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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"#XFeat minimal inference example"
],
"metadata": {
"id": "2tDj94al5GAJ"
}
},
{
"cell_type": "markdown",
"source": [
"## Clone repository"
],
"metadata": {
"id": "X8MPXBro5IFv"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tVkH1ChzNcLW",
"outputId": "da9a9474-76bd-4b66-8ecd-8ba0022f030e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'accelerated_features'...\n",
"remote: Enumerating objects: 27, done.\u001b[K\n",
"remote: Counting objects: 100% (11/11), done.\u001b[K\n",
"remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
"remote: Total 27 (delta 0), reused 5 (delta 0), pack-reused 16\u001b[K\n",
"Receiving objects: 100% (27/27), 13.29 MiB | 23.03 MiB/s, done.\n",
"Resolving deltas: 100% (1/1), done.\n",
"/content/accelerated_features\n"
]
}
],
"source": [
"!cd /content && git clone 'https://github.com/verlab/accelerated_features.git'\n",
"%cd /content/accelerated_features"
]
},
{
"cell_type": "markdown",
"source": [
"## Test on simple input (sparse setting)"
],
"metadata": {
"id": "32T-WzfU5NRH"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import os\n",
"import torch\n",
"import tqdm\n",
"\n",
"from modules.xfeat import XFeat\n",
"\n",
"xfeat = XFeat()\n",
"\n",
"#Random input\n",
"x = torch.randn(1,3,480,640)\n",
"\n",
"#Simple inference with batch = 1\n",
"output = xfeat.detectAndCompute(x, top_k = 4096)[0]\n",
"print(\"----------------\")\n",
"print(\"keypoints: \", output['keypoints'].shape)\n",
"print(\"descriptors: \", output['descriptors'].shape)\n",
"print(\"scores: \", output['scores'].shape)\n",
"print(\"----------------\\n\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o1TMnCEfNfvD",
"outputId": "f59757f5-477a-4642-e955-7a5abefe3c21"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"loading weights from: /content/accelerated_features/modules/../weights/xfeat.pt\n",
"----------------\n",
"keypoints: torch.Size([4096, 2])\n",
"descriptors: torch.Size([4096, 64])\n",
"scores: torch.Size([4096])\n",
"----------------\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Stress test to check FPS on VGA (sparse setting)"
],
"metadata": {
"id": "8b9C09ya5UwL"
}
},
{
"cell_type": "code",
"source": [
"x = torch.randn(1,3,480,640)\n",
"# Stress test\n",
"for i in tqdm.tqdm(range(100), desc=\"Stress test on VGA resolution\"):\n",
"\toutput = xfeat.detectAndCompute(x, top_k = 4096)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zsjz-QT95ZrM",
"outputId": "2df6f545-419f-4cc3-ad8b-bf5e12741dba"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Stress test on VGA resolution: 100%|██████████| 100/100 [00:14<00:00, 6.74it/s]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Test with batched mode (sparse)"
],
"metadata": {
"id": "1jAl-ejS5du_"
}
},
{
"cell_type": "code",
"source": [
"# Batched mode\n",
"x = torch.randn(4,3,480,640)\n",
"outputs = xfeat.detectAndCompute(x, top_k = 4096)\n",
"print(\"# detected features on each batch item:\", [len(o['keypoints']) for o in outputs])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lAarS8UH5gyg",
"outputId": "883f13f8-3fac-48f2-c0a3-656a81b57f2c"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"# detected features on each batch item: [4096, 4096, 4096, 4096]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Matches two images with built-in MNN matcher (sparse mode)"
],
"metadata": {
"id": "H60iMAlh5nqP"
}
},
{
"cell_type": "code",
"source": [
"# Match two images with sparse features\n",
"x1 = torch.randn(1,3,480,640)\n",
"x2 = torch.randn(1,3,480,640)\n",
"mkpts_0, mkpts_1 = xfeat.match_xfeat(x1, x2)"
],
"metadata": {
"id": "6N-ZqoMZ5syf"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Matches two images with semi-dense matching, and batched mode (batch size = 4) for demonstration purpose"
],
"metadata": {
"id": "MOV4vZDp5v9_"
}
},
{
"cell_type": "code",
"source": [
"# Create 4 image pairs\n",
"x1 = torch.randn(4,3,480,640)\n",
"x2 = torch.randn(4,3,480,640)\n",
"\n",
"#Obtain matches for each batch item\n",
"matches_list = xfeat.match_xfeat_star(x1, x2, top_k = 5000)\n",
"print('number of img pairs', len(matches_list))\n",
"print(matches_list[0].shape) # -> output is (x1,y1,x2,y2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Axe0o6U85zGV",
"outputId": "e1257959-24fc-4194-b2f1-ee06cf450b24"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"number of img pairs 4\n",
"torch.Size([182, 4])\n"
]
}
]
}
]
} |