Update detect_deepsort.py

detect_deepsort.py

@@ -216,14 +216,14 @@ def run_deepsort(
             seen += 1
             if webcam:  # batch_size >= 1
                 p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f'{i}: '
+                #s += f'{i}: '
             else:
                 p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
 
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # im.jpg
             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
-            s += '%gx%g ' % im.shape[2:]  # print string
+            #s += '%gx%g ' % im.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             ims = im0.copy()
             if len(det):
@@ -231,9 +231,9 @@ def run_deepsort(
                 det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
 
                 # Print results
-                for c in det[:, -1].unique():
-                    n = (det[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                for c in det[:, 5].unique():
+                    n = (det[:, 5] == c).sum()  # detections per class
+                    #s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
                 xywh_bboxs = []
                 confs = []
                 oids = []
@@ -287,7 +287,7 @@ def run_deepsort(
                 vid_writer[i].write(ims)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+        #LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
     if update:
         strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
     return save_path