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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.dpi = 120, fig.width = 8, fig.height = 6,
out.width = "100%"
)
```
# ovmlpy
<!-- badges: start -->
[](https://www.tidyverse.org/lifecycle/#experimental)
[](https://github.com/openvolley/ovmlpy/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
The `ovmlpy` package provides image and video machine learning tools for volleyball analytics. It provides similar functionality to the [ovml](https://github.com/openvolley/ovml) package, but `ovmlpy` uses a Python-based implementation which should offer improved performance. `ovmlpy` takes care of installing Python and required dependencies: you do not need an existing Python installation on your system.
## Installation
```{r eval = FALSE}
install.packages("ovmlpy", repos = c("https://openvolley.r-universe.dev",
"https://cloud.r-project.org"))
## or
## install.packages("remotes") ## if needed
remotes::install_github("openvolley/ovmlpy")
```
On first use, you need to tell `ovmlpy` to install Python and some required packages. It will install these into a virtual environment, so that they do not interfere with other Python installation(s) that you might already have on your system:
```{r eval = FALSE}
ovmlpy::ovml_yolo7_python_setup()
```
Some other setup/installation notes:
- if you wish to use a GPU card for improved performance you will need to ensure that the drivers are installed on your system
- the first time you use a new type of network, it will also download the associated network weights file (~70MB, depending on network version)
- note that you probably can't use `ovml` and `ovmlpy` in the same R session, because of conflicts in shared libraries.
`ovmlpy` includes the [YOLO v7](https://github.com/WongKinYiu/yolov7) object detection algorithm and an experimental version of this network specifically for detecting volleyballs. This implementation draws heavily from [WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7).
## Example
Create the network object once ...
```{r ex1}
library(ovmlpy)
dn <- ovml_yolo()
```
... then we can use the network to detect objects in an image:
```{r ex2}
img <- ovml_example_image()
dets <- ovml_yolo_detect(dn, img)
ovml_ggplot(img, dets)
```
Or detect human poses (experimental!):
```{r ex3}
dn2 <- ovml_yolo("7-w6-pose")
dets2 <- ovml_yolo_detect(dn2, img)
library(ggplot2)
library(ggsci)
ovml_ggplot(img) +
geom_segment(data = dets2, aes(x1, y1, xend = x2, yend = y2, col = segment), size = 1.5) +
scale_color_d3(palette = "category20", guide = "none")
```