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https://github.com/ropensci/opencv
R bindings for OpenCV
https://github.com/ropensci/opencv
opencv opencv-library r r-package rstats unconf unconf18
Last synced: 2 months ago
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R bindings for OpenCV
- Host: GitHub
- URL: https://github.com/ropensci/opencv
- Owner: ropensci
- License: other
- Created: 2018-05-21T21:44:00.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-09T17:49:51.000Z (4 months ago)
- Last Synced: 2024-09-09T21:57:56.324Z (4 months ago)
- Topics: opencv, opencv-library, r, r-package, rstats, unconf, unconf18
- Language: C++
- Homepage: https://docs.ropensci.org/opencv
- Size: 378 KB
- Stars: 133
- Watchers: 13
- Forks: 27
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Bindings to 'OpenCV' Computer Vision Library
> Experimenting with computer vision and machine learning in R. This
package exposes some of the available 'OpenCV' algorithms,
such as edge, body or face detection. These can either be applied to analyze
static images, or to filter live video footage from a camera device.
[](http://cran.r-project.org/package=opencv)
[](http://cran.r-project.org/web/packages/opencv/index.html)
## Installation
On Windows and MacOS, the package can be installed directoy from CRAN:
```r
install.packages("opencv")
```
### Install from source
To install from source on MacOS, you need to install the opencv library from homebrew:
```sh
brew install opencv
```
On Ubuntu or Fedora you need [`libopencv-dev`](https://packages.debian.org/testing/libopencv-dev) or [`opencv-devel`](https://src.fedoraproject.org/rpms/opencv):
```sh
sudo apt-get install libopencv-dev
```
And then install the R bindings:
```r
install.packages("opencv", type = "source")
```
## Basic stuff:
Face recognition:
```r
unconf <- ocv_read('https://jeroen.github.io/images/unconf18.jpg')
faces <- ocv_face(unconf)
ocv_write(faces, 'faces.jpg')
```
Or get the face location data:
```r
facemask <- ocv_facemask(unconf)
attr(facemask, 'faces')
```
## Live Webcam Examples
Live face detection:
```r
library(opencv)
ocv_video(ocv_face)
```
Edge detection:
```r
library(opencv)
ocv_video(ocv_edges)
```
## Combine with Graphics
Replaces the background with a plot:
```r
library(opencv)
library(ggplot2)
# get webcam size
test <- ocv_picture()
bitmap <- ocv_bitmap(test)
width <- dim(bitmap)[2]
height <- dim(bitmap)[3]
png('bg.png', width = width, height = height)
par(ask=FALSE)
print(ggplot2::qplot(speed, dist, data = cars, geom = c("smooth", "point")))
dev.off()
bg <- ocv_read('bg.png')
unlink('pg.png')
ocv_video(function(input){
mask <- ocv_mog2(input)
return(ocv_copyto(input, bg, mask))
})
```
Put your face in the plot:
```r
# Overlay face filter
ocv_video(function(input){
mask <- ocv_facemask(input)
ocv_copyto(input, bg, mask)
})
```
## Live Face Survey
Go stand on the left if you're a tidier
```r
library(opencv)
# get webcam size
test <- ocv_picture()
bitmap <- ocv_bitmap(test)
width <- dim(bitmap)[2]
height <- dim(bitmap)[3]
# generates the plot
makeplot <- function(x){
png('bg.png', width = width, height = height, res = 96)
on.exit(unlink('bg.png'))
groups <- seq(0, width, length.out = 4)
left <- rep("left", sum(x < groups[2]))
middle <- rep("middle", sum(x >= groups[2] & x < groups[3]))
right <- rep("right", sum(x >= groups[3]))
f <- factor(c(left, middle, right), levels = c('left', 'middle', 'right'),
labels = c("Tidy!", "Whatever Works", "Base!"))
color = I(c("#F1BB7B", "#FD6467", "#5B1A18"))
plot(f, ylim = c(0, 5),
main = "Are you a tidyer or baser?", col = color)
dev.off()
ocv_read('bg.png')
}
# overlays faces on the plot
ocv_video(function(input){
mask <- ocv_facemask(input)
faces <- attr(mask, 'faces')
bg <- makeplot(faces$x)
return(ocv_copyto(input, bg, mask))
})
```