update for bakeup

tracking/contrast_analysis.py

@@ -10,6 +10,43 @@ sys.path.append(r"D:\DetectTracking")
 from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
 from tracking.utils.plotting import draw_tracking_boxes
 
+VideoFormat = ['.mp4', '.avi']
+def video2imgs(videopath, savepath):
+    k = 0
+    have = False
+    for filename in os.listdir(videopath):
+        file, ext = os.path.splitext(filename)
+        if ext not in VideoFormat:
+            continue
+        
+        basename = os.path.basename(videopath)
+        imgbase = basename + '_' + file
+        imgdir = os.path.join(savepath, imgbase)
+        if not os.path.exists(imgdir):
+            os.mkdir(imgdir)
+
+        video = os.path.join(videopath, filename)
+        cap = cv2.VideoCapture(video)
+        i = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            imgp = os.path.join(imgdir, file+f"_{i}.png")
+            i += 1
+            cv2.imwrite(imgp, frame)
+        cap.release()
+        
+        print(filename + f" haved resolved")
+        
+        k+=1
+        if k==1000:
+            break
+
+
+
+
+
 def showHist(err, correct):
     err = np.array(err)
     correct = np.array(correct)
@@ -22,24 +59,28 @@ def showHist(err, correct):
     axs[1].hist(correct, bins=50, edgecolor='black')
     axs[1].set_xlim([0, 1])
     axs[1].set_title('correct')
-    plt.show()
+    # plt.show()
     
-def showgrid(recall, prec, ths):
+    return plt
+
+def show_recall_prec(recall, prec, ths):
     # x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
     fig = plt.figure(figsize=(10, 6))
     plt.plot(ths, recall, color='red', label='recall')
     plt.plot(ths, prec, color='blue', label='PrecisePos')
     plt.legend()
-    plt.xlabel('threshold')
+    plt.xlabel(f'threshold')
     # plt.ylabel('Similarity')
     plt.grid(True, linestyle='--', alpha=0.5)
-    plt.savefig('accuracy_recall_grid.png')
-    plt.show()
+    # plt.savefig('accuracy_recall_grid.png')
+    # plt.show()
     # plt.close()
     
+    return plt
+
 
 def compute_recall_precision(err_similarity, correct_similarity):
-    ths = np.linspace(0, 1, 11)
+    ths = np.linspace(0, 1, 51)
     recall, prec = [], []
     for th in ths:
         TP = len([num for num in correct_similarity if num >= th])
@@ -50,9 +91,7 @@ def compute_recall_precision(err_similarity, correct_similarity):
         else:
             prec.append(TP / (TP + FP))
             recall.append(TP / (len(err_similarity) + len(correct_similarity)))
-            
-    showgrid(recall, prec, ths)
-    return recall, prec
+    return recall, prec, ths
 
 
 # =============================================================================
@@ -291,7 +330,8 @@ def performance_evaluate(all_list, isshow=False):
     
     '''3. 计算比对性能 '''
     if isshow:
-        compute_recall_precision(err_similarity, correct_similarity)
+        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+        show_recall_prec(recall, prec, ths)
         showHist(err_similarity, correct_similarity)
         
     return errpairs, corrpairs, err_similarity, correct_similarity
@@ -307,7 +347,6 @@ def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
     '''1. 读取 deletedBarcode 文件 '''
     all_list = read_deletedBarcode_file(del_barcode_file)
 
-    
     '''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
     errpairs, corrpairs, _, _ = performance_evaluate(all_list)
 
@@ -320,11 +359,61 @@ def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
         if saveimgs:
             save_tracking_imgpairs(errpair, basepath, savepath)
 
-    
-
-    
     return relative_paths
 
+
+def contrast_loop(fpath):
+    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+    # savepath = r'D:\contrast\dataset\1_to_n\illustration'
+    if not os.path.exists(savepath):
+        os.mkdir(savepath)
+        
+    if os.path.isfile(fpath):
+        fpath, filename = os.path.split(fpath)
+    
+    
+        
+    
+    
+    
+    BarLists, blists = {}, []
+    for filename in os.listdir(fpath):
+        file = os.path.splitext(filename)[0][15:]
+
+        filepath = os.path.join(fpath, filename)
+        blist = read_deletedBarcode_file(filepath)
+ 
+        BarLists.update({file: blist})
+        blists.extend(blist)
+        
+        
+        
+    BarLists.update({file: blist})
+    BarLists.update({"Total": blists})
+    for file, blist in BarLists.items():        
+        errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist)
+        
+        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+        
+        plt1 = show_recall_prec(recall, prec, ths)
+        # plt1.show()
+        plt.xlabel(f'threshold, Num: {len(blist)}')
+        plt1.savefig(os.path.join(savepath, file+'_pr.png'))
+        # plt1.close()
+        
+        # plt2 = showHist(err_similarity, correct_similarity)
+        # plt2.show()
+        # plt2.savefig(os.path.join(savepath, file+'_hist.png'))
+        # plt.close()
+
+        
+        
+def main1():
+    fpath = r'D:\contrast\dataset\1_to_n\0719'
+    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\good'
+    
+    contrast_loop(fpath)
+
 def main():
     del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
     basepath = r'D:\contrast\dataset\1_to_n\709'
@@ -335,13 +424,18 @@ def main():
     except Exception as e:
         print(f'Error Type: {e}')
     
+def resolve_vidoes():
+    videopath = r"\\192.168.1.28\share\测试_202406\0719\719_1\20240719-103533_"
+    savepath = r"D:\contrast\result"
     
-
-    
-
+    video2imgs(videopath, savepath)
 
 if __name__ == '__main__':
-    main()
+    # main()
+    main1()
+    
+    # resolve_vidoes()
+    
     
 
 

tracking/module_analysis.py

@@ -205,9 +205,9 @@ def do_tracker_tracking(fpath, save_dir):
 
 def do_tracking(fpath, savedir):
     '''
-    fpath: 算法各模块输出的data文件地址，匹配
-    savedir: 对 fpath 各模块输出的复现
-    分析具体视频时，需指定 fpath 和 savedir
+      fpath: 算法各模块输出的data文件地址，匹配；
+      savedir: 对 fpath 各模块输出的复现；
+      分析具体视频时，需指定 fpath 和 savedir
     '''
     # fpath = r'D:\contrast\dataset\1_to_n\709\20240709-102758_6971558612189\1_track.data'
     # savedir = r'D:\contrast\dataset\result\20240709-102843_6958770005357_6971558612189\error_6971558612189'
@@ -218,17 +218,25 @@ def do_tracking(fpath, savedir):
     bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
     
     tracking_output_path = os.path.join(imgpath, CamerType + '_tracking_output.data')
+    
+    if not os.path.isfile(tracking_output_path): return
+    
     tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
     
 
     
-
+    '''存储画框后的 img'''
     save_dir, basename  = os.path.split(savedir)
     if not os.path.exists(savedir):
         os.makedirs(savedir) 
     
-    ''' 读取 fpath 中 track.data 文件对应的图像 '''
+    '''存储轨迹对应的 boxes子图'''
+    subimg_dir = os.path.join(save_dir, basename.split('_')[0] + '_subimgs')
+    if not os.path.exists(subimg_dir):
+        os.makedirs(subimg_dir)
     
+        
+    ''' 读取 fpath 中 track.data 文件对应的图像 '''
     imgs = read_imgs(imgpath, CamerType)
     
     ''' 在 imgs 上画框并保存，如果 trackerboxes 的帧数和 imgs 数不匹配，返回原图'''
@@ -262,10 +270,28 @@ def do_tracking(fpath, savedir):
         vts.classify()
 
         edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
-        draw_all_trajectories(vts, edgeline, save_dir, traj_graphic)
+        img = draw_all_trajectories(vts, edgeline, save_dir, traj_graphic)
+        
+        imgpth = save_dir.joinpath(f"{traj_graphic}_show.png")
+        cv2.imwrite(str(imgpth), img)
     else:
         print("Please check data file!")
         
+    for track in vts.Residual:
+        for *xyxy, tid, conf, cls, fid, bid in track.boxes:
+            img = imgs[int(fid-1)]
+            x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
+            subimg = img[y1:y2, x1:x2]
+            
+            subimg_path = os.path.join(subimg_dir, f'{CamerType}_{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
+            
+            cv2.imwrite(subimg_path, subimg)
+            
+            
+        
+        
+    
+
 
     '''================== 现场测试的 tracking() 算法输出 =================='''
     if CamerType == '1':
@@ -295,14 +321,14 @@ def do_tracking(fpath, savedir):
     cv2.line(abimg, (int(abW/2), 0), (int(abW/2), abH), (128, 255, 128), 2)
     
     algpath = save_dir.joinpath(f"{traj_graphic}_Alg.png")
-    cv2.imwrite(algpath, abimg)
+    cv2.imwrite(str(algpath), abimg)
     
     return    
     
 
 def main_loop():
-    del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
-    basepath = r'D:\contrast\dataset\1_to_n\709'   # 测试数据文件夹地址
+    del_barcode_file = r'\\192.168.1.28\share\测试_202406\deletedBarcode\bad\deletedBarcode_0719_4.txt'
+    basepath = r'\\192.168.1.28\share\测试_202406\0719\719_4'   # 测试数据文件夹地址
     SavePath = r'D:\contrast\dataset\result'       # 结果保存地址
     prefix = ["getout_", "input_", "error_"]
     
@@ -341,9 +367,7 @@ def main_loop():
                         if fpath.find(name)>0:
                             enent_name = prefix[i] + name
                             break
-         
                     spath = os.path.join(savepath, enent_name)
-
                     do_tracking(fpath, spath)
         
         k +=1
@@ -353,25 +377,24 @@ def main_loop():
 
 
 
-def main_fold():
-    save_dir = Path('./result')
-    if not save_dir.exists():
-        save_dir.mkdir(parents=True, exist_ok=True)
+def main():
+    '''
+    fpath: data文件，包括 Pipeline 各模块输出
+    save_dir：需包含二级目录，其中一级目录为轨迹图像；
+              二级目录为与data文件对应的序列图像存储地址。
+    '''
     
-    files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'   
-    for filename in os.listdir(files_path):   
-        filename = '1_track.data'
+    fpath = r'\\192.168.1.28\share\测试_202406\0719\719_4\20240719-164209_\0_track.data'
+    save_dir = r'D:\contrast\dataset\result\20240719-164209_6971284204320_6902890247777\getout'
     
-        fpath = os.path.join(files_path, filename)   
-        if os.path.isfile(fpath) and filename.find("track.data")>0:
-            # do_tracker_tracking(fpath, save_dir) 
-            do_tracking(fpath, save_dir)
+    do_tracking(fpath, save_dir)
     
     
 if __name__ == "__main__":
     try:
+        # main()
         main_loop()
-        # main_fold()
+        
     except Exception as e:
         print(f'Error: {e}')   
 

tracking/utils/drawtracks.py

@@ -98,11 +98,12 @@ def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
     H, W = img.shape[:2]
     cv2.line(img, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
     
-    imgpth = save_dir.joinpath(f"{file}_show.png")
-    cv2.imwrite(str(imgpth), img)
+    
+    # imgpth = save_dir.joinpath(f"{file}_show.png")
+    # cv2.imwrite(str(imgpth), img)
 
     if not draw5p:
-        return
+        return img
         
     ''' tracks 5点轨迹'''
     trackpth = save_dir / Path("trajectory") / Path(f"{file}")

tracking/utils/read_data.py

@@ -151,7 +151,8 @@ def read_deletedBarcode_file(filePth):
         dict, barcode_list, similarity_list =  {}, [], []
   
         clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
-        for line in clean_lines:
+        
+        for i, line in enumerate(clean_lines):
             stripped_line = line.strip()
             if not stripped_line:
                 if len(barcode_list): dict['barcode'] = barcode_list
@@ -163,8 +164,16 @@ def read_deletedBarcode_file(filePth):
                 continue
             
             # print(line)
-            label = line.split(':')[0]
-            value = line.split(':')[1]
+            try:
+                label = line.split(':')[0]
+                value = line.split(':')[1]
+            except Exception as e:
+                print(f'Error: {e}')    
+            
+            
+            
+            
+            
             if label == 'SeqDir':
                 dict['SeqDir'] = value
             if label == 'Deleted':