Annotate datasets with a semi-trained or fully trained YOLOv5 model
Ubuntu >=20.04
Python >=3.7sudo apt install python3-dev python3-pipcycler==0.11.0
fonttools==4.29.0
kiwisolver==1.3.2
lxml==4.6.4
numpy==1.21.4
opencv-contrib-python==4.5.5.62
opencv-python==4.5.5.62
packaging==21.3
Pillow==9.0.0
pyparsing==3.0.7
python-dateutil==2.8.2
six==1.16.0
tqdm==4.62.3Install with the following command -
pip3 install -r requirements.txtExecute annotate.py in the following format -
usage: annotate.py [-h] [--viewmode] [--imgdir IMGDIR] [--annodir ANNODIR] [--confThreshold CONFTHRESHOLD] [--nmsThreshold NMSTHRESHOLD] [--width WIDTH] [--height HEIGHT] [--onnx_path ONNX_PATH] [--labels_path LABELS_PATH]
optional arguments:
-h, --help show this help message and exit
--viewmode Toggle View Mode
--imgdir IMGDIR Directory of images
--annodir ANNODIR Directory of annotations
--confThreshold CONFTHRESHOLD
Class confidence
--nmsThreshold NMSTHRESHOLD
NMS threshold
--width WIDTH Width of network input
--height HEIGHT Height of network input
--onnx_path ONNX_PATH
Path to onnx file
--labels_path LABELS_PATH
Path to labels fileExample -
python3 annotate.py --imgdir /home/kn1ght/Documents/images --annodir annotations --onnx_path models/YOLOv5s/yolov5s.onnx --labels_path models/YOLOv5s/coco.names --viewmode