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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"\n",
"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",
"project_root = Path().resolve().parent\n",
"sys.path.append(str(project_root))\n",
"\n",
"from yolo import (\n",
" AugmentationComposer, \n",
" Config, \n",
" NMSConfig, \n",
" PostProccess,\n",
" bbox_nms, \n",
" create_model, \n",
" create_converter, \n",
" custom_logger, \n",
" draw_bboxes, \n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CONFIG_PATH = \"../yolo/config\"\n",
"CONFIG_NAME = \"config\"\n",
"MODEL = \"v9-c\"\n",
"\n",
"DEVICE = 'cuda:0'\n",
"CLASS_NUM = 80\n",
"IMAGE_PATH = '../image.png'\n",
"SLIDE = 4\n",
"\n",
"custom_logger()\n",
"device = torch.device(DEVICE)"
]
},
{
"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}\", f\"model={MODEL}\"])\n",
" model = create_model(cfg.model, class_num=CLASS_NUM).to(device)\n",
" transform = AugmentationComposer([], cfg.image_size)\n",
" converter = create_converter(cfg.model.name, model, cfg.model.anchor, cfg.image_size, device)\n",
" post_proccess = PostProccess(converter, NMSConfig(0.5, 0.9))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pil_image = Image.open(IMAGE_PATH)\n",
"image, bbox, rev_tensor = transform(pil_image)\n",
"image = image.to(device)[None]\n",
"rev_tensor = rev_tensor.to(device)[None]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def slide_image(image, slide = 4, device = device):\n",
" up_image = torch.nn.functional.interpolate(image, scale_factor=slide)\n",
" image_list = [image]\n",
" shift_list = []\n",
" *_, w, h = up_image.shape\n",
" for x_slide in range(slide):\n",
" for y_slide in range(slide):\n",
" left_w, right_w = w // slide * x_slide, w // slide * (x_slide + 1)\n",
" left_h, right_h = h // slide * y_slide, h // slide * (y_slide + 1)\n",
" slide_image = up_image[:, :, left_w: right_w, left_h: right_h]\n",
" image_list.append(slide_image)\n",
" shift_list.append(torch.Tensor([left_h, left_w, left_h, left_w]))\n",
" total_image = torch.concat(image_list)\n",
" total_shift = torch.stack(shift_list).to(device)\n",
"\n",
" return total_image, total_shift"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" total_image, total_shift = slide_image(image)\n",
" predict = model(total_image)\n",
" pred_class, _, pred_bbox = converter(predict[\"Main\"])\n",
"pred_bbox[1:] = (pred_bbox[1: ] + total_shift[:, None]) / SLIDE\n",
"pred_bbox = pred_bbox.view(1, -1, 4)\n",
"pred_class = pred_class.view(1, -1, 80)\n",
"pred_bbox = (pred_bbox - rev_tensor[:, None, 1:]) / rev_tensor[:, 0:1, None]\n",
"predict_box = bbox_nms(pred_class, pred_bbox, NMSConfig(0.3, 0.5))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"draw_bboxes(pil_image, predict_box, idx2label=cfg.dataset.class_list)"
]
},
{
"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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