implement std mean

Cargo.toml

@@ -21,9 +21,13 @@ fast_image_resize = "3.0.4"
 candle-core = { version = "0.3.2", optional = true }
 
 [dev-dependencies]
+clap = { version = "4.5.3", features = ["derive"] }
+criterion = { version = "0.5.1", features = ["html_reports"] }
+indicatif = { version = "0.17.8", features = ["rayon"] }
 tempfile = "3.9.0"
+rayon = "1.10.0"
 rerun = "0.14.1"
-criterion = { version = "0.5.1", features = ["html_reports"] }
+walkdir = "2.5.0"
 
 [features]
 candle = ["candle-core"]

examples/std_mean.rs

@@ -0,0 +1,93 @@
+use std::path::PathBuf;
+
+use anyhow::Result;
+use clap::Parser;
+use indicatif::{ParallelProgressIterator, ProgressStyle};
+use rayon::iter::{IntoParallelRefIterator, ParallelIterator};
+
+use kornia_rs::io::functional as F;
+
+#[derive(Parser, Debug)]
+struct Args {
+    #[arg(short, long)]
+    images_dir: PathBuf,
+
+    #[arg(short, long, default_value = "8")]
+    num_threads: usize,
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+
+    rayon::ThreadPoolBuilder::new()
+        .num_threads(args.num_threads)
+        .build_global()
+        .expect("Failed to build thread pool");
+
+    // Walk through the images directory and collect the paths of the images
+    let images_paths: Vec<PathBuf> = walkdir::WalkDir::new(&args.images_dir)
+        .into_iter()
+        .filter_map(|entry| entry.ok())
+        .filter(|entry| {
+            entry.file_type().is_file()
+                && entry
+                    .path()
+                    .extension()
+                    .map(|ext| ext == "jpeg")
+                    .unwrap_or(false)
+        })
+        .map(|entry| entry.path().to_path_buf())
+        .collect();
+
+    if images_paths.is_empty() {
+        println!("No images found in the directory");
+        return Ok(());
+    }
+
+    println!(
+        "🚀 Found {} images. Starting to compute the std and mean !!!",
+        images_paths.len()
+    );
+
+    // Create a progress bar
+    let pb = indicatif::ProgressBar::new(images_paths.len() as u64);
+    pb.set_style(ProgressStyle::default_bar().template(
+        "{spinner:.green} [{elapsed_precise}] [{bar:40.cyan/blue}] {pos:>7}/{len:7} ({eta}) {msg} {per_sec} img/sec",
+    )?.progress_chars("##>-"));
+
+    // compute the std and mean of the images
+    let (total_std, total_mean) = images_paths
+        .par_iter()
+        .progress_with(pb)
+        .map(|image_path| {
+            // read the image
+            let image = F::read_image_jpeg(image_path).expect("Failed to read image");
+            // compute the std and mean
+            kornia_rs::core::std_mean(&image)
+        })
+        .reduce(
+            || (vec![0.0; 3], vec![0.0; 3]),
+            |(mut total_std, mut total_mean), (std, mean)| {
+                total_std
+                    .iter_mut()
+                    .zip(std.iter())
+                    .for_each(|(t, s)| *t += s);
+                total_mean
+                    .iter_mut()
+                    .zip(mean.iter())
+                    .for_each(|(t, m)| *t += m);
+
+                (total_std, total_mean)
+            },
+        );
+
+    // average the measurements
+    let n = images_paths.len() as f64;
+    let total_std = total_std.iter().map(|&s| s / n).collect::<Vec<_>>();
+    let total_mean = total_mean.iter().map(|&m| m / n).collect::<Vec<_>>();
+
+    println!("🔥Total std: {:?}", total_std);
+    println!("🔥Total mean: {:?}", total_mean);
+
+    Ok(())
+}