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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from hydra import compose, initialize\n",
"from PIL import Image \n",
"\n",
"# Ensure that the necessary repository is cloned and installed. You may need to run: \n",
"# git clone [email protected]:WongKinYiu/YOLO.git\n",
"# cd YOLO \n",
"# pip install .\n",
"from yolo import AugmentationComposer, bbox_nms, Config, create_model, custom_logger, draw_bboxes, Vec2Box"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CONFIG_PATH = \"../yolo/config\"\n",
"CONFIG_NAME = \"config\"\n",
"\n",
"DEVICE = 'cuda:0'\n",
"CLASS_NUM = 80\n",
"WEIGHT_PATH = '../weights/v9-c.pt' \n",
"IMAGE_PATH = '../demo/images/inference/image.png'\n",
"\n",
"custom_logger()\n",
"device = torch.device(DEVICE)\n",
"image = Image.open(IMAGE_PATH)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with initialize(config_path=CONFIG_PATH, version_base=None, job_name=\"notebook_job\"):\n",
" cfg: Config = compose(config_name=CONFIG_NAME, overrides=[\"task=inference\", f\"task.data.source={IMAGE_PATH}\", \"model=v9-c-deploy\"])\n",
" model = create_model(cfg.model, class_num=CLASS_NUM, weight_path=WEIGHT_PATH).to(device)\n",
" transform = AugmentationComposer([], cfg.image_size)\n",
" vec2box = Vec2Box(model, cfg.image_size, device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image, bbox = transform(image, torch.zeros(0, 5))\n",
"image = image.to(device)[None]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" predict = model(image)\n",
" predict = vec2box(predict[\"Main\"])\n",
"\n",
"predict_box = bbox_nms(predict[0], predict[2], cfg.task.nms)\n",
"draw_bboxes(image, predict_box, save_path='../demo/images/output/', idx2label=cfg.class_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sample Output:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "yolomit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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