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validation_functions.R
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validation_functions.R
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# Description -------------------------------------------------------------
# functions to create shapefiles from validation data and matching predictions to evaluate model performance and calculate best threshold to use
# both functions expect same arguments
# db_name = path to database where survey data stored
# colony = 4-letter colony code lower case
# date = YYYY-mm of survey of interest
#
# AS 3/2024
# Functions ---------------------------------------------------------------
create_validation_shapefiles <- function(db_name, colony, species, date) {
# Required Libraries ------------------------------------------------------
require(sf)
require(tidyverse)
require(RSQLite)
require(terra)
require(sp)
# Connect to UAV survey db ------------------------------------------------
db <- dbConnect(SQLite(), dbname = db_name)
# List of surveys in db
survey_ls <- dbGetQuery(db, "SELECT * FROM Surveys")
# Get survey ID
survey <- survey_ls %>%
filter(str_detect(SurveyId, glue::glue("{colony}_{species}.*_{date}"))) %>%
pull(SurveyId)
valid_dir <- paste("data", survey, "validate", sep = "/")
valid_labs <-
list.files(valid_dir, pattern = "^label.*\\.csv$", full.names = TRUE) %>%
read_csv()
names(valid_labs) <-
c(
"label_name",
"bbox_x",
"bbox_y",
"bbox_width",
"bbox_height",
"tileName",
"image_width",
"image_height"
)
predict_dir <-
paste("data", survey, "predict", sep = "/")
# Get pixel size of orthos
ortho_info <-
list.files(predict_dir, pattern = "OrthoInfo", full.names = TRUE) %>%
map(.,
~ read_csv(.x, show_col_types = FALSE) %>% mutate(file_name = basename(.x))) %>%
bind_rows() %>%
mutate(ortho_name = sub("^(.*?)_OrthoInfo\\.csv$", "\\1", file_name)) %>%
select(-file_name) # Remove the original file_name column
proj <- CRS(ortho_info$crs_r[1])
georef_ls <-
list.files(predict_dir, pattern = "tilesGeoref", full.names = TRUE)
reformat_string <- function(old_string) {
if (startsWith(old_string, "bird")) {
new_string <-
str_replace(
old_string,
"^(bird)_(middle|north|south)_(\\d{4})(\\d{2})(\\d{2})_(.*)\\.jpg$",
"\\1_\\2_adpe_\\3-\\4-\\5_\\6"
)
if (grepl("middle", new_string)) {
# Replace "middle" with "mid"
new_string <- gsub("middle", "mid", new_string)
}
} else if (startsWith(old_string, "croz") ||
startsWith(old_string, "royd")) {
new_string <-
str_replace(
old_string,
"^(croz|royd)_((?:middle|north|south)_)?(\\d{4})(\\d{2})(\\d{2})_(.*)\\.jpg$",
"\\1_\\2adpe_\\3-\\4-\\5_\\6"
)
# Replace "middle" with "mid" if it's present in the new string
if (grepl("middle", new_string)) {
new_string <- gsub("middle", "mid", new_string)
}
}
# Remove .jpg at the end
new_string <- gsub("\\.jpg$", "", new_string)
return(new_string)
}
georef <- map_dfr(georef_ls, read_csv, show_col_types = FALSE)
# Apply the function to the column of strings
georef$tileName <- sapply(georef$tileName, reformat_string)
# Assuming georef is the table with upper-left corner coordinates of each tile
# and valid_labs is the table with label box coordinates relative to the upper-left corner of each tile
valid_labs_coords <-
valid_labs %>%
mutate(tileName = gsub("\\.jpg$", "", tileName)) %>%
inner_join(georef, . , by = "tileName") %>%
mutate(ortho_name = sub("^(.*?)_\\d+_\\d+$", "\\1", tileName)) %>%
left_join(ortho_info) %>%
mutate(
left_geo = easting + bbox_x * xres,
top_geo = northing - bbox_y * yres,
# Subtract because y-coordinate increases downward in pixel space
right_geo = left_geo + bbox_width * xres,
bottom_geo = top_geo - bbox_height * yres
)
# Create polygons
valid_polygons_list <-
lapply(1:nrow(valid_labs_coords), function(i) {
sp::Polygon(cbind(
c(
valid_labs_coords$left_geo[i],
valid_labs_coords$right_geo[i],
valid_labs_coords$right_geo[i],
valid_labs_coords$left_geo[i],
valid_labs_coords$left_geo[i]
),
c(
valid_labs_coords$bottom_geo[i],
valid_labs_coords$bottom_geo[i],
valid_labs_coords$top_geo[i],
valid_labs_coords$top_geo[i],
valid_labs_coords$bottom_geo[i]
)
))
})
# Convert list of polygons to SpatialPolygons
valid_sp_polygons <-
SpatialPolygons(lapply(1:length(valid_polygons_list), function(i) {
Polygons(list(valid_polygons_list[[i]]), ID = as.character(i))
}), proj4string = proj)
valid_sp_polygons_df <-
SpatialPolygonsDataFrame(valid_sp_polygons, data = valid_labs_coords)
# Convert the SpatialPolygonsDataFrame to an sf object
valid_sf_object <- st_as_sf(valid_sp_polygons_df)
# Write the polygons to a shapefile
st_write(
valid_sf_object,
paste0("data/", survey, "/validate/", survey, "_validation", ".shp"),
append = FALSE
)
# Convert prediction labels to shapefile ----------------------------------
# Get model name
predictions <- dbGetQuery(db, "SELECT * FROM ModelPredictions")
model <- predictions %>%
filter(SurveyId == survey) %>%
pull(ModelName) %>%
.[1]
pred_labs_dir <-
paste(predict_dir, "counts", model, "labels", sep = "/")
labnames <-
unique(paste(sub("\\.jpg$", "", valid_labs$tileName)))
# Table with prediction labels
pred_labs <-
list.files(pred_labs_dir,
pattern = paste(labnames, collapse = "|"),
full.names = TRUE) %>%
map(.,
~ read_delim(
.x,
col_names = c(
"class",
"bbox_x",
"bbox_y",
"bbox_width",
"bbox_height",
"conf"
),
col_types = cols(.default = "n")
) %>%
mutate(file_name = basename(.x))) %>%
bind_rows() %>%
mutate(ortho_name = sub("^(.*?)_\\d+_\\d+\\.txt$", "\\1", file_name))
# Calculate coordinates
pred_labs_coords <- pred_labs %>%
mutate(tileName = gsub("\\.txt$", "", file_name)) %>%
inner_join(georef, . , by = "tileName") %>%
left_join(ortho_info) %>%
mutate(
image_width = valid_labs$image_width[1],
image_height = valid_labs$image_height[1],
half_width = bbox_width * xres * image_width / 2,
# Half the width of the bounding box
half_height = bbox_height * yres * image_height / 2,
# Half the height of the bounding box
left_geo = easting + (bbox_x * xres * image_width) - half_width,
top_geo = northing - (bbox_y * yres * image_height) + half_height,
right_geo = easting + (bbox_x * xres * image_width) + half_width,
bottom_geo = northing - (bbox_y * yres * image_height) - half_height
)
# Create polygons
pred_polygons_list <-
lapply(1:nrow(pred_labs_coords), function(i) {
sp::Polygon(cbind(
c(
pred_labs_coords$left_geo[i],
pred_labs_coords$right_geo[i],
pred_labs_coords$right_geo[i],
pred_labs_coords$left_geo[i],
pred_labs_coords$left_geo[i]
),
c(
pred_labs_coords$bottom_geo[i],
pred_labs_coords$bottom_geo[i],
pred_labs_coords$top_geo[i],
pred_labs_coords$top_geo[i],
pred_labs_coords$bottom_geo[i]
)
))
})
# Convert list of polygons to SpatialPolygons
pred_sp_polygons <-
SpatialPolygons(lapply(1:length(pred_polygons_list), function(i) {
Polygons(list(pred_polygons_list[[i]]), ID = as.character(i))
}), proj4string = proj)
pred_sp_polygons_df <-
SpatialPolygonsDataFrame(pred_sp_polygons, data = pred_labs_coords)
# Convert the SpatialPolygonsDataFrame to an sf object
pred_sf_object <- st_as_sf(pred_sp_polygons_df)
# Write the polygons to a shapefile
st_write(
pred_sf_object,
paste0(
"data/",
survey,
"/validate/",
survey,
"_predictions_to_validate",
".shp"
),
append = FALSE
)
# Close the database connection
dbDisconnect(db)
}
compute_threshold_results <- function(db_name, colony, species, date) {
# Required Libraries ------------------------------------------------------
require(sf)
require(tidyverse)
require(parallel)
require(RSQLite)
# Connect to UAV survey db ------------------------------------------------
db <- dbConnect(SQLite(), dbname = db_name)
# Get survey ID
survey_ls <- dbGetQuery(db, "SELECT * FROM Surveys")
survey <- survey_ls %>%
filter(str_detect(SurveyId, glue::glue("{colony}_{species}.*_{date}"))) %>%
pull(SurveyId)
# Build paths
pred_path <-
paste0("data/",
survey,
"/validate/",
survey,
"_predictions_to_validate.shp")
valid_path <-
paste0("data/", survey, "/validate/", survey, "_validation.shp")
# Read data
preds_sf <- st_read(pred_path) %>%
mutate(PredictId = row_number()) %>%
rename(tileName = tileNam,
confidence = conf)
valid_sf <- st_read(valid_path) %>%
mutate(ValidId = row_number()) %>%
rename(tileName = tileNam)
# List of tiles that have validation data
tile_ls <- unique(valid_sf$tileName)
compute_tile_stats <-
function(tile_index,
tiles,
preds_sf,
valid_sf,
conf_thresh) {
require(sf)
require(dplyr)
tn <- tiles[tile_index]
preds <-
filter(preds_sf, tileName == tn, confidence > conf_thresh)
valids <- filter(valid_sf, tileName == tn)
inter <- st_intersection(preds, valids) %>%
mutate(area_overlap = as.numeric(st_area(geometry))) %>%
filter(area_overlap > 0.05) %>%
group_by(ValidId) %>%
arrange(area_overlap) %>%
slice_tail(n = 1)
tp <- nrow(inter)
fp <- nrow(preds) - nrow(inter)
fn <- nrow(valids) - tp
return(data.frame(
tileName = tn,
tp = tp,
fp = fp,
fn = fn,
conf_thresh = conf_thresh
))
}
conf_thresh_values <- seq(0.3, 1, by = 0.01)
no_cores <- detectCores() - 10
cl <- makeCluster(no_cores)
# Export necessary variables to the cluster
clusterExport(
cl,
c(
"tile_ls",
"compute_tile_stats",
"preds_sf",
"valid_sf",
"conf_thresh_values"
),
envir = environment()
)
results_list <-
parLapply(cl, conf_thresh_values, function(conf) {
valid_stats_list <-
lapply(
1:length(tile_ls),
compute_tile_stats,
tiles = tile_ls,
preds_sf = preds_sf,
valid_sf = valid_sf,
conf_thresh = conf
)
return(do.call(rbind, valid_stats_list))
})
# Stop the cluster once finished
stopCluster(cl)
# Bind all results into one dataframe
all_results <- do.call(rbind, results_list)
# Compute precision/recall curve
results_summ <- all_results %>%
group_by(conf_thresh) %>%
summarise(
tp_all = sum(tp),
fp_all = sum(fp),
fn_all = sum(fn),
actual_positive = tp_all + fn_all,
total_positive = tp_all + fp_all,
precision = tp_all / total_positive,
recall = tp_all / actual_positive
) %>%
mutate(
conf_thresh = conf_thresh,
# correction factor = fraction to adjust by to get real number. Real number is tp + fn
# real/estimate
correction_fact = actual_positive / total_positive,
FScore = (2 * precision * recall / (precision + recall))
)
# Write results to a CSV file
write_csv(
results_summ,
paste0(
"data/",
survey,
"/validate/",
survey,
"_threshold_results.csv"
)
)
# Calculate values at threshold that maximizes F1Score
selected_thresh <-
max(results_summ$conf_thresh[results_summ$FScore == max(results_summ$FScore, na.rm = TRUE)], na.rm = TRUE)
F1Score <-
round(results_summ$FScore[which.max(results_summ$FScore)], 4)
Precision <-
round(results_summ$precision[which.max(results_summ$FScore)], 4)
Recall <-
round(results_summ$recall[which.max(results_summ$FScore)], 4)
CorrFact <-
round(results_summ$correction_fact[which.max(results_summ$FScore)], 4)
# Plot precision/recall curve
recall_precision_plot <-
ggplot(data = results_summ, aes(recall, precision, col = conf_thresh)) +
geom_point(na.rm = TRUE) + # Remove missing values
geom_label(aes(label = conf_thresh), na.rm = TRUE) + # Remove missing values
theme(legend.position = "none")
f_score_plot <-
ggplot(data = results_summ, aes(conf_thresh, FScore, col = conf_thresh)) +
geom_point(na.rm = TRUE) + # Remove missing values
geom_text(aes(
x = conf_thresh[10],
y = 0.5,
label = paste("Selected Threshold =", selected_thresh)
)) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 90,
vjust = 0.5,
hjust = 1
)
)
# Write plots as JPEG files
ggsave(
filename = paste0(
"data/",
survey,
"/validate/",
survey,
"_precision_recall.jpeg"
),
plot = recall_precision_plot,
width = 8,
height = 6,
units = "in",
dpi = 150
)
ggsave(
filename = paste0("data/", survey, "/validate/", survey, "_FScores.jpeg"),
plot = f_score_plot,
width = 8,
height = 4,
units = "in",
dpi = 150
)
# Write plots as JPEG files
ggsave(
filename = paste0(
"data/",
survey,
"/validate/",
survey,
"_precision_recall.jpeg"
),
plot = recall_precision_plot,
width = 8,
height = 6,
units = "in",
dpi = 150
)
ggsave(
filename = paste0("data/",
survey,
"/validate/",
survey,
"_FScores.jpeg"),
plot = f_score_plot,
width = 8,
height = 4,
units = "in",
dpi = 150
)
# Write values to human labels table in db
query <- glue::glue("
UPDATE ModelPredictions
SET F1Score = {F1Score},
Precision = {Precision},
Recall = {Recall},
Threshold = {selected_thresh},
CorrectionFact = {CorrFact}
WHERE SurveyId = '{survey}'
")
dbExecute(db, query)
# Close the database connection
dbDisconnect(db)
message("Selected threshold for max F1Score = ", selected_thresh)
message("F1Score = ", F1Score)
message("Precision = ", Precision)
message("Recall = ", Recall)
message("Correction Factor = ", CorrFact)
}