Around Farrowing

library(dplyr)

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library(lmerTest)

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library(tidyverse)

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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(modelr)
library(purrr)
library(emmeans)

Warning: package 'emmeans' was built under R version 4.4.3

Welcome to emmeans.
Caution: You lose important information if you filter this package's results.
See '? untidy'

library(gridExtra)

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Attaching package: 'gridExtra'

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    combine

library(writexl)

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library(gt)

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library(webshot2)

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library(broom.mixed)
library(ggplot2)
library(isotree)

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library(tidyr)
library(ggforce)

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library(plotrix)

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library(lubridate)
library(KFAS)

Warning: package 'KFAS' was built under R version 4.4.3

Please cite KFAS in publications by using: 

  Jouni Helske (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), 1-39. doi:10.18637/jss.v078.i10.

library(zoo)

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

library(ggforce)

library(dplyr)
library(ggplot2)
first <- read.csv("Z:/Isaac/Piglet_Detection/Around Farrowing/Cropped/Results/Sow_1_1st_detection_metrics.csv")
many <- read.csv("Z:/Isaac/Piglet_Detection/Around Farrowing/Cropped/Results/Sow_1_Many_detection_metrics.csv")

boxplot(first$area)

plot(first$area)

plot(first$centroid_x,first$centroid_y)

boxplot(first$width)

boxplot(many$area)

plot(many$area)

plot(many$centroid_x,many$centroid_y)

boxplot(many$width)

library(dplyr)
library(ggplot2)

many %>% 
  filter(score > 0.3) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

many %>% 
  filter(score > 0.3) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

many %>% 
  filter(score > 0.3) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

first %>% 
  filter(score > 0.2) %>%
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

first %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

first %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() +
    labs(title = "Object Detection Bounding Boxes")

save(first,many,file="Z:/Isaac/Piglet_Detection/Around Farrowing/Cropped/Results/around_farrowing.RData")

First 115-135-155

first$image<- as.numeric(str_sub(first$image, end = -5))
first_before <- first %>% filter(image<135)
first_birth <- first %>% filter(image==135)
first_after <- first %>% filter(image>135)

Many 22-42-62

many$image<- as.numeric(str_sub(many$image, end = -5))
many_before <- many %>% filter(image<42)
many_birth <- many %>% filter(image==42)
many_after <- many %>% filter(image>42)

fb1 <- first_before %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark()

fb2 <- first_before %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

fb3 <- first_before %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

fbi1 <- first_birth %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark()

fbi2 <- first_birth %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

fbi3 <- first_birth %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark()

fa1 <- first_after %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

fa2 <- first_after %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

fa3 <- first_after %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mb1 <- many_before %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mb2 <- many_before %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mb3 <- many_before %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mbi1 <- many_birth %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mbi2 <- many_birth %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

mbi3 <- many_birth %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

ma1 <- many_after %>% 
  filter(score > 0.2) %>% 
  # filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

ma2 <- many_after %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  # filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark()

ma3 <- many_after %>% 
  filter(score > 0.2) %>% 
  filter(area < 15000) %>%
  filter(x1 > 100) %>% # 1. Filter the data first
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) + # 2. Start ggplot
    geom_rect(fill = NA, color = "red") + # 3. Add layers with +
    theme_dark() 

library(cowplot)

Warning: package 'cowplot' was built under R version 4.4.3

Attaching package: 'cowplot'

The following object is masked from 'package:gt':

    as_gtable

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    stamp

plot_grid(fb1,fbi1,fa1,mb1,mbi1,ma1,ncol=2)

plot_grid(fb2,fbi2,fa2,mb2,mbi2,ma2,ncol=2)

plot_grid(fb3,fbi3,fa3,mb3,mbi3,ma3,ncol=2)

library(dplyr)
library(ggplot2)

# Combine everything and add 'type' and 'stage' tags
all_data <- bind_rows(
  mutate(first_before, type = "First", stage = "Before"),
  mutate(many_before,  type = "Many",  stage = "Before"),
  mutate(first_birth,  type = "First", stage = "Birth"),
  mutate(many_birth,   type = "Many",  stage = "Birth"),
  mutate(first_after,  type = "First", stage = "After"),
  mutate(many_after,   type = "Many",  stage = "After")
)

# One single ggplot call for the entire 2x3 panel
all_data %>%
  filter(score > 0.2#, 
         # area < 15000, 
         # x1>100
         )%>%
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) +
  geom_rect(fill = NA, color = "red") +
  facet_grid(stage ~ type) + # Rows = stage, Columns = type
  theme_dark() +
  labs(title = "Detection Panel")

all_data %>%
  filter(score > 0.2, 
         area < 15000#, 
         # x1>100
         ) %>%
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) +
  geom_rect(fill = NA, color = "red") +
  facet_grid(stage ~ type) + # Rows = stage, Columns = type
  theme_dark() +
  labs(title = "Detection Panel")

all_data %>%
  filter(score > 0.2, 
         area < 15000, 
         x1>100) %>%
  ggplot(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2)) +
  geom_rect(fill = NA, color = "red") +
  facet_grid(stage ~ type) + # Rows = stage, Columns = type
  theme_dark() +
  labs(title = "Detection Panel")