examples/notebook_smallobject.ipynb

{
 "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 git@github.com: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": []
  }
 ],
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