box select in a track and feat simi modify in tracker

pipeline.py

@@ -10,6 +10,7 @@ import cv2
 import pickle
 import numpy as np
 from pathlib import Path
+from scipy.spatial.distance import cdist
 from track_reid import yolo_resnet_tracker
 
 from tracking.dotrack.dotracks_back import doBackTracks
@@ -19,13 +20,45 @@ from utils.getsource import get_image_pairs, get_video_pairs
 from tracking.utils.read_data import read_similar
 
 
-def save_subimgs(imgdict, boxes, spath, ctype):
+def save_subimgs(imgdict, boxes, spath, ctype, featdict = None):
+    '''
+    当前 box 特征和该轨迹前一个 box 特征的相似度，可用于和跟踪序列中的相似度进行比较
+    '''
+    boxes = boxes[np.argsort(boxes[:, 7])]
     for i in range(len(boxes)):
-        fid, bid = int(boxes[i, 7]), int(boxes[i, 8])
-        if f"{fid}_{bid}" in imgdict.keys():
-            img = imgdict[f"{fid}_{bid}"]
-            imgpath = spath / f"{ctype}_{fid}_{bid}.png"
-            cv2.imwrite(imgpath, img)
+        simi = None
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        
+        if i>0:
+            _, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
+            if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys():
+                feat0 = featdict[f"{fid0}_{bid0}"]
+                feat1 = featdict[f"{fid}_{bid}"]
+                simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0]
+            
+        img = imgdict[f"{fid}_{bid}"]
+        imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
+        if simi is not None:
+            imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png"
+        
+        cv2.imwrite(imgpath, img)
+        
+        
+def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None):
+    '''
+    当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中，
+    采用该方式记录特征相似度
+    '''
+    for i in range(len(boxes)):
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        
+        key = f"{fid}_{bid}"
+        img = imgdict[key]
+        imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
+        if simidict is not None and key in simidict.keys():
+            imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png"
+        
+        cv2.imwrite(imgpath, img)
         
 
 def pipeline(        
@@ -177,15 +210,18 @@ def pipeline(
             yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
             ctype = 0
             
-        imgdict = {}
+        imgdict, featdict, simidict = {}, {}, {}
         for y in yolos:
             imgdict.update(y["imgs"])
+            featdict.update(y["feats"])
+            simidict.update(y["featsimi"])
+            
  
         for track in vts.Residual:
             if isinstance(track, np.ndarray):
-                save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype)
+                save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict)
             else:
-                save_subimgs(imgdict, track.boxes, savepath_pipeline_subimgs,  ctype)
+                save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs,  ctype, featdict)
   
 
     '''轨迹显示模块'''
@@ -243,14 +279,14 @@ def main():
         if item.is_dir():            
             # item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
             parmDict["eventpath"] = item
-            # pipeline(**parmDict)
+            pipeline(**parmDict)
             
-            try:
-                pipeline(**parmDict)
-            except Exception as e:
-                errEvents.append(str(item))
+            # try:
+            #     pipeline(**parmDict)
+            # except Exception as e:
+            #     errEvents.append(str(item))
         k+=1
-        if k==1:
+        if k==2:
             break
             
     errfile = os.path.join(parmDict["savepath"], f'error_events.txt')