pepeline_extract_subimg.py: extract subimg

contrast/one2one_contrast.py

@@ -404,8 +404,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
                 feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
                 
                 # uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小
-                # stdfeat_uint8, _ = ft16_to_uint8(feature_ft16)
-                stdfeat_uint8 = (feature_ft16*128).astype(np.int8)
+                # feature_uint8, _ = ft16_to_uint8(feature_ft16)
+                feature_uint8 = (feature_ft16*128).astype(np.int8)
   
         except Exception as e:
             print(f"Error accured at: {filename}, with Exception is: {e}")
@@ -414,32 +414,15 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
         ##================== float32
         stdbDict["barcode"] = barcode
         stdbDict["imgpaths"] = imgpaths
-        stdbDict["feats"] = feature
+        stdbDict["feats_ft32"] = feature
+        stdbDict["feats_ft16"] = feature_ft16
+        stdbDict["feats_uint8"] = feature_uint8
         
-        # pkpath = os.path.join(featPath, f"{barcode}.pickle")
         with open(pkpath, 'wb') as f:
             pickle.dump(stdbDict, f)
+        
         stdBarcodeDict[barcode] = feature
-
-
-        ##================== float16
-        # stdbDict_ft16["barcode"] = barcode
-        # stdbDict_ft16["imgpaths"] = imgpaths
-        # stdbDict_ft16["feats"] = feature_ft16
-        # pkpath_ft16 = os.path.join(featPath, f"{barcode}_ft16.pickle")
-        # with open(pkpath_ft16, 'wb') as f:
-        #     pickle.dump(stdbDict_ft16, f)
-        # stdBarcodeDict_ft16[barcode] = pkpath_ft16
-        
-        
-        
-        ##================== uint8
-        # stdbDict_uint8["barcode"] = barcode
-        # stdbDict_uint8["imgpaths"] = imgpaths
-        # stdbDict_uint8["feats"] = stdfeat_uint8
-        # pkpath_uint8 = os.path.join(featPath, f"{barcode}_uint8.pickle")
-        # with open(pkpath_uint8, 'wb') as f:
-        #     pickle.dump(stdbDict_uint8, f)
+        stdBarcodeDict_ft16[barcode] = feature_ft16
         
         t2 = time.time()
         print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
@@ -813,7 +796,7 @@ def main_std():
     
     get_std_barcodeDict(std_sample_path, std_barcode_path)
     
-    # stdfeat_infer(std_barcode_path, std_feature_path, bcdSet=None)
+    stdfeat_infer(std_barcode_path, std_feature_path, bcdSet=None)
     
     # fileList = []
     # for filename in os.listdir(std_barcode_path):
@@ -829,7 +812,7 @@ if __name__ == '__main__':
     # main()
     
     
-    # main_std()
+    main_std()
 
 
 

pipeline.py

@@ -6,6 +6,7 @@ Created on Sun Sep 29 08:59:21 2024
 """
 import os
 import cv2
+import pickle
 from pathlib import Path
 from track_reid import parse_opt, yolo_resnet_tracker
 
@@ -21,21 +22,49 @@ std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_21
 opt = parse_opt()
 optdict = vars(opt)
 
+
+
+
+
+def get_interbcd_inputenents():
+    bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
+    eventpath = r"\\192.168.1.28\share\测试_202406\0918"
+
+    barcodes = []
+    eventpaths = []
+    for featname in os.listdir(bcdpath):
+        barcode, ext = os.path.splitext(featname)
+        barcodes.append(barcode)
+    
+    input_enents = []    
+    for root, dirs, files in os.walk(eventpath):
+        input_enent = [os.path.join(root, d) for d in dirs if d.split('_')[-1] in barcodes]
+        
+        input_enents.extend(input_enent)
+        
+        
+    return input_enents
+        
+
+
 def get_video_pairs(vpath):
     vdieopath = []
     for filename in os.listdir(vpath):
         file, ext = os.path.splitext(filename)
         if ext in VIDFORMATS:
             vdieopath.append(os.path.join(vpath, filename))            
-    return vdieopath
-            
+    return vdieopath          
 
-def pipeline():  
-    eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110913-c3a7e4d9-23d4-4a6f-a23f-a2eeee510536_6939947701616"
+def pipeline(eventpath, stdfeat_path):  
+    # eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110822-1bc3902e-5a8e-4e23-8eca-fb3f02738551_6938314601726"
+    
     savepath = r"D:\contrast\detect"
     
     optdict["project"] = savepath
+    
     eventname = os.path.basename(eventpath)
+    barcode = eventname.split('_')[-1]
+    
 
     vpaths = get_video_pairs(eventpath)
     event_tracks = []
@@ -75,9 +104,19 @@ def pipeline():
         if CamerType == 'front':
             pass
         
+    '''前后摄轨迹选择'''
+    
+    
+    if stdfeat_path is not None:
+        with open(stdfeat_path, 'rb') as f:  
+            featDict = pickle.load(f) 
+        
+    
+
+    
+    
+    
         
-    for featname in os.listdir(std_feature_path):
-        pass
             
         
     
@@ -88,7 +127,63 @@ def pipeline():
     
 
 def main():
-    pipeline()
+    bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
+    eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1"
+    
+    
+    barcodes = []
+    input_enents = []  
+    output_events = []
+    
+    
+    # input_enents = get_interbcd_inputenents()
+    # k = 0
+    # for event in input_enents:
+    #     pipeline(event)
+        
+    #     k += 1
+    #     if k ==1:
+    #         break
+        
+        
+    '''1. 获得barcode标准特征集列表'''    
+    for featname in os.listdir(bcdpath):
+        barcode, ext = os.path.splitext(featname)
+        if not barcode.isdigit() or len(barcode)<=8 or ext != ".pickle" :
+            continue
+        barcodes.append(barcode)
+    
+    
+    
+    
+    
+
+    
+    
+    for filename in os.listdir(eventpath):
+        bcd = filename.split('_')[-1]
+        
+        event_path = os.path.join(eventpath, filename)
+        stdfeat_path = None
+        if bcd in barcodes:
+            stdfeat_path = os.path.join(bcdpath, f"{bcd}.pickle")
+            
+            input_enents.append((event_path, stdfeat_path))
+    
+    
+    
+    
+    for eventpath, stdfeat_path in input_enents:
+        pipeline(eventpath, stdfeat_path)
+        
+            
+        
+        
+        
+
+        
+        
+        
 
 
 if __name__ == "__main__": 

pipeline_extract_subimg.py

@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 29 08:59:21 2024
+
+@author: ym
+"""
+import os
+import cv2
+import pickle
+from pathlib import Path
+from track_reid import parse_opt, yolo_resnet_tracker
+
+from tracking.dotrack.dotracks_back import doBackTracks
+from tracking.dotrack.dotracks_front import doFrontTracks 
+
+IMGFORMATS = '.bmp', '.jpeg', '.jpg', 'png', 'tif', 'tiff', 'webp', 'pfm'
+VIDFORMATS = '.avi', '.gif', '.m4v', '.mkv', '.mov', '.mp4', '.ts', '.wmv'
+
+std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16"
+
+
+opt = parse_opt()
+optdict = vars(opt)
+
+
+def pipeline(eventpath, savepath):  
+    # eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110822-1bc3902e-5a8e-4e23-8eca-fb3f02738551_6938314601726"    
+    optdict["project"] = savepath
+    
+    '''Yolo + Resnet + Tracker'''
+    optdict["source"] = eventpath
+    optdict["save_dir"] = savepath
+    optdict["nosave"] = False
+    
+    tracksdict = yolo_resnet_tracker(**optdict)
+    
+    bboxes = tracksdict['TrackBoxes']
+    
+    basename = os.path.basename(eventpath)
+    base, ext = os.path.splitext(basename)
+    
+    if base.find('front')>=0:
+        vts = doFrontTracks(bboxes, tracksdict)
+        vts.classify()
+        
+    if  base.find('back')>=0:
+        vts = doBackTracks(bboxes, tracksdict)
+        vts.classify()
+        
+    tracks = [t for t in vts.tracks if t.cls>0 and not t.is_static()]
+    # tracks = [t for t in vts.tracks if t.cls>0]
+    
+    for track in tracks:
+    # for track in vts.Residual:
+        for *xyxy, tid, conf, cls, fid, bid in track.boxes:
+            img = tracksdict[f'frame_{int(fid)}']["imgs"][int(bid)]
+            
+            imgpth = savepath / Path(f'{base}_tid-{int(tid)}_fid-{int(fid)}_bid-{int(bid)}.jpg')
+            
+            cv2.imwrite(imgpth, img)
+    
+    return len(vts.Residual)
+        
+            
+
+
+def main():
+    
+    videopath = r"\\192.168.1.28\share\上海中环店采集视频\21-25\videos\1\back"   
+    savepath = r"D:\contrast\barcodes"
+    
+    vpaths = []
+    for root, dirs, files in os.walk(videopath):
+        vpth = [os.path.join(root, f) for f in files if os.path.splitext(f)[-1] in VIDFORMATS]
+        vpaths.extend(vpth)
+    
+    manual_txt = os.path.join(savepath, 'manual_videos.txt')
+    file = open(manual_txt, 'a', encoding='utf-8')
+                              
+                              
+    
+    manual = []
+    k = 0
+    for vpath in vpaths:
+        videoname = os.path.basename(vpath)
+        
+        vname, ext = os.path.splitext(videoname)
+        barcode = videoname.split('_')[0]
+        
+        subpath = os.path.join(savepath, barcode, vname)
+        subpath = Path(subpath)
+        if not subpath.exists():
+            subpath.mkdir(parents=True, exist_ok=True)
+
+        ntract = pipeline(vpath, subpath)
+        
+        if ntract==0:
+            manual.append(vpath)
+            file.write(vpath)
+            file.write("\n")
+
+        print(f"{videoname} done!!!")
+        k += 1
+        if k==10:
+            break
+    
+    file.close()
+
+        
+        
+        
+
+
+if __name__ == "__main__": 
+    main()
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

track_reid.py

@@ -264,12 +264,11 @@ def yolo_resnet_tracker(
             im0 = annotator.result()
             save_path_img, ext = os.path.splitext(save_path)
             if save_img:
-                if dataset.mode == 'image':
-                    imgpath = save_path_img + f"_{dataset}.png"
-                else:
-                    imgpath = save_path_img + f"_{dataset.frame}.png"
-                    
-                cv2.imwrite(Path(imgpath), im0)
+                # if dataset.mode == 'image':
+                #     imgpath = save_path_img + f"_{dataset}.png"
+                # else:
+                #     imgpath = save_path_img + f"_{dataset.frame}.png"           
+                # cv2.imwrite(Path(imgpath), im0)
                 
                 if vid_path[i] != save_path:  # new video
                     vid_path[i] = save_path