import MoveState, Track
class frontTrack(Track):
# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
# 0, 1, 2, 3, 4, 5, 6, 7, 8
def __init__(self, boxes, imgshape=(1024, 1280)):
super().__init__(boxes, imgshape)
self.hands = []
'''5个关键点（中心点、左上点、右上点、左下点、右下点 ）轨迹特征'''
# self.compute_cornpts_feats()
self.CART_HIGH_THRESH1 = imgshape[1]/2.98
# if self.tid==10:
# print(f"ID: {self.tid}")
'''y1、y2静止状态区间，值是 boxes 中对 axis=0 的索引，不是帧索引'''
det_y1 = np.diff(boxes[:, 1], axis=0)
det_y2 = np.diff(boxes[:, 3], axis=0)
self.static_y1, self.dynamic_y1 = self.compute_static_fids(det_y1)
self.static_y2, self.dynamic_y2 = self.compute_static_fids(det_y2)
self.isCornpoint = self.is_left_or_right_cornpoint()
self.isBotmpoint = self.is_bottom_cornpoint()
'''该函数依赖项: self.isCornpoint，不能在父类中初始化'''
self.trajfeature()
'''手部状态分析'''
self.HAND_STATIC_THRESH = 100
self.CART_POSIT_0 = 430
self.CART_POSIT_1 = 620
def is_left_or_right_cornpoint(self):
''' 基于 all(boxes)，
boxes左下角点和图像左下角点重叠 或
boxes右下角点和图像左下角点重叠
'''
x1, y1 = self.boxes[:, 0], self.boxes[:, 1]
x2, y2 = self.boxes[:, 2], self.boxes[:, 3]
# Left-Bottom cornpoint
condt1 = all(x1 < 5) and all(y2 > self.imgshape[1]-5)
# Right-Bottom cornpoint
condt2 = all(x2 > self.imgshape[0]-5) and all(y2 > self.imgshape[1]-5)
condt = condt1 or condt2
return condt
def is_edge_cornpoint(self):
'''基于 all(boxes)，boxes是否和图像左右边缘重叠'''
x1, x2 = self.boxes[:, 0], self.boxes[:, 2]
condt = all(x1 < 3) or all(x2 > self.imgshape[0]-3)
return condt
def is_bottom_cornpoint(self):
'''基于 all(boxes)，boxes是否和图像下边缘重叠'''
condt = all(self.boxes[:, 3] > self.imgshape[1]-20)
return condt
# def is_OutTrack(self):
# isout = False
# if self.posState <= 1:
# isout = True
# return isout
# =============================================================================
# def compute_static_fids(self, det_y, STATIC_THRESH = 8):
# '''
# 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。
# inputs：
# y：1D array，
# parameters：
# STATIC_THRESH：轨迹处于静止状态的阈值。
# outputs：
# 输出为差分值小于 STATIC_THRESH 的y中元素的（start, end）索引
# ranges = [(x1, y1),
# (x1, y1),
# ...]
# '''
# # print(f"The ID is: {self.tid}")
#
# # det_y = np.diff(y, axis=0)
# ranges, rangex = [], []
#
# static_indices = np.where(np.abs(det_y) < STATIC_THRESH)[0]
#
# if len(static_indices) == 0:
# rangex.append((0, len(det_y)))
# return ranges, rangex
#
# start_index = static_indices[0]
#
# for i in range(1, len(static_indices)):
# if static_indices[i] != static_indices[i-1] + 1:
# ranges.append((start_index, static_indices[i-1] + 1))
# start_index = static_indices[i]
# ranges.append((start_index, static_indices[-1] + 1))
#
# if len(ranges) == 0:
# rangex.append((0, len(det_y)))
# return ranges, rangex
#
# idx1, idx2 = ranges[0][0], ranges[-1][1]
#
# if idx1 != 0:
# rangex.append((0, idx1))
#
# # 轨迹的最后阶段是运动状态
# for k in range(1, len(ranges)):
# index1 = ranges[k-1][1]
# index2 = ranges[k][0]
# rangex.append((index1, index2))
#
# if idx2 != len(det_y):
# rangex.append((idx2, len(det_y)))
#
# return ranges, rangex
#
# =============================================================================
def is_static(self):
assert self.frnum > 1, "boxes number must greater than 1"
# print(f"The ID is: {self.tid}")
# 手部和小孩目标不考虑
if self.cls == 0 or self.cls == 9:
return False
# boxes 全部 y2=1280
if self.isBotmpoint:
return True
boxes = self.boxes
y0 = (boxes[:, 1]+boxes[:, 3])/2
## 纵轴矢量和
sum_y0 = y0[-1] - y0[0]
sum_y1 = boxes[-1, 1]-boxes[0, 1]
sum_y2 = boxes[-1, 3]-boxes[0, 3]
# 一些需要考虑的特殊情况
isbottom = max(boxes[:, 3]) > 1280-3
istop = min(boxes[:, 1]) < 3
isincart = min(y0) > self.CART_HIGH_THRESH1
uncert = abs(sum_y1)<100 and abs(sum_y2)<100
'''初始条件：商品中心点始终在购物车内、'''
condt0 = max((boxes[:, 1]+boxes[:, 3])/2) > self.CART_HIGH_THRESH1
'''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：
(1). 检测框可能与图像上下边缘重合，
(2). 上边或下边存在跳动
'''
if isbottom and istop:
condt1 = abs(sum_y0) < 300
elif isbottom: # y2在底部，用y1表征运动
condt1 = sum_y1 > -120 and abs(sum_y0)<80 # 有底部点，方向向上阈值小于100
elif istop: # y1在顶部，用y2表征运动
condt1 = abs(sum_y2) < 100
else:
condt1 = (abs(sum_y1) < 30 or abs(sum_y2)<30)
'''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
a. 商品在购物车内，
b. 检测框的起始阶段和结束阶段均为静止状态
c. 静止帧长度 > 3'''
condt2 = False
if len(self.static_y1)>=2:
condt_s0 = self.static_y1[0][0]==0 and self.static_y1[0][1] - self.static_y1[0][0] >= 3
condt_s1 = self.static_y1[-1][1]==self.frnum-1 and self.static_y1[-1][1] - self.static_y1[-1][0] >= 3
condt2 = condt_s0 and condt_s1 and isincart
condt = condt0 and (condt1 or condt2)
return condt
def is_upward(self):
'''判断商品是否取出，'''
print(f"The ID is: {self.tid}")
def is_free_move(self):
if self.frnum == 1:
return True
# print(f"The ID is: {self.tid}")
y0 = (self.boxes[:, 1] + self.boxes[:, 3]) / 2
det_y0 = np.diff(y0, axis=0)
sum_y0 = y0[-1] - y0[0]
'''情况1：中心点向下 '''
## 初始条件：商品第一次检测到在购物车内
condt0 = y0[0] > self.CART_HIGH_THRESH1
condt_a = False
## 条件1：商品初始为静止状态，静止条件应严格一些
condt11, condt12 = False, False
if len(self.static_y1)>0:
condt11 = self.static_y1[0][0]==0 and self.static_y1[0][1] - self.static_y1[0][0] >= 5
if len(self.static_y2)>0:
condt12 = self.static_y2[0][0]==0 and self.static_y2[0][1] - self.static_y2[0][0] >= 5
# 条件2：商品中心发生向下移动
condt2 = y0[-1] > y0[0]
# 综合判断a
condt_a = condt0 and (condt11 or condt12) and condt2
'''情况2：中心点向上 '''
## 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
condt_b = condt0 and len(self.hands)==0 and y0[-1] < y0[0] and (not self.is_edge_cornpoint())
'''情况3: 商品在购物车内，但运动方向无序'''
## 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
condt_c = False
if self.frnum > 3:
condt_c = all(y0>self.CART_HIGH_THRESH1) and \
(abs(sum_y0) < sum(np.sort(np.abs(det_y0))[::-1][:2])-1)
condt = (condt_a or condt_b or condt_c) and self.cls!=0
return condt