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change yolox preprocessing name #1747

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Nov 20, 2024
Merged

change yolox preprocessing name #1747

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2 changes: 1 addition & 1 deletion boxmot/utils/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)):
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6 changes: 3 additions & 3 deletions examples/det/yolox_boxmot.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
Expand Down Expand Up @@ -128,7 +128,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
"version": "3.11.5"
}
},
"nbformat": 4,
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4 changes: 2 additions & 2 deletions tracking/detectors/yolox.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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)
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