update

ytracking/tracking/test_merge.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 23 11:04:48 2024
+
+@author: ym
+"""
+import numpy as np
+import cv2
+from scipy.spatial.distance import cdist
+# from trackers.utils import matching
+
+def readDict(boxes, feat_dicts):
+    feat = []
+    for i in range(boxes.shape[0]):   
+        tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
+        feat.append(feat_dicts[fid][bid])
+        
+        # img = feat_dicts[fid][f'{bid}_img']
+        # cv2.imwrite(f'./result/imgs/{tid}_{fid}_{bid}.png', img)
+    
+    return np.asarray(feat, dtype=np.float32)
+
+
+
+def track_equal_track(atrack, btrack, feat_dicts):
+    # boxes: [x, y, w, h, track_id, score, cls, frame_index, box_index]
+    aboxes = atrack.boxes
+    bboxes = btrack.boxes
+    
+    ''' 1. 判断轨迹在时序上是否有交集 '''
+    afids = aboxes[:, 7].astype(np.int_)
+    bfids = bboxes[:, 7].astype(np.int_)
+    # 帧索引交集
+    interfid = set(afids).intersection(set(bfids))
+    
+    # 或者直接判断帧索引是否有交集，返回 Ture or False
+    # interfid = set(afids).isdisjoint(set(bfids))
+    
+    
+    ''' 2. 轨迹空间iou'''
+    alabel = np.array([0] * afids.size, dtype=np.int_)
+    blabel = np.array([1] * bfids.size, dtype=np.int_)
+    
+    label = np.concatenate((alabel, blabel), axis=0)
+    fids = np.concatenate((afids, bfids), axis=0)
+    indices = np.argsort(fids)
+    idx_pair = []
+    for i in range(len(indices)-1):
+        idx1, idx2 = indices[i], indices[i+1]
+        if label[idx1] != label[idx2] and fids[idx2] - fids[idx1] == 1:
+            if label[idx1] == 0:
+                a_idx = idx1
+                b_idx = idx2-alabel.size
+            else:
+                a_idx = idx2
+                b_idx = idx1-alabel.size 
+            
+            idx_pair.append((a_idx, b_idx))
+    
+    ious = []
+    for a, b in idx_pair:
+        abox, bbox = aboxes[a, :], bboxes[b, :]
+        
+        xa1, ya1 = abox[0] - abox[2]/2, abox[1] - abox[3]/2 
+        xa2, ya2 = abox[0] + abox[2]/2, abox[1] + abox[3]/2
+        
+        xb1, yb1 = bbox[0] - bbox[2]/2, bbox[1] - bbox[3]/2
+        xb2, yb2 = bbox[0] + bbox[2]/2, bbox[1] + bbox[3]/2 
+        
+        
+        inter = (np.minimum(xb2, xa2) - np.maximum(xb1, xa1)).clip(0) * \
+                     (np.minimum(yb2, ya2) - np.maximum(yb1, ya1)).clip(0)
+                     
+        # Union Area
+        box1_area = abox[2] * abox[3]
+        box2_area = bbox[2] * bbox[3]
+        union = box1_area + box2_area - inter + 1e-6
+
+        ious.append(inter/union)
+    
+    ''' 3. 轨迹特征相似度判断'''
+    afeat = readDict(aboxes, feat_dicts)
+    bfeat = readDict(bboxes, feat_dicts)
+    feat = np.concatenate((afeat, bfeat), axis=0)
+    
+    emb_simil = 1-np.maximum(0.0, cdist(feat, feat, 'cosine'))
+    emb_ = 1-cdist(np.mean(afeat, axis=0)[None, :], np.mean(bfeat, axis=0)[None, :], 'cosine')
+     
+    cont1 = False if len(interfid) else True
+    cont2 = all(iou>0.5 for iou in ious)
+    cont3 = emb_[0, 0]>0.75
+    
+    cont = cont1 and cont2 and cont3
+    
+    
+    
+    
+    
+    
+    
+    return cont
+
+
+
+def track_equal_str(atrack, btrack):
+    if atrack == btrack:
+        return True
+    else:
+        return False
+
+
+def merge_track(Residual):
+    out_list = []
+    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_str(atrack, btrack):
+                cur_list.append(btrack)
+            else:
+                alist.append(btrack)
+                
+        out_list.append(cur_list)
+    return out_list
+    
+def main():
+    Residual = ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'b', 'c', 'd']
+    out_list = merge_track(Residual)
+    
+    print(Residual)
+    print(out_list)
+
+if __name__ == "__main__": 
+    
+    main()
+
+
+
+
+# =============================================================================
+# for i, atrack in enumerate(input_list):
+#     cur_list = []
+#     cur_list.append(atrack)
+#     del input_list[i]
+#     
+#     for j, btrack in enumerate(input_list):
+#         if track_equal(atrack, btrack):
+#             cur_list.append(btrack)
+#             del input_list[j]
+#     
+#     out_list.append(cur_list)
+# =============================================================================
+
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