update

ytracking/tracking/dotrack/dotracks.py

@@ -0,0 +1,357 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  4 18:16:01 2024
+
+@author: ym
+"""
+import numpy as np
+import cv2
+from pathlib import Path
+from scipy.spatial.distance import cdist
+
+from ytracking.tracking.utils.mergetrack import track_equal_track
+# curpath = Path(__file__).resolve().parents[0]
+from tools.config import cfg
+
+
+class MoveState:
+    """商品运动状态标志"""
+    Static = 0
+    DownWard = 1
+    UpWard = 2
+    FreeMove = 3
+    HandHborder = 4
+    Unknown = -1
+    
+class  ShoppingCart:
+    
+    def __init__(self, bboxes):
+        self.bboxes = bboxes
+        self.loadrate = self.load_rate()
+
+    def load_rate(self):
+        bboxes = self.bboxes
+
+        fid = min(bboxes[:, 7])
+        idx = bboxes[:, 7] == fid
+        boxes = bboxes[idx]
+        
+        temp = np.zeros(self.incart.shape, np.uint8)
+        for i in range(boxes.shape[0]):
+            x1, y1, x2, y2, tid = boxes[i, 0:5]
+            cv2.rectangle(temp, (int(x1), int(y1)), (int(x2), int(y2)), 255, cv2.FILLED)
+        
+        '''1. and 滤除购物车边框外的干扰'''
+        loadstate = cv2.bitwise_and(self.incart, temp)
+        
+        '''2. xor 得到购物车内内被填充的区域'''
+        # loadstate = cv2.bitwise_xor(self.incart, temp1)
+        
+        num_loadstate = cv2.countNonZero(loadstate)
+        num_incart = cv2.countNonZero(self.incart)
+        loadrate = num_loadstate / (num_incart+0.01)
+        
+        # edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png", cv2.IMREAD_GRAYSCALE)
+        # cv2.imwrite(f"./test/temp.png", cv2.add(temp, edgeline))
+        # cv2.imwrite(f"./test/incart.png", cv2.add(self.incart, edgeline))
+        # cv2.imwrite(f"./test/loadstate.png", cv2.add(loadstate, edgeline))
+
+        return loadrate
+    
+    
+        
+
+    @property    
+    def incart(self):
+        # img = cv2.imread(curpath/'cart_tempt'/'back_incart.png', cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(cfg.incart, cv2.IMREAD_GRAYSCALE)
+        ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
+        
+        return binary
+        
+    @property    
+    def outcart(self):
+        # img = cv2.imread(curpath/'cart_tempt'/'back_outcart.png', cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(cfg.outcart, cv2.IMREAD_GRAYSCALE)
+        ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
+        
+        return binary
+    
+    @property    
+    def cartedge(self):
+        # img = cv2.imread(curpath/'cart_tempt'/'back_cartedge.png', cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(cfg.cartedge, cv2.IMREAD_GRAYSCALE)
+        ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
+        
+        return binary
+    
+class Track:
+    '''抽象基类，不能实例化对象'''
+    def __init__(self, boxes, imgshape=(1024, 1280)):
+        '''
+        boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 
+                0   1   2   3      4        5     6       7           8     
+        '''
+        self.boxes = boxes
+        self.tid = int(boxes[0, 4])
+        self.cls = int(boxes[0, 6])
+        self.frnum = boxes.shape[0]
+        self.imgBorder = False
+        self.imgshape = imgshape
+        self.state = MoveState.Unknown
+        '''轨迹开始帧、结束帧 ID'''
+        self.start_fid = int(np.min(boxes[:, 7]))
+        self.end_fid = int(np.max(boxes[:, 7]))
+        
+        # 根据需要，可以在子类中实现，降低顺序处理时的计算量
+        self.compute_cornpoints()
+        self.compute_cornpts_feats()
+        
+        ''' 基于 (x, y, w, h) 的一些计算，haved deprecated
+            最后一帧与第一帧间的位移：
+            vshift: 正值为向下，负值为向上
+            hshift: 正值为物品向中心移动，负值为向购物车边框两边移动
+        '''
+        x0, y0 = (boxes[:,0] + boxes[:,2])/2, (boxes[:, 1] + boxes[:, 3])/2
+        mw, mh = np.mean(boxes[:, 2]-boxes[:, 0]), np.mean((boxes[:, 3]-boxes[:, 1]))
+        self.mwh = np.mean((mw, mh))
+        self.Area = mw * mh
+        self.vshift = y0[-1] - boxes[0]
+        self.hshift = abs(x0[0]-self.imgshape[0]/2) - abs(x0[-1]-self.imgshape[0]/2)
+
+        
+        
+        # self.boxmean = [np.mean(self.boxes[:, k]) for k in range(4)]
+        # self.mwh = np.mean(self.boxmean[2:])
+        # self.Area = boxes[:,2] * boxes[:,3]
+        # self.vshift = boxes[-1, 1] - boxes[0, 1]
+        # self.hshift = abs(boxes[0, 0]-self.imgshape[0]/2) - abs(boxes[-1, 0]-self.imgshape[0]/2)
+        
+        
+    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] = (boxes[:, 0] + boxes[:, 2]) / 2
+        cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
+        cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
+        cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
+        cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
+        cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
+
+        self.cornpoints = cornpoints
+    def compute_cornpts_feats(self):
+        '''
+        '''
+        trajectory = []
+        trajlens = []
+        trajdist = []
+        trajrects = []
+        for k in range(5):
+            # diff_xy2 = np.power(np.diff(self.cornpoints[:, 2*k:2*(k+1)], axis = 0), 2)
+            # trajlen = np.sum(np.sqrt(np.sum(diff_xy2, axis = 1)))
+            
+            X = self.cornpoints[:, 2*k:2*(k+1)]
+            
+            traj = np.linalg.norm(np.diff(X, axis=0), axis=1)
+            trajectory.append(traj)
+            
+            trajlen = np.sum(traj)
+            trajlens.append(trajlen)
+            
+            ptdist = np.max(cdist(X, X))
+            trajdist.append(ptdist)
+              
+            rect = cv2.minAreaRect(X.astype(np.int64))
+            trajrects.append(rect)
+        
+        self.trajectory = trajectory
+        self.trajlens = trajlens
+        self.trajdist = trajdist
+        self.trajrects = trajrects
+    
+
+        
+    def trajfeature(self):       
+        '''
+        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）：
+          -最小长度轨迹：trajmin
+          -最小轨迹长度：trajlen_min
+          -最小轨迹欧氏距离：trajdist_max
+        '''
+        idx1 = self.trajlens.index(max(self.trajlens))
+        trajmax = self.trajectory[idx1]
+        trajlen_max = self.trajlens[idx1]
+        trajdist_max = self.trajdist[idx1]
+        if not self.isCornpoint:
+            idx2 = self.trajlens.index(min(self.trajlens))            
+            trajmin = self.trajectory[idx2]
+            trajlen_min = self.trajlens[idx2]
+            trajdist_min = self.trajdist[idx2]
+        else:
+            trajmin = self.trajectory[0]
+            trajlen_min = self.trajlens[0]
+            trajdist_min = self.trajdist[0]
+    
+        
+        '''最小轨迹长度/最大轨迹长度，越小，代表运动幅度越小'''
+        trajlen_rate = trajlen_min/(trajlen_max+0.0001)    
+        
+        '''最小轨迹欧氏距离/目标框尺度均值'''
+        trajdist_rate = trajdist_min/(self.mwh+0.0001)
+        
+        
+        
+        self.trajmin = trajmin
+        self.trajmax = trajmax
+        self.feature = [trajlen_min, trajlen_max, 
+                        trajdist_min,  trajdist_max,
+                        trajlen_rate, trajdist_rate]
+
+
+
+    
+class doTracks:
+    def __init__(self, bboxes, features_dict):
+        
+        self.bboxes = bboxes
+        self.features_dict = features_dict
+        self.frameid = set(bboxes[:, 7])
+        self.trackid = set(bboxes[:, 4])
+        self.lboxes = self.array2list()  
+        
+        '''对 self.tracks 中的元素进行分类，将 track 归入相应列表中'''
+        self.Static = []
+        self.DownWard = []
+        self.UpWard = []
+        self.FreeMove = []
+        
+        self.Hands = []
+        self.Kids = []
+        self.HandHborder = []
+        self.Disruptors = []
+        self.Residual = []
+        self.Merged = []
+    
+    def array2list(self):
+        '''
+            将 bboxes 变换为 track 列表
+            bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+        Return：
+            lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
+                    [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]        
+        '''
+        track_ids = set(self.bboxes[:, 4])
+        lboxes = []
+        for t_id in track_ids: 
+            # print(f"The ID is: {t_id}")
+            idx = np.where(self.bboxes[:, 4] == t_id)[0]
+            box = self.bboxes[idx, :]     
+
+            lboxes.append(box)
+        
+        return lboxes
+    
+    
+    def similarity(self):
+        nt = len(self.tracks)
+        similar_dict = {}
+        if nt >= 2:
+            for i in range(nt):
+                for j in range(i, nt):
+                    tracka = self.tracks[i]
+                    trackb = self.tracks[j]
+                    similar = self.feat_similarity(tracka, trackb)
+                    similar_dict.update({(tracka.tid, trackb.tid): similar})
+        return similar_dict
+    
+    
+    def feat_similarity(self, tracka, trackb, metric='cosine'):
+        boxes_a, boxes_b = tracka.boxes, trackb.boxes
+        na, nb = tracka.boxes.shape[0], trackb.boxes.shape[0]
+        feata, featb = [], []
+        for i in range(na):
+            fid, bid = tracka.boxes[i, 7:9]
+            feata.append(self.features_dict[fid][bid])
+        for i in range(nb):
+            fid, bid = trackb.boxes[i, 7:9]
+            featb.append(self.features_dict[fid][bid])
+        
+        feata = np.asarray(feata, dtype=np.float32)
+        featb = np.asarray(featb, dtype=np.float32)
+        similarity_matrix = 1-np.maximum(0.0, cdist(feata, featb, metric))
+        
+        feata_m = np.mean(feata, axis =0)[None,:]
+        featb_m = np.mean(featb, axis =0)[None,:]
+        simi_ab = 1 - cdist(feata_m, featb_m, metric)
+        print(f'tid {int(boxes_a[0, 4])} vs {int(boxes_b[0, 4])}: {simi_ab[0][0]}')
+        
+        # return np.max(similarity_matrix)   
+        return simi_ab
+    
+    def merge_tracks_loop(self, alist):
+        na, nb = len(alist), 0
+        while na!=nb:
+            na = len(alist)
+            alist = self.merge_tracks(alist)
+            nb = len(alist)
+        return alist
+    
+    def base_merge_tracks(self, Residual):
+        """
+        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, self.features_dict):
+                    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]
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

ytracking/tracking/dotrack/dotracks_back.py

@@ -0,0 +1,204 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  4 18:36:31 2024
+
+@author: ym
+"""
+import numpy as np
+from ytracking.tracking.dotrack.dotracks import doTracks, ShoppingCart
+from ytracking.tracking.dotrack.track_back import backTrack
+
+
+class doBackTracks(doTracks):
+    def __init__(self, bboxes, features_dict):
+        
+        super().__init__(bboxes, features_dict)
+        
+        self.tracks = [backTrack(b) for b in self.lboxes]
+        
+        # self.similar_dict = self.similarity()
+        
+        
+        self.shopcart = ShoppingCart(bboxes)
+
+# =============================================================================
+#     def array2list(self):
+#         '''           0, 1,   2,  3,     4,      5,    6,      7,          8
+#             bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+#             lboxes：[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 
+#         '''
+# 
+#         track_ids = set(self.bboxes[:, 4])
+#         lboxes = []
+#         for t_id in track_ids:
+#             idx = np.where(self.bboxes[:, 4] == t_id)[0]
+#             box = self.bboxes[idx, :]
+#             
+#             x = (box[:, 0] + box[:, 2]) / 2
+#             y = (box[:, 1] + box[:, 3]) / 2
+#             
+#             # box: [x, y, w, h, track_id, score, cls, frame_index]
+#             box[:, 2] = box[:, 2] - box[:, 0]    
+#             box[:, 3] = box[:, 3] - box[:, 1]
+#             box[:, 0] = x
+#             box[:, 1] = y
+#       
+#             lboxes.append(box)
+# 
+#         
+#         return lboxes
+# =============================================================================
+
+            
+    
+    def classify(self):
+        '''
+        功能：对 tracks 中元素分类
+        
+        '''
+        
+        tracks = self.tracks
+        shopcart = self.shopcart
+
+        # 提取手的frame_id，并和动目标的frame_id 进行关联
+        hand_tracks = [t for t in tracks if t.cls==0]
+        self.Hands.extend(hand_tracks)
+        tracks = self.sub_tracks(tracks, hand_tracks)
+        
+        
+        
+        # 提取小孩的track，并计算状态：left, right, incart
+        kid_tracks = [t for t in tracks if t.cls==9]
+        kid_states = [self.kid_state(t) for t in kid_tracks]
+        self.Kids = [x for x in zip(kid_tracks, kid_states)]
+        
+        tracks = self.sub_tracks(tracks, kid_tracks)
+        
+
+
+        '''静态情况 1: 目标关键点最小相对运动轨迹 < 0.2, 指标值偏大
+           feature = [trajlen_min, trajlen_max, 
+                      trajdist_min,  trajdist_max,
+                      trajlen_rate, trajdist_rate]
+        '''        
+        track1 = [t for t in tracks if t.feature[5] < 0.2
+                                    or t.feature[3] < 120
+                                    ] 
+        
+        
+        '''静态情况 2: 目标初始状态为静止，适当放宽关键点最小相对运动轨迹 < 0.5'''
+        track2 = [t for t in tracks if t.static_index.size > 0
+                                   and t.static_index[0, 0] <= 2                      
+                                   and t.feature[5] < 0.5]
+        
+        '''静态情况 3: 目标初始状态和最终状态均为静止'''
+        track3 = [t for t in tracks if t.static_index.shape[0] >= 2
+                                   and t.static_index[0, 0] <= 2
+                                   and t.static_index[-1, 1] >= t.frnum-3]
+        
+        track12 = self.join_tracks(track1, track2) 
+        
+        '''提取静止状态的 track'''
+        static_tracks = self.join_tracks(track12, track3)
+        self.Static.extend(static_tracks)
+        
+        
+        '''剔除静止目标后的 tracks'''
+        tracks = self.sub_tracks(tracks, static_tracks)
+        
+        
+        '''购物框边界外具有运动状态的干扰目标'''
+        trcak4 = [t for t in tracks if self.isouttrack(t)]
+
+        tracks = self.sub_tracks(tracks, trcak4)
+
+        
+        '''轨迹循环归并'''
+        # merged_tracks = self.merge_tracks(tracks)
+        merged_tracks = self.merge_tracks_loop(tracks)
+
+        self.Residual = merged_tracks
+
+
+        
+    def merge_tracks(self, Residual):
+        """
+        对不同id，但可能是同一商品的目标进行归并
+        """ 
+        mergedTracks = self.base_merge_tracks(Residual)
+        
+        oldtracks, newtracks = [], []
+        for tracklist in mergedTracks:
+            if len(tracklist) > 1:
+                boxes = np.empty((0, 9), dtype=np.float32)
+                for i, track in enumerate(tracklist):
+                    if i==0: ntid, ncls=track.boxes[0, 4], track.boxes[0, 6]
+                    iboxes = track.boxes.copy()
+                    
+                    iboxes[:, 4], iboxes[:, 6] = ntid, ncls
+                    boxes = np.concatenate((boxes, iboxes), axis=0)
+                    
+                    oldtracks.append(track)
+                    
+                fid_indices = np.argsort(boxes[:, 7])
+                boxes_fid = boxes[fid_indices]    
+                    
+                newtracks.append(backTrack(boxes_fid))
+            elif len(tracklist) == 1:
+                oldtracks.append(tracklist[0])
+                newtracks.append(tracklist[0])
+                
+
+        redu = self.sub_tracks(Residual, oldtracks)
+        merged = self.join_tracks(redu, newtracks)
+
+        return merged      
+        
+    def kid_state(self, track):
+        
+        left_dist = track.cornpoints[:, 2]
+        right_dist = 1024 - track.cornpoints[:, 4]
+        
+        if np.sum(left_dist<30)/track.frnum>0.8 and np.sum(right_dist>512)/track.frnum>0.7:
+            kidstate = "left"
+        elif np.sum(left_dist>512)/track.frnum>0.7 and np.sum(right_dist<30)/track.frnum>0.8:
+            kidstate = "right"
+        else:
+            kidstate = "incart"
+        
+        return kidstate
+    
+    
+
+    def hand_association(self):
+        """
+        分析商品和手之间的关联性
+        """
+        pass 
+    
+    def isouttrack(self, track):     
+        if track.posState <= 1:
+            isout = True
+        else:
+            isout = False  
+        return isout
+    
+    
+
+      
+       
+
+    def isuptrack(self, track):
+        Flag = False
+        
+        return Flag
+    
+    def isdowntrack(self, track):
+        Flag = False
+        
+        return Flag
+    
+    def isfreetrack(self, track):
+        Flag = False
+        
+        return Flag

ytracking/tracking/dotrack/dotracks_front.py

@@ -0,0 +1,190 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  4 18:38:20 2024
+
+@author: ym
+"""
+import numpy as np
+from ytracking.tracking.utils.mergetrack import track_equal_track
+from .dotracks import doTracks
+from .track_front import frontTrack
+
+class doFrontTracks(doTracks):
+    def __init__(self, bboxes, features_dict):
+        super().__init__(bboxes, features_dict)
+        
+        self.tracks = [frontTrack(b) for b in self.lboxes]
+
+    def classify(self):
+        '''功能：对 tracks 中元素分类 '''
+        
+        tracks = self.tracks
+
+        '''提取手的 tracks'''
+        hand_tracks = [t for t in tracks if t.cls==0]
+        
+        for htrack in hand_tracks:
+            htrack.extract_hand_features()
+        
+        
+        
+        
+        
+        self.Hands.extend(hand_tracks)
+        tracks = self.sub_tracks(tracks, hand_tracks)
+        
+        
+        
+        '''提取小孩的 tracks'''
+        kid_tracks = [t for t in tracks if t.cls==9]        
+        tracks = self.sub_tracks(tracks, kid_tracks)
+
+
+        '''静态 tracks'''        
+        static_tracks = [t for t in tracks if t.frnum>1 and t.is_Static()] 
+
+        
+        '''剔除静止目标后的 tracks'''
+        tracks = self.sub_tracks(tracks, static_tracks)
+         
+        '''轨迹循环归并'''
+        merged_tracks = self.merge_tracks_loop(tracks)
+        
+        tracks = [t for t in merged_tracks if t.frnum > 1]
+
+        for gtrack in tracks:
+            # print(f"Goods ID:{gtrack.tid}")
+            for htrack in hand_tracks:
+                if self.is_associate_with_hand(htrack, gtrack):
+                    gtrack.hands.append(htrack)
+                    
+        freemoved_tracks = [t for t in tracks if t.is_FreeMove()]
+        
+        tracks = self.sub_tracks(tracks, freemoved_tracks)
+                    
+
+        self.Residual = tracks
+  
+    def is_associate_with_hand(self, htrack, gtrack):
+        '''手部 Track、商品 Track 建立关联的依据：
+            a. 运动帧的帧索引有交集
+            b. 帧索引交集部分iou均大于0
+        '''
+        
+        assert htrack.cls==0 and gtrack.cls!=0 and gtrack.cls!=9, 'Track cls is Error!'
+        
+        hboxes = np.empty(shape=(0, 9), dtype = float)
+        gboxes = np.empty(shape=(0, 9), dtype = float)
+        
+        # start, end 为索引值，需要 start:(end+1)
+        for start, end in htrack.dynamic_y2:
+            hboxes = np.concatenate((hboxes, htrack.boxes[start:end+1, :]), axis=0)    
+        for start, end in gtrack.dynamic_y1:
+            gboxes = np.concatenate((gboxes, gtrack.boxes[start:end+1, :]), axis=0)
+        
+        hfids, gfids = hboxes[:, 7], gboxes[:, 7]
+        fids = set(hfids).intersection(set(gfids))
+        
+        if len(fids)==0:
+            return False
+        
+        
+        # print(f"Goods ID: {gtrack.tid}, Hand ID: {htrack.tid}")
+        ious = []
+        for f in fids:
+            h = np.where(hfids==f)[0][0]
+            g = np.where(gfids==f)[0][0]
+            
+            x11, y11, x12, y12 = hboxes[h, 0:4]
+            x21, y21, x22, y22 = gboxes[g, 0:4]
+            
+            x1, y1 = max((x11, x21)), max((y11, y21))
+            x2, y2 = min((x12, x22)), min((y12, y22))
+            
+            union = (x2 - x1).clip(0) * (y2 - y1).clip(0)
+            area1 = (x12 - x11) * (y12 - y11)
+            area2 = (x22 - x21) * (y22 - y21)
+            
+            iou = union / (area1 + area2 - union + 1e-6)
+            
+            if iou>0: 
+                ious.append(iou)
+        
+        return len(ious)
+    
+    
+    
+    def merge_tracks(self, Residual):
+        """
+        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, self.features_dict):
+#                     cur_list.append(btrack)
+#                 else:
+#                     alist.append(btrack)
+#                     
+#             mergedTracks.append(cur_list)
+# =============================================================================
+        mergedTracks = self.base_merge_tracks(Residual)
+        
+        oldtracks, newtracks = [], []
+        for tracklist in mergedTracks:
+            if len(tracklist) > 1:
+                boxes = np.empty((0, 9), dtype=np.float32)
+                for i, track in enumerate(tracklist):
+                    if i==0: ntid, ncls=track.boxes[0, 4], track.boxes[0, 6]
+                    iboxes = track.boxes.copy()
+                    iboxes[:, 4], iboxes[:, 6] = ntid, ncls
+                    boxes = np.concatenate((boxes, iboxes), axis=0)
+                    oldtracks.append(track)
+                
+                fid_indices = np.argsort(boxes[:, 7])
+                boxes_fid = boxes[fid_indices]
+                
+                newtracks.append(frontTrack(boxes_fid))
+            elif len(tracklist) == 1:
+                oldtracks.append(tracklist[0])
+                newtracks.append(tracklist[0])
+                
+
+        redu = self.sub_tracks(Residual, oldtracks)
+        merged = self.join_tracks(redu, newtracks)
+
+        return merged
+            
+        
+    
+        
+# =============================================================================
+#     def array2list(self):
+#         '''
+#             将 bboxes 变换为 track 列表
+#             bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+#         Return：
+#             lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
+#                     [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]        
+#         '''
+#         track_ids = set(self.bboxes[:, 4])
+#         lboxes = []
+#         for t_id in track_ids: 
+#             # print(f"The ID is: {t_id}")
+#             idx = np.where(self.bboxes[:, 4] == t_id)[0]
+#             box = self.bboxes[idx, :]     
+# 
+#             lboxes.append(box)
+#         
+#         return lboxes
+# =============================================================================
+    

ytracking/tracking/dotrack/track_back.py

@@ -0,0 +1,304 @@
+# -*- 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

ytracking/tracking/dotrack/track_front.py

@@ -0,0 +1,275 @@
+# -*- 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 = []
+        
+        self.mwh = np.mean((self.boxes[:, 2] - self.boxes[:, 0]) *
+                           (self.boxes[:, 3] - self.boxes[:, 1]))
+        
+        
+        '''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'''
+        self.static_y1, self.dynamic_y1 = self.compute_static_fids(boxes[:, 1])
+        self.static_y2, self.dynamic_y2 = self.compute_static_fids(boxes[:, 3])
+        
+        self.isCornpoint = self.is_left_right_cornpoint()
+        self.isBotmpoint = self.is_bottom_cornpoint()
+        
+        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_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 compute_static_fids(self, 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(y)-1))
+            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(y)-1))
+            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(y)-1:
+            rangex.append((idx2, len(y)-1))
+            
+        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 extract_hand_features(self):              
+        self.isHandStatic = False
+        
+        x0 = (self.boxes[:, 0] + self.boxes[:, 2]) / 2
+        y0 = (self.boxes[:, 1] + self.boxes[:, 3]) / 2
+        
+        handXy = np.stack((x0, y0), axis=-1)
+        handMaxY0 = np.max(y0)
+        
+        handCenter = np.array([(max(x0)+min(x0))/2, (max(y0)+min(y0))/2])
+        
+        handMaxDist = np.max(np.linalg.norm(handXy - handCenter))
+        
+        if handMaxDist < self.HAND_STATIC_THRESH:
+            self.isHandStatic = True
+            return
+            
+        
+        
+    
+    
+    
+    
+    def is_Upward(self):
+        '''判断商品是否取出，'''
+        print(f"The ID is: {self.tid}")
+        
+    def is_FreeMove(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
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+