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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 4 18:28:47 2024
@author: ym
"""
import cv2
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.decomposition import PCA
from .dotracks import MoveState, Track
from tools.config import cfg
class backTrack(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)
'''(cent_x, cent_y, mean_w, mean_h)'''
'''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()
'''该函数依赖项: self.cornpoints'''
self.isCornpoint = self.isimgborder()
'''该函数依赖项: self.cornpoints、self.isCornpoint，定义 10 个轨迹特征：
self.trajectory, self.trajmin, self.trajlens, self.trajdist
self.feature = [trajlen_min, trajlen_max,
trajdist_min, trajdist_max,
trajlen_rate, trajdist_rate] '''
self.trajfeature()
'''静止点帧索引'''
self.static_index = self.compute_static_fids()
'''运动点帧索引(运动帧两端的静止帧索引)'''
self.moving_index = self.compute_moving_fids()
'''该函数依赖项: self.cornpoints，定义 4 个商品位置变量：
self.Cent_isIncart, self.LB_isIncart, self.RB_isIncart
self.posState = self.Cent_isIncart+self.LB_isIncart+self.RB_isIncart'''
self.PositionState()
'''self.feature_ious = (incart_iou, outcart_iou, cartboarder_iou, maxbox_iou, minbox_iou)
self.incartrates = incartrates'''
self.compute_ious_feat()
# self.PCA()
# =============================================================================
# def compute_cornpoints(self):
# '''
# cornpoints 共10项，分别是个点的坐标值（x, y）
# (center, top_left, top_right, bottom_left, bottom_right)
# '''
# boxes = self.boxes
# cornpoints = np.zeros((self.frnum, 10))
# cornpoints[:,0], cornpoints[:,1] = boxes[:, 0], boxes[:, 1]
# cornpoints[:,2], cornpoints[:,3] = boxes[:, 0] - boxes[:, 2]/2, boxes[:, 1] - boxes[:, 3]/2
# cornpoints[:,4], cornpoints[:,5] = boxes[:, 0] + boxes[:, 2]/2, boxes[:, 1] - boxes[:, 3]/2
# cornpoints[:,6], cornpoints[:,7] = boxes[:, 0] - boxes[:, 2]/2, boxes[:, 1] + boxes[:, 3]/2
# cornpoints[:,8], cornpoints[:,9] = boxes[:, 0] + boxes[:, 2]/2, boxes[:, 1] + boxes[:, 3]/2
#
# self.cornpoints = cornpoints
# =============================================================================
def isimgborder(self, BoundPixel=10, BoundThresh=0.3):
x1, y1 = self.cornpoints[:,2], self.cornpoints[:,3],
x2, y2 = self.cornpoints[:,8], self.cornpoints[:,9]
cont1 = sum(abs(x1)<BoundPixel) / self.frnum > BoundThresh
cont2 = sum(abs(y1)<BoundPixel) / self.frnum > BoundThresh
cont3 = sum(abs(x2-self.imgshape[0])<BoundPixel) / self.frnum > BoundThresh
cont4 = sum(abs(y2-self.imgshape[1])<BoundPixel) / self.frnum > BoundThresh
cont = cont1 or cont2 or cont3 or cont4
isCornpoint = False
if cont:
isCornpoint = True
return isCornpoint
def PositionState(self, camerType="back"):
'''
camerType: back, 后置摄像头
front, 前置摄像头
'''
if camerType=="front":
incart = cv2.imread(cfg.incart, cv2.IMREAD_GRAYSCALE)
else:
incart = cv2.imread(cfg.incart_ftmp, cv2.IMREAD_GRAYSCALE)
xc, yc = self.cornpoints[:,0].clip(0,self.imgshape[0]-1).astype(np.int64), self.cornpoints[:,1].clip(0,self.imgshape[1]-1).astype(np.int64)
x1, y1 = self.cornpoints[:,6].clip(0,self.imgshape[0]-1).astype(np.int64), self.cornpoints[:,7].clip(0,self.imgshape[1]-1).astype(np.int64)
x2, y2 = self.cornpoints[:,8].clip(0,self.imgshape[0]-1).astype(np.int64), self.cornpoints[:,9].clip(0,self.imgshape[1]-1).astype(np.int64)
# print(self.tid)
Cent_inCartnum = np.count_nonzero(incart[(yc, xc)])
LB_inCartnum = np.count_nonzero(incart[(y1, x1)])
RB_inCartnum = np.count_nonzero(incart[(y2, x2)])
self.Cent_isIncart = False
self.LB_isIncart = False
self.RB_isIncart = False
if Cent_inCartnum: self.Cent_isIncart = True
if LB_inCartnum: self.LB_isIncart = True
if RB_inCartnum: self.RB_isIncart = True
self.posState = self.Cent_isIncart+self.LB_isIncart+self.RB_isIncart
def PCA(self):
self.pca = PCA()
X = self.cornpoints[:, 0:2]
self.pca.fit(X)
def compute_ious_feat(self):
'''输出：
self.feature_ious = (incart_iou, outcart_iou, cartboarder_iou, maxbox_iou, minbox_iou)
self.incartrates = incartrates，
其中：
boxes流：track中所有boxes形成的轨迹图，可分为三部分：incart, outcart, cartboarder
incart_iou, outcart_iou, cartboarder_iou：各部分和 boxes流的 iou。
incart_iou = 0，track在购物车外，
outcart_iou = 0，track在购物车内，也可能是通过左下角、右下角置入购物车，
maxbox_iou, minbox_iou：track中最大、最小 box 和boxes流的iou，二者差值越小，越接近 1，表明track的运动型越小。
incartrates: 各box和incart的iou时序，由小变大，反应的是置入过程，由大变小，反应的是取出过程
'''
incart = cv2.imread(cfg.incart, cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread(cfg.outcart, cv2.IMREAD_GRAYSCALE)
cartboarder = cv2.imread(cfg.cartboarder, cv2.IMREAD_GRAYSCALE)
incartrates = []
temp = np.zeros(incart.shape, np.uint8)
maxarea, minarea = 0, self.imgshape[0]*self.imgshape[1]
for i in range(self.frnum):
# x, y, w, h = self.boxes[i, 0:4]
x = (self.boxes[i, 2] + self.boxes[i, 0]) / 2
w = (self.boxes[i, 2] - self.boxes[i, 0]) / 2
y = (self.boxes[i, 3] + self.boxes[i, 1]) / 2
h = (self.boxes[i, 3] - self.boxes[i, 1]) / 2
if w*h > maxarea: maxarea = w*h
if w*h < minarea: minarea = w*h
cv2.rectangle(temp, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), 255, cv2.FILLED)
temp1 = np.zeros(incart.shape, np.uint8)
cv2.rectangle(temp1, (int(x-w/2), int(y-h/2)), (int(x+w/2), int(y+h/2)), 255, cv2.FILLED)
temp2 = cv2.bitwise_and(incart, temp1)
inrate = cv2.countNonZero(temp1)/(w*h)
incartrates.append(inrate)
isincart = cv2.bitwise_and(incart, temp)
isoutcart = cv2.bitwise_and(outcart, temp)
iscartboarder = cv2.bitwise_and(cartboarder, temp)
num_temp = cv2.countNonZero(temp)
num_incart = cv2.countNonZero(isincart)
num_outcart = cv2.countNonZero(isoutcart)
num_cartboarder = cv2.countNonZero(iscartboarder)
incart_iou = num_incart/num_temp
outcart_iou = num_outcart/num_temp
cartboarder_iou = num_cartboarder/num_temp
maxbox_iou = maxarea/num_temp
minbox_iou = minarea/num_temp
self.feature_ious = (incart_iou, outcart_iou, cartboarder_iou, maxbox_iou, minbox_iou)
self.incartrates = incartrates
def compute_static_fids(self, thresh1 = 12, thresh2 = 3):
'''
计算 track 的轨迹中相对处于静止状态的轨迹点的（start_frame_id, end_frame_id）
thresh1: 相邻两帧目标中心点是否静止的的阈值，以像素为单位，
thresh2: 连续捕捉到目标处于静止状态的帧数
'''
BoundPixel = 8
x1, y1 = self.cornpoints[:,2], self.cornpoints[:,3],
x2, y2 = self.cornpoints[:,8], self.cornpoints[:,9]
cont1 = sum(abs(x1)<BoundPixel) > 3
# cont2 = sum(abs(y1)<BoundPixel) > 3
cont3 = sum(abs(x2-self.imgshape[0])<BoundPixel) > 3
# cont4 = sum(abs(y2-self.imgshape[1])<BoundPixel) > 3
cont = not(cont1 or cont3)
## ============== 下一步，启用中心点，选择具有最小运动幅度的角点作为参考点
static_index = []
if self.frnum>=2 and cont:
x1 = self.boxes[1:,7]
x2 = [i for i in range(int(min(x1)), int(max(x1)+1))]
dist_adjc = np.interp(x2, x1, self.trajmin)
# dist_adjc = self.trajmin
static_thresh = (dist_adjc < thresh1)[:, None].astype(np.uint8)
static_cnts, _ = cv2.findContours(static_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
for cnt in static_cnts:
_, start, _, num = cv2.boundingRect(cnt)
end = start + num
if num <= thresh2:
continue
static_index.append((start, end))
# =============================================================================
# '''========= 输出帧id，不太合适 ========='''
# static_fids = []
# for i in range(len(static_index)):
# i1, i2 = static_index[i]
# fid1, fid2 = boxes[i1, 7], boxes[i2, 7]
# static_fids.append([fid1, fid2])
# static_fids = np.array(static_fids)
# =============================================================================
static_index = np.array(static_index)
if static_index.size:
indx = np.argsort(static_index[:, 0])
static_index = static_index[indx]
return static_index
def compute_moving_fids(self, thresh1 = 12, thresh2 = 3):
'''
计算 track 的轨迹中运动轨迹点的（start_frame_id, end_frame_id）
thresh1: 相邻两帧目标中心点是否运动的阈值，以像素为单位，
thresh2: 连续捕捉到目标连续运动的帧数
目标：
1. 计算轨迹方向
2. 计算和手部运动的关联性
'''
moving_index = []
if self.frnum>=2:
x1 = self.boxes[1:,7]
x2 = [i for i in range(int(min(x1)), int(max(x1)+1))]
dist_adjc = np.interp(x2, x1, self.trajmin)
moving_thresh = (dist_adjc >= thresh1)[:, None].astype(np.uint8)
moving_cnts, _ = cv2.findContours(moving_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
for cnt in moving_cnts:
_, start, _, num = cv2.boundingRect(cnt)
if num < thresh2:
continue
end = start + num
moving_index.append((start, end))
# =============================================================================
# '''========= 输出帧id，不太合适 ========='''
# moving_fids = []
# for i in range(len(moving_index)):
# i1, i2 = moving_index[i]
# fid1, fid2 = boxes[i1, 7], boxes[i2, 7]
# moving_fids.append([fid1, fid2])
# moving_fids = np.array(moving_fids)
# =============================================================================
moving_index = np.array(moving_index)
if moving_index.size:
indx = np.argsort(moving_index[:, 0])
moving_index = moving_index[indx]
return moving_index
def compute_distance(self):
pass
def move_start_fid(self):
pass
def move_end_fid(self):
pass