JDWarner

"""
_jaccard.py : Jaccard metric for comparing set similarity.

"""

import numpy as np


def jaccard(im1, im2):
    """

jirihnidek

"""
This example try to create new empty object visualized as
image. Image fits to the background of current active camera.
When you set X,Y coordinates of empty object called 'Pixel',
then this object is position at corresponding X,Y coordinate
at image in 3D space.
"""

import bpy
import math

diegopacheco

sudo apt-get install autoconf automake libtool curl make g++ unzip -y
git clone https://github.com/google/protobuf.git
cd protobuf
git submodule update --init --recursive
./autogen.sh
make
make check
sudo make install
sudo ldconfig

davegreenwood

from __future__ import print_function
import numpy as np
import cv2
import time

np.set_printoptions(formatter={'float': '{: 0.3f}'.format})


def triangulate_nviews(P, ip):
    """

Saafke

Google Summer of Code 2019 with OpenCV

Learning-based Super Resolution

Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori

Link to accomplished work:

• PR in the opencv_contrib repository: opencv_contrib/pull/2231

fannymonori

Google Summer of Code 2019 with OpenCV

DNN based super-resolution module

Student: Fanny Monori
Mentor: Vladimir Tyan
Other student on the project: Xavier Weber
Other mentor on the project: Yida Wang

maky-hnou

# Install Cuda 10.1 and Cudnn 7.6.5 on Ubuntu 18.04  


## Start clean  
sudo apt purge nvidia*
sudo apt remove nvidia-*
sudo rm /etc/apt/sources.list.d/cuda*
sudo apt autoremove && apt autoclean
sudo rm -rf /usr/local/cuda*  

Saafke

cmake \
-D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D WITH_CUDA=ON \
-D WITH_CUDNN=ON \
-D OPENCV_DNN_CUDA=ON \
-D ENABLE_FAST_MATH=1 \

GuyARoss

import {_ as e, L as t} from "./index-4deec983.js";
import {a as n, C as i} from "./Controller-26bd1e9e.js";
import {S as s} from "./ScrollObserver-d0732a2c.js";
import {F as o} from "./index-bee741e4.js";
class r {
    constructor(e, t, n, i=!1) {
        const s = this
          , o = -1 !== document.location.search.toLowerCase().indexOf("debug=webgl");
        s.canvas = e,
        s.gl = s.canvas.getContext("webgl", {

jinyup100

GSoC '20 : Real-time Single Object Tracking using Deep Learning (SiamRPN++)

Overview

Mentors : Liubov Batanina @l-bat, Stefano Fabri @bhack, Ilya Elizarov @ieliz
Student : Jin Yeob Chung @jinyup100
Mentors' Project Proposal : https://summerofcode.withgoogle.com/projects/#4979746967912448
Link to Pull Request : opencv/opencv#17647
Link to video summarising the experience : https://www.youtube.com/watch?v=D9G1vHqJCrc

Introduction

Recent interest in computer vision has led to a great advance in the development of visual trackers. Specifically, various applications of Kernelized Correlation Function (KCF) and deep learning have led to numerous implementations of single object trackers using publicly available libraries. Lately, there has been an increased focus on the function of convolutional features in developing visual trackers. In this particular project, I look to focus on the implementations of visual trackers