feat(bounding-box): add scale_uniform method for consistent scaling

feat(gui): display face ROIs in comparison results refactor(bridge): pad detected face bounding boxes uniformly
feat(gui): add face ROIs to comparison results and update image size
2025-08-22 19:01:34 +05:30 · 2025-08-22 18:26:29 +05:30 · 2025-08-22 18:15:55 +05:30 · 2025-08-22 18:14:58 +05:30 · 2025-08-22 15:27:47 +05:30 · 2025-08-22 15:27:36 +05:30
39 changed files with 4934 additions and 214 deletions
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -203,6 +203,9 @@ name = "arbitrary"
 version = "1.4.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c3d036a3c4ab069c7b410a2ce876bd74808d2d0888a82667669f8e783a898bf1"
+dependencies = [
+ "derive_arbitrary",
+]

 [[package]]
 name = "arg_enum_proc_macro"
@@ -478,6 +481,15 @@ dependencies = [
 "windows-targets 0.52.6",
 ]

+[[package]]
+name = "bbox"
+version = "0.1.0"
+dependencies = [
+ "ndarray",
+ "num",
+ "serde",
+]
+
 [[package]]
 name = "bindgen"
 version = "0.60.1"
@@ -770,6 +782,16 @@ dependencies = [
 "windows-link",
 ]

+[[package]]
+name = "clang"
+version = "2.0.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "84c044c781163c001b913cd018fc95a628c50d0d2dfea8bca77dad71edb16e37"
+dependencies = [
+ "clang-sys",
+ "libc",
+]
+
 [[package]]
 name = "clang-sys"
 version = "1.8.1"
@@ -816,6 +838,7 @@ dependencies = [
 "anstyle",
 "clap_lex 0.7.5",
 "strsim 0.11.1",
+ "terminal_size",
 ]

 [[package]]
@@ -979,6 +1002,12 @@ dependencies = [
 "crossbeam-utils",
 ]

+[[package]]
+name = "condtype"
+version = "1.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "baf0a07a401f374238ab8e2f11a104d2851bf9ce711ec69804834de8af45c7af"
+
 [[package]]
 name = "core-foundation"
 version = "0.9.4"
@@ -1210,6 +1239,17 @@ dependencies = [
 "powerfmt",
 ]

+[[package]]
+name = "derive_arbitrary"
+version = "1.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1e567bd82dcff979e4b03460c307b3cdc9e96fde3d73bed1496d2bc75d9dd62a"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.106",
+]
+
 [[package]]
 name = "detect-desktop-environment"
 version = "0.2.0"
@@ -1320,6 +1360,31 @@ dependencies = [
 "syn 2.0.106",
 ]

+[[package]]
+name = "divan"
+version = "0.1.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a405457ec78b8fe08b0e32b4a3570ab5dff6dd16eb9e76a5ee0a9d9cbd898933"
+dependencies = [
+ "cfg-if",
+ "clap 4.5.45",
+ "condtype",
+ "divan-macros",
+ "libc",
+ "regex-lite",
+]
+
+[[package]]
+name = "divan-macros"
+version = "0.1.21"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "9556bc800956545d6420a640173e5ba7dfa82f38d3ea5a167eb555bc69ac3323"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.106",
+]
+
 [[package]]
 name = "dlib"
 version = "0.5.2"
@@ -2026,7 +2091,7 @@ dependencies = [
 "presser",
 "thiserror 1.0.69",
 "winapi",
- "windows",
+ "windows 0.52.0",
 ]

 [[package]]
@@ -2552,6 +2617,17 @@ dependencies = [
 "rayon",
 ]

+[[package]]
+name = "img-parts"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "19734e3c43b2a850f5889c077056e47c874095f2d87e853c7c41214ae67375f0"
+dependencies = [
+ "bytes",
+ "crc32fast",
+ "miniz_oxide",
+]
+
 [[package]]
 name = "imgref"
 version = "1.11.0"
@@ -3154,6 +3230,7 @@ dependencies = [
 "portable-atomic",
 "portable-atomic-util",
 "rawpointer",
+ "rayon",
 ]

 [[package]]
@@ -3183,6 +3260,20 @@ dependencies = [
 "thiserror 2.0.15",
 ]

+[[package]]
+name = "ndarray-npy"
+version = "0.9.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7b313788c468c49141a9d9b6131fc15f403e6ef4e8446a0b2e18f664ddb278a9"
+dependencies = [
+ "byteorder",
+ "ndarray",
+ "num-complex",
+ "num-traits",
+ "py_literal",
+ "zip",
+]
+
 [[package]]
 name = "ndarray-resize"
 version = "0.1.0"
@@ -3206,6 +3297,27 @@ dependencies = [
 "thiserror 2.0.15",
 ]

+[[package]]
+name = "ndcv-bridge"
+version = "0.1.0"
+dependencies = [
+ "bounding-box",
+ "bytemuck",
+ "divan",
+ "error-stack",
+ "fast_image_resize",
+ "img-parts",
+ "nalgebra 0.34.0",
+ "ndarray",
+ "ndarray-npy",
+ "num",
+ "opencv",
+ "rayon",
+ "thiserror 2.0.15",
+ "tracing",
+ "wide",
+]
+
 [[package]]
 name = "ndk"
 version = "0.9.0"
@@ -3727,6 +3839,41 @@ version = "0.1.11"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "b4ce411919553d3f9fa53a0880544cda985a112117a0444d5ff1e870a893d6ea"

+[[package]]
+name = "opencv"
+version = "0.95.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "0c73b6fccd78797a87cdb885c997351a1a290b0ebde778e996b694dec2a4c04a"
+dependencies = [
+ "cc",
+ "dunce",
+ "jobserver",
+ "libc",
+ "num-traits",
+ "once_cell",
+ "opencv-binding-generator",
+ "pkg-config",
+ "semver",
+ "shlex",
+ "vcpkg",
+ "windows 0.59.0",
+]
+
+[[package]]
+name = "opencv-binding-generator"
+version = "0.97.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "010a78e4cc47ff85cf58fb1cbbbab9dcdb8e5e6718917eac26623f077872d012"
+dependencies = [
+ "clang",
+ "clang-sys",
+ "dunce",
+ "once_cell",
+ "percent-encoding",
+ "regex",
+ "shlex",
+]
+
 [[package]]
 name = "orbclient"
 version = "0.3.48"
@@ -3903,6 +4050,50 @@ version = "2.3.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "e3148f5046208a5d56bcfc03053e3ca6334e51da8dfb19b6cdc8b306fae3283e"

+[[package]]
+name = "pest"
+version = "2.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1db05f56d34358a8b1066f67cbb203ee3e7ed2ba674a6263a1d5ec6db2204323"
+dependencies = [
+ "memchr",
+ "thiserror 2.0.15",
+ "ucd-trie",
+]
+
+[[package]]
+name = "pest_derive"
+version = "2.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bb056d9e8ea77922845ec74a1c4e8fb17e7c218cc4fc11a15c5d25e189aa40bc"
+dependencies = [
+ "pest",
+ "pest_generator",
+]
+
+[[package]]
+name = "pest_generator"
+version = "2.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87e404e638f781eb3202dc82db6760c8ae8a1eeef7fb3fa8264b2ef280504966"
+dependencies = [
+ "pest",
+ "pest_meta",
+ "proc-macro2",
+ "quote",
+ "syn 2.0.106",
+]
+
+[[package]]
+name = "pest_meta"
+version = "2.8.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "edd1101f170f5903fde0914f899bb503d9ff5271d7ba76bbb70bea63690cc0d5"
+dependencies = [
+ "pest",
+ "sha2",
+]
+
 [[package]]
 name = "phf"
 version = "0.11.3"
@@ -4119,6 +4310,19 @@ dependencies = [
 "syn 2.0.106",
 ]

+[[package]]
+name = "py_literal"
+version = "0.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "102df7a3d46db9d3891f178dcc826dc270a6746277a9ae6436f8d29fd490a8e1"
+dependencies = [
+ "num-bigint",
+ "num-complex",
+ "num-traits",
+ "pest",
+ "pest_derive",
+]
+
 [[package]]
 name = "qoi"
 version = "0.4.1"
@@ -4401,6 +4605,12 @@ dependencies = [
 "regex-syntax 0.8.5",
 ]

+[[package]]
+name = "regex-lite"
+version = "0.1.6"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "53a49587ad06b26609c52e423de037e7f57f20d53535d66e08c695f347df952a"
+
 [[package]]
 name = "regex-syntax"
 version = "0.6.29"
@@ -4696,6 +4906,17 @@ dependencies = [
 "digest",
 ]

+[[package]]
+name = "sha2"
+version = "0.10.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283"
+dependencies = [
+ "cfg-if",
+ "cpufeatures",
+ "digest",
+]
+
 [[package]]
 name = "sharded-slab"
 version = "0.1.7"
@@ -5079,6 +5300,16 @@ dependencies = [
 "winapi-util",
 ]

+[[package]]
+name = "terminal_size"
+version = "0.4.3"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "60b8cb979cb11c32ce1603f8137b22262a9d131aaa5c37b5678025f22b8becd0"
+dependencies = [
+ "rustix 1.0.8",
+ "windows-sys 0.60.2",
+]
+
 [[package]]
 name = "textwrap"
 version = "0.16.2"
@@ -5373,6 +5604,12 @@ version = "1.18.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "1dccffe3ce07af9386bfd29e80c0ab1a8205a2fc34e4bcd40364df902cfa8f3f"

+[[package]]
+name = "ucd-trie"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "2896d95c02a80c6d6a5d6e953d479f5ddf2dfdb6a244441010e373ac0fb88971"
+
 [[package]]
 name = "uds_windows"
 version = "1.1.0"
@@ -5955,6 +6192,16 @@ dependencies = [
 "windows-targets 0.52.6",
 ]

+[[package]]
+name = "windows"
+version = "0.59.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7f919aee0a93304be7f62e8e5027811bbba96bcb1de84d6618be56e43f8a32a1"
+dependencies = [
+ "windows-core 0.59.0",
+ "windows-targets 0.53.3",
+]
+
 [[package]]
 name = "windows-core"
 version = "0.52.0"
@@ -5964,17 +6211,41 @@ dependencies = [
 "windows-targets 0.52.6",
 ]

+[[package]]
+name = "windows-core"
+version = "0.59.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "810ce18ed2112484b0d4e15d022e5f598113e220c53e373fb31e67e21670c1ce"
+dependencies = [
+ "windows-implement 0.59.0",
+ "windows-interface",
+ "windows-result",
+ "windows-strings 0.3.1",
+ "windows-targets 0.53.3",
+]
+
 [[package]]
 name = "windows-core"
 version = "0.61.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "c0fdd3ddb90610c7638aa2b3a3ab2904fb9e5cdbecc643ddb3647212781c4ae3"
 dependencies = [
- "windows-implement",
+ "windows-implement 0.60.0",
 "windows-interface",
 "windows-link",
 "windows-result",
- "windows-strings",
+ "windows-strings 0.4.2",
+]
+
+[[package]]
+name = "windows-implement"
+version = "0.59.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "83577b051e2f49a058c308f17f273b570a6a758386fc291b5f6a934dd84e48c1"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.106",
 ]

 [[package]]
@@ -6014,6 +6285,15 @@ dependencies = [
 "windows-link",
 ]

+[[package]]
+name = "windows-strings"
+version = "0.3.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "87fa48cc5d406560701792be122a10132491cff9d0aeb23583cc2dcafc847319"
+dependencies = [
+ "windows-link",
+]
+
 [[package]]
 name = "windows-strings"
 version = "0.4.2"
@@ -6701,6 +6981,35 @@ dependencies = [
 "syn 2.0.106",
 ]

+[[package]]
+name = "zip"
+version = "2.4.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "fabe6324e908f85a1c52063ce7aa26b68dcb7eb6dbc83a2d148403c9bc3eba50"
+dependencies = [
+ "arbitrary",
+ "crc32fast",
+ "crossbeam-utils",
+ "displaydoc",
+ "flate2",
+ "indexmap 2.10.0",
+ "memchr",
+ "thiserror 2.0.15",
+ "zopfli",
+]
+
+[[package]]
+name = "zopfli"
+version = "0.8.2"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "edfc5ee405f504cd4984ecc6f14d02d55cfda60fa4b689434ef4102aae150cd7"
+dependencies = [
+ "bumpalo",
+ "crc32fast",
+ "log",
+ "simd-adler32",
+]
+
 [[package]]
 name = "zune-core"
 version = "0.4.12"
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -1,25 +1,50 @@
 [workspace]
-members = ["ndarray-image", "ndarray-resize", ".", "bounding-box", "ndarray-safetensors", "sqlite3-safetensor-cosine"]
+members = [
+  "ndarray-image",
+  "ndarray-resize",
+  ".",
+  "bounding-box",
+  "ndarray-safetensors",
+  "sqlite3-safetensor-cosine",
+  "ndcv-bridge",
+  "bbox",
+]

 [workspace.package]
 version = "0.1.0"
 edition = "2024"

 [workspace.dependencies]
+bbox = { path = "bbox" }
+divan = { version = "0.1.21" }
+ndarray-npy = "0.9.1"
+serde = { version = "1.0", features = ["derive"] }
 ndarray-image = { path = "ndarray-image" }
 ndarray-resize = { path = "ndarray-resize" }
 mnn = { git = "https://github.com/uttarayan21/mnn-rs", version = "0.2.0", features = [
-    # "metal",
-    # "coreml",
-    "tracing",
+  # "metal",
+  # "coreml",
+  "tracing",
 ], branch = "restructure-tensor-type" }
 mnn-bridge = { git = "https://github.com/uttarayan21/mnn-rs", version = "0.1.0", features = [
-    "ndarray",
+  "ndarray",
 ], branch = "restructure-tensor-type" }
 mnn-sync = { git = "https://github.com/uttarayan21/mnn-rs", version = "0.1.0", features = [
-    "tracing",
+  "tracing",
 ], branch = "restructure-tensor-type" }
 nalgebra = { version = "0.34.0", default-features = false, features = ["std"] }
+opencv = { version = "0.95.1" }
+bounding-box = { path = "bounding-box" }
+ndarray-safetensors = { path = "ndarray-safetensors" }
+wide = "0.7.33"
+rayon = "1.11.0"
+bytemuck = "1.23.2"
+error-stack = "0.5.0"
+thiserror = "2.0"
+fast_image_resize = "5.2.0"
+img-parts = "0.4.0"
+ndarray = { version = "0.16.1", features = ["rayon"] }
+num = "0.4"

 [package]
 name = "detector"
@@ -50,7 +75,11 @@ bounding-box = { version = "0.1.0", path = "bounding-box" }
 color = "0.3.1"
 itertools = "0.14.0"
 ordered-float = "5.0.0"
-ort = { version = "2.0.0-rc.10", default-features = false, features = [ "std", "tracing", "ndarray"]}
+ort = { version = "2.0.0-rc.10", default-features = false, features = [
+  "std",
+  "tracing",
+  "ndarray",
+] }
 ndarray-math = { git = "https://git.darksailor.dev/servius/ndarray-math", version = "0.1.0" }
 ndarray-safetensors = { version = "0.1.0", path = "ndarray-safetensors" }
 sqlite3-safetensor-cosine = { version = "0.1.0", path = "sqlite3-safetensor-cosine" }
@@ -74,4 +103,8 @@ ort-directml = ["ort/directml"]
 mnn-metal = ["mnn/metal"]
 mnn-coreml = ["mnn/coreml"]

-default = ["mnn-metal","mnn-coreml"]
+default = ["mnn-metal", "mnn-coreml"]
+
+[[test]]
+name = "test_bbox_replacement"
+path = "test_bbox_replacement.rs"
--- a/bbox/Cargo.toml
+++ b/bbox/Cargo.toml
@@ -0,0 +1,13 @@
+[package]
+name = "bbox"
+version.workspace = true
+edition.workspace = true
+
+[dependencies]
+ndarray.workspace = true
+num = "0.4.3"
+serde = { workspace = true, features = ["derive"], optional = true }
+
+[features]
+serde = ["dep:serde"]
+default = ["serde"]
--- a/bbox/src/lib.rs
+++ b/bbox/src/lib.rs
@@ -0,0 +1,708 @@
+pub mod traits;
+
+/// A bounding box of co-ordinates whose origin is at the top-left corner.
+#[derive(
+    Debug, Copy, Clone, PartialEq, PartialOrd, Eq, Hash, serde::Serialize, serde::Deserialize,
+)]
+#[non_exhaustive]
+pub struct BBox<T = f32> {
+    pub x: T,
+    pub y: T,
+    pub width: T,
+    pub height: T,
+}
+
+impl<T> From<[T; 4]> for BBox<T> {
+    fn from([x, y, width, height]: [T; 4]) -> Self {
+        Self {
+            x,
+            y,
+            width,
+            height,
+        }
+    }
+}
+
+impl<T: Copy> BBox<T> {
+    pub fn new(x: T, y: T, width: T, height: T) -> Self {
+        Self {
+            x,
+            y,
+            width,
+            height,
+        }
+    }
+
+    /// Casts the internal values to another type using [as] keyword
+    pub fn cast<T2>(self) -> BBox<T2>
+    where
+        T: num::cast::AsPrimitive<T2>,
+        T2: Copy + 'static,
+    {
+        BBox {
+            x: self.x.as_(),
+            y: self.y.as_(),
+            width: self.width.as_(),
+            height: self.height.as_(),
+        }
+    }
+
+    /// Clamps all the internal values to the given min and max.
+    pub fn clamp(&self, min: T, max: T) -> Self
+    where
+        T: std::cmp::PartialOrd,
+    {
+        Self {
+            x: num::clamp(self.x, min, max),
+            y: num::clamp(self.y, min, max),
+            width: num::clamp(self.width, min, max),
+            height: num::clamp(self.height, min, max),
+        }
+    }
+
+    pub fn clamp_box(&self, bbox: BBox<T>) -> Self
+    where
+        T: std::cmp::PartialOrd,
+        T: num::Zero,
+        T: core::ops::Add<Output = T>,
+        T: core::ops::Sub<Output = T>,
+    {
+        let x1 = num::clamp(self.x1(), bbox.x1(), bbox.x2());
+        let y1 = num::clamp(self.y1(), bbox.y1(), bbox.y2());
+        let x2 = num::clamp(self.x2(), bbox.x1(), bbox.x2());
+        let y2 = num::clamp(self.y2(), bbox.y1(), bbox.y2());
+        Self::new_xyxy(x1, y1, x2, y2)
+    }
+
+    pub fn normalize(&self, width: T, height: T) -> Self
+    where
+        T: core::ops::Div<Output = T> + Copy,
+    {
+        Self {
+            x: self.x / width,
+            y: self.y / height,
+            width: self.width / width,
+            height: self.height / height,
+        }
+    }
+
+    /// Normalize after casting to float
+    pub fn normalize_f64(&self, width: T, height: T) -> BBox<f64>
+    where
+        T: core::ops::Div<Output = T> + Copy,
+        T: num::cast::AsPrimitive<f64>,
+    {
+        BBox {
+            x: self.x.as_() / width.as_(),
+            y: self.y.as_() / height.as_(),
+            width: self.width.as_() / width.as_(),
+            height: self.height.as_() / height.as_(),
+        }
+    }
+
+    pub fn denormalize(&self, width: T, height: T) -> Self
+    where
+        T: core::ops::Mul<Output = T> + Copy,
+    {
+        Self {
+            x: self.x * width,
+            y: self.y * height,
+            width: self.width * width,
+            height: self.height * height,
+        }
+    }
+
+    pub fn height(&self) -> T {
+        self.height
+    }
+
+    pub fn width(&self) -> T {
+        self.width
+    }
+
+    pub fn padding(&self, padding: T) -> Self
+    where
+        T: core::ops::Add<Output = T> + core::ops::Sub<Output = T> + Copy,
+    {
+        Self {
+            x: self.x - padding,
+            y: self.y - padding,
+            width: self.width + padding + padding,
+            height: self.height + padding + padding,
+        }
+    }
+
+    pub fn padding_height(&self, padding: T) -> Self
+    where
+        T: core::ops::Add<Output = T> + core::ops::Sub<Output = T> + Copy,
+    {
+        Self {
+            x: self.x,
+            y: self.y - padding,
+            width: self.width,
+            height: self.height + padding + padding,
+        }
+    }
+
+    pub fn padding_width(&self, padding: T) -> Self
+    where
+        T: core::ops::Add<Output = T> + core::ops::Sub<Output = T> + Copy,
+    {
+        Self {
+            x: self.x - padding,
+            y: self.y,
+            width: self.width + padding + padding,
+            height: self.height,
+        }
+    }
+
+    // Enlarge / shrink the bounding box by a factor while
+    // keeping the center point and the aspect ratio fixed
+    pub fn scale(&self, factor: T) -> Self
+    where
+        T: core::ops::Mul<Output = T>,
+        T: core::ops::Sub<Output = T>,
+        T: core::ops::Add<Output = T>,
+        T: core::ops::Div<Output = T>,
+        T: num::One + Copy,
+    {
+        let two = num::one::<T>() + num::one::<T>();
+        let width = self.width * factor;
+        let height = self.height * factor;
+        let width_inc = width - self.width;
+        let height_inc = height - self.height;
+        Self {
+            x: self.x - width_inc / two,
+            y: self.y - height_inc / two,
+            width,
+            height,
+        }
+    }
+
+    pub fn scale_x(&self, factor: T) -> Self
+    where
+        T: core::ops::Mul<Output = T>
+            + core::ops::Sub<Output = T>
+            + core::ops::Add<Output = T>
+            + core::ops::Div<Output = T>
+            + num::One
+            + Copy,
+    {
+        let two = num::one::<T>() + num::one::<T>();
+        let width = self.width * factor;
+        let width_inc = width - self.width;
+        Self {
+            x: self.x - width_inc / two,
+            y: self.y,
+            width,
+            height: self.height,
+        }
+    }
+
+    pub fn scale_y(&self, factor: T) -> Self
+    where
+        T: core::ops::Mul<Output = T>
+            + core::ops::Sub<Output = T>
+            + core::ops::Add<Output = T>
+            + core::ops::Div<Output = T>
+            + num::One
+            + Copy,
+    {
+        let two = num::one::<T>() + num::one::<T>();
+        let height = self.height * factor;
+        let height_inc = height - self.height;
+        Self {
+            x: self.x,
+            y: self.y - height_inc / two,
+            width: self.width,
+            height,
+        }
+    }
+
+    pub fn offset(&self, offset: Point<T>) -> Self
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        Self {
+            x: self.x + offset.x,
+            y: self.y + offset.y,
+            width: self.width,
+            height: self.height,
+        }
+    }
+
+    /// Translate the bounding box by the given offset
+    /// if they are in the same scale
+    pub fn translate(&self, bbox: Self) -> Self
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        Self {
+            x: self.x + bbox.x,
+            y: self.y + bbox.y,
+            width: self.width,
+            height: self.height,
+        }
+    }
+
+    pub fn with_top_left(&self, top_left: Point<T>) -> Self {
+        Self {
+            x: top_left.x,
+            y: top_left.y,
+            width: self.width,
+            height: self.height,
+        }
+    }
+
+    pub fn center(&self) -> Point<T>
+    where
+        T: core::ops::Add<Output = T> + core::ops::Div<Output = T> + Copy,
+        T: num::One,
+    {
+        let two = T::one() + T::one();
+        Point::new(self.x + self.width / two, self.y + self.height / two)
+    }
+
+    pub fn area(&self) -> T
+    where
+        T: core::ops::Mul<Output = T> + Copy,
+    {
+        self.width * self.height
+    }
+
+    // Corresponds to self.x1() and self.y1()
+    pub fn top_left(&self) -> Point<T> {
+        Point::new(self.x, self.y)
+    }
+
+    pub fn top_right(&self) -> Point<T>
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        Point::new(self.x + self.width, self.y)
+    }
+
+    pub fn bottom_left(&self) -> Point<T>
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        Point::new(self.x, self.y + self.height)
+    }
+
+    // Corresponds to self.x2() and self.y2()
+    pub fn bottom_right(&self) -> Point<T>
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        Point::new(self.x + self.width, self.y + self.height)
+    }
+
+    pub const fn x1(&self) -> T {
+        self.x
+    }
+
+    pub const fn y1(&self) -> T {
+        self.y
+    }
+
+    pub fn x2(&self) -> T
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        self.x + self.width
+    }
+
+    pub fn y2(&self) -> T
+    where
+        T: core::ops::Add<Output = T> + Copy,
+    {
+        self.y + self.height
+    }
+
+    pub fn overlap(&self, other: &Self) -> T
+    where
+        T: std::cmp::PartialOrd
+            + traits::min::Min
+            + traits::max::Max
+            + num::Zero
+            + core::ops::Add<Output = T>
+            + core::ops::Sub<Output = T>
+            + core::ops::Mul<Output = T>
+            + Copy,
+    {
+        let x1 = self.x.max(other.x);
+        let y1 = self.y.max(other.y);
+        let x2 = (self.x + self.width).min(other.x + other.width);
+        let y2 = (self.y + self.height).min(other.y + other.height);
+        let width = (x2 - x1).max(T::zero());
+        let height = (y2 - y1).max(T::zero());
+        width * height
+    }
+
+    pub fn iou(&self, other: &Self) -> T
+    where
+        T: std::cmp::Ord
+            + num::Zero
+            + traits::min::Min
+            + traits::max::Max
+            + core::ops::Add<Output = T>
+            + core::ops::Sub<Output = T>
+            + core::ops::Mul<Output = T>
+            + core::ops::Div<Output = T>
+            + Copy,
+    {
+        let overlap = self.overlap(other);
+        let union = self.area() + other.area() - overlap;
+        overlap / union
+    }
+
+    pub fn contains(&self, point: Point<T>) -> bool
+    where
+        T: std::cmp::PartialOrd + core::ops::Add<Output = T> + Copy,
+    {
+        point.x >= self.x
+            && point.x <= self.x + self.width
+            && point.y >= self.y
+            && point.y <= self.y + self.height
+    }
+
+    pub fn contains_bbox(&self, other: Self) -> bool
+    where
+        T: std::cmp::PartialOrd + Copy,
+        T: core::ops::Add<Output = T>,
+    {
+        self.contains(other.top_left())
+            && self.contains(other.top_right())
+            && self.contains(other.bottom_left())
+            && self.contains(other.bottom_right())
+    }
+
+    pub fn new_xywh(x: T, y: T, width: T, height: T) -> Self {
+        Self {
+            x,
+            y,
+            width,
+            height,
+        }
+    }
+    pub fn new_xyxy(x1: T, y1: T, x2: T, y2: T) -> Self
+    where
+        T: core::ops::Sub<Output = T> + Copy,
+    {
+        Self {
+            x: x1,
+            y: y1,
+            width: x2 - x1,
+            height: y2 - y1,
+        }
+    }
+
+    pub fn containing(box1: Self, box2: Self) -> Self
+    where
+        T: traits::min::Min + traits::max::Max + Copy,
+        T: core::ops::Sub<Output = T>,
+        T: core::ops::Add<Output = T>,
+    {
+        let x1 = box1.x.min(box2.x);
+        let y1 = box1.y.min(box2.y);
+        let x2 = box1.x2().max(box2.x2());
+        let y2 = box1.y2().max(box2.y2());
+        Self::new_xyxy(x1, y1, x2, y2)
+    }
+}
+
+impl<T: core::ops::Sub<Output = T> + Copy> core::ops::Sub<T> for BBox<T> {
+    type Output = BBox<T>;
+    fn sub(self, rhs: T) -> Self::Output {
+        BBox {
+            x: self.x - rhs,
+            y: self.y - rhs,
+            width: self.width - rhs,
+            height: self.height - rhs,
+        }
+    }
+}
+
+impl<T: core::ops::Add<Output = T> + Copy> core::ops::Add<T> for BBox<T> {
+    type Output = BBox<T>;
+    fn add(self, rhs: T) -> Self::Output {
+        BBox {
+            x: self.x + rhs,
+            y: self.y + rhs,
+            width: self.width + rhs,
+            height: self.height + rhs,
+        }
+    }
+}
+impl<T: core::ops::Mul<Output = T> + Copy> core::ops::Mul<T> for BBox<T> {
+    type Output = BBox<T>;
+    fn mul(self, rhs: T) -> Self::Output {
+        BBox {
+            x: self.x * rhs,
+            y: self.y * rhs,
+            width: self.width * rhs,
+            height: self.height * rhs,
+        }
+    }
+}
+impl<T: core::ops::Div<Output = T> + Copy> core::ops::Div<T> for BBox<T> {
+    type Output = BBox<T>;
+    fn div(self, rhs: T) -> Self::Output {
+        BBox {
+            x: self.x / rhs,
+            y: self.y / rhs,
+            width: self.width / rhs,
+            height: self.height / rhs,
+        }
+    }
+}
+
+impl<T> core::ops::Add<BBox<T>> for BBox<T>
+where
+    T: core::ops::Sub<Output = T>
+        + core::ops::Add<Output = T>
+        + traits::min::Min
+        + traits::max::Max
+        + Copy,
+{
+    type Output = BBox<T>;
+    fn add(self, rhs: BBox<T>) -> Self::Output {
+        let x1 = self.x1().min(rhs.x1());
+        let y1 = self.y1().min(rhs.y1());
+        let x2 = self.x2().max(rhs.x2());
+        let y2 = self.y2().max(rhs.y2());
+        BBox::new_xyxy(x1, y1, x2, y2)
+    }
+}
+
+#[test]
+fn test_bbox_add() {
+    let bbox1: BBox<usize> = BBox::new_xyxy(0, 0, 10, 10);
+    let bbox2: BBox<usize> = BBox::new_xyxy(5, 5, 15, 15);
+    let bbox3: BBox<usize> = bbox1 + bbox2;
+    assert_eq!(bbox3, BBox::new_xyxy(0, 0, 15, 15).cast());
+}
+
+#[derive(
+    Debug, Copy, Clone, serde::Serialize, serde::Deserialize, PartialEq, PartialOrd, Eq, Ord, Hash,
+)]
+pub struct Point<T = f32> {
+    x: T,
+    y: T,
+}
+
+impl<T> Point<T> {
+    pub const fn new(x: T, y: T) -> Self {
+        Self { x, y }
+    }
+
+    pub const fn x(&self) -> T
+    where
+        T: Copy,
+    {
+        self.x
+    }
+
+    pub const fn y(&self) -> T
+    where
+        T: Copy,
+    {
+        self.y
+    }
+
+    pub fn cast<T2>(&self) -> Point<T2>
+    where
+        T: num::cast::AsPrimitive<T2>,
+        T2: Copy + 'static,
+    {
+        Point {
+            x: self.x.as_(),
+            y: self.y.as_(),
+        }
+    }
+}
+
+impl<T: core::ops::Sub<T, Output = T> + Copy> core::ops::Sub<Point<T>> for Point<T> {
+    type Output = Point<T>;
+    fn sub(self, rhs: Point<T>) -> Self::Output {
+        Point {
+            x: self.x - rhs.x,
+            y: self.y - rhs.y,
+        }
+    }
+}
+
+impl<T: core::ops::Add<T, Output = T> + Copy> core::ops::Add<Point<T>> for Point<T> {
+    type Output = Point<T>;
+    fn add(self, rhs: Point<T>) -> Self::Output {
+        Point {
+            x: self.x + rhs.x,
+            y: self.y + rhs.y,
+        }
+    }
+}
+
+impl<T: core::ops::Sub<Output = T> + Copy> Point<T> {
+    /// If both the boxes are in the same scale then make the translation of the origin to the
+    /// other box
+    pub fn with_origin(&self, origin: Self) -> Self {
+        *self - origin
+    }
+}
+
+impl<T: core::ops::Add<Output = T> + Copy> Point<T> {
+    pub fn translate(&self, point: Point<T>) -> Self {
+        *self + point
+    }
+}
+
+impl<I: num::Zero> BBox<I>
+where
+    I: num::cast::AsPrimitive<usize>,
+{
+    pub fn zeros_ndarray_2d<T: num::Zero + Copy>(&self) -> ndarray::Array2<T> {
+        ndarray::Array2::<T>::zeros((self.height.as_(), self.width.as_()))
+    }
+    pub fn zeros_ndarray_3d<T: num::Zero + Copy>(&self, channels: usize) -> ndarray::Array3<T> {
+        ndarray::Array3::<T>::zeros((self.height.as_(), self.width.as_(), channels))
+    }
+    pub fn ones_ndarray_2d<T: num::One + Copy>(&self) -> ndarray::Array2<T> {
+        ndarray::Array2::<T>::ones((self.height.as_(), self.width.as_()))
+    }
+}
+
+impl<T: num::Float> BBox<T> {
+    pub fn round(&self) -> Self {
+        Self {
+            x: self.x.round(),
+            y: self.y.round(),
+            width: self.width.round(),
+            height: self.height.round(),
+        }
+    }
+}
+
+#[cfg(test)]
+mod bbox_clamp_tests {
+    use super::*;
+    #[test]
+    pub fn bbox_test_clamp_box() {
+        let large_box = BBox::new(0, 0, 100, 100);
+        let small_box = BBox::new(10, 10, 20, 20);
+        let clamped = large_box.clamp_box(small_box);
+        assert_eq!(clamped, small_box);
+    }
+
+    #[test]
+    pub fn bbox_test_clamp_box_offset() {
+        let box_a = BBox::new(0, 0, 100, 100);
+        let box_b = BBox::new(-10, -10, 20, 20);
+        let clamped = box_b.clamp_box(box_a);
+        let expected = BBox::new(0, 0, 10, 10);
+        assert_eq!(expected, clamped);
+    }
+}
+
+#[cfg(test)]
+mod bbox_padding_tests {
+    use super::*;
+    #[test]
+    pub fn bbox_test_padding() {
+        let bbox = BBox::new(0, 0, 10, 10);
+        let padded = bbox.padding(2);
+        assert_eq!(padded, BBox::new(-2, -2, 14, 14));
+    }
+
+    #[test]
+    pub fn bbox_test_padding_height() {
+        let bbox = BBox::new(0, 0, 10, 10);
+        let padded = bbox.padding_height(2);
+        assert_eq!(padded, BBox::new(0, -2, 10, 14));
+    }
+
+    #[test]
+    pub fn bbox_test_padding_width() {
+        let bbox = BBox::new(0, 0, 10, 10);
+        let padded = bbox.padding_width(2);
+        assert_eq!(padded, BBox::new(-2, 0, 14, 10));
+    }
+
+    #[test]
+    pub fn bbox_test_clamped_padding() {
+        let bbox = BBox::new(0, 0, 10, 10);
+        let padded = bbox.padding(2);
+        let clamp = BBox::new(0, 0, 12, 12);
+        let clamped = padded.clamp_box(clamp);
+        assert_eq!(clamped, clamp);
+    }
+
+    #[test]
+    pub fn bbox_clamp_failure() {
+        let og = BBox::new(475.0, 79.625, 37.0, 282.15);
+        let padded = BBox {
+            x: 471.3,
+            y: 51.412499999999994,
+            width: 40.69999999999999,
+            height: 338.54999999999995,
+        };
+        let clamp = BBox::new(0.0, 0.0, 512.0, 512.0);
+        let sus = padded.clamp_box(clamp);
+        assert!(clamp.contains_bbox(sus));
+    }
+}
+
+#[cfg(test)]
+mod bbox_scale_tests {
+    use super::*;
+    #[test]
+    pub fn bbox_test_scale_int() {
+        let bbox = BBox::new(0, 0, 10, 10);
+        let scaled = bbox.scale(2);
+        assert_eq!(scaled, BBox::new(-5, -5, 20, 20));
+    }
+
+    #[test]
+    pub fn bbox_test_scale_float() {
+        let bbox = BBox::new(0, 0, 10, 10).cast();
+        let scaled = bbox.scale(1.05); // 5% increase
+        let l = 10.0 * 0.05;
+        assert_eq!(scaled, BBox::new(-l / 2.0, -l / 2.0, 10.0 + l, 10.0 + l));
+    }
+
+    #[test]
+    pub fn bbox_test_scale_float_negative() {
+        let bbox = BBox::new(0, 0, 10, 10).cast();
+        let scaled = bbox.scale(0.95); // 5% decrease
+        let l = -10.0 * 0.05;
+        assert_eq!(scaled, BBox::new(-l / 2.0, -l / 2.0, 10.0 + l, 10.0 + l));
+    }
+
+    #[test]
+    pub fn bbox_scale_float() {
+        let bbox = BBox::new_xywh(0, 0, 200, 200);
+        let scaled = bbox.cast::<f64>().scale(1.1).cast::<i32>().clamp(0, 1000);
+        let expected = BBox::new(0, 0, 220, 220);
+        assert_eq!(scaled, expected);
+    }
+    #[test]
+    pub fn add_padding_bbox_example() {
+        // let result = add_padding_bbox(
+        //     vec![Rect::new(100, 200, 300, 400)],
+        //     (0.1, 0.1),
+        //     (1000, 1000),
+        // );
+        //   assert_eq!(result[0], Rect::new(70, 160, 360, 480));
+        let bbox = BBox::new(100, 200, 300, 400);
+        let scaled = bbox.cast::<f64>().scale(1.2).cast::<i32>().clamp(0, 1000);
+        assert_eq!(bbox, BBox::new(100, 200, 300, 400));
+        assert_eq!(scaled, BBox::new(70, 160, 360, 480));
+    }
+    #[test]
+    pub fn scale_bboxes() {
+        // let result = scale_bboxes(Rect::new(100, 200, 300, 400), (1000, 1000), (500, 500));
+        // assert_eq!(result[0], Rect::new(200, 400, 600, 800));
+        let bbox = BBox::new(100, 200, 300, 400);
+        let scaled = bbox.scale(2);
+        assert_eq!(scaled, BBox::new(200, 400, 600, 800));
+    }
+}
--- a/bbox/src/traits.rs
+++ b/bbox/src/traits.rs
@@ -0,0 +1,2 @@
+pub mod max;
+pub mod min;
--- a/bbox/src/traits/max.rs
+++ b/bbox/src/traits/max.rs
@@ -0,0 +1,27 @@
+pub trait Max: Sized + Copy {
+    fn max(self, other: Self) -> Self;
+}
+
+macro_rules! impl_max {
+    ($($t:ty),*) => {
+        $(
+            impl Max for $t {
+                fn max(self, other: Self) -> Self {
+                    Ord::max(self, other)
+                }
+            }
+        )*
+    };
+    (float $($t:ty),*) => {
+        $(
+            impl Max for $t {
+                fn max(self, other: Self) -> Self {
+                    Self::max(self, other)
+                }
+            }
+        )*
+    };
+}
+
+impl_max!(usize, u8, u16, u32, u64, u128, isize, i8, i16, i32, i64, i128);
+impl_max!(float f32, f64);
--- a/bbox/src/traits/min.rs
+++ b/bbox/src/traits/min.rs
@@ -0,0 +1,27 @@
+pub trait Min: Sized + Copy {
+    fn min(self, other: Self) -> Self;
+}
+
+macro_rules! impl_min {
+    ($($t:ty),*) => {
+        $(
+            impl Min for $t {
+                fn min(self, other: Self) -> Self {
+                    Ord::min(self, other)
+                }
+            }
+        )*
+    };
+    (float $($t:ty),*) => {
+        $(
+            impl Min for $t {
+                fn min(self, other: Self) -> Self {
+                    Self::min(self, other)
+                }
+            }
+        )*
+    };
+}
+
+impl_min!(usize, u8, u16, u32, u64, u128, isize, i8, i16, i32, i64, i128);
+impl_min!(float f32, f64);
--- a/bounding-box/src/lib.rs
+++ b/bounding-box/src/lib.rs
@@ -163,6 +163,21 @@ impl<T: Num, const D: usize> AxisAlignedBoundingBox<T, D> {
        }
    }

+    pub fn scale_uniform(self, scalar: T) -> Self
+    where
+        T: core::ops::MulAssign,
+        T: core::ops::DivAssign,
+        T: core::ops::SubAssign,
+    {
+        let two = T::one() + T::one();
+        let new_size = self.size * scalar;
+        let new_point = self.point.coords - (new_size - self.size) / two;
+        Self {
+            point: Point::from(new_point),
+            size: new_size,
+        }
+    }
+
    pub fn contains_bbox(&self, other: &Self) -> bool
    where
        T: core::ops::AddAssign,
@@ -270,15 +285,17 @@ impl<T: Num, const D: usize> AxisAlignedBoundingBox<T, D> {
        })
    }

-    // pub fn as_<T2>(&self) -> Option<Aabb<T2, D>>
-    // where
-    //     T2: Num + simba::scalar::SubsetOf<T>,
-    // {
-    //     Some(Aabb {
-    //         point: Point::from(self.point.coords.as_()),
-    //         size: self.size.as_(),
-    //     })
-    // }
+    pub fn as_<T2>(&self) -> Aabb<T2, D>
+    where
+        T2: Num,
+        T: num::cast::AsPrimitive<T2>,
+    {
+        Aabb {
+            point: Point::from(self.point.coords.map(|x| x.as_())),
+            size: self.size.map(|x| x.as_()),
+        }
+    }
+
    pub fn measure(&self) -> T
    where
        T: core::ops::MulAssign,
--- a/flake.nix
+++ b/flake.nix
@@ -211,6 +211,7 @@
                  mnn
                  cargo-make
                  hyperfine
+                  opencv
                ]
                ++ (lib.optionals pkgs.stdenv.isDarwin [
                  apple-sdk_13
--- a/ndcv-bridge/Cargo.toml
+++ b/ndcv-bridge/Cargo.toml
@@ -0,0 +1,36 @@
+[package]
+name = "ndcv-bridge"
+version.workspace = true
+edition.workspace = true
+
+[dependencies]
+bounding-box.workspace = true
+nalgebra.workspace = true
+bytemuck.workspace = true
+error-stack.workspace = true
+fast_image_resize.workspace = true
+ndarray = { workspace = true, features = ["rayon"] }
+num.workspace = true
+opencv = { workspace = true, optional = true }
+rayon = "1.10.0"
+thiserror.workspace = true
+tracing = "0.1.41"
+wide = "0.7.32"
+img-parts.workspace = true
+
+[dev-dependencies]
+divan.workspace = true
+ndarray-npy.workspace = true
+
+[features]
+opencv = ["dep:opencv"]
+default = ["opencv"]
+
+
+[[bench]]
+name = "conversions"
+harness = false
+
+[[bench]]
+name = "gaussian"
+harness = false
--- a/ndcv-bridge/benches/conversions.rs
+++ b/ndcv-bridge/benches/conversions.rs
@@ -0,0 +1,75 @@
+use divan::black_box;
+use ndcv_bridge::*;
+
+// #[global_allocator]
+// static ALLOC: AllocProfiler = AllocProfiler::system();
+
+fn main() {
+    divan::main();
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_512() {
+    bench_mat_to_3d_ndarray(512);
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_1024() {
+    bench_mat_to_3d_ndarray(1024);
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_2k() {
+    bench_mat_to_3d_ndarray(2048);
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_4k() {
+    bench_mat_to_3d_ndarray(4096);
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_8k() {
+    bench_mat_to_3d_ndarray(8192);
+}
+
+#[divan::bench]
+fn bench_3d_mat_to_ndarray_8k_ref() {
+    bench_mat_to_3d_ndarray_ref(8192);
+}
+
+#[divan::bench]
+fn bench_2d_mat_to_ndarray_8k_ref() {
+    bench_mat_to_2d_ndarray(8192);
+}
+
+fn bench_mat_to_2d_ndarray(size: i32) -> ndarray::Array2<u8> {
+    let mat =
+        opencv::core::Mat::new_nd_with_default(&[size, size], opencv::core::CV_8UC1, (200).into())
+            .expect("failed");
+    let ndarray: ndarray::Array2<u8> = mat.as_ndarray().expect("failed").to_owned();
+    ndarray
+}
+
+fn bench_mat_to_3d_ndarray(size: i32) -> ndarray::Array3<u8> {
+    let mat = opencv::core::Mat::new_nd_with_default(
+        &[size, size],
+        opencv::core::CV_8UC3,
+        (200, 100, 10).into(),
+    )
+    .expect("failed");
+    // ndarray::Array3::<u8>::from_mat(black_box(mat)).expect("failed")
+    let ndarray: ndarray::Array3<u8> = mat.as_ndarray().expect("failed").to_owned();
+    ndarray
+}
+
+fn bench_mat_to_3d_ndarray_ref(size: i32) {
+    let mut mat = opencv::core::Mat::new_nd_with_default(
+        &[size, size],
+        opencv::core::CV_8UC3,
+        (200, 100, 10).into(),
+    )
+    .expect("failed");
+    let array: ndarray::ArrayView3<u8> = black_box(&mut mat).as_ndarray().expect("failed");
+    let _ = black_box(array);
+}
--- a/ndcv-bridge/benches/gaussian.rs
+++ b/ndcv-bridge/benches/gaussian.rs
@@ -0,0 +1,265 @@
+use divan::black_box;
+use ndarray::*;
+use ndcv_bridge::*;
+
+// #[global_allocator]
+// static ALLOC: AllocProfiler = AllocProfiler::system();
+
+fn main() {
+    divan::main();
+}
+
+// Helper function to create test images with different patterns
+fn create_test_image(size: usize, pattern: &str) -> Array3<u8> {
+    let mut arr = Array3::<u8>::zeros((size, size, 3));
+    match pattern {
+        "edges" => {
+            // Create a pattern with sharp edges
+            arr.slice_mut(s![size / 4..3 * size / 4, size / 4..3 * size / 4, ..])
+                .fill(255);
+        }
+        "gradient" => {
+            // Create a gradual gradient
+            for i in 0..size {
+                let val = (i * 255 / size) as u8;
+                arr.slice_mut(s![i, .., ..]).fill(val);
+            }
+        }
+        "checkerboard" => {
+            // Create a checkerboard pattern
+            for i in 0..size {
+                for j in 0..size {
+                    if (i / 20 + j / 20) % 2 == 0 {
+                        arr[[i, j, 0]] = 255;
+                        arr[[i, j, 1]] = 255;
+                        arr[[i, j, 2]] = 255;
+                    }
+                }
+            }
+        }
+        _ => arr.fill(255), // Default to solid white
+    }
+    arr
+}
+
+#[divan::bench_group]
+mod sizes {
+    use super::*;
+    // Benchmark different image sizes
+    #[divan::bench(args = [512, 1024, 2048, 4096])]
+    fn bench_gaussian_sizes_u8(size: usize) {
+        let arr = Array3::<u8>::ones((size, size, 3));
+        let _out = black_box(
+            arr.gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+
+    #[divan::bench(args = [512, 1024, 2048, 4096])]
+    fn bench_gaussian_sizes_u8_inplace(size: usize) {
+        let mut arr = Array3::<u8>::ones((size, size, 3));
+        black_box(
+            arr.gaussian_blur_inplace((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+
+    #[divan::bench(args = [512, 1024, 2048, 4096])]
+    fn bench_gaussian_sizes_f32(size: usize) {
+        let arr = Array3::<f32>::ones((size, size, 3));
+        let _out = black_box(
+            arr.gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+
+    #[divan::bench(args = [512, 1024, 2048, 4096])]
+    fn bench_gaussian_sizes_f32_inplace(size: usize) {
+        let mut arr = Array3::<f32>::ones((size, size, 3));
+        black_box(
+            arr.gaussian_blur_inplace((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+}
+
+// Benchmark different kernel sizes
+#[divan::bench(args = [(3, 3), (5, 5), (7, 7), (9, 9), (11, 11)])]
+fn bench_gaussian_kernels(kernel_size: (u8, u8)) {
+    let mut arr = Array3::<u8>::ones((1000, 1000, 3));
+    arr.gaussian_blur_inplace(kernel_size, 1.0, 1.0, BorderType::BorderConstant)
+        .unwrap();
+}
+
+// Benchmark different sigma values
+#[divan::bench(args = [0.5, 1.0, 2.0, 5.0])]
+fn bench_gaussian_sigmas(sigma: f64) {
+    let mut arr = Array3::<u8>::ones((1000, 1000, 3));
+    arr.gaussian_blur_inplace((3, 3), sigma, sigma, BorderType::BorderConstant)
+        .unwrap();
+}
+
+// Benchmark different sigma_x and sigma_y combinations
+#[divan::bench(args = [(0.5, 2.0), (1.0, 1.0), (2.0, 0.5), (3.0, 1.0)])]
+fn bench_gaussian_asymmetric_sigmas(sigmas: (f64, f64)) {
+    let mut arr = Array3::<u8>::ones((1000, 1000, 3));
+    arr.gaussian_blur_inplace((3, 3), sigmas.0, sigmas.1, BorderType::BorderConstant)
+        .unwrap();
+}
+
+// Benchmark different border types
+#[divan::bench]
+fn bench_gaussian_border_types() -> Vec<()> {
+    let border_types = [
+        BorderType::BorderConstant,
+        BorderType::BorderReplicate,
+        BorderType::BorderReflect,
+        BorderType::BorderReflect101,
+    ];
+
+    let mut arr = Array3::<u8>::ones((1000, 1000, 3));
+    border_types
+        .iter()
+        .map(|border_type| {
+            arr.gaussian_blur_inplace((3, 3), 1.0, 1.0, *border_type)
+                .unwrap();
+        })
+        .collect()
+}
+
+// Benchmark different image patterns
+#[divan::bench]
+fn bench_gaussian_patterns() {
+    let patterns = ["edges", "gradient", "checkerboard", "solid"];
+
+    patterns.iter().for_each(|&pattern| {
+        let mut arr = create_test_image(1000, pattern);
+        arr.gaussian_blur_inplace((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+            .unwrap();
+    })
+}
+
+#[divan::bench_group]
+mod allocation {
+    use super::*;
+    #[divan::bench]
+    fn bench_gaussian_allocation_inplace() {
+        let mut arr = Array3::<f32>::ones((3840, 2160, 3));
+
+        black_box(
+            arr.gaussian_blur_inplace((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+
+    #[divan::bench]
+    fn bench_gaussian_allocation_allocate() {
+        let arr = Array3::<f32>::ones((3840, 2160, 3));
+
+        let _out = black_box(
+            arr.gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+}
+
+#[divan::bench_group]
+mod realistic {
+    use super::*;
+    #[divan::bench]
+    fn small_800_600_3x3() {
+        let small_blur = Array3::<u8>::ones((800, 600, 3));
+        let _blurred = black_box(
+            small_blur
+                .gaussian_blur((3, 3), 0.5, 0.5, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn small_800_600_3x3_inplace() {
+        let mut small_blur = Array3::<u8>::ones((800, 600, 3));
+        small_blur
+            .gaussian_blur_inplace((3, 3), 0.5, 0.5, BorderType::BorderConstant)
+            .unwrap();
+    }
+    #[divan::bench]
+    fn medium_1920x1080_5x5() {
+        let mut medium_blur = Array3::<u8>::ones((1920, 1080, 3));
+        let _blurred = black_box(
+            medium_blur
+                .gaussian_blur_inplace((5, 5), 2.0, 2.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn medium_1920x1080_5x5_inplace() {
+        let mut medium_blur = Array3::<u8>::ones((1920, 1080, 3));
+        medium_blur
+            .gaussian_blur_inplace((5, 5), 2.0, 2.0, BorderType::BorderConstant)
+            .unwrap();
+    }
+    #[divan::bench]
+    fn large_3840x2160_9x9() {
+        let large_blur = Array3::<u8>::ones((3840, 2160, 3));
+        let _blurred = black_box(
+            large_blur
+                .gaussian_blur((9, 9), 5.0, 5.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn large_3840x2160_9x9_inplace() {
+        let mut large_blur = Array3::<u8>::ones((3840, 2160, 3));
+        large_blur
+            .gaussian_blur_inplace((9, 9), 5.0, 5.0, BorderType::BorderConstant)
+            .unwrap();
+    }
+    #[divan::bench]
+    fn small_800_600_3x3_f32() {
+        let small_blur = Array3::<f32>::ones((800, 600, 3));
+        let _blurred = black_box(
+            small_blur
+                .gaussian_blur((3, 3), 0.5, 0.5, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn small_800_600_3x3_inplace_f32() {
+        let mut small_blur = Array3::<f32>::ones((800, 600, 3));
+        small_blur
+            .gaussian_blur_inplace((3, 3), 0.5, 0.5, BorderType::BorderConstant)
+            .unwrap();
+    }
+    #[divan::bench]
+    fn medium_1920x1080_5x5_f32() {
+        let mut medium_blur = Array3::<f32>::ones((1920, 1080, 3));
+        let _blurred = black_box(
+            medium_blur
+                .gaussian_blur_inplace((5, 5), 2.0, 2.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn medium_1920x1080_5x5_inplace_f32() {
+        let mut medium_blur = Array3::<f32>::ones((1920, 1080, 3));
+        medium_blur
+            .gaussian_blur_inplace((5, 5), 2.0, 2.0, BorderType::BorderConstant)
+            .unwrap();
+    }
+    #[divan::bench]
+    fn large_3840x2160_9x9_f32() {
+        let large_blur = Array3::<f32>::ones((3840, 2160, 3));
+        let _blurred = black_box(
+            large_blur
+                .gaussian_blur((9, 9), 5.0, 5.0, BorderType::BorderConstant)
+                .unwrap(),
+        );
+    }
+    #[divan::bench]
+    fn large_3840x2160_9x9_inplace_f32() {
+        let mut large_blur = Array3::<f32>::ones((3840, 2160, 3));
+        large_blur
+            .gaussian_blur_inplace((9, 9), 5.0, 5.0, BorderType::BorderConstant)
+            .unwrap();
+    }
+}
--- a/ndcv-bridge/src/blend.rs
+++ b/ndcv-bridge/src/blend.rs
@@ -0,0 +1,180 @@
+use crate::prelude_::*;
+use ndarray::*;
+
+type Result<T, E = Report<NdCvError>> = std::result::Result<T, E>;
+
+mod seal {
+    pub trait Sealed {}
+    impl<T: ndarray::Data<Elem = f32>> Sealed for ndarray::ArrayBase<T, ndarray::Ix3> {}
+}
+pub trait NdBlend<T, D: ndarray::Dimension>: seal::Sealed {
+    fn blend(
+        &self,
+        mask: ndarray::ArrayView<T, D::Smaller>,
+        other: ndarray::ArrayView<T, D>,
+        alpha: T,
+    ) -> Result<ndarray::Array<T, D>>;
+    fn blend_inplace(
+        &mut self,
+        mask: ndarray::ArrayView<T, D::Smaller>,
+        other: ndarray::ArrayView<T, D>,
+        alpha: T,
+    ) -> Result<()>;
+}
+
+impl<S> NdBlend<f32, Ix3> for ndarray::ArrayBase<S, Ix3>
+where
+    S: ndarray::DataMut<Elem = f32>,
+{
+    fn blend(
+        &self,
+        mask: ndarray::ArrayView<f32, Ix2>,
+        other: ndarray::ArrayView<f32, Ix3>,
+        alpha: f32,
+    ) -> Result<ndarray::Array<f32, Ix3>> {
+        if self.shape() != other.shape() {
+            return Err(NdCvError)
+                .attach_printable("Shapes of image and other imagge do not match");
+        }
+        if self.shape()[0] != mask.shape()[0] || self.shape()[1] != mask.shape()[1] {
+            return Err(NdCvError).attach_printable("Shapes of image and mask do not match");
+        }
+
+        let mut output = ndarray::Array3::zeros(self.dim());
+        let (_height, _width, channels) = self.dim();
+
+        Zip::from(output.lanes_mut(Axis(2)))
+            .and(self.lanes(Axis(2)))
+            .and(other.lanes(Axis(2)))
+            .and(mask)
+            .par_for_each(|mut out, this, other, mask| {
+                let this = wide::f32x4::from(this.as_slice().expect("Invalid self array"));
+                let other = wide::f32x4::from(other.as_slice().expect("Invalid other array"));
+                let mask = wide::f32x4::splat(mask * alpha);
+                let o = this * (1.0 - mask) + other * mask;
+                out.as_slice_mut()
+                    .expect("Failed to get mutable slice")
+                    .copy_from_slice(&o.as_array_ref()[..channels]);
+            });
+
+        Ok(output)
+    }
+
+    fn blend_inplace(
+        &mut self,
+        mask: ndarray::ArrayView<f32, <Ix3 as Dimension>::Smaller>,
+        other: ndarray::ArrayView<f32, Ix3>,
+        alpha: f32,
+    ) -> Result<()> {
+        if self.shape() != other.shape() {
+            return Err(NdCvError)
+                .attach_printable("Shapes of image and other imagge do not match");
+        }
+        if self.shape()[0] != mask.shape()[0] || self.shape()[1] != mask.shape()[1] {
+            return Err(NdCvError).attach_printable("Shapes of image and mask do not match");
+        }
+
+        let (_height, _width, channels) = self.dim();
+
+        // Zip::from(self.lanes_mut(Axis(2)))
+        //     .and(other.lanes(Axis(2)))
+        //     .and(mask)
+        //     .par_for_each(|mut this, other, mask| {
+        //         let this_wide = wide::f32x4::from(this.as_slice().expect("Invalid self array"));
+        //         let other = wide::f32x4::from(other.as_slice().expect("Invalid other array"));
+        //         let mask = wide::f32x4::splat(mask * alpha);
+        //         let o = this_wide * (1.0 - mask) + other * mask;
+        //         this.as_slice_mut()
+        //             .expect("Failed to get mutable slice")
+        //             .copy_from_slice(&o.as_array_ref()[..channels]);
+        //     });
+        let this = self
+            .as_slice_mut()
+            .ok_or(NdCvError)
+            .attach_printable("Failed to get source image as a continuous slice")?;
+        let other = other
+            .as_slice()
+            .ok_or(NdCvError)
+            .attach_printable("Failed to get other image as a continuous slice")?;
+        let mask = mask
+            .as_slice()
+            .ok_or(NdCvError)
+            .attach_printable("Failed to get mask as a continuous slice")?;
+
+        use rayon::prelude::*;
+        this.par_chunks_exact_mut(channels)
+            .zip(other.par_chunks_exact(channels))
+            .zip(mask)
+            .for_each(|((this, other), mask)| {
+                let this_wide = wide::f32x4::from(&*this);
+                let other = wide::f32x4::from(other);
+                let mask = wide::f32x4::splat(mask * alpha);
+                this.copy_from_slice(
+                    &(this_wide * (1.0 - mask) + other * mask).as_array_ref()[..channels],
+                );
+            });
+
+        // for h in 0.._height {
+        //     for w in 0.._width {
+        //         let mask_index = h * _width + w;
+        //         let mask = mask[mask_index];
+        //         let mask = wide::f32x4::splat(mask * alpha);
+        //         let this = &mut this[mask_index * channels..(mask_index + 1) * channels];
+        //         let other = &other[mask_index * channels..(mask_index + 1) * channels];
+        //         let this_wide = wide::f32x4::from(&*this);
+        //         let other = wide::f32x4::from(other);
+        //         let o = this_wide * (1.0 - mask) + other * mask;
+        //         this.copy_from_slice(&o.as_array_ref()[..channels]);
+        //     }
+        // }
+        Ok(())
+    }
+}
+
+#[test]
+pub fn test_blend() {
+    let img = Array3::<f32>::from_shape_fn((10, 10, 3), |(i, j, k)| match (i, j, k) {
+        (0..=3, _, 0) => 1f32, // red
+        (4..=6, _, 1) => 1f32, // green
+        (7..=9, _, 2) => 1f32, // blue
+        _ => 0f32,
+    });
+    let other = img.clone().permuted_axes([1, 0, 2]).to_owned();
+    let mask = Array2::<f32>::from_shape_fn((10, 10), |(_, j)| if j > 5 { 1f32 } else { 0f32 });
+    // let other = Array3::<f32>::zeros((10, 10, 3));
+    let out = img.blend(mask.view(), other.view(), 1f32).unwrap();
+    let out_u8 = out.mapv(|v| (v * 255f32) as u8);
+    let expected = Array3::<u8>::from_shape_fn((10, 10, 3), |(i, j, k)| {
+        match (i, j, k) {
+            (0..=3, 0..=5, 0) => u8::MAX,                  // red
+            (4..=6, 0..=5, 1) | (_, 6, 1) => u8::MAX,      // green
+            (7..=9, 0..=5, 2) | (_, 7..=10, 2) => u8::MAX, // blue
+            _ => u8::MIN,
+        }
+    });
+    assert_eq!(out_u8, expected);
+}
+
+// #[test]
+// pub fn test_blend_inplace() {
+//     let mut img = Array3::<f32>::from_shape_fn((10, 10, 3), |(i, j, k)| match (i, j, k) {
+//         (0..=3, _, 0) => 1f32, // red
+//         (4..=6, _, 1) => 1f32, // green
+//         (7..=9, _, 2) => 1f32, // blue
+//         _ => 0f32,
+//     });
+//     let other = img.clone().permuted_axes([1, 0, 2]);
+//     let mask = Array2::<f32>::from_shape_fn((10, 10), |(_, j)| if j > 5 { 1f32 } else { 0f32 });
+//     // let other = Array3::<f32>::zeros((10, 10, 3));
+//     img.blend_inplace(mask.view(), other.view(), 1f32).unwrap();
+//     let out_u8 = img.mapv(|v| (v * 255f32) as u8);
+//     let expected = Array3::<u8>::from_shape_fn((10, 10, 3), |(i, j, k)| {
+//         match (i, j, k) {
+//             (0..=3, 0..=5, 0) => u8::MAX,                  // red
+//             (4..=6, 0..=5, 1) | (_, 6, 1) => u8::MAX,      // green
+//             (7..=9, 0..=5, 2) | (_, 7..=10, 2) => u8::MAX, // blue
+//             _ => u8::MIN,
+//         }
+//     });
+//     assert_eq!(out_u8, expected);
+// }
--- a/ndcv-bridge/src/bounding_rect.rs
+++ b/ndcv-bridge/src/bounding_rect.rs
@@ -0,0 +1,48 @@
+//! Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.
+//! The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.
+use crate::{NdAsImage, prelude_::*};
+pub trait BoundingRect: seal::SealedInternal {
+    fn bounding_rect(&self) -> Result<bounding_box::Aabb2<i32>, NdCvError>;
+}
+
+mod seal {
+    pub trait SealedInternal {}
+    impl<T, S: ndarray::Data<Elem = T>> SealedInternal for ndarray::ArrayBase<S, ndarray::Ix2> {}
+}
+
+impl<S: ndarray::Data<Elem = u8>> BoundingRect for ndarray::ArrayBase<S, ndarray::Ix2> {
+    fn bounding_rect(&self) -> Result<bounding_box::Aabb2<i32>, NdCvError> {
+        let mat = self.as_image_mat()?;
+        let rect = opencv::imgproc::bounding_rect(mat.as_ref()).change_context(NdCvError)?;
+        Ok(bounding_box::Aabb2::from_xywh(
+            rect.x,
+            rect.y,
+            rect.width,
+            rect.height,
+        ))
+    }
+}
+
+#[test]
+fn test_bounding_rect_empty() {
+    let arr = ndarray::Array2::<u8>::zeros((10, 10));
+    let rect = arr.bounding_rect().unwrap();
+    assert_eq!(rect, bounding_box::Aabb2::from_xywh(0, 0, 0, 0));
+}
+
+#[test]
+fn test_bounding_rect_valued() {
+    let mut arr = ndarray::Array2::<u8>::zeros((10, 10));
+    crate::NdRoiMut::roi_mut(&mut arr, bounding_box::Aabb2::from_xywh(1, 1, 3, 3)).fill(1);
+    let rect = arr.bounding_rect().unwrap();
+    assert_eq!(rect, bounding_box::Aabb2::from_xywh(1, 1, 3, 3));
+}
+
+#[test]
+fn test_bounding_rect_complex() {
+    let mut arr = ndarray::Array2::<u8>::zeros((10, 10));
+    crate::NdRoiMut::roi_mut(&mut arr, bounding_box::Aabb2::from_xywh(1, 3, 3, 3)).fill(1);
+    crate::NdRoiMut::roi_mut(&mut arr, bounding_box::Aabb2::from_xywh(2, 3, 3, 5)).fill(5);
+    let rect = arr.bounding_rect().unwrap();
+    assert_eq!(rect, bounding_box::Aabb2::from_xywh(1, 3, 4, 5));
+}
--- a/ndcv-bridge/src/codec.rs
+++ b/ndcv-bridge/src/codec.rs
@@ -0,0 +1,4 @@
+pub mod codecs;
+pub mod decode;
+pub mod encode;
+pub mod error;
--- a/ndcv-bridge/src/codec/codecs.rs
+++ b/ndcv-bridge/src/codec/codecs.rs
@@ -0,0 +1,218 @@
+use super::decode::Decoder;
+use super::encode::Encoder;
+use crate::NdCvError;
+use crate::conversions::matref::MatRef;
+use error_stack::*;
+use img_parts::{
+    Bytes,
+    jpeg::{Jpeg, markers},
+};
+use opencv::{
+    core::{Mat, Vector, VectorToVec},
+    imgcodecs::{ImreadModes, ImwriteFlags, imdecode, imencode},
+};
+
+#[derive(Debug)]
+pub enum CvEncoder {
+    Jpeg(CvJpegEncFlags),
+    Tiff(CvTiffEncFlags),
+}
+
+pub enum EncKind {
+    Jpeg,
+    Tiff,
+}
+
+impl CvEncoder {
+    fn kind(&self) -> EncKind {
+        match self {
+            Self::Jpeg(_) => EncKind::Jpeg,
+            Self::Tiff(_) => EncKind::Tiff,
+        }
+    }
+
+    fn extension(&self) -> &'static str {
+        match self {
+            Self::Jpeg(_) => ".jpg",
+            Self::Tiff(_) => ".tiff",
+        }
+    }
+
+    fn to_cv_param_list(&self) -> Vector<i32> {
+        match self {
+            Self::Jpeg(flags) => flags.to_cv_param_list(),
+            Self::Tiff(flags) => flags.to_cv_param_list(),
+        }
+    }
+}
+
+#[derive(Default, Debug)]
+pub struct CvJpegEncFlags {
+    quality: Option<usize>,
+    progressive: Option<bool>,
+    optimize: Option<bool>,
+    remove_app0: Option<bool>,
+}
+
+#[derive(Default, Debug)]
+pub struct CvTiffEncFlags {
+    compression: Option<i32>,
+}
+
+impl CvTiffEncFlags {
+    pub fn new() -> Self {
+        Self::default().with_compression(1)
+    }
+
+    pub fn with_compression(mut self, compression: i32) -> Self {
+        self.compression = Some(compression);
+        self
+    }
+
+    fn to_cv_param_list(&self) -> Vector<i32> {
+        let iter = [(
+            ImwriteFlags::IMWRITE_TIFF_COMPRESSION as i32,
+            self.compression.map(|i| i as i32),
+        )]
+        .into_iter()
+        .filter_map(|(flag, opt)| opt.map(|o| [flag, o]))
+        .flatten();
+
+        Vector::from_iter(iter)
+    }
+}
+
+impl CvJpegEncFlags {
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    pub fn with_quality(mut self, quality: usize) -> Self {
+        self.quality = Some(quality);
+        self
+    }
+
+    pub fn remove_app0_marker(mut self, val: bool) -> Self {
+        self.remove_app0 = Some(val);
+        self
+    }
+
+    fn to_cv_param_list(&self) -> Vector<i32> {
+        let iter = [
+            (
+                ImwriteFlags::IMWRITE_JPEG_QUALITY as i32,
+                self.quality.map(|i| i as i32),
+            ),
+            (
+                ImwriteFlags::IMWRITE_JPEG_PROGRESSIVE as i32,
+                self.progressive.map(|i| i as i32),
+            ),
+            (
+                ImwriteFlags::IMWRITE_JPEG_OPTIMIZE as i32,
+                self.optimize.map(|i| i as i32),
+            ),
+        ]
+        .into_iter()
+        .filter_map(|(flag, opt)| opt.map(|o| [flag, o]))
+        .flatten();
+
+        Vector::from_iter(iter)
+    }
+}
+
+impl Encoder for CvEncoder {
+    type Input<'a>
+        = MatRef<'a>
+    where
+        Self: 'a;
+
+    fn encode(&self, input: Self::Input<'_>) -> Result<Vec<u8>, NdCvError> {
+        let mut buf = Vector::default();
+
+        let params = self.to_cv_param_list();
+
+        imencode(self.extension(), &input.as_ref(), &mut buf, &params).change_context(NdCvError)?;
+
+        match self.kind() {
+            EncKind::Jpeg => {
+                let bytes = Bytes::from(buf.to_vec());
+                let mut jpg = Jpeg::from_bytes(bytes).change_context(NdCvError)?;
+                jpg.remove_segments_by_marker(markers::APP0);
+                let bytes = jpg.encoder().bytes();
+                Ok(bytes.to_vec())
+            }
+            EncKind::Tiff => Ok(buf.to_vec()),
+        }
+    }
+}
+
+pub enum CvDecoder {
+    Jpeg(CvJpegDecFlags),
+}
+
+impl CvDecoder {
+    fn to_cv_decode_flag(&self) -> i32 {
+        match self {
+            Self::Jpeg(flags) => flags.to_cv_decode_flag(),
+        }
+    }
+}
+
+#[derive(Default)]
+pub enum ColorMode {
+    #[default]
+    Color,
+    GrayScale,
+}
+
+impl ColorMode {
+    fn to_cv_decode_flag(&self) -> i32 {
+        match self {
+            Self::Color => ImreadModes::IMREAD_ANYCOLOR as i32,
+            Self::GrayScale => ImreadModes::IMREAD_GRAYSCALE as i32,
+        }
+    }
+}
+
+#[derive(Default)]
+pub struct CvJpegDecFlags {
+    color_mode: ColorMode,
+    ignore_orientation: bool,
+}
+
+impl CvJpegDecFlags {
+    pub fn new() -> Self {
+        Self::default()
+    }
+
+    pub fn with_color_mode(mut self, color_mode: ColorMode) -> Self {
+        self.color_mode = color_mode;
+        self
+    }
+
+    pub fn with_ignore_orientation(mut self, ignore_orientation: bool) -> Self {
+        self.ignore_orientation = ignore_orientation;
+        self
+    }
+
+    fn to_cv_decode_flag(&self) -> i32 {
+        let flag = self.color_mode.to_cv_decode_flag();
+
+        if self.ignore_orientation {
+            flag | ImreadModes::IMREAD_IGNORE_ORIENTATION as i32
+        } else {
+            flag
+        }
+    }
+}
+
+impl Decoder for CvDecoder {
+    type Output = Mat;
+
+    fn decode(&self, input: impl AsRef<[u8]>) -> Result<Self::Output, NdCvError> {
+        let flag = self.to_cv_decode_flag();
+        let out = imdecode(&Vector::from_slice(input.as_ref()), flag).change_context(NdCvError)?;
+
+        Ok(out)
+    }
+}
--- a/ndcv-bridge/src/codec/decode.rs
+++ b/ndcv-bridge/src/codec/decode.rs
@@ -0,0 +1,61 @@
+#![deny(warnings)]
+
+use super::codecs::CvDecoder;
+use super::error::ErrorReason;
+use crate::NdCvError;
+use crate::{NdAsImage, conversions::NdCvConversion};
+use error_stack::*;
+use ndarray::Array;
+use std::path::Path;
+
+pub trait Decodable<D: Decoder>: Sized {
+    fn decode(buf: impl AsRef<[u8]>, decoder: &D) -> Result<Self, NdCvError> {
+        let output = decoder.decode(buf)?;
+        Self::transform(output)
+    }
+
+    fn read(&self, path: impl AsRef<Path>, decoder: &D) -> Result<Self, NdCvError> {
+        let buf = std::fs::read(path)
+            .map_err(|e| match e.kind() {
+                std::io::ErrorKind::NotFound => {
+                    Report::new(e).attach_printable(ErrorReason::ImageWriteFileNotFound)
+                }
+                std::io::ErrorKind::PermissionDenied => {
+                    Report::new(e).attach_printable(ErrorReason::ImageWritePermissionDenied)
+                }
+                std::io::ErrorKind::OutOfMemory => {
+                    Report::new(e).attach_printable(ErrorReason::OutOfMemory)
+                }
+                std::io::ErrorKind::StorageFull => {
+                    Report::new(e).attach_printable(ErrorReason::OutOfStorage)
+                }
+                _ => Report::new(e).attach_printable(ErrorReason::ImageWriteOtherError),
+            })
+            .change_context(NdCvError)?;
+        Self::decode(buf, decoder)
+    }
+
+    fn transform(input: D::Output) -> Result<Self, NdCvError>;
+}
+
+pub trait Decoder {
+    type Output: Sized;
+    fn decode(&self, buf: impl AsRef<[u8]>) -> Result<Self::Output, NdCvError>;
+}
+
+impl<T: bytemuck::Pod + Copy, D: ndarray::Dimension> Decodable<CvDecoder> for Array<T, D>
+where
+    Self: NdAsImage<T, D>,
+{
+    fn transform(input: <CvDecoder as Decoder>::Output) -> Result<Self, NdCvError> {
+        Self::from_mat(input)
+    }
+}
+
+#[test]
+fn decode_image() {
+    use crate::codec::codecs::*;
+    let img = std::fs::read("/Users/fs0c131y/Projects/face-detector/assets/selfie.jpg").unwrap();
+    let decoder = CvDecoder::Jpeg(CvJpegDecFlags::new().with_ignore_orientation(true));
+    let _out = ndarray::Array3::<u8>::decode(img, &decoder).unwrap();
+}
--- a/ndcv-bridge/src/codec/encode.rs
+++ b/ndcv-bridge/src/codec/encode.rs
@@ -0,0 +1,56 @@
+use super::codecs::CvEncoder;
+use super::error::ErrorReason;
+use crate::conversions::NdAsImage;
+use crate::NdCvError;
+use error_stack::*;
+use ndarray::ArrayBase;
+use std::path::Path;
+
+pub trait Encodable<E: Encoder> {
+    fn encode(&self, encoder: &E) -> Result<Vec<u8>, NdCvError> {
+        let input = self.transform()?;
+        encoder.encode(input)
+    }
+
+    fn write(&self, path: impl AsRef<Path>, encoder: &E) -> Result<(), NdCvError> {
+        let buf = self.encode(encoder)?;
+
+        std::fs::write(path, buf)
+            .map_err(|e| match e.kind() {
+                std::io::ErrorKind::NotFound => {
+                    Report::new(e).attach_printable(ErrorReason::ImageWriteFileNotFound)
+                }
+                std::io::ErrorKind::PermissionDenied => {
+                    Report::new(e).attach_printable(ErrorReason::ImageWritePermissionDenied)
+                }
+                std::io::ErrorKind::OutOfMemory => {
+                    Report::new(e).attach_printable(ErrorReason::OutOfMemory)
+                }
+                std::io::ErrorKind::StorageFull => {
+                    Report::new(e).attach_printable(ErrorReason::OutOfStorage)
+                }
+                _ => Report::new(e).attach_printable(ErrorReason::ImageWriteOtherError),
+            })
+            .change_context(NdCvError)
+    }
+
+    fn transform(&self) -> Result<<E as Encoder>::Input<'_>, NdCvError>;
+}
+
+pub trait Encoder {
+    type Input<'a>
+    where
+        Self: 'a;
+
+    fn encode(&self, input: Self::Input<'_>) -> Result<Vec<u8>, NdCvError>;
+}
+
+impl<T: bytemuck::Pod + Copy, S: ndarray::Data<Elem = T>, D: ndarray::Dimension>
+    Encodable<CvEncoder> for ArrayBase<S, D>
+where
+    Self: NdAsImage<T, D>,
+{
+    fn transform(&self) -> Result<<CvEncoder as Encoder>::Input<'_>, NdCvError> {
+        self.as_image_mat()
+    }
+}
--- a/ndcv-bridge/src/codec/error.rs
+++ b/ndcv-bridge/src/codec/error.rs
@@ -0,0 +1,19 @@
+#[derive(Debug)]
+pub enum ErrorReason {
+    ImageReadFileNotFound,
+    ImageReadPermissionDenied,
+    ImageReadOtherError,
+
+    ImageWriteFileNotFound,
+    ImageWritePermissionDenied,
+    ImageWriteOtherError,
+
+    OutOfMemory,
+    OutOfStorage,
+}
+
+impl std::fmt::Display for ErrorReason {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{:?}", self)
+    }
+}
--- a/ndcv-bridge/src/color_space.rs
+++ b/ndcv-bridge/src/color_space.rs
@@ -0,0 +1,88 @@
+//! Colorspace conversion functions
+//! ## Example
+//! ```rust
+//! let arr = Array3::<u8>::ones((100, 100, 3));
+//! let out: Array3<u8> = arr.cvt::<Rgba<u8>, Rgb<u8>>()
+//! ```
+use crate::prelude_::*;
+use ndarray::*;
+
+pub trait ColorSpace {
+    type Elem: seal::Sealed;
+    type Dim: ndarray::Dimension;
+    const CHANNELS: usize;
+}
+
+mod seal {
+    pub trait Sealed: bytemuck::Pod {}
+    // impl<T> Sealed for T {}
+    impl Sealed for u8 {} // 0 to 255
+    impl Sealed for u16 {} // 0 to 65535
+    impl Sealed for f32 {} // 0 to 1
+}
+
+macro_rules! define_color_space {
+    ($name:ident, $channels:expr, $depth:ty) => {
+        pub struct $name<T> {
+            __phantom: core::marker::PhantomData<T>,
+        }
+        impl<T: seal::Sealed> ColorSpace for $name<T> {
+            type Elem = T;
+            type Dim = $depth;
+            const CHANNELS: usize = $channels;
+        }
+    };
+}
+
+define_color_space!(Rgb, 3, Ix3);
+define_color_space!(Bgr, 3, Ix3);
+define_color_space!(Rgba, 4, Ix3);
+
+pub trait NdArray<T, D: ndarray::Dimension> {}
+impl<T, D: ndarray::Dimension, S: ndarray::Data<Elem = T>> NdArray<S, D> for ArrayBase<S, D> {}
+
+pub trait ConvertColor<T, U>
+where
+    T: ColorSpace,
+    U: ColorSpace,
+    Self: NdArray<T::Elem, T::Dim>,
+{
+    type Output: NdArray<U::Elem, U::Dim>;
+    fn cvt(&self) -> Self::Output;
+}
+
+// impl<T: seal::Sealed, S: ndarray::Data<Elem = T>> ConvertColor<Rgb<T>, Bgr<T>> for ArrayBase<S, Ix3>
+// where
+//     Self: NdArray<T, Ix3>,
+// {
+//     type Output = ArrayView3<'a, T>;
+//     fn cvt(&self) -> CowArray<T, Ix3> {
+//         self.view().permuted_axes([2, 1, 0]).into()
+//     }
+// }
+//
+// impl<T: seal::Sealed, S: ndarray::Data<Elem = T>> ConvertColor<Bgr<T>, Rgb<T>> for ArrayBase<S, Ix3>
+// where
+//     Self: NdArray<T, Ix3>,
+// {
+//     type Output = ArrayView3<'a, T>;
+//     fn cvt(&self) -> CowArray<T, Ix3> {
+//         self.view().permuted_axes([2, 1, 0]).into()
+//     }
+// }
+
+
+// impl<T: seal::Sealed + num::One + num::Zero, S: ndarray::Data<Elem = T>>
+//     ConvertColor<Rgb<T>, Rgba<T>> for ArrayBase<S, Ix3>
+// {
+//     fn cvt(&self) -> CowArray<T, Ix3> {
+//         let mut out = Array3::<T>::zeros((self.height(), self.width(), 4));
+//         // Zip::from(&mut out).and(self).for_each(|out, &in_| {
+//         //     out[0] = in_[0];
+//         //     out[1] = in_[1];
+//         //     out[2] = in_[2];
+//         //     out[3] = T::one();
+//         // });
+//         out.into()
+//     }
+// }
--- a/ndcv-bridge/src/connected_components.rs
+++ b/ndcv-bridge/src/connected_components.rs
@@ -0,0 +1,113 @@
+use crate::{NdAsImage, NdAsImageMut, conversions::MatAsNd, prelude_::*};
+
+pub(crate) mod seal {
+    pub trait ConnectedComponentOutput: Sized + Copy + bytemuck::Pod + num::Zero {
+        fn as_cv_type() -> i32 {
+            crate::type_depth::<Self>()
+        }
+    }
+    impl ConnectedComponentOutput for i32 {}
+    impl ConnectedComponentOutput for u16 {}
+}
+
+pub trait NdCvConnectedComponents<T> {
+    fn connected_components<O: seal::ConnectedComponentOutput>(
+        &self,
+        connectivity: Connectivity,
+    ) -> Result<ndarray::Array2<O>, NdCvError>;
+    fn connected_components_with_stats<O: seal::ConnectedComponentOutput>(
+        &self,
+        connectivity: Connectivity,
+    ) -> Result<ConnectedComponentStats<O>, NdCvError>;
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
+pub enum Connectivity {
+    Four = 4,
+    #[default]
+    Eight = 8,
+}
+
+#[derive(Debug, Clone)]
+pub struct ConnectedComponentStats<O: seal::ConnectedComponentOutput> {
+    pub num_labels: i32,
+    pub labels: ndarray::Array2<O>,
+    pub stats: ndarray::Array2<i32>,
+    pub centroids: ndarray::Array2<f64>,
+}
+
+// use crate::conversions::NdCvConversionRef;
+impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> NdCvConnectedComponents<T>
+    for ndarray::ArrayBase<S, ndarray::Ix2>
+where
+    ndarray::Array2<T>: NdAsImage<T, ndarray::Ix2>,
+{
+    fn connected_components<O: seal::ConnectedComponentOutput>(
+        &self,
+        connectivity: Connectivity,
+    ) -> Result<ndarray::Array2<O>, NdCvError> {
+        let mat = self.as_image_mat()?;
+        let mut labels = ndarray::Array2::<O>::zeros(self.dim());
+        let mut cv_labels = labels.as_image_mat_mut()?;
+        opencv::imgproc::connected_components(
+            mat.as_ref(),
+            cv_labels.as_mut(),
+            connectivity as i32,
+            O::as_cv_type(),
+        )
+        .change_context(NdCvError)?;
+        Ok(labels)
+    }
+
+    fn connected_components_with_stats<O: seal::ConnectedComponentOutput>(
+        &self,
+        connectivity: Connectivity,
+    ) -> Result<ConnectedComponentStats<O>, NdCvError> {
+        let mut labels = ndarray::Array2::<O>::zeros(self.dim());
+        let mut stats = opencv::core::Mat::default();
+        let mut centroids = opencv::core::Mat::default();
+        let num_labels = opencv::imgproc::connected_components_with_stats(
+            self.as_image_mat()?.as_ref(),
+            labels.as_image_mat_mut()?.as_mut(),
+            &mut stats,
+            &mut centroids,
+            connectivity as i32,
+            O::as_cv_type(),
+        )
+        .change_context(NdCvError)?;
+        let stats = stats.as_ndarray()?.to_owned();
+        let centroids = centroids.as_ndarray()?.to_owned();
+        Ok(ConnectedComponentStats {
+            labels,
+            stats,
+            centroids,
+            num_labels,
+        })
+    }
+}
+
+// #[test]
+// fn test_connected_components() {
+//     use opencv::core::MatTrait as _;
+//     let mat = opencv::core::Mat::new_nd_with_default(&[10, 10], opencv::core::CV_8UC1, 0.into())
+//         .expect("failed");
+//     let roi1 = opencv::core::Rect::new(2, 2, 2, 2);
+//     let roi2 = opencv::core::Rect::new(6, 6, 3, 3);
+//     let mut mat1 = opencv::core::Mat::roi(&mat, roi1).expect("failed");
+//     mat1.set_scalar(1.into()).expect("failed");
+//     let mut mat2 = opencv::core::Mat::roi(&mat, roi2).expect("failed");
+//     mat2.set_scalar(1.into()).expect("failed");
+
+//     let array2: ndarray::ArrayView2<u8> = mat.as_ndarray().expect("failed");
+//     let output = array2
+//         .connected_components::<u16>(Connectivity::Four)
+//         .expect("failed");
+//     let expected = {
+//         let mut expected = ndarray::Array2::zeros((10, 10));
+//         expected.slice_mut(ndarray::s![2..4, 2..4]).fill(1);
+//         expected.slice_mut(ndarray::s![6..9, 6..9]).fill(2);
+//         expected
+//     };
+
+//     assert_eq!(output, expected);
+// }
--- a/ndcv-bridge/src/contours.rs
+++ b/ndcv-bridge/src/contours.rs
@@ -0,0 +1,270 @@
+//! <https://docs.rs/opencv/latest/opencv/imgproc/fn.find_contours.html>
+
+#![deny(warnings)]
+
+use crate::conversions::*;
+use crate::prelude_::*;
+use nalgebra::Point2;
+use ndarray::*;
+
+#[repr(C)]
+#[derive(Default, Debug, Copy, Clone, PartialEq, Eq)]
+pub enum ContourRetrievalMode {
+    #[default]
+    External = 0, // RETR_EXTERNAL
+    List = 1,      // RETR_LIST
+    CComp = 2,     // RETR_CCOMP
+    Tree = 3,      // RETR_TREE
+    FloodFill = 4, // RETR_FLOODFILL
+}
+
+#[repr(C)]
+#[derive(Default, Debug, Copy, Clone, PartialEq, Eq)]
+pub enum ContourApproximationMethod {
+    #[default]
+    None = 1, // CHAIN_APPROX_NONE
+    Simple = 2,   // CHAIN_APPROX_SIMPLE
+    Tc89L1 = 3,   // CHAIN_APPROX_TC89_L1
+    Tc89Kcos = 4, // CHAIN_APPROX_TC89_KCOS
+}
+
+#[derive(Debug, Clone)]
+pub struct ContourHierarchy {
+    pub next: i32,
+    pub previous: i32,
+    pub first_child: i32,
+    pub parent: i32,
+}
+
+#[derive(Debug, Clone)]
+pub struct ContourResult {
+    pub contours: Vec<Vec<Point2<i32>>>,
+    pub hierarchy: Vec<ContourHierarchy>,
+}
+
+mod seal {
+    pub trait Sealed {}
+    impl Sealed for u8 {}
+}
+
+pub trait NdCvFindContours<T: bytemuck::Pod + seal::Sealed>:
+    crate::image::NdImage + crate::conversions::NdAsImage<T, ndarray::Ix2>
+{
+    fn find_contours(
+        &self,
+        mode: ContourRetrievalMode,
+        method: ContourApproximationMethod,
+    ) -> Result<Vec<Vec<Point2<i32>>>, NdCvError>;
+
+    fn find_contours_with_hierarchy(
+        &self,
+        mode: ContourRetrievalMode,
+        method: ContourApproximationMethod,
+    ) -> Result<ContourResult, NdCvError>;
+
+    fn find_contours_def(&self) -> Result<Vec<Vec<Point2<i32>>>, NdCvError> {
+        self.find_contours(
+            ContourRetrievalMode::External,
+            ContourApproximationMethod::Simple,
+        )
+    }
+
+    fn find_contours_with_hierarchy_def(&self) -> Result<ContourResult, NdCvError> {
+        self.find_contours_with_hierarchy(
+            ContourRetrievalMode::External,
+            ContourApproximationMethod::Simple,
+        )
+    }
+}
+
+pub trait NdCvContourArea<T: bytemuck::Pod> {
+    fn contours_area(&self, oriented: bool) -> Result<f64, NdCvError>;
+
+    fn contours_area_def(&self) -> Result<f64, NdCvError> {
+        self.contours_area(false)
+    }
+}
+
+impl<T: ndarray::RawData + ndarray::Data<Elem = u8>> NdCvFindContours<u8> for ArrayBase<T, Ix2> {
+    fn find_contours(
+        &self,
+        mode: ContourRetrievalMode,
+        method: ContourApproximationMethod,
+    ) -> Result<Vec<Vec<Point2<i32>>>, NdCvError> {
+        let cv_self = self.as_image_mat()?;
+        let mut contours = opencv::core::Vector::<opencv::core::Vector<opencv::core::Point>>::new();
+
+        opencv::imgproc::find_contours(
+            &*cv_self,
+            &mut contours,
+            mode as i32,
+            method as i32,
+            opencv::core::Point::new(0, 0),
+        )
+        .change_context(NdCvError)
+        .attach_printable("Failed to find contours")?;
+        let mut result: Vec<Vec<Point2<i32>>> = Vec::new();
+
+        for i in 0..contours.len() {
+            let contour = contours.get(i).change_context(NdCvError)?;
+            let points: Vec<Point2<i32>> =
+                contour.iter().map(|pt| Point2::new(pt.x, pt.y)).collect();
+            result.push(points);
+        }
+
+        Ok(result)
+    }
+
+    fn find_contours_with_hierarchy(
+        &self,
+        mode: ContourRetrievalMode,
+        method: ContourApproximationMethod,
+    ) -> Result<ContourResult, NdCvError> {
+        let cv_self = self.as_image_mat()?;
+        let mut contours = opencv::core::Vector::<opencv::core::Vector<opencv::core::Point>>::new();
+        let mut hierarchy = opencv::core::Vector::<opencv::core::Vec4i>::new();
+
+        opencv::imgproc::find_contours_with_hierarchy(
+            &*cv_self,
+            &mut contours,
+            &mut hierarchy,
+            mode as i32,
+            method as i32,
+            opencv::core::Point::new(0, 0),
+        )
+        .change_context(NdCvError)
+        .attach_printable("Failed to find contours with hierarchy")?;
+        let mut contour_list: Vec<Vec<Point2<i32>>> = Vec::new();
+
+        for i in 0..contours.len() {
+            let contour = contours.get(i).change_context(NdCvError)?;
+            let points: Vec<Point2<i32>> =
+                contour.iter().map(|pt| Point2::new(pt.x, pt.y)).collect();
+            contour_list.push(points);
+        }
+
+        let mut hierarchy_list = Vec::new();
+        for i in 0..hierarchy.len() {
+            let h = hierarchy.get(i).change_context(NdCvError)?;
+            hierarchy_list.push(ContourHierarchy {
+                next: h[0],
+                previous: h[1],
+                first_child: h[2],
+                parent: h[3],
+            });
+        }
+
+        Ok(ContourResult {
+            contours: contour_list,
+            hierarchy: hierarchy_list,
+        })
+    }
+}
+
+impl<T> NdCvContourArea<T> for Vec<Point2<T>>
+where
+    T: bytemuck::Pod + num::traits::AsPrimitive<i32> + std::cmp::PartialEq + std::fmt::Debug + Copy,
+{
+    fn contours_area(&self, oriented: bool) -> Result<f64, NdCvError> {
+        if self.is_empty() {
+            return Ok(0.0);
+        }
+
+        let mut cv_contour: opencv::core::Vector<opencv::core::Point> = opencv::core::Vector::new();
+        self.iter().for_each(|point| {
+            cv_contour.push(opencv::core::Point::new(
+                point.coords[0].as_(),
+                point.coords[1].as_(),
+            ));
+        });
+
+        opencv::imgproc::contour_area(&cv_contour, oriented)
+            .change_context(NdCvError)
+            .attach_printable("Failed to calculate contour area")
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::Array2;
+
+    fn simple_binary_rect_image() -> Array2<u8> {
+        let mut img = Array2::<u8>::zeros((10, 10));
+        for i in 2..8 {
+            for j in 3..7 {
+                img[(i, j)] = 255;
+            }
+        }
+        img
+    }
+
+    #[test]
+    fn test_find_contours_external_simple() {
+        let img = simple_binary_rect_image();
+        let contours = img
+            .find_contours(
+                ContourRetrievalMode::External,
+                ContourApproximationMethod::Simple,
+            )
+            .expect("Failed to find contours");
+        assert_eq!(contours.len(), 1);
+        assert!(contours[0].len() >= 4);
+    }
+
+    #[test]
+    fn test_find_contours_with_hierarchy() {
+        let img = simple_binary_rect_image();
+        let res = img
+            .find_contours_with_hierarchy(
+                ContourRetrievalMode::External,
+                ContourApproximationMethod::Simple,
+            )
+            .expect("Failed to find contours with hierarchy");
+        assert_eq!(res.contours.len(), 1);
+        assert_eq!(res.hierarchy.len(), 1);
+
+        let h = &res.hierarchy[0];
+        assert_eq!(h.parent, -1);
+        assert_eq!(h.first_child, -1);
+    }
+
+    #[test]
+    fn test_default_methods() {
+        let img = simple_binary_rect_image();
+        let contours = img.find_contours_def().unwrap();
+        let res = img.find_contours_with_hierarchy_def().unwrap();
+        assert_eq!(contours.len(), 1);
+        assert_eq!(res.contours.len(), 1);
+    }
+
+    #[test]
+    fn test_contour_area_calculation() {
+        let img = simple_binary_rect_image();
+        let contours = img.find_contours_def().unwrap();
+        let expected_area = 15.;
+        let area = contours[0].contours_area_def().unwrap();
+        assert!(
+            (area - expected_area).abs() < 1.0,
+            "Area mismatch: got {area}, expected {expected_area}",
+        );
+    }
+
+    #[test]
+    fn test_empty_input_returns_no_contours() {
+        let img = Array2::<u8>::zeros((10, 10));
+        let contours = img.find_contours_def().unwrap();
+        assert!(contours.is_empty());
+
+        let res = img.find_contours_with_hierarchy_def().unwrap();
+        assert!(res.contours.is_empty());
+        assert!(res.hierarchy.is_empty());
+    }
+
+    #[test]
+    fn test_contour_area_empty_contour() {
+        let contour: Vec<Point2<i32>> = vec![];
+        let area = contour.contours_area_def().unwrap();
+        assert_eq!(area, 0.0);
+    }
+}
--- a/ndcv-bridge/src/conversions.rs
+++ b/ndcv-bridge/src/conversions.rs
@@ -0,0 +1,337 @@
+//! Mat <--> ndarray conversion traits
+//!
+//! Conversion Table
+//!
+//! | ndarray           | Mat     |
+//! |---------          |-----    |
+//! | Array<T, Ix1>     | Mat(ndims = 1, channels = 1)   |
+//! | Array<T, Ix2>     | Mat(ndims = 2, channels = 1)   |
+//! | Array<T, Ix2>     | Mat(ndims = 1, channels = X)   |
+//! | Array<T, Ix3>     | Mat(ndims = 3, channels = 1)   |
+//! | Array<T, Ix3>     | Mat(ndims = 2, channels = X)   |
+//! | Array<T, Ix4>     | Mat(ndims = 4, channels = 1)   |
+//! | Array<T, Ix4>     | Mat(ndims = 3, channels = X)   |
+//! | Array<T, Ix5>     | Mat(ndims = 5, channels = 1)   |
+//! | Array<T, Ix5>     | Mat(ndims = 4, channels = X)   |
+//! | Array<T, Ix6>     | Mat(ndims = 6, channels = 1)   |
+//! | Array<T, Ix6>     | Mat(ndims = 5, channels = X)   |
+//!
+//! // X is the last dimension
+use crate::NdCvError;
+use crate::type_depth;
+use error_stack::*;
+use ndarray::{Ix2, Ix3};
+use opencv::core::MatTraitConst;
+mod impls;
+pub(crate) mod matref;
+use matref::{MatRef, MatRefMut};
+
+pub(crate) mod seal {
+    pub trait SealedInternal {}
+    impl<T, S: ndarray::Data<Elem = T>, D> SealedInternal for ndarray::ArrayBase<S, D> {}
+    // impl<T, S: ndarray::DataMut<Elem = T>, D> SealedInternal for ndarray::ArrayBase<S, D> {}
+}
+
+pub trait NdCvConversion<T: bytemuck::Pod + Copy, D: ndarray::Dimension>:
+    seal::SealedInternal + Sized
+{
+    fn to_mat(&self) -> Result<opencv::core::Mat, NdCvError>;
+    fn from_mat(
+        mat: opencv::core::Mat,
+    ) -> Result<ndarray::ArrayBase<ndarray::OwnedRepr<T>, D>, NdCvError>;
+}
+
+impl<T: bytemuck::Pod + Copy, S: ndarray::Data<Elem = T>, D: ndarray::Dimension>
+    NdCvConversion<T, D> for ndarray::ArrayBase<S, D>
+where
+    Self: NdAsImage<T, D>,
+{
+    fn to_mat(&self) -> Result<opencv::core::Mat, NdCvError> {
+        Ok(self.as_image_mat()?.mat.clone())
+    }
+
+    fn from_mat(
+        mat: opencv::core::Mat,
+    ) -> Result<ndarray::ArrayBase<ndarray::OwnedRepr<T>, D>, NdCvError> {
+        let ndarray = unsafe { impls::mat_to_ndarray::<T, D>(&mat) }.change_context(NdCvError)?;
+        Ok(ndarray.to_owned())
+    }
+}
+
+pub trait MatAsNd {
+    fn as_ndarray<T: bytemuck::Pod, D: ndarray::Dimension>(
+        &self,
+    ) -> Result<ndarray::ArrayView<T, D>, NdCvError>;
+}
+
+impl MatAsNd for opencv::core::Mat {
+    fn as_ndarray<T: bytemuck::Pod, D: ndarray::Dimension>(
+        &self,
+    ) -> Result<ndarray::ArrayView<T, D>, NdCvError> {
+        unsafe { impls::mat_to_ndarray::<T, D>(self) }.change_context(NdCvError)
+    }
+}
+
+pub trait NdAsMat<T: bytemuck::Pod + Copy, D: ndarray::Dimension> {
+    fn as_single_channel_mat(&self) -> Result<MatRef, NdCvError>;
+    fn as_multi_channel_mat(&self) -> Result<MatRef, NdCvError>;
+}
+
+pub trait NdAsMatMut<T: bytemuck::Pod + Copy, D: ndarray::Dimension>: NdAsMat<T, D> {
+    fn as_single_channel_mat_mut(&mut self) -> Result<MatRefMut, NdCvError>;
+    fn as_multi_channel_mat_mut(&mut self) -> Result<MatRefMut, NdCvError>;
+}
+
+impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>, D: ndarray::Dimension> NdAsMat<T, D>
+    for ndarray::ArrayBase<S, D>
+{
+    fn as_single_channel_mat(&self) -> Result<MatRef, NdCvError> {
+        let mat = unsafe { impls::ndarray_to_mat_regular(self) }.change_context(NdCvError)?;
+        Ok(MatRef::new(mat))
+    }
+    fn as_multi_channel_mat(&self) -> Result<MatRef, NdCvError> {
+        let mat = unsafe { impls::ndarray_to_mat_consolidated(self) }.change_context(NdCvError)?;
+        Ok(MatRef::new(mat))
+    }
+}
+
+impl<T: bytemuck::Pod, S: ndarray::DataMut<Elem = T>, D: ndarray::Dimension> NdAsMatMut<T, D>
+    for ndarray::ArrayBase<S, D>
+{
+    fn as_single_channel_mat_mut(&mut self) -> Result<MatRefMut, NdCvError> {
+        let mat = unsafe { impls::ndarray_to_mat_regular(self) }.change_context(NdCvError)?;
+        Ok(MatRefMut::new(mat))
+    }
+
+    fn as_multi_channel_mat_mut(&mut self) -> Result<MatRefMut, NdCvError> {
+        let mat = unsafe { impls::ndarray_to_mat_consolidated(self) }.change_context(NdCvError)?;
+        Ok(MatRefMut::new(mat))
+    }
+}
+
+pub trait NdAsImage<T: bytemuck::Pod, D: ndarray::Dimension> {
+    fn as_image_mat(&self) -> Result<MatRef, NdCvError>;
+}
+
+pub trait NdAsImageMut<T: bytemuck::Pod, D: ndarray::Dimension> {
+    fn as_image_mat_mut(&mut self) -> Result<MatRefMut, NdCvError>;
+}
+
+impl<T, S> NdAsImage<T, Ix2> for ndarray::ArrayBase<S, Ix2>
+where
+    T: bytemuck::Pod + Copy,
+    S: ndarray::Data<Elem = T>,
+{
+    fn as_image_mat(&self) -> Result<MatRef, NdCvError> {
+        self.as_single_channel_mat()
+    }
+}
+
+impl<T, S> NdAsImageMut<T, Ix2> for ndarray::ArrayBase<S, Ix2>
+where
+    T: bytemuck::Pod + Copy,
+    S: ndarray::DataMut<Elem = T>,
+{
+    fn as_image_mat_mut(&mut self) -> Result<MatRefMut, NdCvError> {
+        self.as_single_channel_mat_mut()
+    }
+}
+
+impl<T, S> NdAsImage<T, Ix3> for ndarray::ArrayBase<S, Ix3>
+where
+    T: bytemuck::Pod + Copy,
+    S: ndarray::Data<Elem = T>,
+{
+    fn as_image_mat(&self) -> Result<MatRef, NdCvError> {
+        self.as_multi_channel_mat()
+    }
+}
+
+impl<T, S> NdAsImageMut<T, Ix3> for ndarray::ArrayBase<S, Ix3>
+where
+    T: bytemuck::Pod + Copy,
+    S: ndarray::DataMut<Elem = T>,
+{
+    fn as_image_mat_mut(&mut self) -> Result<MatRefMut, NdCvError> {
+        self.as_multi_channel_mat_mut()
+    }
+}
+
+// #[test]
+// fn test_1d_mat_to_ndarray() {
+//     let mat = opencv::core::Mat::new_nd_with_default(
+//         &[10],
+//         opencv::core::CV_MAKE_TYPE(opencv::core::CV_8U, 1),
+//         200.into(),
+//     )
+//     .expect("failed");
+//     let array: ndarray::ArrayView1<u8> = mat.as_ndarray().expect("failed");
+//     array.into_iter().for_each(|&x| assert_eq!(x, 200));
+// }
+
+// #[test]
+// fn test_2d_mat_to_ndarray() {
+//     let mat = opencv::core::Mat::new_nd_with_default(
+//         &[10],
+//         opencv::core::CV_16SC3,
+//         (200, 200, 200).into(),
+//     )
+//     .expect("failed");
+//     let array2: ndarray::ArrayView2<i16> = mat.as_ndarray().expect("failed");
+//     assert_eq!(array2.shape(), [10, 3]);
+//     array2.into_iter().for_each(|&x| {
+//         assert_eq!(x, 200);
+//     });
+// }
+
+// #[test]
+// fn test_3d_mat_to_ndarray() {
+//     let mat = opencv::core::Mat::new_nd_with_default(
+//         &[20, 30],
+//         opencv::core::CV_32FC3,
+//         (200, 200, 200).into(),
+//     )
+//     .expect("failed");
+//     let array2: ndarray::ArrayView3<f32> = mat.as_ndarray().expect("failed");
+//     array2.into_iter().for_each(|&x| {
+//         assert_eq!(x, 200f32);
+//     });
+// }
+
+// #[test]
+// fn test_mat_to_dyn_ndarray() {
+//     let mat = opencv::core::Mat::new_nd_with_default(&[10], opencv::core::CV_8UC1, 200.into())
+//         .expect("failed");
+//     let array2: ndarray::ArrayViewD<u8> = mat.as_ndarray().expect("failed");
+//     array2.into_iter().for_each(|&x| assert_eq!(x, 200));
+// }
+
+// #[test]
+// fn test_3d_mat_to_ndarray_4k() {
+//     let mat = opencv::core::Mat::new_nd_with_default(
+//         &[4096, 4096],
+//         opencv::core::CV_8UC3,
+//         (255, 0, 255).into(),
+//     )
+//     .expect("failed");
+//     let array2: ndarray::ArrayView3<u8> = (mat).as_ndarray().expect("failed");
+//     array2.exact_chunks((1, 1, 3)).into_iter().for_each(|x| {
+//         assert_eq!(x[(0, 0, 0)], 255);
+//         assert_eq!(x[(0, 0, 1)], 0);
+//         assert_eq!(x[(0, 0, 2)], 255);
+//     });
+// }
+
+// // #[test]
+// // fn test_3d_mat_to_ndarray_8k() {
+// //     let mat = opencv::core::Mat::new_nd_with_default(
+// //         &[8192, 8192],
+// //         opencv::core::CV_8UC3,
+// //         (255, 0, 255).into(),
+// //     )
+// //     .expect("failed");
+// //     let array2 = ndarray::Array3::<u8>::from_mat(mat).expect("failed");
+// //     array2.exact_chunks((1, 1, 3)).into_iter().for_each(|x| {
+// //         assert_eq!(x[(0, 0, 0)], 255);
+// //         assert_eq!(x[(0, 0, 1)], 0);
+// //         assert_eq!(x[(0, 0, 2)], 255);
+// //     });
+// // }
+
+// #[test]
+// pub fn test_mat_to_nd_default_strides() {
+//     let mat = opencv::core::Mat::new_rows_cols_with_default(
+//         10,
+//         10,
+//         opencv::core::CV_8UC3,
+//         opencv::core::VecN([10f64, 0.0, 0.0, 0.0]),
+//     )
+//     .expect("failed");
+//     let array = unsafe { impls::mat_to_ndarray::<u8, Ix3>(&mat) }.expect("failed");
+//     assert_eq!(array.shape(), [10, 10, 3]);
+//     assert_eq!(array.strides(), [30, 3, 1]);
+//     assert_eq!(array[(0, 0, 0)], 10);
+// }
+
+// #[test]
+// pub fn test_mat_to_nd_custom_strides() {
+//     let mat = opencv::core::Mat::new_rows_cols_with_default(
+//         10,
+//         10,
+//         opencv::core::CV_8UC3,
+//         opencv::core::VecN([10f64, 0.0, 0.0, 0.0]),
+//     )
+//     .unwrap();
+//     let mat_roi = opencv::core::Mat::roi(&mat, opencv::core::Rect::new(3, 2, 3, 5))
+//         .expect("failed to get roi");
+//     let array = unsafe { impls::mat_to_ndarray::<u8, Ix3>(&mat_roi) }.expect("failed");
+//     assert_eq!(array.shape(), [5, 3, 3]);
+//     assert_eq!(array.strides(), [30, 3, 1]);
+//     assert_eq!(array[(0, 0, 0)], 10);
+// }
+
+// #[test]
+// pub fn test_non_continuous_3d() {
+//     let array = ndarray::Array3::<f32>::from_shape_fn((10, 10, 4), |(i, j, k)| {
+//         ((i + 1) * (j + 1) * (k + 1)) as f32
+//     });
+//     let slice = array.slice(ndarray::s![3..7, 3..7, 0..4]);
+//     let mat = unsafe { impls::ndarray_to_mat_consolidated(&slice) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, Ix3>(&mat).unwrap() };
+//     assert!(slice == arr);
+// }
+
+// #[test]
+// pub fn test_5d_array() {
+//     let array = ndarray::Array5::<f32>::ones((1, 2, 3, 4, 5));
+//     let mat = unsafe { impls::ndarray_to_mat_consolidated(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix5>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// pub fn test_3d_array() {
+//     let array = ndarray::Array3::<f32>::ones((23, 31, 33));
+//     let mat = unsafe { impls::ndarray_to_mat_consolidated(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix3>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// pub fn test_2d_array() {
+//     let array = ndarray::Array2::<f32>::ones((23, 31));
+//     let mat = unsafe { impls::ndarray_to_mat_consolidated(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix2>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// #[should_panic]
+// pub fn test_1d_array_consolidated() {
+//     let array = ndarray::Array1::<f32>::ones(23);
+//     let mat = unsafe { impls::ndarray_to_mat_consolidated(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix1>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// pub fn test_1d_array_regular() {
+//     let array = ndarray::Array1::<f32>::ones(23);
+//     let mat = unsafe { impls::ndarray_to_mat_regular(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix1>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// pub fn test_2d_array_regular() {
+//     let array = ndarray::Array2::<f32>::ones((23, 31));
+//     let mat = unsafe { impls::ndarray_to_mat_regular(&array) }.unwrap();
+//     let arr = unsafe { impls::mat_to_ndarray::<f32, ndarray::Ix2>(&mat).unwrap() };
+//     assert_eq!(array, arr);
+// }
+
+// #[test]
+// pub fn test_ndcv_1024_1024_to_mat() {
+//     let array = ndarray::Array2::<f32>::ones((1024, 1024));
+//     let _mat = array.to_mat().unwrap();
+// }
--- a/ndcv-bridge/src/conversions/impls.rs
+++ b/ndcv-bridge/src/conversions/impls.rs
@@ -0,0 +1,168 @@
+use super::*;
+use core::ffi::*;
+use opencv::core::prelude::*;
+pub(crate) unsafe fn ndarray_to_mat_regular<
+    T,
+    S: ndarray::Data<Elem = T>,
+    D: ndarray::Dimension,
+>(
+    input: &ndarray::ArrayBase<S, D>,
+) -> Result<opencv::core::Mat, NdCvError> {
+    let shape = input.shape();
+    let strides = input.strides();
+
+    // let channels = shape.last().copied().unwrap_or(1);
+    // if channels > opencv::core::CV_CN_MAX as usize {
+    //     Err(Report::new(NdCvError).attach_printable(format!(
+    //             "Number of channels({channels}) exceeds CV_CN_MAX({}) use the regular version of the function", opencv::core::CV_CN_MAX
+    //         )))?;
+    // }
+
+    // let size_len = shape.len();
+    let size = shape.iter().copied().map(|f| f as i32).collect::<Vec<_>>();
+    // Step len for ndarray is always 1 less than ndims
+    let step_len = strides.len() - 1;
+    let step = strides
+        .iter()
+        .take(step_len)
+        .copied()
+        .map(|f| f as usize * core::mem::size_of::<T>())
+        .collect::<Vec<_>>();
+
+    let data_ptr = input.as_ptr() as *const c_void;
+
+    let typ = opencv::core::CV_MAKETYPE(type_depth::<T>(), 1);
+    let mat = opencv::core::Mat::new_nd_with_data_unsafe(
+        size.as_slice(),
+        typ,
+        data_ptr.cast_mut(),
+        Some(step.as_slice()),
+    )
+    .change_context(NdCvError)?;
+
+    Ok(mat)
+}
+
+pub(crate) unsafe fn ndarray_to_mat_consolidated<
+    T,
+    S: ndarray::Data<Elem = T>,
+    D: ndarray::Dimension,
+>(
+    input: &ndarray::ArrayBase<S, D>,
+) -> Result<opencv::core::Mat, NdCvError> {
+    let shape = input.shape();
+    let strides = input.strides();
+
+    let channels = shape.last().copied().unwrap_or(1);
+    if channels > opencv::core::CV_CN_MAX as usize {
+        Err(Report::new(NdCvError).attach_printable(format!(
+                "Number of channels({channels}) exceeds CV_CN_MAX({}) use the regular version of the function", opencv::core::CV_CN_MAX
+            )))?;
+    }
+
+    if shape.len() > 2 {
+        // Basically the second last stride is used to jump from one column to next
+        // But opencv only keeps ndims - 1 strides so we can't have the column stride as that
+        // will be lost
+        if shape.last() != strides.get(strides.len() - 2).map(|x| *x as usize).as_ref() {
+            Err(Report::new(NdCvError).attach_printable(
+                "You cannot slice into the last axis in ndarray when converting to mat",
+            ))?;
+        }
+    } else if shape.len() == 1 {
+        return Err(Report::new(NdCvError).attach_printable(
+            "You cannot convert a 1D array to a Mat while using the consolidated version",
+        ));
+    }
+
+    // Since this is the consolidated version we should always only have ndims - 1 sizes and
+    // ndims - 2 strides
+
+    let size_len = shape.len() - 1; // Since we move last axis into the channel
+    let size = shape
+        .iter()
+        .take(size_len)
+        .map(|f| *f as i32)
+        .collect::<Vec<_>>();
+
+    let step_len = strides.len() - 1;
+    let step = strides
+        .iter()
+        .take(step_len)
+        .map(|f| *f as usize * core::mem::size_of::<T>())
+        .collect::<Vec<_>>();
+
+    let data_ptr = input.as_ptr() as *const c_void;
+
+    let typ = opencv::core::CV_MAKETYPE(type_depth::<T>(), channels as i32);
+
+    let mat = opencv::core::Mat::new_nd_with_data_unsafe(
+        size.as_slice(),
+        typ,
+        data_ptr.cast_mut(),
+        Some(step.as_slice()),
+    )
+    .change_context(NdCvError)?;
+
+    Ok(mat)
+}
+
+pub(crate) unsafe fn mat_to_ndarray<T: bytemuck::Pod, D: ndarray::Dimension>(
+    mat: &opencv::core::Mat,
+) -> Result<ndarray::ArrayView<'_, T, D>, NdCvError> {
+    let depth = mat.depth();
+    if type_depth::<T>() != depth {
+        return Err(Report::new(NdCvError).attach_printable(format!(
+            "Expected type Mat<{}> ({}), got Mat<{}> ({})",
+            std::any::type_name::<T>(),
+            type_depth::<T>(),
+            crate::depth_type(depth),
+            depth,
+        )));
+    }
+
+    // Since a dims always returns >= 2 we can't use this to check if it's a 1D array
+    // So we compare the first axis to the total to see if its a 1D array
+    let is_1d = mat.total() as i32 == mat.rows();
+    let dims = is_1d.then_some(1).unwrap_or(mat.dims());
+    let channels = mat.channels();
+    let ndarray_size = (channels != 1).then_some(dims + 1).unwrap_or(dims) as usize;
+    if let Some(ndim) = D::NDIM {
+        // When channels is not 1,
+        // the last dimension is the channels
+        // Array1 -> Mat(ndims = 1, channels = 1)
+        // Array2 -> Mat(ndims = 1, channels = X)
+        // Array2 -> Mat(ndims = 2, channels = 1)
+        // Array3 -> Mat(ndims = 2, channels = X)
+        // Array3 -> Mat(ndims = 3, channels = 1)
+        // ...
+        if ndim != dims as usize && channels == 1 {
+            return Err(Report::new(NdCvError)
+                .attach_printable(format!("Expected {}D array, got {}D", ndim, ndarray_size)));
+        }
+    }
+
+    let mat_size = mat.mat_size();
+    let sizes = (0..dims)
+        .map(|i| mat_size.get(i).change_context(NdCvError))
+        .chain([Ok(channels)])
+        .map(|x| x.map(|x| x as usize))
+        .take(ndarray_size)
+        .collect::<Result<Vec<_>, NdCvError>>()
+        .change_context(NdCvError)?;
+    let strides = (0..(dims - 1))
+        .map(|i| mat.step1(i).change_context(NdCvError))
+        .chain([
+            Ok(channels as usize),
+            Ok((channels == 1).then_some(0).unwrap_or(1)),
+        ])
+        .take(ndarray_size)
+        .collect::<Result<Vec<_>, NdCvError>>()
+        .change_context(NdCvError)?;
+    use ndarray::ShapeBuilder;
+    let shape = sizes.strides(strides);
+    let raw_array = ndarray::RawArrayView::from_shape_ptr(shape, mat.data() as *const T)
+        .into_dimensionality()
+        .change_context(NdCvError)?;
+    Ok(unsafe { raw_array.deref_into_view() })
+}
--- a/ndcv-bridge/src/conversions/matref.rs
+++ b/ndcv-bridge/src/conversions/matref.rs
@@ -0,0 +1,73 @@
+#[derive(Debug, Clone)]
+pub struct MatRef<'a> {
+    pub(crate) mat: opencv::core::Mat,
+    pub(crate) _marker: core::marker::PhantomData<&'a ()>,
+}
+
+impl MatRef<'_> {
+    pub fn clone_pointee(&self) -> opencv::core::Mat {
+        self.mat.clone()
+    }
+}
+
+impl MatRef<'_> {
+    pub fn new<'a>(mat: opencv::core::Mat) -> MatRef<'a> {
+        MatRef {
+            mat,
+            _marker: core::marker::PhantomData,
+        }
+    }
+}
+
+impl AsRef<opencv::core::Mat> for MatRef<'_> {
+    fn as_ref(&self) -> &opencv::core::Mat {
+        &self.mat
+    }
+}
+
+impl AsRef<opencv::core::Mat> for MatRefMut<'_> {
+    fn as_ref(&self) -> &opencv::core::Mat {
+        &self.mat
+    }
+}
+
+impl AsMut<opencv::core::Mat> for MatRefMut<'_> {
+    fn as_mut(&mut self) -> &mut opencv::core::Mat {
+        &mut self.mat
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct MatRefMut<'a> {
+    pub(crate) mat: opencv::core::Mat,
+    pub(crate) _marker: core::marker::PhantomData<&'a mut ()>,
+}
+
+impl MatRefMut<'_> {
+    pub fn new<'a>(mat: opencv::core::Mat) -> MatRefMut<'a> {
+        MatRefMut {
+            mat,
+            _marker: core::marker::PhantomData,
+        }
+    }
+}
+
+impl core::ops::Deref for MatRef<'_> {
+    type Target = opencv::core::Mat;
+    fn deref(&self) -> &Self::Target {
+        &self.mat
+    }
+}
+
+impl core::ops::Deref for MatRefMut<'_> {
+    type Target = opencv::core::Mat;
+    fn deref(&self) -> &Self::Target {
+        &self.mat
+    }
+}
+
+impl core::ops::DerefMut for MatRefMut<'_> {
+    fn deref_mut(&mut self) -> &mut Self::Target {
+        &mut self.mat
+    }
+}
--- a/ndcv-bridge/src/fir.rs
+++ b/ndcv-bridge/src/fir.rs
@@ -0,0 +1,262 @@
+use error_stack::*;
+use fast_image_resize::*;
+use images::{Image, ImageRef};
+#[derive(Debug, Clone, thiserror::Error)]
+#[error("NdFirError")]
+pub struct NdFirError;
+type Result<T, E = Report<NdFirError>> = std::result::Result<T, E>;
+
+pub trait NdAsImage<T: seal::Sealed, D: ndarray::Dimension>: Sized {
+    fn as_image_ref(&self) -> Result<ImageRef<'_>>;
+}
+
+pub trait NdAsImageMut<T: seal::Sealed, D: ndarray::Dimension>: Sized {
+    fn as_image_ref_mut(&mut self) -> Result<Image<'_>>;
+}
+
+pub struct NdarrayImageContainer<'a, T: seal::Sealed, D: ndarray::Dimension> {
+    #[allow(dead_code)]
+    data: ndarray::ArrayView<'a, T, D>,
+    pub _phantom: std::marker::PhantomData<(T, D)>,
+}
+
+impl<'a, T: seal::Sealed> NdarrayImageContainer<'a, T, ndarray::Ix3> {
+    pub fn new<S: ndarray::Data<Elem = T>>(array: &'a ndarray::ArrayBase<S, ndarray::Ix3>) -> Self {
+        Self {
+            data: array.view(),
+            _phantom: std::marker::PhantomData,
+        }
+    }
+}
+
+impl<'a, T: seal::Sealed> NdarrayImageContainer<'a, T, ndarray::Ix2> {
+    pub fn new<S: ndarray::Data<Elem = T>>(array: &'a ndarray::ArrayBase<S, ndarray::Ix2>) -> Self {
+        Self {
+            data: array.view(),
+            _phantom: std::marker::PhantomData,
+        }
+    }
+}
+pub struct NdarrayImageContainerMut<'a, T: seal::Sealed, D: ndarray::Dimension> {
+    #[allow(dead_code)]
+    data: ndarray::ArrayViewMut<'a, T, D>,
+}
+
+impl<'a, T: seal::Sealed> NdarrayImageContainerMut<'a, T, ndarray::Ix3> {
+    pub fn new<S: ndarray::DataMut<Elem = T>>(
+        array: &'a mut ndarray::ArrayBase<S, ndarray::Ix3>,
+    ) -> Self {
+        Self {
+            data: array.view_mut(),
+        }
+    }
+}
+
+impl<'a, T: seal::Sealed> NdarrayImageContainerMut<'a, T, ndarray::Ix2> {
+    pub fn new<S: ndarray::DataMut<Elem = T>>(
+        array: &'a mut ndarray::ArrayBase<S, ndarray::Ix2>,
+    ) -> Self {
+        Self {
+            data: array.view_mut(),
+        }
+    }
+}
+
+pub struct NdarrayImageContainerTyped<'a, T: seal::Sealed, D: ndarray::Dimension, P: PixelTrait> {
+    #[allow(dead_code)]
+    data: ndarray::ArrayView<'a, T, D>,
+    __marker: std::marker::PhantomData<P>,
+}
+
+// unsafe impl<'a, T: seal::Sealed + Sync + InnerPixel, P: PixelTrait> ImageView
+//     for NdarrayImageContainerTyped<'a, T, ndarray::Ix3, P>
+// where
+//     T: bytemuck::Pod,
+// {
+//     type Pixel = P;
+//     fn width(&self) -> u32 {
+//         self.data.shape()[1] as u32
+//     }
+//     fn height(&self) -> u32 {
+//         self.data.shape()[0] as u32
+//     }
+//     fn iter_rows(&self, start_row: u32) -> impl Iterator<Item = &[Self::Pixel]> {
+//         self.data
+//             .rows()
+//             .into_iter()
+//             .skip(start_row as usize)
+//             .map(|row| {
+//                 row.as_slice()
+//                     .unwrap_or_default()
+//                     .chunks_exact(P::CHANNELS as usize)
+//             })
+//     }
+// }
+
+// impl<'a, T: fast_image_resize::pixels::InnerPixel + seal::Sealed, D: ndarray::Dimension>
+//     fast_image_resize::IntoImageView for NdarrayImageContainer<'a, T, D>
+// {
+//     fn pixel_type(&self) -> Option<PixelType> {
+//         match D::NDIM {
+//             Some(2) => Some(to_pixel_type::<T>(1).expect("Failed to convert to pixel type")),
+//             Some(3) => Some(
+//                 to_pixel_type::<T>(self.data.shape()[2]).expect("Failed to convert to pixel type"),
+//             ),
+//             _ => None,
+//         }
+//     }
+//     fn width(&self) -> u32 {
+//         self.data.shape()[1] as u32
+//     }
+//     fn height(&self) -> u32 {
+//         self.data.shape()[0] as u32
+//     }
+//     fn image_view<P: PixelTrait>(&'a self) -> Option<NdarrayImageContainerTyped<'a, T, D, P>> {
+//         Some(NdarrayImageContainerTyped {
+//             data: self.data.view(),
+//             __marker: std::marker::PhantomData,
+//         })
+//     }
+// }
+
+pub fn to_pixel_type<T: seal::Sealed>(u: usize) -> Result<PixelType> {
+    match (core::any::type_name::<T>(), u) {
+        ("u8", 1) => Ok(PixelType::U8),
+        ("u8", 2) => Ok(PixelType::U8x2),
+        ("u8", 3) => Ok(PixelType::U8x3),
+        ("u8", 4) => Ok(PixelType::U8x4),
+        ("u16", 1) => Ok(PixelType::U16),
+        ("i32", 1) => Ok(PixelType::I32),
+        ("f32", 1) => Ok(PixelType::F32),
+        ("f32", 2) => Ok(PixelType::F32x2),
+        ("f32", 3) => Ok(PixelType::F32x3),
+        ("f32", 4) => Ok(PixelType::F32x4),
+        _ => Err(Report::new(NdFirError).attach_printable("Unsupported pixel type")),
+    }
+}
+
+mod seal {
+    pub trait Sealed {}
+    impl Sealed for u8 {}
+    impl Sealed for u16 {}
+    impl Sealed for i32 {}
+    impl Sealed for f32 {}
+}
+
+impl<S: ndarray::Data<Elem = T>, T: seal::Sealed + bytemuck::Pod, D: ndarray::Dimension>
+    NdAsImage<T, D> for ndarray::ArrayBase<S, D>
+{
+    /// Clones self and makes a new image
+    fn as_image_ref(&self) -> Result<ImageRef> {
+        let shape = self.shape();
+        let rows = *shape
+            .first()
+            .ok_or_else(|| Report::new(NdFirError).attach_printable("Failed to get rows"))?
+            as u32;
+        let cols = *shape.get(1).unwrap_or(&1) as u32;
+        let channels = *shape.get(2).unwrap_or(&1);
+        let data = self
+            .as_slice()
+            .ok_or(NdFirError)
+            .attach_printable("The ndarray is non continuous")?;
+        let data_bytes: &[u8] = bytemuck::cast_slice(data);
+
+        let pixel_type = to_pixel_type::<T>(channels)?;
+        ImageRef::new(cols, rows, data_bytes, pixel_type)
+            .change_context(NdFirError)
+            .attach_printable("Failed to create Image from ndarray")
+    }
+}
+
+impl<S: ndarray::DataMut<Elem = T>, T: seal::Sealed + bytemuck::Pod, D: ndarray::Dimension>
+    NdAsImageMut<T, D> for ndarray::ArrayBase<S, D>
+{
+    fn as_image_ref_mut(&mut self) -> Result<Image<'_>>
+    where
+        S: ndarray::DataMut<Elem = T>,
+    {
+        let shape = self.shape();
+        let rows = *shape
+            .first()
+            .ok_or_else(|| Report::new(NdFirError).attach_printable("Failed to get rows"))?
+            as u32;
+        let cols = *shape.get(1).unwrap_or(&1) as u32;
+        let channels = *shape.get(2).unwrap_or(&1);
+        let data = self
+            .as_slice_mut()
+            .ok_or(NdFirError)
+            .attach_printable("The ndarray is non continuous")?;
+        let data_bytes: &mut [u8] = bytemuck::cast_slice_mut(data);
+
+        let pixel_type = to_pixel_type::<T>(channels)?;
+        Image::from_slice_u8(cols, rows, data_bytes, pixel_type)
+            .change_context(NdFirError)
+            .attach_printable("Failed to create Image from ndarray")
+    }
+}
+
+pub trait NdFir<T, D> {
+    fn fast_resize<'o>(
+        &self,
+        height: usize,
+        width: usize,
+        options: impl Into<Option<&'o ResizeOptions>>,
+    ) -> Result<ndarray::Array<T, D>>;
+}
+
+impl<T: seal::Sealed + bytemuck::Pod + num::Zero, S: ndarray::Data<Elem = T>> NdFir<T, ndarray::Ix3>
+    for ndarray::ArrayBase<S, ndarray::Ix3>
+{
+    fn fast_resize<'o>(
+        &self,
+        height: usize,
+        width: usize,
+        options: impl Into<Option<&'o ResizeOptions>>,
+    ) -> Result<ndarray::Array3<T>> {
+        let source = self.as_image_ref()?;
+        let (_height, _width, channels) = self.dim();
+        let mut dest = ndarray::Array3::<T>::zeros((height, width, channels));
+        let mut dest_image = dest.as_image_ref_mut()?;
+        let mut resizer = fast_image_resize::Resizer::default();
+        resizer
+            .resize(&source, &mut dest_image, options)
+            .change_context(NdFirError)?;
+        Ok(dest)
+    }
+}
+
+impl<T: seal::Sealed + bytemuck::Pod + num::Zero, S: ndarray::Data<Elem = T>> NdFir<T, ndarray::Ix2>
+    for ndarray::ArrayBase<S, ndarray::Ix2>
+{
+    fn fast_resize<'o>(
+        &self,
+        height: usize,
+        width: usize,
+        options: impl Into<Option<&'o ResizeOptions>>,
+    ) -> Result<ndarray::Array<T, ndarray::Ix2>> {
+        let source = self.as_image_ref()?;
+        let (_height, _width) = self.dim();
+        let mut dest = ndarray::Array::<T, ndarray::Ix2>::zeros((height, width));
+        let mut dest_image = dest.as_image_ref_mut()?;
+        let mut resizer = fast_image_resize::Resizer::default();
+        resizer
+            .resize(&source, &mut dest_image, options)
+            .change_context(NdFirError)?;
+        Ok(dest)
+    }
+}
+
+#[test]
+pub fn test_ndarray_fast_image_resize_u8() {
+    let source_fhd = ndarray::Array3::<u8>::ones((1920, 1080, 3));
+    let mut resized_hd = ndarray::Array3::<u8>::zeros((1280, 720, 3));
+    let mut resizer = fast_image_resize::Resizer::default();
+    resizer
+        .resize(
+            &source_fhd.as_image_ref().unwrap(),
+            &mut resized_hd.as_image_ref_mut().unwrap(),
+            None,
+        )
+        .unwrap();
+    assert_eq!(resized_hd.shape(), [1280, 720, 3]);
+}
--- a/ndcv-bridge/src/gaussian.rs
+++ b/ndcv-bridge/src/gaussian.rs
@@ -0,0 +1,307 @@
+//! <https://docs.rs/opencv/latest/opencv/imgproc/fn.gaussian_blur.html>
+use crate::conversions::*;
+use crate::prelude_::*;
+use ndarray::*;
+
+#[repr(C)]
+#[derive(Default, Debug, Copy, Clone)]
+pub enum BorderType {
+    #[default]
+    BorderConstant = 0,
+    BorderReplicate = 1,
+    BorderReflect = 2,
+    BorderWrap = 3,
+    BorderReflect101 = 4,
+    BorderTransparent = 5,
+    BorderIsolated = 16,
+}
+
+#[repr(C)]
+#[derive(Default, Debug, Copy, Clone)]
+pub enum AlgorithmHint {
+    #[default]
+    AlgoHintDefault = 0,
+    AlgoHintAccurate = 1,
+    AlgoHintApprox = 2,
+}
+
+mod seal {
+    pub trait Sealed {}
+    // src: input image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
+    impl Sealed for u8 {}
+    impl Sealed for u16 {}
+    impl Sealed for i16 {}
+    impl Sealed for f32 {}
+    impl Sealed for f64 {}
+}
+
+pub trait NdCvGaussianBlur<T: bytemuck::Pod + seal::Sealed, D: ndarray::Dimension>:
+    crate::image::NdImage + crate::conversions::NdAsImage<T, D>
+{
+    fn gaussian_blur(
+        &self,
+        kernel_size: (u8, u8),
+        sigma_x: f64,
+        sigma_y: f64,
+        border_type: BorderType,
+    ) -> Result<ndarray::Array<T, D>, NdCvError>;
+    fn gaussian_blur_def(
+        &self,
+        kernel: (u8, u8),
+        sigma_x: f64,
+    ) -> Result<ndarray::Array<T, D>, NdCvError> {
+        self.gaussian_blur(kernel, sigma_x, sigma_x, BorderType::BorderConstant)
+    }
+}
+
+impl<
+        T: bytemuck::Pod + num::Zero + seal::Sealed,
+        S: ndarray::RawData + ndarray::Data<Elem = T>,
+        D: ndarray::Dimension,
+    > NdCvGaussianBlur<T, D> for ArrayBase<S, D>
+where
+    ndarray::ArrayBase<S, D>: crate::image::NdImage + crate::conversions::NdAsImage<T, D>,
+    ndarray::Array<T, D>: crate::conversions::NdAsImageMut<T, D>,
+{
+    fn gaussian_blur(
+        &self,
+        kernel_size: (u8, u8),
+        sigma_x: f64,
+        sigma_y: f64,
+        border_type: BorderType,
+    ) -> Result<ndarray::Array<T, D>, NdCvError> {
+        let mut dst = ndarray::Array::zeros(self.dim());
+        let cv_self = self.as_image_mat()?;
+        let mut cv_dst = dst.as_image_mat_mut()?;
+        opencv::imgproc::gaussian_blur(
+            &*cv_self,
+            &mut *cv_dst,
+            opencv::core::Size {
+                width: kernel_size.0 as i32,
+                height: kernel_size.1 as i32,
+            },
+            sigma_x,
+            sigma_y,
+            border_type as i32,
+        )
+        .change_context(NdCvError)
+        .attach_printable("Failed to apply gaussian blur")?;
+        Ok(dst)
+    }
+}
+
+// impl<
+//         T: bytemuck::Pod + num::Zero + seal::Sealed,
+//         S: ndarray::RawData + ndarray::Data<Elem = T>,
+//     > NdCvGaussianBlur<T, Ix3> for ArrayBase<S, Ix3>
+// {
+//     fn gaussian_blur(
+//         &self,
+//         kernel_size: (u8, u8),
+//         sigma_x: f64,
+//         sigma_y: f64,
+//         border_type: BorderType,
+//     ) -> Result<ndarray::Array<T, Ix3>, NdCvError> {
+//         let mut dst = ndarray::Array::zeros(self.dim());
+//         let cv_self = self.as_image_mat()?;
+//         let mut cv_dst = dst.as_image_mat_mut()?;
+//         opencv::imgproc::gaussian_blur(
+//             &*cv_self,
+//             &mut *cv_dst,
+//             opencv::core::Size {
+//                 width: kernel_size.0 as i32,
+//                 height: kernel_size.1 as i32,
+//             },
+//             sigma_x,
+//             sigma_y,
+//             border_type as i32,
+//         )
+//         .change_context(NdCvError)
+//         .attach_printable("Failed to apply gaussian blur")?;
+//         Ok(dst)
+//     }
+// }
+//
+// impl<
+//         T: bytemuck::Pod + num::Zero + seal::Sealed,
+//         S: ndarray::RawData + ndarray::Data<Elem = T>,
+//     > NdCvGaussianBlur<T, Ix2> for ArrayBase<S, Ix2>
+// {
+//     fn gaussian_blur(
+//         &self,
+//         kernel_size: (u8, u8),
+//         sigma_x: f64,
+//         sigma_y: f64,
+//         border_type: BorderType,
+//     ) -> Result<ndarray::Array<T, Ix2>, NdCvError> {
+//         let mut dst = ndarray::Array::zeros(self.dim());
+//         let cv_self = self.as_image_mat()?;
+//         let mut cv_dst = dst.as_image_mat_mut()?;
+//         opencv::imgproc::gaussian_blur(
+//             &*cv_self,
+//             &mut *cv_dst,
+//             opencv::core::Size {
+//                 width: kernel_size.0 as i32,
+//                 height: kernel_size.1 as i32,
+//             },
+//             sigma_x,
+//             sigma_y,
+//             border_type as i32,
+//         )
+//         .change_context(NdCvError)
+//         .attach_printable("Failed to apply gaussian blur")?;
+//         Ok(dst)
+//     }
+// }
+
+/// For smaller values it is faster to use the allocated version
+/// For example in a 4k f32 image this is about 50% faster than the allocated one
+pub trait NdCvGaussianBlurInPlace<T: bytemuck::Pod + seal::Sealed, D: ndarray::Dimension>:
+    crate::image::NdImage + crate::conversions::NdAsImageMut<T, D>
+{
+    fn gaussian_blur_inplace(
+        &mut self,
+        kernel_size: (u8, u8),
+        sigma_x: f64,
+        sigma_y: f64,
+        border_type: BorderType,
+    ) -> Result<&mut Self, NdCvError>;
+    fn gaussian_blur_def_inplace(
+        &mut self,
+        kernel: (u8, u8),
+        sigma_x: f64,
+    ) -> Result<&mut Self, NdCvError> {
+        self.gaussian_blur_inplace(kernel, sigma_x, sigma_x, BorderType::BorderConstant)
+    }
+}
+
+impl<
+        T: bytemuck::Pod + num::Zero + seal::Sealed,
+        S: ndarray::RawData + ndarray::DataMut<Elem = T>,
+        D: ndarray::Dimension,
+    > NdCvGaussianBlurInPlace<T, D> for ArrayBase<S, D>
+where
+    Self: crate::image::NdImage + crate::conversions::NdAsImageMut<T, D>,
+{
+    fn gaussian_blur_inplace(
+        &mut self,
+        kernel_size: (u8, u8),
+        sigma_x: f64,
+        sigma_y: f64,
+        border_type: BorderType,
+    ) -> Result<&mut Self, NdCvError> {
+        let mut cv_self = self.as_image_mat_mut()?;
+
+        unsafe {
+            crate::inplace::op_inplace(&mut *cv_self, |this, out| {
+                opencv::imgproc::gaussian_blur(
+                    this,
+                    out,
+                    opencv::core::Size {
+                        width: kernel_size.0 as i32,
+                        height: kernel_size.1 as i32,
+                    },
+                    sigma_x,
+                    sigma_y,
+                    border_type as i32,
+                )
+            })
+        }
+        .change_context(NdCvError)
+        .attach_printable("Failed to apply gaussian blur")?;
+        Ok(self)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::Array3;
+
+    #[test]
+    fn test_gaussian_basic() {
+        let arr = Array3::<u8>::ones((10, 10, 3));
+        let kernel_size = (3, 3);
+        let sigma_x = 0.0;
+        let sigma_y = 0.0;
+        let border_type = BorderType::BorderConstant;
+        let res = arr
+            .gaussian_blur(kernel_size, sigma_x, sigma_y, border_type)
+            .unwrap();
+        assert_eq!(res.shape(), &[10, 10, 3]);
+    }
+
+    #[test]
+    fn test_gaussian_edge_preservation() {
+        // Create an image with a sharp edge
+        let mut arr = Array3::<u8>::zeros((10, 10, 3));
+        arr.slice_mut(s![..5, .., ..]).fill(255); // Top half white, bottom half black
+
+        let res = arr
+            .gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+            .unwrap();
+
+        // Check that the middle row (edge) has intermediate values
+        let middle_row = res.slice(s![4..6, 5, 0]);
+        assert!(middle_row.iter().all(|&x| x > 0 && x < 255));
+    }
+
+    #[test]
+    fn test_gaussian_different_kernel_sizes() {
+        let arr = Array3::<u8>::ones((20, 20, 3));
+
+        // Test different kernel sizes
+        let kernel_sizes = [(3, 3), (5, 5), (7, 7)];
+        for &kernel_size in &kernel_sizes {
+            let res = arr
+                .gaussian_blur(kernel_size, 1.0, 1.0, BorderType::BorderConstant)
+                .unwrap();
+            assert_eq!(res.shape(), &[20, 20, 3]);
+        }
+    }
+
+    #[test]
+    fn test_gaussian_different_border_types() {
+        let mut arr = Array3::<u8>::zeros((10, 10, 3));
+        arr.slice_mut(s![4..7, 4..7, ..]).fill(255); // White square in center
+
+        let border_types = [
+            BorderType::BorderConstant,
+            BorderType::BorderReplicate,
+            BorderType::BorderReflect,
+            BorderType::BorderReflect101,
+        ];
+
+        for border_type in border_types {
+            let res = arr.gaussian_blur((3, 3), 1.0, 1.0, border_type).unwrap();
+            assert_eq!(res.shape(), &[10, 10, 3]);
+        }
+    }
+
+    #[test]
+    fn test_gaussian_different_types() {
+        // Test with different numeric types
+        let arr_u8 = Array3::<u8>::ones((10, 10, 3));
+        let arr_f32 = Array3::<f32>::ones((10, 10, 3));
+
+        let res_u8 = arr_u8
+            .gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+            .unwrap();
+        let res_f32 = arr_f32
+            .gaussian_blur((3, 3), 1.0, 1.0, BorderType::BorderConstant)
+            .unwrap();
+
+        assert_eq!(res_u8.shape(), &[10, 10, 3]);
+        assert_eq!(res_f32.shape(), &[10, 10, 3]);
+    }
+
+    #[test]
+    #[should_panic]
+    fn test_gaussian_invalid_kernel_size() {
+        let arr = Array3::<u8>::ones((10, 10, 3));
+        // Even kernel sizes should fail
+        let _ = arr
+            .gaussian_blur((2, 2), 1.0, 1.0, BorderType::BorderConstant)
+            .unwrap();
+    }
+}
--- a/ndcv-bridge/src/image.rs
+++ b/ndcv-bridge/src/image.rs
@@ -0,0 +1,30 @@
+use ndarray::*;
+pub trait NdImage {
+    fn width(&self) -> usize;
+    fn height(&self) -> usize;
+    fn channels(&self) -> usize;
+}
+
+impl<T, S: RawData<Elem = T>> NdImage for ArrayBase<S, Ix3> {
+    fn width(&self) -> usize {
+        self.dim().1
+    }
+    fn height(&self) -> usize {
+        self.dim().0
+    }
+    fn channels(&self) -> usize {
+        self.dim().2
+    }
+}
+
+impl<T, S: RawData<Elem = T>> NdImage for ArrayBase<S, Ix2> {
+    fn width(&self) -> usize {
+        self.dim().1
+    }
+    fn height(&self) -> usize {
+        self.dim().0
+    }
+    fn channels(&self) -> usize {
+        1
+    }
+}
--- a/ndcv-bridge/src/inplace.rs
+++ b/ndcv-bridge/src/inplace.rs
@@ -0,0 +1,14 @@
+use opencv::core::Mat;
+use opencv::prelude::*;
+use opencv::Result;
+
+#[inline(always)]
+pub(crate) unsafe fn op_inplace<T>(
+    m: &mut Mat,
+    f: impl FnOnce(&Mat, &mut Mat) -> Result<T>,
+) -> Result<T> {
+    let mut m_alias = Mat::from_raw(m.as_raw_mut());
+    let out = f(m, &mut m_alias);
+    let _ = m_alias.into_raw();
+    out
+}
--- a/ndcv-bridge/src/lib.rs
+++ b/ndcv-bridge/src/lib.rs
@@ -0,0 +1,83 @@
+//! Methods and type conversions for ndarray to opencv and vice versa
+mod blend;
+// mod dilate;
+pub mod fir;
+mod image;
+mod inplace;
+pub mod percentile;
+mod roi;
+
+#[cfg(feature = "opencv")]
+pub mod bounding_rect;
+// #[cfg(feature = "opencv")]
+// pub mod color_space;
+#[cfg(feature = "opencv")]
+pub mod connected_components;
+#[cfg(feature = "opencv")]
+pub mod contours;
+#[cfg(feature = "opencv")]
+pub mod conversions;
+// #[cfg(feature = "opencv")]
+// pub mod gaussian;
+#[cfg(feature = "opencv")]
+pub mod resize;
+
+pub mod codec;
+pub mod orient;
+pub use blend::NdBlend;
+pub use fast_image_resize::{FilterType, ResizeAlg, ResizeOptions, Resizer};
+pub use fir::NdFir;
+// pub use gaussian::{BorderType, NdCvGaussianBlur, NdCvGaussianBlurInPlace};
+pub use roi::{NdRoi, NdRoiMut, NdRoiZeroPadded};
+
+#[cfg(feature = "opencv")]
+pub use contours::{
+    ContourApproximationMethod, ContourHierarchy, ContourResult, ContourRetrievalMode,
+    NdCvContourArea, NdCvFindContours,
+};
+
+#[cfg(feature = "opencv")]
+pub use bounding_rect::BoundingRect;
+#[cfg(feature = "opencv")]
+pub use connected_components::{Connectivity, NdCvConnectedComponents};
+#[cfg(feature = "opencv")]
+pub use conversions::{MatAsNd, NdAsImage, NdAsImageMut, NdAsMat, NdAsMatMut, NdCvConversion};
+#[cfg(feature = "opencv")]
+pub use resize::{Interpolation, NdCvResize};
+
+pub(crate) mod prelude_ {
+    pub use crate::NdCvError;
+    pub use error_stack::*;
+}
+
+#[derive(Debug, thiserror::Error)]
+#[error("NdCvError")]
+pub struct NdCvError;
+
+#[cfg(feature = "opencv")]
+pub fn type_depth<T>() -> i32 {
+    match std::any::type_name::<T>() {
+        "u8" => opencv::core::CV_8U,
+        "i8" => opencv::core::CV_8S,
+        "u16" => opencv::core::CV_16U,
+        "i16" => opencv::core::CV_16S,
+        "i32" => opencv::core::CV_32S,
+        "f32" => opencv::core::CV_32F,
+        "f64" => opencv::core::CV_64F,
+        _ => panic!("Unsupported type"),
+    }
+}
+
+#[cfg(feature = "opencv")]
+pub fn depth_type(depth: i32) -> &'static str {
+    match depth {
+        opencv::core::CV_8U => "u8",
+        opencv::core::CV_8S => "i8",
+        opencv::core::CV_16U => "u16",
+        opencv::core::CV_16S => "i16",
+        opencv::core::CV_32S => "i32",
+        opencv::core::CV_32F => "f32",
+        opencv::core::CV_64F => "f64",
+        _ => panic!("Unsupported depth"),
+    }
+}
--- a/ndcv-bridge/src/orient.rs
+++ b/ndcv-bridge/src/orient.rs
@@ -0,0 +1,188 @@
+use ndarray::{Array, ArrayBase, ArrayView};
+
+#[derive(Clone, Copy)]
+pub enum Orientation {
+    NoRotation,
+    Mirror,
+    Clock180,
+    Water,
+    MirrorClock270,
+    Clock90,
+    MirrorClock90,
+    Clock270,
+    Unknown,
+}
+
+impl Orientation {
+    pub fn inverse(&self) -> Self {
+        match self {
+            Self::Clock90 => Self::Clock270,
+            Self::Clock270 => Self::Clock90,
+            _ => *self,
+        }
+    }
+}
+
+impl Orientation {
+    pub fn from_raw(flip: u8) -> Self {
+        match flip {
+            1 => Orientation::NoRotation,
+            2 => Orientation::Mirror,
+            3 => Orientation::Clock180,
+            4 => Orientation::Water,
+            5 => Orientation::MirrorClock270,
+            6 => Orientation::Clock90,
+            7 => Orientation::MirrorClock90,
+            8 => Orientation::Clock270,
+            _ => Orientation::Unknown,
+        }
+    }
+}
+
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum RotationFlag {
+    Clock90,
+    Clock180,
+    Clock270,
+}
+
+impl RotationFlag {
+    pub fn neg(&self) -> Self {
+        match self {
+            RotationFlag::Clock90 => RotationFlag::Clock270,
+            RotationFlag::Clock180 => RotationFlag::Clock180,
+            RotationFlag::Clock270 => RotationFlag::Clock90,
+        }
+    }
+
+    pub fn to_orientation(&self) -> Orientation {
+        match self {
+            RotationFlag::Clock90 => Orientation::Clock90,
+            RotationFlag::Clock180 => Orientation::Clock180,
+            RotationFlag::Clock270 => Orientation::Clock270,
+        }
+    }
+}
+
+#[derive(Clone, Copy)]
+pub enum FlipFlag {
+    Mirror,
+    Water,
+    Both,
+}
+
+pub trait Orient<T: bytemuck::Pod, D: ndarray::Dimension> {
+    fn flip(&self, flip: FlipFlag) -> Array<T, D>;
+    fn rotate(&self, rotation: RotationFlag) -> Array<T, D>;
+    fn owned(&self) -> Array<T, D>;
+
+    fn unorient(&self, orientation: Orientation) -> Array<T, D>
+    where
+        Array<T, D>: Orient<T, D>,
+        Self: ToOwned<Owned = Array<T, D>>,
+    {
+        let inverse_orientation = orientation.inverse();
+        self.orient(inverse_orientation)
+
+        // match orientation {
+        //     Orientation::NoRotation | Orientation::Unknown => self.to_owned(),
+        //     Orientation::Mirror => self.flip(FlipFlag::Mirror).to_owned(),
+        //     Orientation::Clock180 => self.rotate(RotationFlag::Clock180),
+        //     Orientation::Water => self.flip(FlipFlag::Water).to_owned(),
+        //     Orientation::MirrorClock270 => self
+        //         .rotate(RotationFlag::Clock90)
+        //         .flip(FlipFlag::Mirror)
+        //         .to_owned(),
+        //     Orientation::Clock90 => self.rotate(RotationFlag::Clock270),
+        //     Orientation::MirrorClock90 => self
+        //         .rotate(RotationFlag::Clock270)
+        //         .flip(FlipFlag::Mirror)
+        //         .to_owned(),
+        //     Orientation::Clock270 => self.rotate(RotationFlag::Clock90),
+        // }
+    }
+
+    fn orient(&self, orientation: Orientation) -> Array<T, D>
+    where
+        Array<T, D>: Orient<T, D>,
+    {
+        match orientation {
+            Orientation::NoRotation | Orientation::Unknown => self.owned(),
+            Orientation::Mirror => self.flip(FlipFlag::Mirror).to_owned(),
+            Orientation::Clock180 => self.rotate(RotationFlag::Clock180),
+            Orientation::Water => self.flip(FlipFlag::Water).to_owned(),
+            Orientation::MirrorClock270 => self
+                .flip(FlipFlag::Mirror)
+                .rotate(RotationFlag::Clock270)
+                .to_owned(),
+            Orientation::Clock90 => self.rotate(RotationFlag::Clock90),
+            Orientation::MirrorClock90 => self
+                .flip(FlipFlag::Mirror)
+                .rotate(RotationFlag::Clock90)
+                .to_owned(),
+            Orientation::Clock270 => self.rotate(RotationFlag::Clock270),
+        }
+        .as_standard_layout()
+        .to_owned()
+    }
+}
+
+impl<T: bytemuck::Pod + Copy, S: ndarray::Data<Elem = T>> Orient<T, ndarray::Ix3>
+    for ArrayBase<S, ndarray::Ix3>
+{
+    fn flip(&self, flip: FlipFlag) -> Array<T, ndarray::Ix3> {
+        match flip {
+            FlipFlag::Mirror => self.slice(ndarray::s![.., ..;-1, ..]),
+            FlipFlag::Water => self.slice(ndarray::s![..;-1, .., ..]),
+            FlipFlag::Both => self.slice(ndarray::s![..;-1, ..;-1, ..]),
+        }
+        .as_standard_layout()
+        .to_owned()
+    }
+
+    fn owned(&self) -> Array<T, ndarray::Ix3> {
+        self.to_owned()
+    }
+
+    fn rotate(&self, rotation: RotationFlag) -> Array<T, ndarray::Ix3> {
+        match rotation {
+            RotationFlag::Clock90 => self
+                .view()
+                .permuted_axes([1, 0, 2])
+                .flip(FlipFlag::Mirror)
+                .to_owned(),
+            RotationFlag::Clock180 => self.flip(FlipFlag::Both).to_owned(),
+            RotationFlag::Clock270 => self
+                .view()
+                .permuted_axes([1, 0, 2])
+                .flip(FlipFlag::Water)
+                .to_owned(),
+        }
+    }
+}
+
+impl<T: bytemuck::Pod + Copy, S: ndarray::Data<Elem = T>> Orient<T, ndarray::Ix2>
+    for ArrayBase<S, ndarray::Ix2>
+{
+    fn flip(&self, flip: FlipFlag) -> Array<T, ndarray::Ix2> {
+        match flip {
+            FlipFlag::Mirror => self.slice(ndarray::s![.., ..;-1,]),
+            FlipFlag::Water => self.slice(ndarray::s![..;-1, ..,]),
+            FlipFlag::Both => self.slice(ndarray::s![..;-1, ..;-1,]),
+        }
+        .as_standard_layout()
+        .to_owned()
+    }
+
+    fn owned(&self) -> Array<T, ndarray::Ix2> {
+        self.to_owned()
+    }
+
+    fn rotate(&self, rotation: RotationFlag) -> Array<T, ndarray::Ix2> {
+        match rotation {
+            RotationFlag::Clock90 => self.t().flip(FlipFlag::Mirror).to_owned(),
+            RotationFlag::Clock180 => self.flip(FlipFlag::Both).to_owned(),
+            RotationFlag::Clock270 => self.t().flip(FlipFlag::Water).to_owned(),
+        }
+    }
+}
--- a/ndcv-bridge/src/percentile.rs
+++ b/ndcv-bridge/src/percentile.rs
@@ -0,0 +1,63 @@
+use error_stack::*;
+use ndarray::{ArrayBase, Ix1};
+use num::cast::AsPrimitive;
+
+use crate::NdCvError;
+
+pub trait Percentile {
+    fn percentile(&self, qth_percentile: f64) -> Result<f64, NdCvError>;
+}
+
+impl<T: std::cmp::Ord + Clone + AsPrimitive<f64>, S: ndarray::Data<Elem = T>> Percentile
+    for ArrayBase<S, Ix1>
+{
+    fn percentile(&self, qth_percentile: f64) -> Result<f64, NdCvError> {
+        if self.len() == 0 {
+            return Err(error_stack::Report::new(NdCvError).attach_printable("Empty Input"));
+        }
+
+        if !(0_f64..1_f64).contains(&qth_percentile) {
+            return Err(error_stack::Report::new(NdCvError)
+                .attach_printable("Qth percentile must be between 0 and 1"));
+        }
+
+        let mut standard_array = self.as_standard_layout();
+        let mut raw_data = standard_array
+            .as_slice_mut()
+            .expect("An array in standard layout will always return its inner slice");
+
+        raw_data.sort();
+
+        let actual_index = qth_percentile * (raw_data.len() - 1) as f64;
+
+        let lower_index = (actual_index.floor() as usize).clamp(0, raw_data.len() - 1);
+        let upper_index = (actual_index.ceil() as usize).clamp(0, raw_data.len() - 1);
+
+        if lower_index == upper_index {
+            Ok(raw_data[lower_index].as_())
+        } else {
+            let weight = actual_index - lower_index as f64;
+            Ok(raw_data[lower_index].as_() * (1.0 - weight) + raw_data[upper_index].as_() * weight)
+        }
+    }
+}
+
+// fn percentile(data: &Array1<f64>, p: f64) -> f64 {
+//     if data.len() == 0 {
+//         return 0.0;
+//     }
+//
+//     let mut sorted_data = data.to_vec();
+//     sorted_data.sort_by(|a, b| a.partial_cmp(b).unwrap());
+//
+//     let index = (p / 100.0) * (sorted_data.len() - 1) as f64;
+//     let lower = index.floor() as usize;
+//     let upper = index.ceil() as usize;
+//
+//     if lower == upper {
+//         sorted_data[lower] as f64
+//     } else {
+//         let weight = index - lower as f64;
+//         sorted_data[lower] as f64 * (1.0 - weight) + sorted_data[upper] as f64 * weight
+//     }
+// }
--- a/ndcv-bridge/src/resize.rs
+++ b/ndcv-bridge/src/resize.rs
@@ -0,0 +1,108 @@
+use crate::{prelude_::*, NdAsImage, NdAsImageMut};
+
+/// Resize ndarray using OpenCV resize functions
+pub trait NdCvResize<T, D>: seal::SealedInternal {
+    /// The input array must be a continuous 2D or 3D ndarray
+    fn resize(
+        &self,
+        height: u16,
+        width: u16,
+        interpolation: Interpolation,
+    ) -> Result<ndarray::ArrayBase<ndarray::OwnedRepr<T>, D>, NdCvError>;
+}
+
+#[repr(i32)]
+#[derive(Debug, Copy, Clone)]
+pub enum Interpolation {
+    Linear = opencv::imgproc::INTER_LINEAR,
+    LinearExact = opencv::imgproc::INTER_LINEAR_EXACT,
+    Max = opencv::imgproc::INTER_MAX,
+    Area = opencv::imgproc::INTER_AREA,
+    Cubic = opencv::imgproc::INTER_CUBIC,
+    Nearest = opencv::imgproc::INTER_NEAREST,
+    NearestExact = opencv::imgproc::INTER_NEAREST_EXACT,
+    Lanczos4 = opencv::imgproc::INTER_LANCZOS4,
+}
+
+mod seal {
+    pub trait SealedInternal {}
+    impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> SealedInternal
+        for ndarray::ArrayBase<S, ndarray::Ix3>
+    {
+    }
+    impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> SealedInternal
+        for ndarray::ArrayBase<S, ndarray::Ix2>
+    {
+    }
+}
+
+impl<T: bytemuck::Pod + num::Zero, S: ndarray::Data<Elem = T>> NdCvResize<T, ndarray::Ix2>
+    for ndarray::ArrayBase<S, ndarray::Ix2>
+{
+    fn resize(
+        &self,
+        height: u16,
+        width: u16,
+        interpolation: Interpolation,
+    ) -> Result<ndarray::Array2<T>, NdCvError> {
+        let mat = self.as_image_mat()?;
+        let mut dest = ndarray::Array2::zeros((height.into(), width.into()));
+        let mut dest_mat = dest.as_image_mat_mut()?;
+        opencv::imgproc::resize(
+            mat.as_ref(),
+            dest_mat.as_mut(),
+            opencv::core::Size {
+                height: height.into(),
+                width: width.into(),
+            },
+            0.,
+            0.,
+            interpolation as i32,
+        )
+        .change_context(NdCvError)?;
+        Ok(dest)
+    }
+}
+
+impl<T: bytemuck::Pod + num::Zero, S: ndarray::Data<Elem = T>> NdCvResize<T, ndarray::Ix3>
+    for ndarray::ArrayBase<S, ndarray::Ix3>
+{
+    fn resize(
+        &self,
+        height: u16,
+        width: u16,
+        interpolation: Interpolation,
+    ) -> Result<ndarray::ArrayBase<ndarray::OwnedRepr<T>, ndarray::Ix3>, NdCvError> {
+        let mat = self.as_image_mat()?;
+        let mut dest =
+            ndarray::Array3::zeros((height.into(), width.into(), self.len_of(ndarray::Axis(2))));
+        let mut dest_mat = dest.as_image_mat_mut()?;
+        opencv::imgproc::resize(
+            mat.as_ref(),
+            dest_mat.as_mut(),
+            opencv::core::Size {
+                height: height.into(),
+                width: width.into(),
+            },
+            0.,
+            0.,
+            interpolation as i32,
+        )
+        .change_context(NdCvError)?;
+        Ok(dest)
+    }
+}
+
+#[test]
+fn test_resize_simple() {
+    let foo = ndarray::Array2::<u8>::ones((10, 10));
+    let foo_resized = foo.resize(15, 20, Interpolation::Linear).unwrap();
+    assert_eq!(foo_resized, ndarray::Array2::<u8>::ones((15, 20)));
+}
+
+#[test]
+fn test_resize_3d() {
+    let foo = ndarray::Array3::<u8>::ones((10, 10, 3));
+    let foo_resized = foo.resize(15, 20, Interpolation::Linear).unwrap();
+    assert_eq!(foo_resized, ndarray::Array3::<u8>::ones((15, 20, 3)));
+}
--- a/ndcv-bridge/src/roi.rs
+++ b/ndcv-bridge/src/roi.rs
@@ -0,0 +1,274 @@
+pub trait NdRoi<T, D>: seal::Sealed {
+    fn roi(&self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayView<T, D>;
+}
+
+pub trait NdRoiMut<T, D>: seal::Sealed {
+    fn roi_mut(&mut self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayViewMut<T, D>;
+}
+
+mod seal {
+    use ndarray::{Ix2, Ix3};
+    pub trait Sealed {}
+    impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> Sealed for ndarray::ArrayBase<S, Ix2> {}
+    impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> Sealed for ndarray::ArrayBase<S, Ix3> {}
+}
+
+impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> NdRoi<T, ndarray::Ix3>
+    for ndarray::ArrayBase<S, ndarray::Ix3>
+{
+    fn roi(&self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayView3<T> {
+        let y1 = rect.y1();
+        let y2 = rect.y2();
+        let x1 = rect.x1();
+        let x2 = rect.x2();
+        self.slice(ndarray::s![y1..y2, x1..x2, ..])
+    }
+}
+
+impl<T: bytemuck::Pod, S: ndarray::DataMut<Elem = T>> NdRoiMut<T, ndarray::Ix3>
+    for ndarray::ArrayBase<S, ndarray::Ix3>
+{
+    fn roi_mut(&mut self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayViewMut3<T> {
+        let y1 = rect.y1();
+        let y2 = rect.y2();
+        let x1 = rect.x1();
+        let x2 = rect.x2();
+        self.slice_mut(ndarray::s![y1..y2, x1..x2, ..])
+    }
+}
+
+impl<T: bytemuck::Pod, S: ndarray::Data<Elem = T>> NdRoi<T, ndarray::Ix2>
+    for ndarray::ArrayBase<S, ndarray::Ix2>
+{
+    fn roi(&self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayView2<T> {
+        let y1 = rect.y1();
+        let y2 = rect.y2();
+        let x1 = rect.x1();
+        let x2 = rect.x2();
+        self.slice(ndarray::s![y1..y2, x1..x2])
+    }
+}
+
+impl<T: bytemuck::Pod, S: ndarray::DataMut<Elem = T>> NdRoiMut<T, ndarray::Ix2>
+    for ndarray::ArrayBase<S, ndarray::Ix2>
+{
+    fn roi_mut(&mut self, rect: bounding_box::Aabb2<usize>) -> ndarray::ArrayViewMut2<T> {
+        let y1 = rect.y1();
+        let y2 = rect.y2();
+        let x1 = rect.x1();
+        let x2 = rect.x2();
+        self.slice_mut(ndarray::s![y1..y2, x1..x2])
+    }
+}
+
+#[test]
+fn test_roi() {
+    let arr = ndarray::Array3::<u8>::zeros((10, 10, 3));
+    let rect = bounding_box::Aabb2::from_xywh(1, 1, 3, 3);
+    let roi = arr.roi(rect);
+    assert_eq!(roi.shape(), &[3, 3, 3]);
+}
+
+#[test]
+fn test_roi_2d() {
+    let arr = ndarray::Array2::<u8>::zeros((10, 10));
+    let rect = bounding_box::Aabb2::from_xywh(1, 1, 3, 3);
+    let roi = arr.roi(rect);
+    assert_eq!(roi.shape(), &[3, 3]);
+}
+
+/// ```text
+///    ┌──────────────────┐
+///    │      padded      │
+///    │    ┌────────┐    │
+///    │    │        │    │
+///    │    │original│    │
+///    │    │        │    │
+///    │    └────────┘    │
+///    │      zeroed      │
+///    └──────────────────┘
+/// ```
+///
+/// Returns the padded bounding box and the padded segment
+/// The padded is the padded bounding box
+/// The original is the original bounding box
+/// Returns the padded bounding box as zeros and the original bbox as the original segment
+// Helper functions for missing methods from old bbox crate
+fn bbox_top_left_usize(bbox: &bounding_box::Aabb2<usize>) -> (usize, usize) {
+    (bbox.x1(), bbox.y1())
+}
+
+fn bbox_with_top_left_usize(
+    bbox: &bounding_box::Aabb2<usize>,
+    x: usize,
+    y: usize,
+) -> bounding_box::Aabb2<usize> {
+    let width = bbox.x2() - bbox.x1();
+    let height = bbox.y2() - bbox.y1();
+    bounding_box::Aabb2::from_xywh(x, y, width, height)
+}
+
+fn bbox_with_origin_usize(point: (usize, usize), origin: (usize, usize)) -> (usize, usize) {
+    (point.0 - origin.0, point.1 - origin.1)
+}
+
+fn bbox_zeros_ndarray_2d<T: num::Zero + Copy>(
+    bbox: &bounding_box::Aabb2<usize>,
+) -> ndarray::Array2<T> {
+    let width = bbox.x2() - bbox.x1();
+    let height = bbox.y2() - bbox.y1();
+    ndarray::Array2::<T>::zeros((height, width))
+}
+
+fn bbox_zeros_ndarray_3d<T: num::Zero + Copy>(
+    bbox: &bounding_box::Aabb2<usize>,
+    channels: usize,
+) -> ndarray::Array3<T> {
+    let width = bbox.x2() - bbox.x1();
+    let height = bbox.y2() - bbox.y1();
+    ndarray::Array3::<T>::zeros((height, width, channels))
+}
+
+fn bbox_round_f64(bbox: &bounding_box::Aabb2<f64>) -> bounding_box::Aabb2<f64> {
+    let x1 = bbox.x1().round();
+    let y1 = bbox.y1().round();
+    let x2 = bbox.x2().round();
+    let y2 = bbox.y2().round();
+    bounding_box::Aabb2::from_x1y1x2y2(x1, y1, x2, y2)
+}
+
+fn bbox_cast_f64_to_usize(bbox: &bounding_box::Aabb2<f64>) -> bounding_box::Aabb2<usize> {
+    let x1 = bbox.x1() as usize;
+    let y1 = bbox.y1() as usize;
+    let x2 = bbox.x2() as usize;
+    let y2 = bbox.y2() as usize;
+    bounding_box::Aabb2::from_x1y1x2y2(x1, y1, x2, y2)
+}
+
+pub trait NdRoiZeroPadded<T, D: ndarray::Dimension>: seal::Sealed {
+    fn roi_zero_padded(
+        &self,
+        original: bounding_box::Aabb2<usize>,
+        padded: bounding_box::Aabb2<usize>,
+    ) -> (bounding_box::Aabb2<usize>, ndarray::Array<T, D>);
+}
+
+impl<T: bytemuck::Pod + num::Zero> NdRoiZeroPadded<T, ndarray::Ix2> for ndarray::Array2<T> {
+    fn roi_zero_padded(
+        &self,
+        original: bounding_box::Aabb2<usize>,
+        padded: bounding_box::Aabb2<usize>,
+    ) -> (bounding_box::Aabb2<usize>, ndarray::Array2<T>) {
+        // The co-ordinates of both the original and the padded bounding boxes must be contained in
+        // self or it will panic
+
+        let self_bbox = bounding_box::Aabb2::from_xywh(0, 0, self.shape()[1], self.shape()[0]);
+        if !self_bbox.contains_bbox(&original) {
+            panic!("original bounding box is not contained in self");
+        }
+        if !self_bbox.contains_bbox(&padded) {
+            panic!("padded bounding box is not contained in self");
+        }
+
+        let original_top_left = bbox_top_left_usize(&original);
+        let padded_top_left = bbox_top_left_usize(&padded);
+        let origin_offset = bbox_with_origin_usize(original_top_left, padded_top_left);
+        let original_roi_in_padded =
+            bbox_with_top_left_usize(&original, origin_offset.0, origin_offset.1);
+
+        let original_segment = self.roi(original);
+        let mut padded_segment = bbox_zeros_ndarray_2d::<T>(&padded);
+        padded_segment
+            .roi_mut(original_roi_in_padded)
+            .assign(&original_segment);
+
+        (padded, padded_segment)
+    }
+}
+
+impl<T: bytemuck::Pod + num::Zero> NdRoiZeroPadded<T, ndarray::Ix3> for ndarray::Array3<T> {
+    fn roi_zero_padded(
+        &self,
+        original: bounding_box::Aabb2<usize>,
+        padded: bounding_box::Aabb2<usize>,
+    ) -> (bounding_box::Aabb2<usize>, ndarray::Array3<T>) {
+        let self_bbox = bounding_box::Aabb2::from_xywh(0, 0, self.shape()[1], self.shape()[0]);
+        if !self_bbox.contains_bbox(&original) {
+            panic!("original bounding box is not contained in self");
+        }
+        if !self_bbox.contains_bbox(&padded) {
+            panic!("padded bounding box is not contained in self");
+        }
+
+        let original_top_left = bbox_top_left_usize(&original);
+        let padded_top_left = bbox_top_left_usize(&padded);
+        let origin_offset = bbox_with_origin_usize(original_top_left, padded_top_left);
+        let original_roi_in_padded =
+            bbox_with_top_left_usize(&original, origin_offset.0, origin_offset.1);
+
+        let original_segment = self.roi(original);
+        let mut padded_segment = bbox_zeros_ndarray_3d::<T>(&padded, self.len_of(ndarray::Axis(2)));
+        padded_segment
+            .roi_mut(original_roi_in_padded)
+            .assign(&original_segment);
+
+        (padded, padded_segment)
+    }
+}
+
+#[test]
+fn test_roi_zero_padded() {
+    let arr = ndarray::Array2::<u8>::ones((10, 10));
+    let original = bounding_box::Aabb2::from_xywh(1.0, 1.0, 3.0, 3.0);
+    let clamp = bounding_box::Aabb2::from_xywh(0.0, 0.0, 10.0, 10.0);
+    let padded = original.padding(2.0).clamp(&clamp).unwrap();
+    let padded_cast = bbox_cast_f64_to_usize(&padded);
+    let original_cast = bbox_cast_f64_to_usize(&original);
+    let (padded_result, padded_segment) = arr.roi_zero_padded(original_cast, padded_cast);
+    assert_eq!(padded_result, bounding_box::Aabb2::from_xywh(0, 0, 6, 6));
+    assert_eq!(padded_segment.shape(), &[6, 6]);
+}
+
+#[test]
+pub fn bbox_clamp_failure_preload() {
+    let segment_mask = ndarray::Array2::<u8>::zeros((512, 512));
+    let og = bounding_box::Aabb2::from_xywh(475.0, 79.625, 37.0, 282.15);
+    let clamp = bounding_box::Aabb2::from_xywh(0.0, 0.0, 512.0, 512.0);
+    let padded = og
+        .scale(nalgebra::Vector2::new(1.2, 1.2))
+        .clamp(&clamp)
+        .unwrap();
+    let padded = bbox_round_f64(&padded);
+    let og_cast = bbox_cast_f64_to_usize(&bbox_round_f64(&og));
+    let padded_cast = bbox_cast_f64_to_usize(&padded);
+    let (_bbox, _segment) = segment_mask.roi_zero_padded(og_cast, padded_cast);
+}
+
+#[test]
+pub fn bbox_clamp_failure_preload_2() {
+    let segment_mask = ndarray::Array2::<u8>::zeros((512, 512));
+    let bbox = bounding_box::Aabb2::from_xywh(354.25, 98.5, 116.25, 413.5);
+    // let padded = bounding_box::Aabb2::from_xywh(342.625, 57.150000000000006, 139.5, 454.85);
+    let clamp = bounding_box::Aabb2::from_xywh(0.0, 0.0, 512.0, 512.0);
+    let padded = bbox
+        .scale(nalgebra::Vector2::new(1.2, 1.2))
+        .clamp(&clamp)
+        .unwrap();
+    let padded = bbox_round_f64(&padded);
+    let bbox_cast = bbox_cast_f64_to_usize(&bbox_round_f64(&bbox));
+    let padded_cast = bbox_cast_f64_to_usize(&padded);
+    let (_bbox, _segment) = segment_mask.roi_zero_padded(bbox_cast, padded_cast);
+}
+
+#[test]
+fn test_roi_zero_padded_3d() {
+    let arr = ndarray::Array3::<u8>::ones((10, 10, 3));
+    let original = bounding_box::Aabb2::from_xywh(1.0, 1.0, 3.0, 3.0);
+    let clamp = bounding_box::Aabb2::from_xywh(0.0, 0.0, 10.0, 10.0);
+    let padded = original.padding(2.0).clamp(&clamp).unwrap();
+    let padded_cast = bbox_cast_f64_to_usize(&padded);
+    let original_cast = bbox_cast_f64_to_usize(&original);
+    let (padded_result, padded_segment) = arr.roi_zero_padded(original_cast, padded_cast);
+    assert_eq!(padded_result, bounding_box::Aabb2::from_xywh(0, 0, 6, 6));
+    assert_eq!(padded_segment.shape(), &[6, 6, 3]);
+}
--- a/src/bin/gui.rs
+++ b/src/bin/gui.rs
@@ -1,10 +1,12 @@
-fn main() -> Result<(), Box<dyn std::error::Error>> {
+use detector::errors::*;
+fn main() -> Result<()> {
    // Initialize logging
    tracing_subscriber::fmt()
        .with_env_filter("info")
-        .with_thread_ids(true)
-        .with_thread_names(true)
-        .with_target(false)
+        // .with_thread_ids(true)
+        // .with_file(true)
+        // .with_thread_names(true)
+        .with_target(true)
        .init();

    // Run the GUI
--- a/src/facedet/retinaface.rs
+++ b/src/facedet/retinaface.rs
@@ -174,7 +174,6 @@ impl FaceDetectionModelOutput {
        // let mut decoded_landmarks = Vec::new();
        // let mut confidences = Vec::new();

-        dbg!(priors.shape());
        let (decoded_boxes, decoded_landmarks, confidences) = (0..priors.shape()[0])
            .filter(|&i| scores[i] > config.threshold)
            .map(|i| {
--- a/src/gui/app.rs
+++ b/src/gui/app.rs
@@ -10,6 +10,7 @@ use std::path::PathBuf;
 use std::sync::Arc;

 use crate::gui::bridge::FaceDetectionBridge;
+use ::image::{DynamicImage, ImageFormat, RgbImage};

 #[derive(Debug, Clone)]
 pub enum Message {
@@ -43,6 +44,7 @@ pub enum Message {
    ImageLoaded(Option<Arc<Vec<u8>>>),
    SecondImageLoaded(Option<Arc<Vec<u8>>>),
    ProcessedImageUpdated(Option<Vec<u8>>),
+    FaceRoisLoaded(Vec<image::Handle>, Vec<image::Handle>),
 }

 #[derive(Debug, Clone, PartialEq)]
@@ -76,6 +78,8 @@ pub enum ComparisonResult {
    Success {
        image1_faces: usize,
        image2_faces: usize,
+        image1_face_rois: Vec<ndarray::Array3<u8>>,
+        image2_face_rois: Vec<ndarray::Array3<u8>>,
        best_similarity: f32,
        processing_time: f64,
    },
@@ -110,6 +114,10 @@ pub struct FaceDetectorApp {
    current_image_handle: Option<image::Handle>,
    processed_image_handle: Option<image::Handle>,
    second_image_handle: Option<image::Handle>,
+
+    // Face ROI handles for comparison display
+    image1_face_roi_handles: Vec<image::Handle>,
+    image2_face_roi_handles: Vec<image::Handle>,
 }

 impl Default for FaceDetectorApp {
@@ -130,6 +138,8 @@ impl Default for FaceDetectorApp {
            current_image_handle: None,
            processed_image_handle: None,
            second_image_handle: None,
+            image1_face_roi_handles: Vec::new(),
+            image2_face_roi_handles: Vec::new(),
        }
    }
 }
@@ -313,6 +323,8 @@ impl FaceDetectorApp {
                self.detection_result = None;
                self.comparison_result = None;
                self.processed_image_handle = None;
+                self.image1_face_roi_handles.clear();
+                self.image2_face_roi_handles.clear();
                self.status_message = "Results cleared".to_string();
                Task::none()
            }
@@ -356,6 +368,8 @@ impl FaceDetectorApp {
                    ComparisonResult::Success {
                        best_similarity,
                        processing_time,
+                        image1_face_rois,
+                        image2_face_rois,
                        ..
                    } => {
                        let interpretation = if *best_similarity > 0.8 {
@@ -372,6 +386,16 @@ impl FaceDetectorApp {
                            "Comparison complete! Similarity: {:.3} - {} (Processing time: {:.2}s)",
                            best_similarity, interpretation, processing_time
                        );
+
+                        // Convert face ROIs to image handles
+                        let image1_handles = convert_face_rois_to_handles(image1_face_rois.clone());
+                        let image2_handles = convert_face_rois_to_handles(image2_face_rois.clone());
+
+                        self.comparison_result = Some(result);
+                        return Task::perform(
+                            async move { (image1_handles, image2_handles) },
+                            |(h1, h2)| Message::FaceRoisLoaded(h1, h2),
+                        );
                    }
                    ComparisonResult::Error(error) => {
                        self.status_message = format!("Comparison failed: {}", error);
@@ -382,6 +406,12 @@ impl FaceDetectorApp {
                Task::none()
            }

+            Message::FaceRoisLoaded(image1_handles, image2_handles) => {
+                self.image1_face_roi_handles = image1_handles;
+                self.image2_face_roi_handles = image2_handles;
+                Task::none()
+            }
+
            Message::ProgressUpdate(progress) => {
                self.progress = progress;
                Task::none()
@@ -765,6 +795,8 @@ impl FaceDetectorApp {
                ComparisonResult::Success {
                    image1_faces,
                    image2_faces,
+                    image1_face_rois: _,
+                    image2_face_rois: _,
                    best_similarity,
                    processing_time,
                } => {
@@ -790,7 +822,7 @@ impl FaceDetectorApp {
                        )
                    };

-                    column![
+                    let mut result_column = column![
                        text("Comparison Results").size(18),
                        text(format!("First image faces: {}", image1_faces)),
                        text(format!("Second image faces: {}", image2_faces)),
@@ -800,7 +832,89 @@ impl FaceDetectorApp {
                        }),
                        text(format!("Processing time: {:.2}s", processing_time)),
                    ]
-                    .spacing(5)
+                    .spacing(5);
+
+                    // Add face ROI displays if available
+                    if !self.image1_face_roi_handles.is_empty()
+                        || !self.image2_face_roi_handles.is_empty()
+                    {
+                        result_column = result_column.push(text("Detected Faces").size(16));
+
+                        // Create face ROI rows
+                        let image1_faces_row = if !self.image1_face_roi_handles.is_empty() {
+                            let faces: Element<'_, Message> = self
+                                .image1_face_roi_handles
+                                .iter()
+                                .enumerate()
+                                .fold(row![].spacing(5), |row, (i, handle)| {
+                                    row.push(
+                                        column![
+                                            text(format!("Face {}", i + 1)).size(12),
+                                            container(
+                                                image(handle.clone())
+                                                    .width(80)
+                                                    .height(80)
+                                                    .content_fit(iced::ContentFit::Cover)
+                                            )
+                                            .style(container::bordered_box)
+                                            .padding(2),
+                                        ]
+                                        .spacing(2)
+                                        .align_x(Alignment::Center),
+                                    )
+                                })
+                                .into();
+
+                            column![
+                                text("First Image Faces:").size(14),
+                                scrollable(faces).direction(scrollable::Direction::Horizontal(
+                                    scrollable::Scrollbar::new()
+                                )),
+                            ]
+                            .spacing(5)
+                        } else {
+                            column![text("First Image Faces: None detected").size(14)]
+                        };
+
+                        let image2_faces_row = if !self.image2_face_roi_handles.is_empty() {
+                            let faces: Element<'_, Message> = self
+                                .image2_face_roi_handles
+                                .iter()
+                                .enumerate()
+                                .fold(row![].spacing(5), |row, (i, handle)| {
+                                    row.push(
+                                        column![
+                                            text(format!("Face {}", i + 1)).size(12),
+                                            container(
+                                                image(handle.clone())
+                                                    .width(80)
+                                                    .height(80)
+                                                    .content_fit(iced::ContentFit::Cover)
+                                            )
+                                            .style(container::bordered_box)
+                                            .padding(2),
+                                        ]
+                                        .spacing(2)
+                                        .align_x(Alignment::Center),
+                                    )
+                                })
+                                .into();
+
+                            column![
+                                text("Second Image Faces:").size(14),
+                                scrollable(faces).direction(scrollable::Direction::Horizontal(
+                                    scrollable::Scrollbar::new()
+                                )),
+                            ]
+                            .spacing(5)
+                        } else {
+                            column![text("Second Image Faces: None detected").size(14)]
+                        };
+
+                        result_column = result_column.push(image1_faces_row).push(image2_faces_row);
+                    }
+
+                    result_column
                }
                ComparisonResult::Error(error) => column![
                    text("Comparison Results").size(18),
@@ -816,10 +930,12 @@ impl FaceDetectorApp {
            ]
        };

-        column![file_section, comparison_image_section, controls, results]
-            .spacing(20)
-            .padding(20)
-            .into()
+        scrollable(
+            column![file_section, comparison_image_section, controls, results]
+                .spacing(20)
+                .padding(20),
+        )
+        .into()
    }

    fn settings_view(&self) -> Element<'_, Message> {
@@ -881,6 +997,31 @@ impl std::fmt::Display for ExecutorType {
    }
 }

+// Helper function to convert face ROIs to image handles
+fn convert_face_rois_to_handles(face_rois: Vec<ndarray::Array3<u8>>) -> Vec<image::Handle> {
+    face_rois
+        .into_iter()
+        .filter_map(|roi| {
+            // Convert ndarray to image::RgbImage
+            let (height, width, _) = roi.dim();
+            let (raw_data, _offset) = roi.into_raw_vec_and_offset();
+
+            if let Some(img) = RgbImage::from_raw(width as u32, height as u32, raw_data) {
+                // Convert to PNG bytes
+                let mut buffer = Vec::new();
+                let mut cursor = std::io::Cursor::new(&mut buffer);
+                if DynamicImage::ImageRgb8(img)
+                    .write_to(&mut cursor, ImageFormat::Png)
+                    .is_ok()
+                {
+                    return Some(image::Handle::from_bytes(buffer));
+                }
+            }
+            None
+        })
+        .collect()
+}
+
 pub fn run() -> iced::Result {
    iced::application(
        "Face Detector",
--- a/src/gui/bridge.rs
+++ b/src/gui/bridge.rs
@@ -1,9 +1,17 @@
 use std::path::PathBuf;

+use crate::errors;
 use crate::facedet::{FaceDetectionConfig, FaceDetector, retinaface};
 use crate::faceembed::facenet;
 use crate::gui::app::{ComparisonResult, DetectionResult, ExecutorType};
+use bounding_box::Aabb2;
+use bounding_box::roi::MultiRoi as _;
+use error_stack::ResultExt;
+use fast_image_resize::ResizeOptions;
+use ndarray::{Array1, Array2, Array3, Array4};
 use ndarray_image::ImageToNdarray;
+use ndarray_math::CosineSimilarity;
+use ndarray_resize::NdFir;

 const RETINAFACE_MODEL_MNN: &[u8] = include_bytes!("../../models/retinaface.mnn");
 const FACENET_MODEL_MNN: &[u8] = include_bytes!("../../models/facenet.mnn");
@@ -62,11 +70,19 @@ impl FaceDetectionBridge {
        )
        .await
        {
-            Ok((image1_faces, image2_faces, best_similarity)) => {
+            Ok((
+                image1_faces,
+                image2_faces,
+                image1_face_rois,
+                image2_face_rois,
+                best_similarity,
+            )) => {
                let processing_time = start_time.elapsed().as_secs_f64();
                ComparisonResult::Success {
                    image1_faces,
                    image2_faces,
+                    image1_face_rois,
+                    image2_face_rois,
                    best_similarity,
                    processing_time,
                }
@@ -172,196 +188,216 @@ impl FaceDetectionBridge {
        threshold: f32,
        nms_threshold: f32,
        executor_type: ExecutorType,
-    ) -> Result<(usize, usize, f32), Box<dyn std::error::Error + Send + Sync>> {
-        // Load both images
-        let img1 = image::open(&image1_path)?.to_rgb8();
-        let img2 = image::open(&image2_path)?.to_rgb8();
-
-        // Convert to ndarray format
-        let image1_array = img1.as_ndarray()?;
-        let image2_array = img2.as_ndarray()?;
-
-        // Create detection configuration
-        let config1 = FaceDetectionConfig::default()
-            .with_threshold(threshold)
-            .with_nms_threshold(nms_threshold)
-            .with_input_width(1024)
-            .with_input_height(1024);
-
-        let config2 = FaceDetectionConfig::default()
-            .with_threshold(threshold)
-            .with_nms_threshold(nms_threshold)
-            .with_input_width(1024)
-            .with_input_height(1024);
-
+    ) -> Result<
+        (usize, usize, Vec<Array3<u8>>, Vec<Array3<u8>>, f32),
+        Box<dyn std::error::Error + Send + Sync>,
+    > {
        // Create detector and embedder, detect faces and generate embeddings
-        let (faces1, faces2, best_similarity) = match executor_type {
-            ExecutorType::MnnCpu | ExecutorType::MnnMetal | ExecutorType::MnnCoreML => {
-                let forward_type = match executor_type {
-                    ExecutorType::MnnCpu => mnn::ForwardType::CPU,
-                    ExecutorType::MnnMetal => mnn::ForwardType::Metal,
-                    ExecutorType::MnnCoreML => mnn::ForwardType::CoreML,
-                    _ => unreachable!(),
-                };
+        let (image1_faces, image2_faces, image1_rois, image2_rois, best_similarity) =
+            match executor_type {
+                ExecutorType::MnnCpu | ExecutorType::MnnMetal | ExecutorType::MnnCoreML => {
+                    let forward_type = match executor_type {
+                        ExecutorType::MnnCpu => mnn::ForwardType::CPU,
+                        ExecutorType::MnnMetal => mnn::ForwardType::Metal,
+                        ExecutorType::MnnCoreML => mnn::ForwardType::CoreML,
+                        _ => unreachable!(),
+                    };

-                let mut detector = retinaface::mnn::FaceDetection::builder(RETINAFACE_MODEL_MNN)
-                    .map_err(|e| format!("Failed to create MNN detector: {}", e))?
-                    .with_forward_type(forward_type.clone())
-                    .build()
-                    .map_err(|e| format!("Failed to build MNN detector: {}", e))?;
+                    let mut detector =
+                        retinaface::mnn::FaceDetection::builder(RETINAFACE_MODEL_MNN)
+                            .map_err(|e| format!("Failed to create MNN detector: {}", e))?
+                            .with_forward_type(forward_type.clone())
+                            .build()
+                            .map_err(|e| format!("Failed to build MNN detector: {}", e))?;

-                let embedder = facenet::mnn::EmbeddingGenerator::builder(FACENET_MODEL_MNN)
-                    .map_err(|e| format!("Failed to create MNN embedder: {}", e))?
-                    .with_forward_type(forward_type)
-                    .build()
-                    .map_err(|e| format!("Failed to build MNN embedder: {}", e))?;
+                    let mut embedder = facenet::mnn::EmbeddingGenerator::builder(FACENET_MODEL_MNN)
+                        .map_err(|e| format!("Failed to create MNN embedder: {}", e))?
+                        .with_forward_type(forward_type)
+                        .build()
+                        .map_err(|e| format!("Failed to build MNN embedder: {}", e))?;

-                // Detect faces in both images
-                let faces1 = detector
-                    .detect_faces(image1_array.view(), &config1)
-                    .map_err(|e| format!("Detection failed for image 1: {}", e))?;
-                let faces2 = detector
-                    .detect_faces(image2_array.view(), &config2)
-                    .map_err(|e| format!("Detection failed for image 2: {}", e))?;
-
-                // Extract face crops and generate embeddings
-                let mut best_similarity = 0.0f32;
-
-                for bbox1 in &faces1.bbox {
-                    let crop1 = Self::crop_face_from_image(&img1, bbox1)?;
-                    let crop1_array = ndarray::Array::from_shape_vec(
-                        (1, crop1.height() as usize, crop1.width() as usize, 3),
-                        crop1
-                            .pixels()
-                            .flat_map(|p| [p.0[0], p.0[1], p.0[2]])
-                            .collect(),
+                    let img_1 = run_detection(
+                        image1_path,
+                        &mut detector,
+                        &mut embedder,
+                        threshold,
+                        nms_threshold,
+                        2,
+                    )?;
+                    let img_2 = run_detection(
+                        image2_path,
+                        &mut detector,
+                        &mut embedder,
+                        threshold,
+                        nms_threshold,
+                        2,
                    )?;

-                    let embedding1 = embedder
-                        .run_models(crop1_array.view())
-                        .map_err(|e| format!("Embedding generation failed: {}", e))?;
+                    let image1_rois = img_1.rois;
+                    let image2_rois = img_2.rois;
+                    let image1_bbox_len = img_1.bbox.len();
+                    let image2_bbox_len = img_2.bbox.len();
+                    let best_similarity = compare_faces(&img_1.embeddings, &img_2.embeddings)?;

-                    for bbox2 in &faces2.bbox {
-                        let crop2 = Self::crop_face_from_image(&img2, bbox2)?;
-                        let crop2_array = ndarray::Array::from_shape_vec(
-                            (1, crop2.height() as usize, crop2.width() as usize, 3),
-                            crop2
-                                .pixels()
-                                .flat_map(|p| [p.0[0], p.0[1], p.0[2]])
-                                .collect(),
-                        )?;
-
-                        let embedding2 = embedder
-                            .run_models(crop2_array.view())
-                            .map_err(|e| format!("Embedding generation failed: {}", e))?;
-
-                        let similarity = Self::cosine_similarity(
-                            embedding1.row(0).as_slice().unwrap(),
-                            embedding2.row(0).as_slice().unwrap(),
-                        );
-                        best_similarity = best_similarity.max(similarity);
-                    }
+                    (
+                        image1_bbox_len,
+                        image2_bbox_len,
+                        image1_rois,
+                        image2_rois,
+                        best_similarity,
+                    )
                }
+                ExecutorType::OnnxCpu => unimplemented!(),
+            };

-                (faces1, faces2, best_similarity)
-            }
-            ExecutorType::OnnxCpu => {
-                let mut detector = retinaface::ort::FaceDetection::builder(RETINAFACE_MODEL_ONNX)
-                    .map_err(|e| format!("Failed to create ONNX detector: {}", e))?
-                    .build()
-                    .map_err(|e| format!("Failed to build ONNX detector: {}", e))?;
-
-                let mut embedder = facenet::ort::EmbeddingGenerator::builder(FACENET_MODEL_ONNX)
-                    .map_err(|e| format!("Failed to create ONNX embedder: {}", e))?
-                    .build()
-                    .map_err(|e| format!("Failed to build ONNX embedder: {}", e))?;
-
-                // Detect faces in both images
-                let faces1 = detector
-                    .detect_faces(image1_array.view(), &config1)
-                    .map_err(|e| format!("Detection failed for image 1: {}", e))?;
-                let faces2 = detector
-                    .detect_faces(image2_array.view(), &config2)
-                    .map_err(|e| format!("Detection failed for image 2: {}", e))?;
-
-                // Extract face crops and generate embeddings
-                let mut best_similarity = 0.0f32;
-
-                for bbox1 in &faces1.bbox {
-                    let crop1 = Self::crop_face_from_image(&img1, bbox1)?;
-                    let crop1_array = ndarray::Array::from_shape_vec(
-                        (1, crop1.height() as usize, crop1.width() as usize, 3),
-                        crop1
-                            .pixels()
-                            .flat_map(|p| [p.0[0], p.0[1], p.0[2]])
-                            .collect(),
-                    )?;
-
-                    let embedding1 = embedder
-                        .run_models(crop1_array.view())
-                        .map_err(|e| format!("Embedding generation failed: {}", e))?;
-
-                    for bbox2 in &faces2.bbox {
-                        let crop2 = Self::crop_face_from_image(&img2, bbox2)?;
-                        let crop2_array = ndarray::Array::from_shape_vec(
-                            (1, crop2.height() as usize, crop2.width() as usize, 3),
-                            crop2
-                                .pixels()
-                                .flat_map(|p| [p.0[0], p.0[1], p.0[2]])
-                                .collect(),
-                        )?;
-
-                        let embedding2 = embedder
-                            .run_models(crop2_array.view())
-                            .map_err(|e| format!("Embedding generation failed: {}", e))?;
-
-                        let similarity = Self::cosine_similarity(
-                            embedding1.row(0).as_slice().unwrap(),
-                            embedding2.row(0).as_slice().unwrap(),
-                        );
-                        best_similarity = best_similarity.max(similarity);
-                    }
-                }
-
-                (faces1, faces2, best_similarity)
-            }
-        };
-
-        Ok((faces1.bbox.len(), faces2.bbox.len(), best_similarity))
-    }
-
-    fn crop_face_from_image(
-        img: &image::RgbImage,
-        bbox: &bounding_box::Aabb2<usize>,
-    ) -> Result<image::RgbImage, Box<dyn std::error::Error + Send + Sync>> {
-        let min_point = bbox.min_vertex();
-        let size = bbox.size();
-        let x = min_point.x as u32;
-        let y = min_point.y as u32;
-        let width = size.x as u32;
-        let height = size.y as u32;
-
-        // Ensure crop bounds are within image
-        let img_width = img.width();
-        let img_height = img.height();
-
-        let crop_x = x.min(img_width.saturating_sub(1));
-        let crop_y = y.min(img_height.saturating_sub(1));
-        let crop_width = width.min(img_width - crop_x);
-        let crop_height = height.min(img_height - crop_y);
-
-        Ok(image::imageops::crop_imm(img, crop_x, crop_y, crop_width, crop_height).to_image())
-    }
-
-    fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
-        let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
-        let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
-        let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
-
-        if norm_a == 0.0 || norm_b == 0.0 {
-            0.0
-        } else {
-            dot_product / (norm_a * norm_b)
-        }
+        Ok((
+            image1_faces,
+            image2_faces,
+            image1_rois,
+            image2_rois,
+            best_similarity,
+        ))
    }
 }
+
+use crate::errors::Error;
+pub fn compare_faces(
+    faces_1: &[Array1<f32>],
+    faces_2: &[Array1<f32>],
+) -> Result<f32, error_stack::Report<crate::errors::Error>> {
+    use error_stack::Report;
+
+    if faces_1.is_empty() || faces_2.is_empty() {
+        Err(Report::new(crate::errors::Error))
+            .attach_printable("One or both images have no detected faces")?;
+    }
+    if faces_1.len() != faces_2.len() {
+        Err(Report::new(crate::errors::Error))
+            .attach_printable("Face count mismatch between images")?;
+    }
+    Ok(faces_1
+        .iter()
+        .zip(faces_2)
+        .flat_map(|(face_1, face_2)| face_1.cosine_similarity(face_2))
+        .inspect(|v| tracing::info!("Cosine similarity: {}", v))
+        .map(|v| ordered_float::OrderedFloat(v))
+        .max()
+        .map(|v| v.0)
+        .ok_or(Report::new(Error))?)
+}
+
+#[derive(Debug)]
+pub struct DetectionOutput {
+    bbox: Vec<Aabb2<usize>>,
+    rois: Vec<ndarray::Array3<u8>>,
+    embeddings: Vec<Array1<f32>>,
+}
+
+fn run_detection<D, E>(
+    image: impl AsRef<std::path::Path>,
+    retinaface: &mut D,
+    facenet: &mut E,
+    threshold: f32,
+    nms_threshold: f32,
+    chunk_size: usize,
+) -> crate::errors::Result<DetectionOutput>
+where
+    D: crate::facedet::FaceDetector,
+    E: crate::faceembed::FaceEmbedder,
+{
+    use errors::*;
+    // Initialize database if requested
+    let image = image.as_ref();
+    let image = image::open(image)
+        .change_context(Error)
+        .attach_printable(image.to_string_lossy().to_string())?;
+    let image = image.into_rgb8();
+    let mut array = image
+        .into_ndarray()
+        .change_context(errors::Error)
+        .attach_printable("Failed to convert image to ndarray")?;
+    let output = retinaface
+        .detect_faces(
+            array.view(),
+            &FaceDetectionConfig::default()
+                .with_threshold(threshold)
+                .with_nms_threshold(nms_threshold),
+        )
+        .change_context(errors::Error)
+        .attach_printable("Failed to detect faces")?;
+
+    let bboxes = output
+        .bbox
+        .iter()
+        .inspect(|bbox| tracing::info!("Raw bbox: {:?}", bbox))
+        .map(|bbox| bbox.as_::<f32>().scale_uniform(1.30).as_::<usize>())
+        .inspect(|bbox| tracing::info!("Padded bbox: {:?}", bbox))
+        .collect_vec();
+    for bbox in &bboxes {
+        tracing::info!("Detected face: {:?}", bbox);
+        use bounding_box::draw::*;
+        array.draw(bbox, color::palette::css::GREEN_YELLOW.to_rgba8(), 1);
+    }
+    use itertools::Itertools;
+    let face_rois = array
+        .view()
+        .multi_roi(&bboxes)
+        .change_context(Error)?
+        .into_iter()
+        .map(|roi| {
+            roi.as_standard_layout()
+                .fast_resize(224, 224, &ResizeOptions::default())
+                .change_context(Error)
+        })
+        .collect::<Result<Vec<_>>>()?;
+    let face_roi_views = face_rois.iter().map(|roi| roi.view()).collect::<Vec<_>>();
+
+    let embeddings: Vec<Array1<f32>> = face_roi_views
+        .chunks(chunk_size)
+        .map(|chunk| {
+            tracing::info!("Processing chunk of size: {}", chunk.len());
+
+            let og_size = chunk.len();
+            if chunk.len() < chunk_size {
+                tracing::warn!("Chunk size is less than 8, padding with zeros");
+                let zeros = Array3::zeros((224, 224, 3));
+                let chunk: Vec<_> = chunk
+                    .iter()
+                    .map(|arr| arr.reborrow())
+                    .chain(core::iter::repeat(zeros.view()))
+                    .take(chunk_size)
+                    .collect();
+                let face_rois: Array4<u8> = ndarray::stack(ndarray::Axis(0), chunk.as_slice())
+                    .change_context(errors::Error)
+                    .attach_printable("Failed to stack rois together")?;
+                let output = facenet.run_models(face_rois.view()).change_context(Error)?;
+                Ok((output, og_size))
+            } else {
+                let face_rois: Array4<u8> = ndarray::stack(ndarray::Axis(0), chunk)
+                    .change_context(errors::Error)
+                    .attach_printable("Failed to stack rois together")?;
+                let output = facenet.run_models(face_rois.view()).change_context(Error)?;
+                Ok((output, og_size))
+            }
+        })
+        .collect::<Result<Vec<(Array2<f32>, usize)>>>()?
+        .into_iter()
+        .map(|(chunk, size): (Array2<f32>, usize)| {
+            use itertools::Itertools;
+            chunk
+                .rows()
+                .into_iter()
+                .take(size)
+                .map(|row| row.to_owned())
+                .collect_vec()
+                .into_iter()
+        })
+        .flatten()
+        .collect::<Vec<Array1<f32>>>();
+
+    Ok(DetectionOutput {
+        bbox: bboxes,
+        rois: face_rois,
+        embeddings,
+    })
+}
--- a/test_bbox_replacement.rs
+++ b/test_bbox_replacement.rs
@@ -0,0 +1,65 @@
+//! Simple test to verify that the bbox replacement from bbox::BBox to bounding_box::Aabb2 works correctly
+
+use bounding_box::Aabb2;
+
+#[test]
+fn test_bbox_replacement() {
+    // Test basic construction
+    let bbox1 = Aabb2::from_xywh(10.0, 20.0, 100.0, 200.0);
+    assert_eq!(bbox1.x1(), 10.0);
+    assert_eq!(bbox1.y1(), 20.0);
+    assert_eq!(bbox1.x2(), 110.0);
+    assert_eq!(bbox1.y2(), 220.0);
+
+    // Test from coordinates
+    let bbox2 = Aabb2::from_x1y1x2y2(10.0, 20.0, 110.0, 220.0);
+    assert_eq!(bbox2.x1(), 10.0);
+    assert_eq!(bbox2.y1(), 20.0);
+    assert_eq!(bbox2.x2(), 110.0);
+    assert_eq!(bbox2.y2(), 220.0);
+
+    // Test equality
+    assert_eq!(bbox1.x1(), bbox2.x1());
+    assert_eq!(bbox1.y1(), bbox2.y1());
+    assert_eq!(bbox1.x2(), bbox2.x2());
+    assert_eq!(bbox1.y2(), bbox2.y2());
+
+    // Test contains
+    let smaller = Aabb2::from_xywh(20.0, 30.0, 50.0, 100.0);
+    assert!(bbox1.contains_bbox(&smaller));
+
+    // Test padding
+    let padded = bbox1.padding(10.0);
+    assert_eq!(padded.x1(), 5.0);
+    assert_eq!(padded.y1(), 15.0);
+    assert_eq!(padded.x2(), 115.0);
+    assert_eq!(padded.y2(), 225.0);
+
+    // Test scale
+    let scaled = bbox1.scale(nalgebra::Vector2::new(2.0, 2.0));
+    assert_eq!(scaled.x1(), -40.0);
+    assert_eq!(scaled.y1(), -80.0);
+    assert_eq!(scaled.x2(), 160.0);
+    assert_eq!(scaled.y2(), 320.0);
+
+    println!("All bbox replacement tests passed!");
+}
+
+#[test]
+fn test_integer_bbox() {
+    // Test with integer types
+    let bbox = Aabb2::from_xywh(1, 2, 10, 20);
+    assert_eq!(bbox.x1(), 1);
+    assert_eq!(bbox.y1(), 2);
+    assert_eq!(bbox.x2(), 11);
+    assert_eq!(bbox.y2(), 22);
+
+    let area = bbox.area();
+    assert_eq!(area, 200);
+}
+
+fn main() {
+    test_bbox_replacement();
+    test_integer_bbox();
+    println!("Test completed successfully!");
+}
Author	SHA1	Message	Date
uttarayan21	3eec262076	feat(bounding-box): add scale_uniform method for consistent scaling Some checks failed build / checks-matrix (push) Successful in 19m22s Details build / codecov (push) Failing after 19m26s Details docs / docs (push) Failing after 28m51s Details build / checks-build (push) Has been cancelled Details feat(gui): display face ROIs in comparison results refactor(bridge): pad detected face bounding boxes uniformly	2025-08-22 19:01:34 +05:30
uttarayan21	c758fd8d41	feat(gui): add face ROIs to comparison results and update image size	2025-08-22 18:26:29 +05:30
uttarayan21	34eaf9348a	refactor(gui): remove commented-out code in face detection function Some checks failed build / checks-matrix (push) Successful in 19m20s Details build / codecov (push) Failing after 19m18s Details docs / docs (push) Has been cancelled Details build / checks-build (push) Has been cancelled Details	2025-08-22 18:15:55 +05:30
uttarayan21	dab7719206	refactor: replace bbox::BBox with bounding_box::Aabb2 across codebase Some checks failed build / checks-matrix (push) Has been cancelled Details build / checks-build (push) Has been cancelled Details build / codecov (push) Has been cancelled Details docs / docs (push) Has been cancelled Details	2025-08-22 18:14:58 +05:30
uttarayan21	4b4d23d1d4	feat(bbox): add bounding box implementation with serialization Add initial implementation of the `BBox` struct in the `bbox` module, including basic operations and serialization/deserialization support with Serde.	2025-08-22 15:27:47 +05:30
uttarayan21	aab3d84db0	feat(ndcv-bridge): add ndcv-bridge for ndarray and opencv interaction	2025-08-22 15:27:36 +05:30