| import itertools | |
| def jaccard(labels1, labels2): | |
| """ | |
| Computes the Jaccard similarity between two sets of clustering labels. | |
| The value returned is between 0 and 1, inclusively. A value of 1 indicates | |
| perfect agreement between two clustering algorithms, whereas a value of 0 | |
| indicates no agreement. For details on the Jaccard index, see: | |
| http://en.wikipedia.org/wiki/Jaccard_index |
| 0 input_1 | |
| 1 stem_conv1 | |
| 2 stem_bn1 | |
| 3 activation_1 | |
| 4 reduction_conv_1_stem_1 | |
| 5 reduction_bn_1_stem_1 | |
| 6 activation_2 | |
| 7 activation_4 | |
| 8 separable_conv_1_reduction_left1_stem_1 | |
| 9 separable_conv_1_reduction_1_stem_1 |
This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.
###Array ####Definition:
| Below are the Big O performance of common functions of different Java Collections. | |
| List | Add | Remove | Get | Contains | Next | Data Structure | |
| ---------------------|------|--------|------|----------|------|--------------- | |
| ArrayList | O(1) | O(n) | O(1) | O(n) | O(1) | Array | |
| LinkedList | O(1) | O(1) | O(n) | O(n) | O(1) | Linked List | |
| CopyOnWriteArrayList | O(n) | O(n) | O(1) | O(n) | O(1) | Array | |
| import org.opencv.core.*; | |
| import org.opencv.highgui.Highgui; | |
| import org.opencv.highgui.VideoCapture; | |
| import processing.core.PApplet; | |
| import processing.core.PConstants; | |
| import processing.core.PImage; | |
| import javax.imageio.ImageIO; | |
| import java.awt.image.BufferedImage; | |
| import java.io.ByteArrayInputStream; | |
| import java.io.InputStream; |
| /* | |
| * Build command: | |
| * g++ -o opencvni opencvni.cpp `pkg-config --cflags --libs opencv` | |
| */ | |
| #include "opencv2/videoio/videoio.hpp" | |
| #include "opencv2/highgui/highgui.hpp" | |
| #include <iostream> |
I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.
After a day or two figuring out how to compile the examples, I finally made some progress:
dlib from Github to your local machine:
| #!/bin/bash | |
| # Bash script to install latest version of ffmpeg and its dependencies on Ubuntu 12.04 or 14.04 | |
| # Inspired from https://gist.github.com/faleev/3435377 | |
| # Remove any existing packages: | |
| sudo apt-get -y remove ffmpeg x264 libav-tools libvpx-dev libx264-dev | |
| # Get the dependencies (Ubuntu Server or headless users): | |
| sudo apt-get update |