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03_analysis/SAM2.csv

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03_analysis/analysis_Emerson.R

@@ -0,0 +1,427 @@
+# Data Analysis for Coffee Leaf Rust Severity
+# Author: [User's Name/Project Group]
+# Description: Comparative analysis of segmentation methods (ImageJ, pliman, DeepLabV3+, SAM_CLR, SAM3).
+
+# ==============================================================================
+# 1. Setup and Libraries
+# ==============================================================================
+library(readxl)
+library(dplyr)
+library(tidyverse)
+library(purrr)
+library(epiR)
+library(cowplot)
+
+# Set global plotting theme
+theme_set(cowplot::theme_half_open(font_size = 12))
+
+# ==============================================================================
+# 2. Data Loading
+# ==============================================================================
+# Function to load and clean CSVs ensuring numeric types where needed
+load_csv <- function(path) {
+  read.csv(path, stringsAsFactors = FALSE)
+}
+
+# Load Gold Standard (Severity Index)
+# Removed R1OLD, R2OLD, R1NEW, R2NEW. Renamed SAM3_2 as SAM3.
+severity_final <- load_csv("severity_final.csv") %>%
+  select(-R1OLD, -R2OLD, -R1NEW, -R2NEW, -SAM3) %>%
+  rename(SAM3 = SAM3_2) %>%
+  rename(SAM_CLR = SAM100) %>%
+  mutate(across(-image, as.numeric))
+
+# Load Method Results
+deeplab <- load_csv("deeplab.csv")
+imagej <- load_csv("ImageJ.csv")
+pliman <- load_csv("pliman.csv")
+SAM_CLR <- load_csv("SAM2.csv")
+SAM3 <- load_csv("SAM3.csv")
+
+# ==============================================================================
+# 3. Helper Functions
+# ==============================================================================
+
+#' Extract CCC results from epiR object
+extract_epi_ccc <- function(res) {
+  data.frame(
+    CCC        = res$rho.c$est,
+    CCC.lwr95  = res$rho.c$lower,
+    CCC.upr95  = res$rho.c$upper,
+    r          = res$rho,
+    Cb         = res$C.b
+  )
+}
+
+
+#' Compute R-squared and RSE
+compute_r2_rse <- function(gs, method) {
+  fit <- lm(method ~ gs)
+  data.frame(
+    R_squared = summary(fit)$r.squared,
+    RSE       = summary(fit)$sigma
+  )
+}
+
+#' Summarise segmentation metrics (IoU, Dice, etc.)
+summarise_metrics <- function(df, method_name, fun = median) {
+  df %>%
+    mutate(across(c(iou, dice, precision, recall, f1), ~ as.numeric(gsub(",", "", .x)))) %>%
+    summarise(
+      Method    = method_name,
+      IoU       = fun(iou, na.rm = TRUE),
+      Dice      = fun(dice, na.rm = TRUE),
+      Precision = fun(precision, na.rm = TRUE),
+      Recall    = fun(recall, na.rm = TRUE),
+      F1        = fun(f1, na.rm = TRUE)
+    )
+}
+
+#' Plot segmentation metrics vs gold standard severity
+plot_method_vs_severity <- function(df_metrics, method_name, gs_data) {
+  # Prepare data for plotting
+  df_long <- df_metrics %>%
+    select(image_id, iou, dice, precision, recall, f1) %>%
+    left_join(select(gs_data, image_id, GS), by = "image_id") %>%
+    mutate(
+      GS = as.numeric(GS),
+      across(c(iou, dice, precision, recall, f1), ~ as.numeric(gsub(",", "", .x)))
+    ) %>%
+    pivot_longer(
+      cols = c(iou, dice, precision, recall, f1),
+      names_to = "metric",
+      values_to = "value"
+    ) %>%
+    mutate(
+      metric = case_when(
+        metric == "iou" ~ "IoU",
+        TRUE ~ toupper(metric)
+      )
+    ) %>%
+    filter(!is.na(GS), !is.na(value))
+
+  # Create plot
+  ggplot(df_long, aes(x = GS, y = value)) +
+    geom_point(alpha = 0.6, size = 1.5, aes(color = metric)) +
+    geom_smooth(method = "loess", span = 0.75, se = FALSE, linewidth = 1, color = "black") +
+    facet_wrap(~metric, ncol = 3) +
+    scale_y_continuous(limits = c(0, 1)) +
+    scale_color_viridis_d() +
+    labs(
+      title = paste("Segmentation performance vs severity —", ifelse(method_name == "pliman", "pliman", toupper(method_name))),
+      x = "Gold standard severity",
+      y = "Metric value"
+    ) +
+    theme(
+      strip.text = element_text(face = "bold"),
+      panel.grid.minor = element_blank(),
+      legend.position = "none"
+    )
+}
+
+# ==============================================================================
+# 4. Analysis: Agreement (CCC)
+# ==============================================================================
+gold_standard <- "GS"
+# Ordered as requested: IMAGEJ, PLIMAN, DEEPLABV3, SAM_CLR AND SAM3
+methods_cols <- c("ImageJ", "Pliman", "DeeplabV3", "SAM_CLR", "SAM3")
+
+# Calculate CCC and Regression stats for each method
+final_results <- map_dfr(methods_cols, function(m) {
+  res_ccc <- epi.ccc(
+    x = severity_final[[gold_standard]],
+    y = severity_final[[m]],
+    ci = "z-transform",
+    conf.level = 0.95
+  )
+
+  cbind(
+    Method = m,
+    extract_epi_ccc(res_ccc),
+    compute_r2_rse(severity_final[[gold_standard]], severity_final[[m]])
+  )
+})
+
+# Plot Agreement
+plot_ccc <- final_results %>%
+  arrange(CCC) %>%
+  ggplot(aes(x = CCC, y = reorder(Method, CCC))) +
+  geom_point(size = 3) +
+  geom_errorbar(aes(xmin = CCC.lwr95, xmax = CCC.upr95), height = 0.2, orientation = "y" ) +
+  geom_vline(xintercept = 0.90, linetype = "dashed") +
+  scale_x_continuous(limits = c(0.3, 1)) +
+  scale_color_viridis_d() +
+  labs(
+    title = "Agreement with Gold Standard (CCC)",
+    x = "Lin’s CCC",
+    y = "Method"
+  )
+
+print(plot_ccc)
+
+ggsave("ccc_agreement.png", plot_ccc, width = 8, height = 6, dpi = 300)
+
+# ==============================================================================
+# 5. CCC Scatterplots (All dots + CCC stats like publication figures)
+# ==============================================================================
+
+plot_ccc_method <- function(df, method_col, gs_col = "GS", method_name = NULL) {
+  
+  x <- df[[gs_col]]
+  y <- df[[method_col]]
+  
+  keep <- is.finite(x) & is.finite(y)
+  x <- x[keep]
+  y <- y[keep]
+  
+  # ---- CCC ----
+  res <- epi.ccc(x, y, ci = "z-transform", conf.level = 0.95)
+  
+  rho_c <- res$rho.c$est
+  lwr   <- res$rho.c$lower
+  upr   <- res$rho.c$upper
+  r     <- res$rho
+  Cb    <- res$C.b
+  
+  # ---- Regression ----
+  fit   <- lm(y ~ x)
+  slope <- coef(fit)[2]
+  mu    <- coef(fit)[1]
+  rmse  <- sqrt(mean((y - x)^2))
+  bias  <- mean(y - x)
+  
+  title <- ifelse(is.null(method_name), method_col, toupper(method_name))
+  
+  subtitle <- sprintf(
+    "ρc = %.2f [%.2f–%.2f], Cb = %.2f, r = %.2f\nμ = %.2f, β = %.2f, RMSE = %.2f, Bias = %.2f",
+    rho_c, lwr, upr, Cb, r, mu, slope, rmse, bias
+  )
+  
+  ggplot(data.frame(x, y), aes(x, y)) +
+    geom_point(color = "#F28E2B",size = 2, alpha = 0.8) +
+    geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
+    geom_smooth(method = "lm", se = FALSE, color = "black", linewidth = 0.8) +
+    labs(
+      title = title,
+      subtitle = subtitle,
+      x = "Reference Severity (%)",
+      y = "Predicted Severity (%)"
+    ) +
+    coord_equal(xlim = c(0, 60), ylim = c(0, 60)) +
+    theme_half_open(font_size = 12)
+}
+
+# ---- Create plots for all methods in the requested order ----
+plots_ccc <- map(methods_cols, ~ plot_ccc_method(
+  severity_final,
+  method_col = .x,
+  gs_col = gold_standard,
+  method_name = .x
+))
+
+# ---- Combine into one figure ----
+figure_ccc <- plot_grid(plotlist = plots_ccc, ncol = 3)
+
+print(figure_ccc)
+
+ggsave("CCC_scatter_models.png", figure_ccc,
+       width = 10, height = 7, dpi = 300)
+
+# ==============================================================================
+# 6. Analysis: Metrics Summary
+# ==============================================================================
+method_names <- c("deeplab", "imagej", "pliman", "SAM_CLR", "SAM3")
+
+# Generate Median and Mean summary tables
+metrics_median <- map_dfr(method_names, ~ summarise_metrics(get(.x), .x, fun = median))
+metrics_mean <- map_dfr(method_names, ~ summarise_metrics(get(.x), .x, fun = mean))
+
+cat("\n--- Median Metrics ---\n")
+print(metrics_median)
+cat("\n--- Mean Metrics ---\n")
+print(metrics_mean)
+
+# ==============================================================================
+# 7. Analysis: Visualization
+# ==============================================================================
+# Clean Image IDs (remove extensions)
+clean_id <- function(df) mutate(df, image_id = sub("\\.[Pp][Nn][Gg]$", "", image))
+
+severity_final <- clean_id(severity_final)
+methods_list <- list(
+  deeplab = clean_id(deeplab),
+  imagej  = clean_id(imagej),
+  pliman  = clean_id(pliman),
+  SAM_CLR    = clean_id(SAM_CLR),
+  SAM3    = clean_id(SAM3)
+)
+
+# Plot 1: Performance vs Severity for each method
+plots_perf <- imap(methods_list, ~ plot_method_vs_severity(.x, .y, severity_final))
+
+# Display all performance plots
+walk(plots_perf, print)
+
+# Plot 2: Density Distribution of Metrics
+# Combine all data into one long format dataframe
+metrics_long <- imap_dfr(methods_list, ~ .x %>% mutate(Method = .y)) %>%
+  select(image_id, Method, iou, dice, precision, recall, f1) %>%
+  left_join(select(severity_final, image_id, GS), by = "image_id") %>%
+  mutate(across(c(iou, dice, precision, recall, f1), ~ as.numeric(gsub(",", "", .x)))) %>%
+  pivot_longer(
+    cols = c(iou, dice, precision, recall, f1),
+    names_to = "metric",
+    values_to = "value"
+  ) %>%
+  mutate(
+    Method = ifelse(Method == "pliman", "pliman", toupper(Method)),
+    metric = case_when(
+      metric == "iou" ~ "IoU",
+      TRUE ~ toupper(metric)
+    )
+  ) %>%
+  filter(!is.na(value))
+
+# Calculate medians for the plot overlay
+metrics_medians <- metrics_long %>%
+  group_by(Method, metric) %>%
+  summarise(grp_median = median(value, na.rm = TRUE), .groups = "drop")
+
+# Create Density Plot
+plot_density <- ggplot(metrics_long, aes(x = value, fill = metric)) +
+  geom_density(alpha = 0.6, color = NA) +
+  # Add dashed vertical line for the median
+  geom_vline(
+    data = metrics_medians,
+    aes(xintercept = grp_median),
+    linetype = "dashed",
+    linewidth = 0.4
+  ) +
+  geom_text(
+    data = metrics_medians,
+    aes(x = 0, y = Inf, label = paste0("tilde(x):~", round(grp_median, 2))),
+    parse = TRUE,
+    hjust = -0.1,
+    vjust = 5,
+    size = 3,
+    inherit.aes = FALSE
+  ) +
+  facet_grid(Method ~ metric) +
+  scale_fill_viridis_d() +
+  # Clean x-axis labels (remove trailing decimal zeros)
+  scale_x_continuous(
+    breaks = seq(0, 1, 0.25),
+    labels = function(x) ifelse(x %in% c(0, 1), as.character(x), as.character(x))
+  ) +
+  labs(
+    x = "Metric value",
+    y = "Density",
+    fill = "Metric",
+    title = "Distribution of segmentation metrics by method"
+  ) +
+  theme(
+    strip.background = element_rect(fill = "grey90", color = NA),
+    strip.text = element_text(face = "bold"),
+    legend.position = "bottom"
+  )
+
+print(plot_density)
+
+ggsave("models.png", plot_density, bg = "white", width = 8, height = 6, dpi = 300)
+
+
+
+# ============================================================
+# gt + gtExtras: metrics in columns, with (median + mini-plot)
+# Requires: metrics_long with columns Method, metric, value
+# ============================================================
+
+library(tidyr)
+library(gt)
+library(gtExtras)
+
+# --- 0) (Optional) ensure expected order/names of metrics ---
+metrics  <- c("IoU","DICE","PRECISION","RECALL","F1")
+methods  <- c("pliman", "IMAGEJ", "DEEPLAB", "SAM_CLR", "SAM3")
+
+metrics_long2 <- metrics_long %>%
+  filter(!is.na(value)) %>%
+  mutate(
+    metric = factor(metric, levels = metrics),
+    Method = factor(Method, levels = methods)
+  )
+# --- 1) List-column with values (to become a mini-plot) ---
+dist_wide <- metrics_long2 %>%
+  group_by(Method, metric) %>%
+  summarise(dist = list(as.numeric(value)), .groups = "drop") %>%
+  mutate(metric = as.character(metric)) %>%
+  pivot_wider(names_from = metric, values_from = dist)
+
+# --- 2) Medians per metric (becomes numeric columns) ---
+med_wide <- metrics_long2 %>%
+  group_by(Method, metric) %>%
+  summarise(med = median(as.numeric(value), na.rm = TRUE), .groups = "drop") %>%
+  mutate(metric = as.character(metric)) %>%
+  pivot_wider(names_from = metric, values_from = med, names_prefix = "med_")
+
+# --- 3) Combine everything (1 row per Method) ---
+tbl_wide <- dist_wide %>%
+  left_join(med_wide, by = "Method") %>%
+  mutate(Method = factor(Method, levels = methods)) %>%
+  arrange(Method)
+
+# --- 4) gt Table: mini-plots + spanners with unique id ---
+tab <- tbl_wide %>%
+  gt(rowname_col = "Method") %>%
+  # format medians
+  fmt_number(columns = all_of(paste0("med_", metrics)), decimals = 2) %>%
+  # mini-plots (one per metric column with list-column)
+  { purrr::reduce(metrics, .init = ., .f = \(gt_tbl, m) {
+    gt_tbl %>% gt_plt_dist(column = all_of(m), fill = "steelblue", type = "density")
+  })
+  } %>%
+  # labels
+  cols_label(.list = c(
+    setNames(rep("Median", length(metrics)), paste0("med_", metrics)),
+    setNames(rep("Dist.",  length(metrics)), metrics)
+  )) %>%
+  # spanners per metric (with unique id to avoid errors)
+  { purrr::reduce(metrics, .init = ., .f = \(gt_tbl, m) {
+    gt_tbl %>%
+      tab_spanner(
+        label = m,
+        columns = c(paste0("med_", m), m),
+        id = paste0("sp_", m)
+      )
+  })
+  } %>%
+  # column widths (fine tuning)
+  cols_width(
+    all_of(paste0("med_", metrics)) ~ px(70),
+    all_of(metrics) ~ px(100)
+  ) %>%
+  cols_align(align = "center") %>%
+  # font style, sizes and borders
+  tab_options(
+    table.font.names = "Arial",
+    column_labels.font.size = px(20),
+    column_labels.font.weight = "bold",
+    table.font.size = px(16),
+    data_row.padding = px(15), 
+    # Black borders (top and bottom only)
+    table.border.top.color = "black",
+    table.border.bottom.color = "black",
+    column_labels.border.top.color = "black",
+    column_labels.border.bottom.color = "black",
+    # Remove internal lines
+    table_body.hlines.style = "none",
+    column_labels.border.bottom.width = px(2), # keep a slightly thicker line below labels
+    table_body.border.bottom.color = "black",
+    stub.border.style = "none"
+  ) %>%
+  tab_style(
+    style = cell_text(size = px(22), weight = "bold", align = "center"),
+    locations = cells_column_spanners()
+  )
+
+tab

03_analysis/deeplab.csv

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