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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Session 5: Computer vision in R</title>
<meta charset="utf-8" />
<meta name="author" content="Ben Raymond, Adrien Ickowicz" />
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<link rel="stylesheet" href="extra/extra.css" type="text/css" />
</head>
<body>
<textarea id="source">
layout: true
<div class="my-footer">
<div class="my-footer-box"><a href="https://openvolley.org/"><img style="display:inline;" src="extra/ovoutline-w.png"/>openvolley.org</a></div>
<div class="my-footer-box"><a href="https://https://volleyball.ca/"><img src="extra/vc-w-wide.png"/></a></div>
<div class="my-footer-box"><a href="https://untan.gl/"><img src="extra/su_title-w.png"/></a></div>
</div>
---
class: inverse, logo, center
<img src="extra/3logo2.png" style="width:65%; margin-bottom:50px;" />
## Session 5: Computer vision in R
### Ben Raymond, Adrien Ickowicz
##### with valuable contributions from many others...
---
## Session 5 setup
Update your copy of the workshop repo:
If you are in the 'R_Workshop_2022' project, then from the RStudio menu:
`Tools` -> `Version Control` -> `Pull Branches`
Or from the command line in your 'R_Workshop_2022' directory: `git pull`
<br />
Install the ovml package if you don't already have it:
```r
install.packages("ovml",
repos = c("https://openvolley.r-universe.dev",
"https://cloud.r-project.org"))
```
---
## Limitations of scout data
- only the player playing the ball
- doesn't capture all details
- unavoidable subjectivity
- manually intensive
---
## A brief history of computer vision, AI and 'deep learning'
#### AI, deep learning:
1940s&ndash;1980s: artificial neural networks, backpropagation
1990s&ndash;2000s: use of GPUs, quantities of data
Around 2010: "modern" computer vision approaches
<br />
<hr />
See also: [Peter Norvig - The Unreasonable Effectiveness of Data](https://www.youtube.com/watch?v=yvDCzhbjYWs)
---
## A biased perspective
For a prospective user of these approaches:
- developing new models is hard
- there are models available (with code), many to choose from
- but they likely don't do quite what (or everything) you want
- training networks is fiddly, time consuming, and requires lots of data ... but likely feasible
- beyond trivial usage, most require domain-specific coding (Python)
---
## Openvolley aims
- to make these approaches more accessible
- in R
- not necessarily the most up-to-date or best (from a ML perspective)
- not necessarily the most efficient implementation
- BUT integrated with all the other openvolley tools
---
## The ovml package
```r
library(ovml)
dn <- ovml_yolo()
image_file <- ovml_example_image()
res <- ovml_yolo_detect(dn, image_file)
res
```
```
image_number class score xmin xmax ymin ymax
1 1 person 0.9997922 559.29555 655.0639 78.05564 259.7696
2 1 person 0.9996709 828.48042 953.8777 91.11566 280.6085
3 1 person 0.9996420 742.20545 837.7763 207.96229 397.8552
4 1 person 0.9998480 655.88811 718.8102 249.73282 449.8958
5 1 person 0.9996897 81.39468 123.2998 245.04192 408.4309
6 1 person 0.9991708 982.23695 1086.7565 148.34806 346.2865
7 1 person 0.9984150 622.42059 674.1802 268.68086 430.3307
8 1 person 0.9973027 802.84929 862.9306 310.86588 469.4443
9 1 person 0.9997010 355.70534 471.9602 99.31968 256.9752
10 1 person 0.9996446 742.64417 800.3177 419.89205 577.3802
11 1 person 0.9996887 1162.08036 1223.8335 450.21562 529.3872
```
---
```r
ovml_ggplot(image_file, res)
```
<!-- -->
---
## The ovml package
Helpers &mdash; extracting video frames
```r
my_video_file <- ovdata::ovdata_example_video("190301_kats_beds")
my_video_file
```
```
[1] "/home/ben/R/x86_64-pc-linux-gnu-library/4.2/ovdata/extdata/video/2019_03_01-KATS-BEDS-clip.mp4"
```
```r
library(ovideo)
image_file <- ov_video_frame(my_video_file, t = 3.2)
image_file
```
```
[1] "/tmp/Rtmpu8a1Kh/file3560a3855f2cc.jpg"
```
---
## The ovml package
Helpers &mdash; converting to court coordinates
Also in the ovideo package:
- `ov_shiny_court_ref` to define the transformation function
- `ov_transform_points` to transform from image coordinates to court coordinates and vice-versa
---
## The ovml package
Helpers &mdash; converting to court coordinates
```r
ref <- ov_shiny_court_ref(image_file)
ref$court_ref
```
```
image_x image_y court_x court_y
1 0.05397063 0.02129301 0.5 0.5
2 0.95402573 0.02294600 3.5 0.5
3 0.75039756 0.52049712 3.5 6.5
4 0.28921230 0.51884413 0.5 6.5
```
---
## The ovml package
Helpers &mdash; putting those together
```r
res <- ovml_yolo_detect(dn, image_file)
head(res)
```
```
image_number class score xmin xmax ymin ymax
1 1 person 0.9987562 249.3282 370.6062 93.12426 298.4178
2 1 person 0.9997475 796.2333 869.6249 260.13521 433.4992
3 1 person 0.9996364 712.9836 786.1387 224.97480 396.0520
4 1 person 0.9996997 535.7915 595.6890 266.06239 430.9295
5 1 person 0.9999343 652.9980 724.1900 247.44727 452.2819
6 1 person 0.9990908 1125.6339 1217.8855 170.70665 379.4568
```
---
## The ovml package
Helpers &mdash; putting those together
```r
library(magick)
image_meta <- image_info(image_read(image_file))
res <- res %>% mutate(x = (xmin + xmax) / 2 / image_meta$width,
y = ymin / image_meta$height)
head(res)
```
```
image_number class score xmin xmax ymin ymax
1 1 person 0.9987562 249.3282 370.6062 93.12426 298.4178
2 1 person 0.9997475 796.2333 869.6249 260.13521 433.4992
3 1 person 0.9996364 712.9836 786.1387 224.97480 396.0520
4 1 person 0.9996997 535.7915 595.6890 266.06239 430.9295
5 1 person 0.9999343 652.9980 724.1900 247.44727 452.2819
6 1 person 0.9990908 1125.6339 1217.8855 170.70665 379.4568
x y
1 0.2421619 0.1293393
2 0.6507259 0.3612989
3 0.5855947 0.3124650
4 0.4419846 0.3695311
5 0.5379641 0.3436768
6 0.9154373 0.2370926
```
---
## The ovml package
```r
court_xy <- ov_transform_points(res[, c("x", "y")],
ref = ref$court_ref,
direction = "to_court")
res <- bind_cols(res, setNames(court_xy, c("court_x", "court_y")))
head(res)
```
```
image_number class score xmin xmax ymin ymax
1 1 person 0.9987562 249.3282 370.6062 93.12426 298.4178
2 1 person 0.9997475 796.2333 869.6249 260.13521 433.4992
3 1 person 0.9996364 712.9836 786.1387 224.97480 396.0520
4 1 person 0.9996997 535.7915 595.6890 266.06239 430.9295
5 1 person 0.9999343 652.9980 724.1900 247.44727 452.2819
6 1 person 0.9990908 1125.6339 1217.8855 170.70665 379.4568
x y court_x court_y
1 0.2421619 0.1293393 1.012716 1.244800
2 0.6507259 0.3612989 2.679859 3.633504
3 0.5855947 0.3124650 2.338898 3.004974
4 0.4419846 0.3695311 1.632314 3.757907
5 0.5379641 0.3436768 2.117389 3.400127
6 0.9154373 0.2370926 3.706100 2.172419
```
---
## The ovml package
Helpers &mdash; putting those together
```r
ggplot(res %>% filter(court_y < 7 & court_x < 4),
aes(x = court_x, y = court_y)) +
ggcourt(labels = NULL) +
geom_point()
```
<!-- -->
---
## ovml &mdash; Application ideas
#### Court positioning
Recall from yesterday, the volleydef app output:
- uses one defender position per dug attack
- what can we get via ovml?
---
## ovml &mdash; Court positioning
```r
library(datavolley)
library(ovdata)
dv <- ovdata_example("190301_kats_beds-clip", as = "parsed")
vt <- plays(dv) %>%
filter(attack_code %in% c("X6", "V6") & team == "MKS Będzin") %>%
pull(video_time)
image_files <- ov_video_frame(my_video_file, t = vt)
res <- ovml_yolo_detect(dn, image_files)
res <- res %>% mutate(x = (xmin + xmax) / 2 / image_meta$width,
y = ymin / image_meta$height)
court_xy <- ov_transform_points(res[, c("x", "y")],
ref = ref$court_ref, direction = "to_court")
res <- bind_cols(res, setNames(court_xy, c("court_x", "court_y")))
res <- res %>% filter(class == "person" & court_y >= 3.5 &
court_y < 7 & court_x > 0 & court_x < 4)
```
---
## ovml &mdash; Court positioning
```r
ggplot(res, aes(x = court_x, y = court_y)) +
ggcourt(labels = NULL, court = "upper") +
geom_point(color = "blue")
```
<!-- -->
---
## ovml &mdash; Court positioning
Positions vs left-side attacks:
<img src="extra/x5-v5-def.png" style="max-height:45vh;" />
---
## ovml &mdash; Court positioning (beach)
Images courtesy Tyler Widdison
<img src="extra/tw-image1.png" style="float:left; clear:none; width:60%;" />
<img src="extra/tw-image2.png" style="float:right; clear:none; max-height:50vh;" />
---
## Next steps
- tracking of players, and the ball
- identify individuals, follow their movements
- fill in gaps (occlusions, missed detections)
- some unique challenges
---
## Next steps &mdash; tracking
<img src="extra/players-tracked.png" style="max-height:55vh;" />
---
## Next steps &mdash; tracking
<video src="extra/kats3_tracked.mp4" controls style="margin-top:-20vh;"/>
---
## Next steps &mdash; height
<img src="/home/ben/R/x86_64-pc-linux-gnu-library/4.2/ovdata/extdata/images/2019_03_01-KATS-BEDS-frame.png" style="max-height:45vh;" />
- multiple cameras?
---
class: logo
<img src="extra/leal_two_views.png" />
---
## Next steps &mdash; two camera example
<img src="extra/leal_apparent_positions.png" />
---
## Next steps &mdash; two camera example
<img src="extra/leal_resolved_positions.png" style="max-height:100vh; margin-top:-20px;" />
Estimated height: 58cm
---
## Next steps &mdash; two camera example
<video id="vid2c" src="extra/demo_ball_tracking.mp4" controls onplay="var vid = document.getElementById('vid2c'); vid.playbackRate = 0.5; "/>
---
## Next steps &mdash; two camera example
Estimated contact heights:
- serve height (Isac, Brazil #12) is 3.49m (top of the ball)
- back-row spike by Alan (Brazil #21) at 3.43m
(See https://untan.gl/multicamera-volleyball-tracking.html)
---
class: center, middle
## Enough with the machine learning already
---
## openvolley
- GitHub issues
- analytics snippets: https://openvolley.github.io/volley-analytics-snippets
</textarea>
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