Merge branch 'develop'

Release 0.1.1

Author: xd009642

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "ndarray-vision"
-version = "0.1.0"
+version = "0.1.1"
 authors = ["xd009642 <danielmckenna93@gmail.com>"]
 description = "A computer vision library built on top of ndarray"
 repository = "https://github.com/xd009642/ndarray-vision"

src/processing/conv.rs

@@ -20,6 +20,12 @@ where
     fn conv2d_inplace(&mut self, kernel: ArrayView3<Self::Data>) -> Result<(), Error>;
 }
 
+fn kernel_centre(rows: usize, cols: usize) -> (usize, usize) {
+    let row_offset = rows / 2 - ((rows % 2 == 0) as usize);
+    let col_offset = cols / 2 - ((cols % 2 == 0) as usize);
+    (row_offset, col_offset) 
+}
+
 impl<T> ConvolutionExt for Array3<T>
 where
     T: Copy + Clone + Num + NumAssignOps,
@@ -33,9 +39,7 @@ where
             let k_s = kernel.shape();
             // Bit icky but handles fact that uncentred convolutions will cross the bounds
             // otherwise
-            let row_offset = k_s[0] / 2 - ((k_s[0] % 2 == 0) as usize);
-            let col_offset = k_s[1] / 2 - ((k_s[1] % 2 == 0) as usize);
-
+            let (row_offset, col_offset) = kernel_centre(k_s[0], k_s[1]);
             // row_offset * 2 may not equal k_s[0] due to truncation
             let shape = (
                 self.shape()[0] - row_offset * 2,
@@ -60,8 +64,14 @@ where
 
     fn conv2d_inplace(&mut self, kernel: ArrayView3<Self::Data>) -> Result<(), Error> {
         let data = self.conv2d(kernel)?;
+        let shape = kernel.shape();
+        let centre = kernel_centre(shape[0], shape[1]);
         for (d, v) in self.indexed_iter_mut() {
-            if let Some(d) = data.get(d) {
+            if d.0 < centre.0 || d.1 < centre.1 {
+                continue;
+            }
+            let centred = (d.0 - centre.0, d.1 - centre.1, d.2);
+            if let Some(d) = data.get(centred) {
                 *v = *d;
             }
         }
@@ -91,7 +101,8 @@ where
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::core::colour_models::RGB;
+    use crate::core::colour_models::{Gray, RGB};
+    use ndarray::arr3;
 
     #[test]
     fn bad_dimensions() {
@@ -111,4 +122,51 @@ mod tests {
         assert!(i.conv2d(good_kern.view()).is_ok());
         assert!(i.conv2d_inplace(good_kern.view()).is_ok());
     }
+
+    #[test]
+    fn basic_conv() {
+        let input_pixels = vec![1, 1, 1, 0, 0,
+                                0, 1, 1, 1, 0,
+                                0, 0, 1, 1, 1,
+                                0, 0, 1, 1, 0,
+                                0, 1, 1, 0, 0];
+        let output_pixels = vec![4, 3, 4,
+                                 2, 4, 3,
+                                 2, 3, 4];
+
+        let kern = arr3(&[[[1], [0], [1]],
+                          [[0], [1], [0]],
+                          [[1], [0], [1]]]);
+
+        let input = Image::<u8, Gray>::from_shape_data(5, 5, input_pixels);
+        let expected = Image::<u8, Gray>::from_shape_data(3, 3, output_pixels);
+
+        assert_eq!(Ok(expected), input.conv2d(kern.view()));
+    }
+
+    #[test]
+    fn basic_conv_inplace() {
+        let input_pixels = vec![1, 1, 1, 0, 0,
+                                0, 1, 1, 1, 0,
+                                0, 0, 1, 1, 1,
+                                0, 0, 1, 1, 0,
+                                0, 1, 1, 0, 0];
+
+        let output_pixels = vec![1, 1, 1, 0, 0,
+                                 0, 4, 3, 4, 0,
+                                 0, 2, 4, 3, 1,
+                                 0, 2, 3, 4, 0,
+                                 0, 1, 1, 0, 0];
+
+        let kern = arr3(&[[[1], [0], [1]],
+                          [[0], [1], [0]],
+                          [[1], [0], [1]]]);
+
+        let mut input = Image::<u8, Gray>::from_shape_data(5, 5, input_pixels);
+        let expected = Image::<u8, Gray>::from_shape_data(5, 5, output_pixels);
+        
+        input.conv2d_inplace(kern.view()).unwrap();
+
+        assert_eq!(expected, input);
+    }
 }