library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionConfidence threshold comparison
# 1. Set your specific directory
folder_path <- "Z:/Isaac/Piglet_Detection/SAM3/Results"
# 2. Get only the 'summary' CSV files
# Pattern looks for 'summary' followed by the .csv extension
files <- list.files(path = folder_path,
pattern = "_summary\\.csv$",
full.names = TRUE)
# 3. Loop through and rename
for (file in files) {
# Get the raw filename: "Sow1_1_detection_summary"
raw_name <- tools::file_path_sans_ext(basename(file))
# Logic to turn "Sow1_1_detection_summary" into "sow_1_1"
# This grabs the "Sow1_1" part, adds an underscore, and makes it lowercase
clean_name <- tolower(gsub("(Sow\\d)_(\\d).*", "\\1_\\2", raw_name))
# Read and assign to the environment
assign(clean_name, read.csv(file))
message(paste("Imported", file, "as", clean_name))
}Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_1_detection_summary.csv as sow1_1Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_2_detection_summary.csv as sow1_2Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_3_detection_summary.csv as sow1_3Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_4_detection_summary.csv as sow1_4Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_5_detection_summary.csv as sow1_5Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_6_detection_summary.csv as sow1_6Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_7_detection_summary.csv as sow1_7Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_8_detection_summary.csv as sow1_8Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow1_9_detection_summary.csv as sow1_9Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_1_detection_summary.csv as sow2_1Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_2_detection_summary.csv as sow2_2Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_3_detection_summary.csv as sow2_3Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_4_detection_summary.csv as sow2_4Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_5_detection_summary.csv as sow2_5Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_6_detection_summary.csv as sow2_6Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_7_detection_summary.csv as sow2_7Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_8_detection_summary.csv as sow2_8Imported Z:/Isaac/Piglet_Detection/SAM3/Results/Sow2_9_detection_summary.csv as sow2_9# 1. Get objects starting with 'sow' followed immediately by a digit
# This matches 'sow1_1', 'sow2_9', etc.
all_sow_names <- ls(pattern = "^sow\\d")
# 2. Build the master table
final_summary_table <- do.call(rbind, lapply(all_sow_names, function(x) {
df <- get(x)
# Calculate Sums
tp <- sum(df$TP, na.rm = TRUE)
fp <- sum(df$FP, na.rm = TRUE)
fn <- sum(df$FN, na.rm = TRUE)
# Calculate Precision, Recall, and F1 (with checks to avoid dividing by zero)
precision <- ifelse((tp + fp) > 0, tp / (tp + fp), 0)
recall <- ifelse((tp + fn) > 0, tp / (tp + fn), 0)
f1 <- ifelse((precision + recall) > 0,
2 * (precision * recall) / (precision + recall), 0)
# Return as a row
data.frame(
Source_DF = x,
Total_TP = tp,
Total_FP = fp,
Total_FN = fn,
# Precision = round(precision, 4),
# Recall = round(recall, 4),
# F1_Score = round(f1, 4),
stringsAsFactors = FALSE
)
}))
# 3. View the full results
View(final_summary_table)sow1sum <- final_summary_table[1:9,]
sow2sum <- final_summary_table[10:18,]
# Calculating metrics for Sow 1
sow1sum <- sow1sum %>%
mutate(
Precision = Total_TP / (Total_TP + Total_FP),
Recall = Total_TP / (Total_TP + Total_FN),
F1 = 2 * ((Precision * Recall) / (Precision + Recall))
)
# Calculating metrics for Sow 2
sow2sum <- sow2sum %>%
mutate(
Precision = Total_TP / (Total_TP + Total_FP),
Recall = Total_TP / (Total_TP + Total_FN),
F1 = 2 * ((Precision * Recall) / (Precision + Recall))
)
sow1sum Source_DF Total_TP Total_FP Total_FN Precision Recall F1
1 sow1_1 78 739 22 0.09547124 0.78 0.1701200
2 sow1_2 77 184 23 0.29501916 0.77 0.4265928
3 sow1_3 71 68 29 0.51079137 0.71 0.5941423
4 sow1_4 64 20 36 0.76190476 0.64 0.6956522
5 sow1_5 56 11 44 0.83582090 0.56 0.6706587
6 sow1_6 45 7 55 0.86538462 0.45 0.5921053
7 sow1_7 30 3 70 0.90909091 0.30 0.4511278
8 sow1_8 12 3 88 0.80000000 0.12 0.2086957
9 sow1_9 0 0 100 NaN 0.00 NaNsow2sum Source_DF Total_TP Total_FP Total_FN Precision Recall F1
10 sow2_1 20 1136 113 0.01730104 0.150375940 0.03103181
11 sow2_2 13 174 120 0.06951872 0.097744361 0.08125000
12 sow2_3 13 172 120 0.07027027 0.097744361 0.08176101
13 sow2_4 9 83 124 0.09782609 0.067669173 0.08000000
14 sow2_5 7 38 126 0.15555556 0.052631579 0.07865169
15 sow2_6 3 16 130 0.15789474 0.022556391 0.03947368
16 sow2_7 1 6 132 0.14285714 0.007518797 0.01428571
17 sow2_8 0 1 133 0.00000000 0.000000000 NaN
18 sow2_9 0 0 133 NaN 0.000000000 NaN