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204 lines
1.6 MiB
204 lines
1.6 MiB
4 years ago
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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python388jvsc74a57bd0e2d1507a0fcefcbd70c2e8d5c2edae879585b2c8df0be6cdbe280bf251175c7f",
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"display_name": "Python 3.8.8 64-bit ('loftr': conda)"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"source": [
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"# Demo LoFTR-DS on a single pair of images\n",
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"\n",
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"This notebook shows how to use the loftr matcher with default config(dual-softmax) and the pretrained weights."
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.chdir(\"..\")\n",
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"import torch\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.cm as cm\n",
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"from src.utils.plotting import make_matching_figure"
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]
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},
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{
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"source": [
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"## Indoor Example"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.loftr import LoFTR, default_cfg\n",
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"\n",
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"# The default config uses dual-softmax.\n",
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"# The outdoor and indoor models share the same config.\n",
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"# You can change the default values like thr and coarse_match_type.\n",
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"matcher = LoFTR(config=default_cfg)\n",
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"matcher.load_state_dict(torch.load(\"weights/indoor_ds.ckpt\")['state_dict'])\n",
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"matcher = matcher.eval().cuda()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load example images\n",
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"img0_pth = \"assets/scannet_sample_images/scene0711_00_frame-001680.jpg\"\n",
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"img1_pth = \"assets/scannet_sample_images/scene0711_00_frame-001995.jpg\"\n",
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"img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)\n",
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"img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)\n",
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"img0_raw = cv2.resize(img0_raw, (640, 480))\n",
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"img1_raw = cv2.resize(img1_raw, (640, 480))\n",
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"\n",
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"img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.\n",
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"img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.\n",
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"batch = {'image0': img0, 'image1': img1}\n",
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"\n",
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"# Inference with LoFTR and get prediction\n",
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"with torch.no_grad():\n",
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" matcher(batch)\n",
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" mkpts0 = batch['mkpts0_f'].cpu().numpy()\n",
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" mkpts1 = batch['mkpts1_f'].cpu().numpy()\n",
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" mconf = batch['mconf'].cpu().numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 750x450 with 2 Axes>",
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"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"273.15pt\" version=\"1.1\" viewBox=\"0 0 714.4 273.15\" width=\"714.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n <cc:Work>\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n <dc:date>2021-04-07T10:41:21.832467</dc:date>\n <dc:format>image/svg+xml</dc:format>\n <dc:creator>\n <cc:Agent>\n <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n </cc:Agent>\n </dc:creator>\n </cc:Work>\n </rdf:RDF>\n </metadata>\n <defs>\n <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 273.15 \nL 714.4 273.15 \nL 714.4 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 7.2 265.95 \nL 352.2 265.95 \nL 352.2 7.2 \nL 7.2 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g clip-path=\"url(#p7b0b3a4453)\">\n <image height=\"259.2\" id=\"image139b1066a2\" transform=\"scale(1 -1)translate(0 -259.2)\" width=\"345.6\" x=\"7.2\" xlink:href=\"data:image/png;base64,\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"image/png": "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},
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"metadata": {}
|
||
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}
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||
|
|
],
|
||
|
|
"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, text)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"source": [
|
||
|
|
"## Outdoor Example"
|
||
|
|
],
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"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": 6,
|
||
|
|
"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": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 750x450 with 2 Axes>",
|
||
|
|
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"272.470866pt\" version=\"1.1\" viewBox=\"0 0 714.4 272.470866\" width=\"714.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n <cc:Work>\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n <dc:date>2021-04-07T10:41:43.419556</dc:date>\n <dc:format>image/svg+xml</dc:format>\n <dc:creator>\n <cc:Agent>\n <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n </cc:Agent>\n </dc:creator>\n </cc:Work>\n </rdf:RDF>\n </metadata>\n <defs>\n <style type=\"text/css\">*{stroke-linecap:butt;stroke-linejoin:round;}</style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 272.470866 \nL 714.4 272.470866 \nL 714.4 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 7.2 238.945357 \nL 352.2 238.945357 \nL 352.2 33.525509 \nL 7.2 33.525509 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g clip-path=\"url(#p4da5760efc)\">\n <image height=\"205.44\" id=\"image96b0692821\" transform=\"scale(1 -1)translate(0 -205.44)\" width=\"345.6\" x=\"7.2\" xlink:href=\"data:image/png;base64,\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
|
||
|
|
"image/png": "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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, text)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|