Cargo.lock

@@ -765,7 +765,7 @@ dependencies = [
 
 [[package]]
 name = "qtizer"
-version = "0.7.0"
+version = "0.6.1"
 dependencies = [
  "clap",
  "image",

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "qtizer"
-version = "0.7.0"
+version = "0.6.1"
 edition = "2024"
 description = "Quantization/palette-generation tool using k-means clustering on pixel data"
 readme = "README.md"

src/colors.rs

@@ -16,14 +16,13 @@ impl Kmeansable for Color {
         vec![0; 4]
     }
 
-    /// distance function, according to which clustering is performed
-    /// (impl avoids sqrt for performance -- uses squared distance)
     fn distance(&self, other: &Self) -> f64 {
         self.data
             .iter()
             .zip(other.data.iter())
             .map(|(a, b)| ((*a as f64) - (*b as f64)).powi(2))
             .sum::<f64>()
+            .sqrt()
     }
 
     fn add(sum: &Self::Sum, other: &Self) -> Self::Sum {

src/kmeans.rs

@@ -44,70 +44,22 @@ impl Context<SmallRng> {
             .cloned()
             .collect::<Vec<_>>();
 
-        // make cursor invisible
-        eprint!("\x1b[?25l");
-
         for i in 0..iterations {
             // TODO: implement static logger functionality for progress
-            //       once implemented, replace other eprint(ln)! calls too
+            eprintln!("processing k-means iteration {}/{}...", i + 1, iterations);
-            eprintln!(
-                "processing k-means iteration: [ {:>9} / {:>9} ]...",
-                i + 1,
-                iterations
-            );
-
-            // precompute cluster distances to skip some distance calculations later
-            // only set for i < j -- note: dist[i][j] == dist[j][i]
-            let mut cluster_distances = vec![vec![0.0; k]; k];
-            for i in 0..k {
-                for j in (i + 1)..k {
-                    let dist = clusters[i].distance(&clusters[j]);
-                    cluster_distances[i][j] = dist; // i < j case only
-                }
-            }
 
             // assign each point to the nearest cluster
             for (i, point) in data.iter().enumerate() {
-                let mut closest_idx = 0;
+                let min_idx = clusters
-                let mut closest_dist = clusters[0].distance(point);
+                    .iter()
+                    .enumerate()
+                    .min_by(|(_, a), (_, b)| f64::total_cmp(&a.distance(point), &b.distance(point)))
+                    .unwrap()
+                    .0;
 
-                // print progress every 500 points
+                assignments[i] = min_idx;
-                if i % 500 == 0 {
-                    if i > 0 {
-                        // restore cursor position (write over previous status)
-                        eprint!("\x1b[1F");
-                    }
-                    let label_len = "processing k-means iteration".len();
-                    eprintln!(
-                        "{:>label_len$}: [ {:>9} / {:>9} ]...",
-                        "assigning point",
-                        i,
-                        data.len()
-                    );
-                }
-
-                for j in 1..k {
-                    // skip distance calculation if the cluster is too far away
-                    let (a, b) = (closest_idx.min(j), closest_idx.max(j));
-                    if cluster_distances[a][b] >= 2.0 * closest_dist {
-                        // d(c_j, c_min) >= 2 * d(p, c_min)
-                        // d(p,   c_j  ) >=     d(p, c_min)
-                        continue;
-                    }
-
-                    let dist = clusters[j].distance(point);
-                    if dist < closest_dist {
-                        closest_dist = dist;
-                        closest_idx = j;
-                    }
-                }
-
-                assignments[i] = closest_idx;
             }
 
-            // restore cursor position (write over previous status)
-            eprint!("\x1b[2F");
-
             // move cluster to mean of its assigned points
             let mut counts: Vec<usize> = vec![0; k];
             let mut sums = vec![T::zero(); k];
@@ -125,9 +77,6 @@ impl Context<SmallRng> {
             }
         }
 
-        // make cursor visible again
-        eprint!("\x1b[?25h");
-
         (clusters, assignments)
     }