modify byte.trackerupdate, add list first_finded

track_reid.py

@@ -206,7 +206,12 @@ def run(
     handpose = hand_pose()
     handlocals_dict = {}
     
-    boxes_and_imgs = []
+    boxes_and_imgs  = []
+    
+    BoxesFeats = []
+    
+    
+    
     track_boxes = np.empty((0, 9), dtype = np.float32)
     det_boxes = np.empty((0, 9), dtype = np.float32)
     
@@ -263,27 +268,40 @@ def run(
                 det = det.cpu().numpy()
                 det = np.concatenate([det[:, :4], np.arange(nd).reshape(-1, 1), det[:, 4:]], axis=-1) 
                 
-                '''FeatFlag为相对于上一帧boxes，当前boxes是否为静止的标志。'''
+                #
-                # def static_estimate(box1, box2, TH1=8, TH2=12):
+                def static_estimate(box1, box2, TH1=8, TH2=12):
-                #     dij_abs = max(np.abs(box1 - box2))
+                    dij_abs = max(np.abs(box1 - box2))
-                #     dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])), 
+                    dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])), 
-                #                    np.linalg.norm((box1[2:4] - box2[2:4]))
+                                    np.linalg.norm((box1[2:4] - box2[2:4]))
-                #                    ])
+                                    ])
-                #     if dij_abs < TH1 and dij_euc < TH2:
+                    if dij_abs < TH1 and dij_euc < TH2:
-                #         return True
+                        return True
-                #     else:
+                    else:
-                #         return False
+                        return False
-                # FeatFlag = [-1] * nd  
+
-                # if len(boxes_and_imgs):
+                nw = 3                      # 向前递推检查的窗口大小
-                #     detj = boxes_and_imgs[-1][0]
+                nf = len(BoxesFeats)        # 已经检测+特征提取的帧数
-                #     frmj = boxes_and_imgs[-1][-1]  
+                feat_curr = [None] * nd     # nd: 当前帧检测出的boxes数
-                #     for ii in range(nd):
+                for ii in range(nd):
-                #         ## flag 中保存的是box索引
+                    box = det[ii, :4]
-                #         condt1 = frame-frmj==1
+                    
-                #         flag = [idx for jj, idx in enumerate(detj[:, 4]) if condt1 and static_estimate(det[ii, :4], detj[jj, :4])]
+                    kk=1
-                #         if len(flag) == 1:
+                    feat = None
-                #             FeatFlag[ii] = flag[0]
+                    while kk <= nw and nf>=kk:
-                # boxes_and_imgs.append((det, im0, frame)) 
+                        ki = -1 * kk
+                        boxes_ = BoxesFeats[ki][0]
+                        feats_ = BoxesFeats[ki][1]
+
+                        flag = [jj for jj in range(len(boxes_)) if static_estimate(box, boxes_[jj, :4])]
+                        if len(flag) == 1:
+                            feat = feats_[flag[0]]
+                            break 
+                        kk += 1
+                    if feat is not None:
+                        feat_curr[ii] = feat
+                        
+                        
+
                 
                 ## ================================================================ writed by WQG
                 
@@ -301,6 +319,9 @@ def run(
                 '''================== 1. 存储 dets/subimgs/features Dict ============='''
                 imgs, features = inference_image(im0, tracks)
                 
+                BoxesFeats.append((tracks, features))
+                
+                
                 imgdict = {}
                 boxdict = {}
                 featdict = {}
@@ -504,17 +525,20 @@ def main_loop(opt):
     if os.path.isdir(p):
         files = find_files_in_nested_dirs(p)
         
-        # files = [#r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4",
+        # files = [r"D:\datasets\ym\广告板遮挡测试\8\6926636301004_20240508-175300_back_addGood_70f754088050_215_17327712807.mp4",
-                  # r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4"]
+        #           r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4",
+        #           r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4",
+        #           r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"
+        #           ]
         
-        # files = [r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"]
+        files = [r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4"]
         for file in files:
             optdict["source"] = file
             run(**optdict)
             
-            k += 1
+            # k += 1
-            if k == 3:
+            # if k == 3:
-                break
+            #     break
     elif os.path.isfile(p):
         optdict["source"] = p
         run(**vars(opt))

tracking/dotrack/dotracks.py

@@ -216,7 +216,7 @@ class Track:
                         trajdist_min,  trajdist_max,
                         trajlen_rate, trajdist_rate]
         
-    def compute_static_fids(self, det_y, STATIC_THRESH = 8):
+    def pt_state_fids(self, det_y, STATIC_THRESH = 8):
         '''
         前摄时，y一般选择为 box 的 y1 坐标，且需限定商品在购物车内。
         inputs：
@@ -326,10 +326,13 @@ class doTracks:
             idx = np.where(track_ids == t_id)[0]
             box = self.bboxes[idx, :]  
             
+            assert len(set(box[:, 7])) == len(box), "Please check!!!"
+
             lboxes.append(box)
         
         return lboxes
     
+    '''
     def classify(self):
         
         tracks = self.tracks
@@ -354,10 +357,11 @@ class doTracks:
         self.Static.extend(static_tracks)
         
         
-        '''剔除静止目标后的 tracks'''
+        # 剔除静止目标后的 tracks
         tracks = self.sub_tracks(tracks, static_tracks)
         
         return tracks
+    '''
         
     
         

tracking/dotrack/dotracks_back.py

@@ -51,40 +51,28 @@ class doBackTracks(doTracks):
 #         return lboxes
 # =============================================================================
 
-            
-    
     def classify(self):
-        '''
+        '''功能：对 tracks 中元素分类 '''
-        功能：对 tracks 中元素分类
 
-        '''
+        tracks = self.tracks
-        tracks = super().classify()
+        # 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.tracks
+        tracks = self.sub_tracks(tracks, hand_tracks)
-        # shopcart = self.shopcart
 
-        # # 提取手的frame_id，并和动目标的frame_id 进行关联
+        # 提取小孩的track，并计算状态：left, right, incart
-        # hand_tracks = [t for t in tracks if t.cls==0]
+        kid_tracks = [t for t in tracks if t.cls==9]
-        # self.Hands.extend(hand_tracks)
+        kid_states = [self.kid_state(t) for t in kid_tracks]
-        # tracks = self.sub_tracks(tracks, hand_tracks)
+        self.Kids = [x for x in zip(kid_tracks, kid_states)]
         
+        tracks = self.sub_tracks(tracks, kid_tracks)
 
+        static_tracks = [t for t in tracks if t.frnum>1 and t.is_static()] 
+        self.Static.extend(static_tracks)
         
-        # # 提取小孩的track，并计算状态：left, right, incart
+        '''剔除静止目标后的 tracks'''
-        # kid_tracks = [t for t in tracks if t.cls==9]
+        tracks = self.sub_tracks(tracks, static_tracks)
-        # 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)
-
-
-        # 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)
-        
 
         
         '''购物框边界外具有运动状态的干扰目标'''

tracking/dotrack/track_back.py

@@ -31,7 +31,7 @@ class backTrack(Track):
         '''运动点帧索引(运动帧两端的静止帧索引)'''
         self.moving_index = self.compute_dynamic_fids()
         
-        self.static_dynamic_fids = self.compute_static_dynamic_fids()
+        # self.static_index, self.moving_index = self.compute_static_dynamic_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
@@ -250,13 +250,35 @@ class backTrack(Track):
         return moving_index     
     
     def compute_static_dynamic_fids(self):
-        static_dynamic_fids = []
-        for traj in self.trajectory:
-            static, dynamic = self.compute_static_fids(traj)
         
-            static_dynamic_fids.append((static, dynamic))
+        idx2 = self.trajlens.index(min(self.trajlens))            
+        trajmin = self.trajectory[idx2]
         
-        return static_dynamic_fids
+        static, dynamic = self.pt_state_fids(trajmin) 
+        
+        static = np.array(static)
+        dynamic = np.array(dynamic)
+        
+        if static.size:
+            indx = np.argsort(static[:, 0])
+            static = static[indx]
+        if dynamic.size:
+            indx = np.argsort(dynamic[:, 0])
+            dynamic = dynamic[indx]
+            
+        return static, dynamic
+        
+        
+        
+        
+# =============================================================================
+#         static_dynamic_fids = []
+#         for traj in self.trajectory:
+#             static, dynamic = self.pt_state_fids(traj)            
+#             static_dynamic_fids.append((static, dynamic))
+#                
+#         return static_dynamic_fids
+# =============================================================================
     
     
     
@@ -270,6 +292,10 @@ class backTrack(Track):
                       trajlen_rate, trajdist_rate]
         '''
         
+        # print(f"TrackID: {self.tid}")
+        boxes = self.boxes
+        
+        
         condt1 = self.feature[5] < 0.2 or self.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'''

tracking/dotrack/track_front.py

@@ -31,8 +31,8 @@ class frontTrack(Track):
         '''y1、y2静止状态区间，值是 boxes 中对 axis=0 的索引，不是帧索引'''
         det_y1 = np.diff(boxes[:, 1], axis=0)
         det_y2 = np.diff(boxes[:, 3], axis=0)
-        self.static_y1, self.dynamic_y1 = self.compute_static_fids(det_y1)
+        self.static_y1, self.dynamic_y1 = self.pt_state_fids(det_y1)
-        self.static_y2, self.dynamic_y2 = self.compute_static_fids(det_y2)
+        self.static_y2, self.dynamic_y2 = self.pt_state_fids(det_y2)
         
         self.isCornpoint = self.is_left_or_right_cornpoint()
         self.isBotmpoint = self.is_bottom_cornpoint()

tracking/test_tracking.py

@@ -90,7 +90,7 @@ def have_tracked():
     k = 0
     gt = Profile()
     for filename in os.listdir(trackdict):
-        filename = 'test_20240402-173935_6920152400975_front_174037379.pkl'
+        filename = 'test_20240402-173935_6920152400975_back_174037372.pkl'
         file, ext = os.path.splitext(filename)
         filepath = os.path.join(trackdict, filename)
         TracksDict = np.load(filepath, allow_pickle=True)
@@ -121,9 +121,9 @@ def have_tracked():
         
         
         
-        # k += 1
+        k += 1
-        # if k==1:
+        if k==1:
-        #     break
+            break
     
     if len(alltracks):
         drawFeatures(alltracks, save_dir)

tracking/trackers/byte_tracker.py

@@ -215,6 +215,8 @@ class BYTETracker:
         lost_stracks = []
         removed_stracks = []
         
+        first_finded = []
+
         scores = results.conf
         cls = results.cls
         
@@ -328,6 +330,9 @@ class BYTETracker:
                 continue
             track.activate(self.kalman_filter, self.frame_id)
             activated_stracks.append(track)
+            
+            first_finded.append(track)
+            
         # Step 5: Update state
         for track in self.lost_stracks:
             if self.frame_id - track.end_frame > self.max_time_lost:
@@ -359,7 +364,7 @@ class BYTETracker:
                               for x in self.tracked_stracks if x.is_activated]
         
         output2 = [x.tlwh_to_tlbr(x._tlwh).tolist() + [x.track_id, x.score, x.cls, x.frame_id, x.idx] 
-                              for x in activated_stracks if x.first_find]
+                              for x in first_finded if x.first_find]
         
         output = np.asarray(output1+output2, dtype=np.float32)
 

tracking/utils/drawtracks.py

@@ -11,6 +11,7 @@ import matplotlib.pyplot as plt
 from sklearn.decomposition import PCA
 from utils.annotator import TrackAnnotator
 from utils.plotting import colors
+from pathlib import Path
 
 
 def plot_frameID_y2(vts):
@@ -88,14 +89,18 @@ def draw_all_trajectories(vts, edgeline, save_dir, filename):
     
 # =============================================================================
 #     '''1. tracks 5点轨迹'''
+#     trackpth = save_dir.parent /Path("trajectory")/ Path(f"{file}")
+#     if not trackpth.exists():
+#         trackpth.mkdir(parents=True, exist_ok=True)
 #     for track in vts.tracks:
-#         if track.cls != 0:
+#         # if track.cls != 0:
-#             img = edgeline.copy()
+#         img = edgeline.copy()
-#             img = draw5points(track, img)
+#         img = draw5points(track, img)
-#             pth = save_dir.joinpath(f"{file}_{track.tid}.png")
+# 
-#             cv2.imwrite(pth, img)
+#         pth = trackpth.joinpath(f"{track.tid}.png")        
+#         cv2.imwrite(str(pth), img)
+#     
 # =============================================================================
-    
     '''2. all tracks 中心轨迹'''
     img1, img2 = edgeline.copy(), edgeline.copy()