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feat : add tracking files

bae.sangwoo 2 vuotta sitten
vanhempi
commit
8bf88c953e

+ 1 - 0
yolov7-deepsort-tracking

@@ -0,0 +1 @@
+Subproject commit 067d25dcc8741b25726457cef1763f8a947dbb98

+ 2 - 2
yolov7-main/detect.py

@@ -91,7 +91,7 @@ def detect(save_img=False):
         # Apply NMS
         # Apply NMS
         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
         t3 = time_synchronized()
         t3 = time_synchronized()
-
+        
         # Apply Classifier
         # Apply Classifier
         if classify:
         if classify:
             pred = apply_classifier(pred, modelc, img, im0s)
             pred = apply_classifier(pred, modelc, img, im0s)
@@ -126,7 +126,7 @@ def detect(save_img=False):
 
 
                     if save_img or view_img:  # Add bbox to image
                     if save_img or view_img:  # Add bbox to image
                         label = f'{names[int(cls)]} {conf:.2f}'
                         label = f'{names[int(cls)]} {conf:.2f}'
-                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
+                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thinckness=1)
 
 
             # Print time (inference + NMS)
             # Print time (inference + NMS)
             print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
             print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')

+ 1 - 0
yolov7-main/requirements.txt

@@ -4,6 +4,7 @@
 matplotlib>=3.2.2
 matplotlib>=3.2.2
 numpy>=1.18.5
 numpy>=1.18.5
 opencv-python>=4.1.1
 opencv-python>=4.1.1
+opencv-python-headless<4.3
 Pillow>=7.1.2
 Pillow>=7.1.2
 PyYAML>=5.3.1
 PyYAML>=5.3.1
 requests>=2.23.0
 requests>=2.23.0

+ 1 - 1
yolov7-main/utils/datasets.py

@@ -136,7 +136,7 @@ class LoadImages:  # for inference
             files = [p]  # files
             files = [p]  # files
         else:
         else:
             raise Exception(f'ERROR: {p} does not exist')
             raise Exception(f'ERROR: {p} does not exist')
-
+        print(files)
         images = [x for x in files if x.split('.')[-1].lower() in img_formats]
         images = [x for x in files if x.split('.')[-1].lower() in img_formats]
         videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
         videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
         ni, nv = len(images), len(videos)
         ni, nv = len(images), len(videos)