Merge pull request #1747 from mikel-brostrom/cleanup2

change yolox preprocessing name

Author: mikel-brostrom

boxmot/utils/ops.py

@@ -190,7 +190,7 @@ def letterbox(
 
 # This preprocess differs from the current version of YOLOX preprocess, but ByteTrack uses it
 # https://github.com/ifzhang/ByteTrack/blob/d1bf0191adff59bc8fcfeaa0b33d3d1642552a99/yolox/data/data_augment.py#L189
-def bytetrack_preprocess(image, input_size, 
+def yolox_preprocess(image, input_size, 
                          mean=(0.485, 0.456, 0.406), 
                          std=(0.229, 0.224, 0.225), 
                          swap=(2, 0, 1)):

examples/det/yolox_boxmot.ipynb

@@ -27,7 +27,7 @@
     "from yolox.utils import postprocess\n",
     "from yolox.utils.model_utils import fuse_model\n",
     "from boxmot import BotSort\n",
-    "from boxmot.utils.ops import bytetrack_preprocess\n",
+    "from boxmot.utils.ops import yolox_preprocess\n",
     "\n",
     "\n",
     "# Dictionary for YOLOX model weights URLs\n",
@@ -77,7 +77,7 @@
     "        break\n",
     "\n",
     "    # Preprocess frame\n",
-    "    frame_img, ratio = bytetrack_preprocess(frame, input_size=input_size)\n",
+    "    frame_img, ratio = yolox_preprocess(frame, input_size=input_size)\n",
     "    frame_tensor = torch.Tensor(frame_img).unsqueeze(0).to(device)\n",
     "\n",
     "    # Detection with YOLOX\n",
@@ -128,7 +128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

tracking/detectors/yolox.py

@@ -10,7 +10,7 @@
 from yolox.utils.model_utils import fuse_model
 
 from boxmot.utils import logger as LOGGER
-from boxmot.utils.ops import bytetrack_preprocess
+from boxmot.utils.ops import yolox_preprocess
 from tracking.detectors.yolo_interface import YoloInterface
 
 # default model weigths for these model names
@@ -126,7 +126,7 @@ def preprocess(self, im) -> torch.Tensor:
         im_preprocessed = []
         self._preproc_data = []
         for i, img in enumerate(im):
-            img_pre, ratio = bytetrack_preprocess(img, input_size=self.imgsz)
+            img_pre, ratio = yolox_preprocess(img, input_size=self.imgsz)
             img_pre = torch.Tensor(img_pre).unsqueeze(0).to(self.device)
 
             im_preprocessed.append(img_pre)