495 lines
17 KiB
Python
495 lines
17 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 4 18:16:01 2024
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@author: ym
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"""
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import numpy as np
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import cv2
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from pathlib import Path
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from scipy.spatial.distance import cdist
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from utils.mergetrack import track_equal_track, readDict
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curpath = Path(__file__).resolve().parents[0]
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curpath = Path(curpath)
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class MoveState:
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"""商品运动状态标志"""
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Static = 0
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DownWard = 1
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UpWard = 2
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FreeMove = 3
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Unknown = -1
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class ShoppingCart:
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def __init__(self, bboxes):
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self.bboxes = bboxes
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self.loadrate = self.load_rate()
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def load_rate(self):
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bboxes = self.bboxes
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fid = min(bboxes[:, 7])
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idx = bboxes[:, 7] == fid
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boxes = bboxes[idx]
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temp = np.zeros(self.incart.shape, np.uint8)
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for i in range(boxes.shape[0]):
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x1, y1, x2, y2, tid = boxes[i, 0:5]
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cv2.rectangle(temp, (int(x1), int(y1)), (int(x2), int(y2)), 255, cv2.FILLED)
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'''1. and 滤除购物车边框外的干扰'''
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loadstate = cv2.bitwise_and(self.incart, temp)
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'''2. xor 得到购物车内内被填充的区域'''
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# loadstate = cv2.bitwise_xor(self.incart, temp1)
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num_loadstate = cv2.countNonZero(loadstate)
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num_incart = cv2.countNonZero(self.incart)
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loadrate = num_loadstate / (num_incart+0.01)
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# edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png", cv2.IMREAD_GRAYSCALE)
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# cv2.imwrite(f"./test/temp.png", cv2.add(temp, edgeline))
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# cv2.imwrite(f"./test/incart.png", cv2.add(self.incart, edgeline))
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# cv2.imwrite(f"./test/loadstate.png", cv2.add(loadstate, edgeline))
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return loadrate
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@property
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def incart(self):
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img = cv2.imread(str(curpath/'cart_tempt/back_incart.png'), cv2.IMREAD_GRAYSCALE)
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ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
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return binary
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@property
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def outcart(self):
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img = cv2.imread(str(curpath/'cart_tempt/back_outcart.png'), cv2.IMREAD_GRAYSCALE)
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ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
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return binary
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@property
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def cartedge(self):
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img = cv2.imread(str(curpath/'cart_tempt/back_cartedge.png'), cv2.IMREAD_GRAYSCALE)
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ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
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return binary
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class Track:
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'''抽象基类，不能实例化对象'''
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def __init__(self, boxes, features, imgshape=(1024, 1280)):
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'''
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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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'''
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# assert len(set(boxes[:, 4].astype(int))) == 1, "For a Track, track_id more than 1"
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# assert len(set(boxes[:, 6].astype(int))) == 1, "For a Track, class number more than 1"
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self.boxes = boxes
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self.features = features
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self.tid = int(boxes[0, 4])
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self.cls = int(boxes[0, 6])
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self.frnum = boxes.shape[0]
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self.imgBorder = False
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self.imgshape = imgshape
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self.state = MoveState.Unknown
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'''轨迹开始帧、结束帧 ID'''
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self.start_fid = int(np.min(boxes[:, 7]))
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self.end_fid = int(np.max(boxes[:, 7]))
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self.Hands = []
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self.HandsIou = []
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'''5个关键点（中心点、左上点、右上点、左下点、右下点 ）坐标'''
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self.compute_cornpoints()
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'''5个关键点轨迹特征，可以在子类中实现，降低顺序处理时的计算量
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（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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mw, mh = np.mean(boxes[:, 2]-boxes[:, 0]), np.mean((boxes[:, 3]-boxes[:, 1]))
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self.mwh = np.mean((mw, mh))
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self.Area = mw * mh
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'''
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最后一帧与第一帧间的位移：
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vshift: 正值为向下，负值为向上
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hshift: 负值为向购物车边框两边移动，正值为物品向中心移动
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'''
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self.vshift = self.cornpoints[-1, 1] - self.cornpoints[0, 1] # 纵向位移
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self.hshift = abs(self.cornpoints[0, 0] - self.imgshape[0]/2) - \
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abs(self.cornpoints[-1, 0] - self.imgshape[0]/2)
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'''手部状态分析'''
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self.HAND_STATIC_THRESH = 100
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if self.cls == 0:
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self.extract_hand_features()
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def compute_cornpoints(self):
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'''
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cornpoints 共10项，分别是个点的坐标值（x, y）
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(center, top_left, top_right, bottom_left, bottom_right)
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'''
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boxes = self.boxes
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cornpoints = np.zeros((self.frnum, 10))
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cornpoints[:,0] = (boxes[:, 0] + boxes[:, 2]) / 2
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cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
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cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
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cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
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cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
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cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
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self.cornpoints = cornpoints
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def compute_cornpts_feats(self):
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'''
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'''
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trajectory = []
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trajlens = []
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trajdist = []
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trajrects = []
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for k in range(5):
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# diff_xy2 = np.power(np.diff(self.cornpoints[:, 2*k:2*(k+1)], axis = 0), 2)
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# trajlen = np.sum(np.sqrt(np.sum(diff_xy2, axis = 1)))
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X = self.cornpoints[:, 2*k:2*(k+1)]
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traj = np.linalg.norm(np.diff(X, axis=0), axis=1)
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trajectory.append(traj)
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trajlen = np.sum(traj)
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trajlens.append(trajlen)
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ptdist = np.max(cdist(X, X))
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trajdist.append(ptdist)
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'''最小外接矩形：
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rect[0]: 中心(x, y)
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rect[1]: (w, h)
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rect[0]: 旋转角度 (-90°, 0]
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'''
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rect = cv2.minAreaRect(X.astype(np.int64))
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trajrects.append(rect)
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self.trajectory = trajectory
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self.trajlens = trajlens
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self.trajdist = trajdist
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self.trajrects = trajrects
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def trajfeature(self):
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'''
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分两种情况计算轨迹特征（检测框边界不在图像边界范围内，在图像边界范围内）：
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-最小长度轨迹：trajmin
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-最小轨迹长度：trajlen_min
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-最小轨迹欧氏距离：trajdist_max
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'''
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idx1 = self.trajlens.index(max(self.trajlens))
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trajmax = self.trajectory[idx1]
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trajlen_max = self.trajlens[idx1]
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trajdist_max = self.trajdist[idx1]
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if not self.isCornpoint:
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idx2 = self.trajlens.index(min(self.trajlens))
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trajmin = self.trajectory[idx2]
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trajlen_min = self.trajlens[idx2]
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trajdist_min = self.trajdist[idx2]
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else:
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trajmin = self.trajectory[0]
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trajlen_min = self.trajlens[0]
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trajdist_min = self.trajdist[0]
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'''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
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trajlen_rate = trajlen_min/(trajlen_max+0.0001)
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'''最小轨迹欧氏距离/目标框尺度均值'''
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trajdist_rate = trajdist_min/(self.mwh+0.0001)
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self.trajmin = trajmin
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self.trajmax = trajmax
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self.TrajFeat = [trajlen_min, trajlen_max,
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trajdist_min, trajdist_max,
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trajlen_rate, trajdist_rate]
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def pt_state_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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# det_y = np.diff(y, axis=0)
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ranges, rangex = [], []
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static_indices = np.where(np.abs(det_y) < STATIC_THRESH)[0]
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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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start_index = static_indices[0]
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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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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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idx1, idx2 = ranges[0][0], ranges[-1][1]
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if idx1 != 0:
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rangex.append((0, idx1))
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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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if idx2 != len(det_y):
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rangex.append((idx2, len(det_y)))
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return ranges, rangex
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def PositionState(self, camerType="back"):
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'''
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camerType: back, 后置摄像头
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front, 前置摄像头
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'''
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if camerType=="front":
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incart = cv2.imread("./shopcart/cart_tempt/incart.png", cv2.IMREAD_GRAYSCALE)
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outcart = cv2.imread("./shopcart/cart_tempt/outcart.png", cv2.IMREAD_GRAYSCALE)
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else:
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incart = cv2.imread("./shopcart/cart_tempt/incart_ftmp.png", cv2.IMREAD_GRAYSCALE)
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outcart = cv2.imread("./shopcart/cart_tempt/outcart_ftmp.png", cv2.IMREAD_GRAYSCALE)
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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)
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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)
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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)
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# print(self.tid)
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Cent_inCartnum = np.count_nonzero(incart[(yc, xc)])
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LB_inCartnum = np.count_nonzero(incart[(y1, x1)])
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RB_inCartnum = np.count_nonzero(incart[(y2, x2)])
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Cent_outCartnum = np.count_nonzero(outcart[(yc, xc)])
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LB_outCartnum = np.count_nonzero(outcart[(y1, x1)])
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RB_outCartnum = np.count_nonzero(outcart[(y2, x2)])
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'''Track完全在车内：左下角点、右下角点与 outcart 的交集为 0'''
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self.isWholeInCart = False
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if LB_outCartnum + RB_outCartnum == 0:
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self.isWholeInCart = True
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'''Track完全在车外：左下角点、中心点与 incart 的交集为 0
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右下角点、中心点与 incart 的交集为 0
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'''
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self.isWholeOutCart = False
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if Cent_inCartnum + LB_inCartnum == 0 or Cent_inCartnum + RB_inCartnum == 0:
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self.isWholeOutCart = True
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self.Cent_isIncart = False
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self.LB_isIncart = False
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self.RB_isIncart = False
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if Cent_inCartnum: self.Cent_isIncart = True
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if LB_inCartnum: self.LB_isIncart = True
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if RB_inCartnum: self.RB_isIncart = True
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self.posState = self.Cent_isIncart+self.LB_isIncart+self.RB_isIncart
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def extract_hand_features(self):
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assert self.cls == 0, "The class of traj must be HAND!"
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self.isHandStatic = False
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x0 = (self.boxes[:, 0] + self.boxes[:, 2]) / 2
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y0 = (self.boxes[:, 1] + self.boxes[:, 3]) / 2
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handXY = np.stack((x0, y0), axis=-1)
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# handMaxY0 = np.max(y0)
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handCenter = np.array([(max(x0)+min(x0))/2, (max(y0)+min(y0))/2])
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handMaxDist = np.max(np.linalg.norm(handXY - handCenter))
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if handMaxDist < self.HAND_STATIC_THRESH:
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self.isHandStatic = True
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return
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class doTracks:
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def __init__(self, bboxes, TracksDict):
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'''fundamental property'''
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self.bboxes = bboxes
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# self.TracksDict = TracksDict
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self.frameID = np.unique(bboxes[:, 7].astype(int))
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self.trackID = np.unique(bboxes[:, 4].astype(int))
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self.lboxes = self.array2list()
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self.lfeats = self.getfeats(TracksDict)
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'''对 self.tracks 中的元素进行分类，将 track 归入相应列表中'''
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self.Hands = []
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self.Kids = []
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self.Static = []
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self.Residual = []
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self.DownWard = [] # subset of self.Residual
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self.UpWard = [] # subset of self.Residual
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self.FreeMove = [] # subset of self.Residual
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def array2list(self):
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'''
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将 bboxes 变换为 track 列表
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bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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Return：
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lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
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[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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'''
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track_ids = self.bboxes[:, 4].astype(int)
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lboxes = []
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for t_id in self.trackID:
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# print(f"The ID is: {t_id}")
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idx = np.where(track_ids == t_id)[0]
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box = self.bboxes[idx, :]
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assert len(set(box[:, 7])) == len(box), "Please check!!!"
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lboxes.append(box)
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return lboxes
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def getfeats(self, TracksDict):
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lboxes = self.lboxes
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lfeats = []
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for boxes in lboxes:
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afeat = readDict(boxes, TracksDict)
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lfeats.append(afeat)
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return lfeats
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def similarity(self):
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nt = len(self.tracks)
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similar_dict = {}
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if nt >= 2:
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for i in range(nt):
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for j in range(i, nt):
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tracka = self.tracks[i]
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trackb = self.tracks[j]
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similar = self.feat_similarity(tracka, trackb)
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similar_dict.update({(tracka.tid, trackb.tid): similar})
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return similar_dict
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def feat_similarity(self, tracka, trackb, metric='cosine'):
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boxes_a, boxes_b = tracka.boxes, trackb.boxes
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na, nb = tracka.boxes.shape[0], trackb.boxes.shape[0]
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feata, featb = [], []
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for i in range(na):
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fid, bid = tracka.boxes[i, 7:9]
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feata.append(self.features_dict[fid][bid])
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for i in range(nb):
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fid, bid = trackb.boxes[i, 7:9]
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featb.append(self.features_dict[fid][bid])
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feata = np.asarray(feata, dtype=np.float32)
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featb = np.asarray(featb, dtype=np.float32)
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similarity_matrix = 1-np.maximum(0.0, cdist(feata, featb, metric))
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feata_m = np.mean(feata, axis =0)[None,:]
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featb_m = np.mean(featb, axis =0)[None,:]
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simi_ab = 1 - cdist(feata_m, featb_m, metric)
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print(f'tid {int(boxes_a[0, 4])} vs {int(boxes_b[0, 4])}: {simi_ab[0][0]}')
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# return np.max(similarity_matrix)
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return simi_ab
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def merge_tracks_loop(self, alist):
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na, nb = len(alist), 0
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while na!=nb:
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na = len(alist)
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alist = self.merge_tracks(alist) #func is from subclass
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nb = len(alist)
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return alist
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def base_merge_tracks(self, Residual):
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"""
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对不同id，但可能是同一商品的目标进行归并
|
||
"""
|
||
mergedTracks = []
|
||
alist = [t for t in Residual]
|
||
while alist:
|
||
atrack = alist[0]
|
||
cur_list = []
|
||
cur_list.append(atrack)
|
||
alist.pop(0)
|
||
|
||
blist = [b for b in alist]
|
||
alist = []
|
||
for btrack in blist:
|
||
if track_equal_track(atrack, btrack):
|
||
cur_list.append(btrack)
|
||
else:
|
||
alist.append(btrack)
|
||
|
||
mergedTracks.append(cur_list)
|
||
|
||
return mergedTracks
|
||
|
||
|
||
@staticmethod
|
||
def join_tracks(tlista, tlistb):
|
||
"""Combine two lists of stracks into a single one."""
|
||
exists = {}
|
||
res = []
|
||
for t in tlista:
|
||
exists[t.tid] = 1
|
||
res.append(t)
|
||
for t in tlistb:
|
||
tid = t.tid
|
||
if not exists.get(tid, 0):
|
||
exists[tid] = 1
|
||
res.append(t)
|
||
return res
|
||
|
||
@staticmethod
|
||
def sub_tracks(tlista, tlistb):
|
||
track_ids_b = {t.tid for t in tlistb}
|
||
return [t for t in tlista if t.tid not in track_ids_b]
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|