Loftr/notebooks/demo_single_pair.ipynb

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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
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 "cells": [
  {
   "source": [
    "# Demo LoFTR-DS on a single pair of images\n",
    "\n",
    "This notebook shows how to use the loftr matcher with default config(dual-softmax) and the pretrained weights."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"..\")\n",
    "import torch\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "from src.utils.plotting import make_matching_figure"
   ]
  },
  {
   "source": [
    "## Indoor Example"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.loftr import LoFTR, default_cfg\n",
    "\n",
    "# The default config uses dual-softmax.\n",
    "# The outdoor and indoor models share the same config.\n",
    "# You can change the default values like thr and coarse_match_type.\n",
    "matcher = LoFTR(config=default_cfg)\n",
    "matcher.load_state_dict(torch.load(\"weights/indoor_ds.ckpt\")['state_dict'])\n",
    "matcher = matcher.eval().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load example images\n",
    "img0_pth = \"assets/scannet_sample_images/scene0711_00_frame-001680.jpg\"\n",
    "img1_pth = \"assets/scannet_sample_images/scene0711_00_frame-001995.jpg\"\n",
    "img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n",
    "img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n",
    "img0_raw = cv2.resize(img0_raw, (640, 480))\n",
    "img1_raw = cv2.resize(img1_raw, (640, 480))\n",
    "\n",
    "img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n",
    "img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n",
    "batch = {'image0': img0, 'image1': img1}\n",
    "\n",
    "# Inference with LoFTR and get prediction\n",
    "with torch.no_grad():\n",
    "    matcher(batch)\n",
    "    mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
    "    mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
    "    mconf = batch['mconf'].cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 750x450 with 2 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# Draw\n",
    "color = cm.jet(mconf)\n",
    "text = [\n",
    "    'LoFTR',\n",
    "    'Matches: {}'.format(len(mkpts0)),\n",
    "]\n",
    "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, mkpts0, mkpts1, text)"
   ]
  },
  {
   "source": [
    "## Outdoor Example"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.loftr import LoFTR, default_cfg\n",
    "\n",
    "# The default config uses dual-softmax.\n",
    "# The outdoor and indoor models share the same config.\n",
    "# You can change the default values like thr and coarse_match_type.\n",
    "matcher = LoFTR(config=default_cfg)\n",
    "matcher.load_state_dict(torch.load(\"weights/outdoor_ds.ckpt\")['state_dict'])\n",
    "matcher = matcher.eval().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load example images\n",
    "img0_pth = \"assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg\"\n",
    "img1_pth = \"assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg\"\n",
    "img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n",
    "img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n",
    "img0_raw = cv2.resize(img0_raw, (img0_raw.shape[1]//8*8, img0_raw.shape[0]//8*8))  # input size shuold be divisible by 8\n",
    "img1_raw = cv2.resize(img1_raw, (img1_raw.shape[1]//8*8, img1_raw.shape[0]//8*8))\n",
    "\n",
    "img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n",
    "img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n",
    "batch = {'image0': img0, 'image1': img1}\n",
    "\n",
    "# Inference with LoFTR and get prediction\n",
    "with torch.no_grad():\n",
    "    matcher(batch)\n",
    "    mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
    "    mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
    "    mconf = batch['mconf'].cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 750x450 with 2 Axes>",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# Draw\n",
    "color = cm.jet(mconf)\n",
    "text = [\n",
    "    'LoFTR',\n",
    "    'Matches: {}'.format(len(mkpts0)),\n",
    "]\n",
    "fig = make_matching_figure(img0_raw, img1_raw, mkpts0, mkpts1, color, mkpts0, mkpts1, text)"
   ]
  }
 ]
}