回传数据解析，兼容v5和v10

tracking/dotrack/dotracks_back.py

@@ -0,0 +1,277 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  4 18:36:31 2024
+
+@author: ym
+"""
+import numpy as np
+import cv2
+import copy
+import sys
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[2]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))
+
+from tracking.utils.mergetrack import track_equal_track
+
+
+from scipy.spatial.distance import cdist
+
+curpath = Path(__file__).resolve().parents[0]
+curpath = Path(curpath)
+parpath = curpath.parent
+
+
+from .dotracks import doTracks, ShoppingCart
+from .track_back import backTrack
+
+
+class doBackTracks(doTracks):
+    def __init__(self, bboxes, trackefeats):
+        
+        super().__init__(bboxes, trackefeats)
+        
+        self.tracks = [backTrack(b, f) for b, f in zip(self.lboxes, self.lfeats)]
+        
+        # self.similar_dict = self.similarity()        
+        # self.shopcart = ShoppingCart(bboxes)
+
+        self.incart = self.getincart()
+        
+        
+    def getincart(self):
+        img1 = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
+        img2 = cv2.imread(str(parpath/'shopcart/cart_tempt/cartedge.png'), cv2.IMREAD_GRAYSCALE)
+        
+        
+        ret, binary1 = cv2.threshold(img1, 250, 255, cv2.THRESH_BINARY)
+        ret, binary2 = cv2.threshold(img2, 250, 255, cv2.THRESH_BINARY)
+        
+        binary = cv2.bitwise_or(binary1, binary2)
+        
+        
+        return binary
+        
+        
+
+    def classify(self):
+        '''功能：对 tracks 中元素分类 '''
+
+        tracks = self.tracks
+        # 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)
+                
+        out_trcak = [t for t in tracks if t.isWholeOutCart]
+        tracks = self.sub_tracks(tracks, out_trcak)
+
+        static_tracks = [t for t in tracks if t.frnum>1 and t.is_static()] 
+        self.Static.extend(static_tracks)
+        
+        '''剔除静止目标后的 tracks'''
+        tracks = self.sub_tracks(tracks, static_tracks)
+
+        
+        tracks_free = [t for t in tracks if t.frnum>1 and t.is_freemove()]
+        self.FreeMove.extend(tracks_free)
+        tracks = self.sub_tracks(tracks, tracks_free)
+        
+        
+        
+        # '''购物框边界外具有运动状态的干扰目标'''
+        # out_trcak = [t for t in tracks if t.is_OutTrack()]
+        # tracks = self.sub_tracks(tracks, out_trcak)
+
+        
+        
+        '''轨迹循环归并'''
+        # merged_tracks = self.merge_tracks(tracks)
+        merged_tracks = self.merge_tracks_loop(tracks)
+        
+        [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
+        
+        
+        tracks = [t for t in merged_tracks if t.frnum > 1]
+        
+        self.merged_tracks = merged_tracks
+        
+        static_tracks = [t for t in tracks if t.frnum>1 and t.is_static()] 
+        self.Static.extend(static_tracks)
+        
+        tracks = self.sub_tracks(tracks, static_tracks)
+
+        # for gtrack in tracks:
+        #     for htrack in hand_tracks:
+        #         hand_ious = self.associate_with_hand(htrack, gtrack)
+                # if len(hand_ious):
+                #     gtrack.Hands.append(htrack)
+                #     gtrack.HandsIou.append(hand_ious)    
+                #     htrack.Goods.append((gtrack, hand_ious))
+                       
+        # for htrack in  hand_tracks:
+        #     self.merge_based_hands(htrack)
+       
+        self.Residual = tracks
+        self.Confirmed = self.confirm_track()
+        
+    def confirm_track(self):
+        Confirmed = None
+        mindist = 0
+        for track in self.Residual:
+            md = min(track.trajrects_wh)
+            if md > mindist:
+                mindist = copy.deepcopy(md)
+                Confirmed = copy.deepcopy(track)
+                
+        if Confirmed is not None:
+            return [Confirmed]
+
+        return []
+        
+    
+    # def merge_based_hands(self, htrack):
+    #     gtracks = htrack.Goods
+        
+    #     if len(gtracks) >= 2:
+    #         atrack, afious = gtracks[0]
+    #         btrack, bfious = gtracks[1]
+    
+    
+
+    
+    def associate_with_hand(self, htrack, gtrack):
+        ''' 
+        迁移至基类：
+            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!'
+        
+        hand_ious = []
+        
+        hboxes = np.empty(shape=(0, 9), dtype = np.float64)
+        gboxes = np.empty(shape=(0, 9), dtype = np.float64)
+
+        
+        # start, end 为索引值，需要 start:(end+1)
+        for start, end in htrack.moving_index:
+            hboxes = np.concatenate((hboxes, htrack.boxes[start:end+1, :]), axis=0)    
+        for start, end in gtrack.moving_index:
+            gboxes = np.concatenate((gboxes, gtrack.boxes[start:end+1, :]), axis=0)
+        
+        hfids, gfids = hboxes[:, 7], gboxes[:, 7]
+        fids = sorted(set(hfids).intersection(set(gfids)))
+        
+        
+        if len(fids)==0:
+            return None
+
+        # print(f"Goods ID: {gtrack.tid}, Hand ID: {htrack.tid}")
+        
+        for f in fids:
+            h = np.where(hboxes[:,7] == f)[0][0]
+            g = np.where(gboxes[:,7] == 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 >= 0.01:                 
+                gtrack.Hands.append((htrack.tid, f, iou))
+               
+        
+        return gtrack.Hands
+        
+    def merge_tracks(self, Residual):
+        """
+        对不同id，但可能是同一商品的目标进行归并
+        和 dotrack_front.py中函数相同，可以合并，可以合并至基类
+        """ 
+        mergedTracks = self.base_merge_tracks(Residual)
+        
+        oldtracks, newtracks = [], []
+        for tracklist in mergedTracks:
+            if len(tracklist) > 1:
+                boxes = np.empty((0, 9), dtype=np.float32)
+                feats = np.empty((0, 256), 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()
+                    ifeats = track.features.copy()
+                    
+                    # iboxes[:, 4], iboxes[:, 6] = ntid, ncls
+                    
+                    boxes = np.concatenate((boxes, iboxes), axis=0)
+                    feats = np.concatenate((feats, ifeats), axis=0)
+                    
+                    oldtracks.append(track)
+                    
+                fid_indices = np.argsort(boxes[:, 7])
+                
+                boxes_fid = boxes[fid_indices]    
+                feats_fid = feats[fid_indices]
+                
+                
+                
+                newtracks.append(backTrack(boxes_fid, feats_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 isuptrack(self, track):
+        Flag = False
+        
+        return Flag
+    
+    def isdowntrack(self, track):
+        Flag = False
+        
+        return Flag
+    
+    def isfreetrack(self, track):
+        Flag = False
+        
+        return Flag