Create stats module

src/hough.rs

@@ -40,8 +40,8 @@ pub fn lines(
 ) -> Vec<Line> {
     let hough = transform(img);
 
-    let mu = mean((&hough.pixels).into_iter().map(|p| *p));
-    let sigma = stddev((&hough.pixels).into_iter().map(|p| *p), mu);
+    let mu = stats::mean((&hough.pixels).into_iter().map(|p| *p));
+    let sigma = stats::stddev((&hough.pixels).into_iter().map(|p| *p), mu);
 
     let mut lines = Vec::<Line>::new();
     for y in 0..hough.extent.y {
@@ -65,23 +65,3 @@ pub fn lines(
 fn theta_from(x: usize) -> f32 {
     x as f32 * std::f32::consts::PI / THETA_N as f32
 }
-
-fn mean(xs: impl Iterator<Item = f32>) -> f32 {
-    let mut n = 0;
-    let mut sum = 0.0;
-    for x in xs {
-        sum += x;
-        n += 1;
-    }
-    sum / n as f32
-}
-
-fn stddev(xs: impl Iterator<Item = f32>, mean: f32) -> f32 {
-    let mut n = 0;
-    let mut sum = 0.0;
-    for x in xs {
-        sum += (x - mean).powf(2.0);
-        n += 1;
-    }
-    (sum / (n - 1) as f32).sqrt()
-}

src/lib.rs

@@ -5,3 +5,4 @@ pub mod filter;
 pub mod hough;
 pub mod image;
 pub mod kernel;
+pub mod stats;

src/stats.rs

@@ -0,0 +1,19 @@
+pub fn mean(xs: impl Iterator<Item = f32>) -> f32 {
+    let mut n = 0;
+    let mut sum = 0.0;
+    for x in xs {
+        sum += x;
+        n += 1;
+    }
+    sum / n as f32
+}
+
+pub fn stddev(xs: impl Iterator<Item = f32>, mean: f32) -> f32 {
+    let mut n = 0;
+    let mut sum = 0.0;
+    for x in xs {
+        sum += (x - mean).powf(2.0);
+        n += 1;
+    }
+    (sum / (n - 1) as f32).sqrt()
+}