Add color to PCA and UMAP visualisations

.gitignore

@@ -110,3 +110,5 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+OUT/

cfg/ccmcpe.json

@@ -1,5 +1,5 @@
 {
-    "root":"/home/wangzd/datasets/MOT",
+    "root":"/Towards-Realtime-MOT/datasets/MOT",
     "train":
     {
         "mot17":"./data/mot17.train",

demo.py

@@ -49,9 +49,14 @@ def track(opt):
     n_frame = 0
 
     logger.info('Starting tracking...')
-    dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
+    if os.path.isdir(opt.input_video):
+        print('Use image sequence')
+        dataloader = datasets.LoadImages(opt.input_video, opt.img_size)
+        frame_rate = 30 # hack for now; see https://motchallenge.net/data/MOT16/
+    else:
+        dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
+        frame_rate = dataloader.frame_rate 
     result_filename = os.path.join(result_root, 'results.txt')
-    frame_rate = dataloader.frame_rate 
 
     frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
     try:

docker/Dockerfile

@@ -1,5 +1,20 @@
-FROM  pytorch/pytorch:1.3-cuda10.1-cudnn7-devel
+FROM  pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel
 
 RUN apt update && apt install -y ffmpeg libsm6 libxrender-dev
 RUN pip install Cython
 RUN pip install opencv-python cython_bbox motmetrics numba matplotlib sklearn
+RUN pip install lap
+RUN pip install umap-learn
+ENV NUMBA_CACHE_DIR=/tmp/numba_cache
+RUN pip install bokeh
+RUN pip install ipykernel
+RUN pip install ipython
+# Vscode bug: https://github.com/microsoft/vscode-jupyter/issues/8552
+RUN pip install ipywidgets==7.7.2
+#RUN pip install panel jupyter_bokeh
+
+
+# for bokeh
+EXPOSE 5006
+
+CMD python -m ipykernel_launcher -f $DOCKERNEL_CONNECTION_FILE

track.py

@@ -1,8 +1,11 @@
 import os
 import os.path as osp
+import pickle
 import cv2
 import logging
 import argparse
+
+from tqdm.auto import tqdm
 import motmetrics as mm
 
 import torch
@@ -38,7 +41,7 @@ def write_results(filename, results, data_type):
     logger.info('save results to {}'.format(filename))
 
 
-def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
+def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, save_img=False, save_figures=False, show_image=True, frame_rate=30):
     '''
        Processes the video sequence given and provides the output of tracking result (write the results in video file)
 
@@ -59,7 +62,9 @@ def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_im
                          The name(path) of the file for storing results.
 
        save_dir : String
-                  Path to the folder for storing the frames containing bounding box information (Result frames).
+                  Path to the folder for storing the frames containing bounding box information (Result frames). If given, featuers will be save there as pickle
+       save_figures : bool
+                  If set, individual crops of all embedded figures will be saved
 
        show_image : bool
                     Option for shhowing individial frames during run-time.
@@ -79,15 +84,20 @@ def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_im
     tracker = JDETracker(opt, frame_rate=frame_rate)
     timer = Timer()
     results = []
-    frame_id = 0
-    for path, img, img0 in dataloader:
-        if frame_id % 20 == 0:
-            logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
-
+    frame_id = -1
+    for path, img, img0 in tqdm(dataloader):
+        frame_id += 1
+        # if frame_id % 20 == 0:
+        #     logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
+        frame_pickle_fn = os.path.join(save_dir, f'{frame_id:05d}.pcl')
+        if os.path.exists(frame_pickle_fn):
+            continue 
         # run tracking
         timer.tic()
         blob = torch.from_numpy(img).cuda().unsqueeze(0)
-        online_targets = tracker.update(blob, img0)
+        # online targets: all tartgets that are not timed out
+        # frame_embeddings: the embeddings of objects visible only in the current frame
+        online_targets, frame_embeddings = tracker.update(blob, img0)
         online_tlwhs = []
         online_ids = []
         for t in online_targets:
@@ -106,10 +116,27 @@ def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_im
         if show_image:
             cv2.imshow('online_im', online_im)
         if save_dir is not None:
-            cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
-        frame_id += 1
+            base_fn = os.path.join(save_dir, '{:05d}'.format(frame_id))
+            if save_img:
+                cv2.imwrite(base_fn+'.jpg', online_im)
+            if save_figures:
+                for i, fe in enumerate(frame_embeddings):
+                    tlwh, curr_feat = fe
+                    x,y,w,h = round(tlwh[0]), round(tlwh[1]), round(tlwh[2]), round(tlwh[3])
+                    # print(x,y,w,h, tlwh)
+                    crop_img = img0[y:y+h, x:x+w]
+                    cv2.imwrite(f'{base_fn}-{i}.jpg', crop_img)
+                    with open(os.path.join(save_dir, f'{frame_id:05d}-{i}.pcl'), 'wb') as fp:
+                        pickle.dump(fe, fp)
+                    
+            with open(frame_pickle_fn, 'wb') as fp:
+                pickle.dump(frame_embeddings, fp)
+
+        
     # save results
-    write_results(result_filename, results, data_type)
+    if result_filename is not None:
+        write_results(result_filename, results, data_type)
+
     return frame_id, timer.average_time, timer.calls
 
 
@@ -131,7 +158,7 @@ def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',),
     for seq in seqs:
         output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
 
-        logger.info('start seq: {}'.format(seq))
+        # logger.info('start seq: {}'.format(seq))
         dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
         result_filename = os.path.join(result_root, '{}.txt'.format(seq))
         meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read() 

tracker/multitracker.py

@@ -203,6 +203,8 @@ class JDETracker(object):
         lost_stracks = []           # The tracks which are not obtained in the current frame but are not removed.(Lost for some time lesser than the threshold for removing)
         removed_stracks = []
 
+        frame_embeddings = []
+
         t1 = time.time()
         ''' Step 1: Network forward, get detections & embeddings'''
         with torch.no_grad():
@@ -220,8 +222,12 @@ class JDETracker(object):
 
             detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
                           (tlbrs, f) in zip(dets[:, :5], dets[:, 6:])]
+            # Surfacing Suspicion: extract features + frame id + bbox
+            frame_embeddings = [[track.tlwh, track.curr_feat] for track in detections]
+            
         else:
             detections = []
+            frame_embeddings = []
 
         t2 = time.time()
         # print('Forward: {} s'.format(t2-t1))
@@ -346,7 +352,7 @@ class JDETracker(object):
         logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
         logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
         # print('Final {} s'.format(t5-t4))
-        return output_stracks
+        return output_stracks, frame_embeddings
 
 def joint_stracks(tlista, tlistb):
     exists = {}

utils/log.py

@@ -10,7 +10,7 @@ def get_logger(name='root'):
     handler.setFormatter(formatter)
 
     logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
+    logger.setLevel(logging.INFO)
     logger.addHandler(handler)
     return logger