From 956894c07e103d4ceba7470753971673a0c5fce0 Mon Sep 17 00:00:00 2001
From: SirTalksalot75 <132705706+SirTalksalot75@users.noreply.github.com>
Date: Sat, 17 Jun 2023 12:05:24 +0200
Subject: [PATCH] Added FeatureExtr

FeatureExtractor provided by Servostar
---
 src/Feature/mod.rs | 137 +++++++++++++++++++++++++++------------------
 1 file changed, 81 insertions(+), 56 deletions(-)

diff --git a/src/Feature/mod.rs b/src/Feature/mod.rs
index 01074f2..78c4b0c 100644
--- a/src/Feature/mod.rs
+++ b/src/Feature/mod.rs
@@ -1,69 +1,94 @@
-use std::collections::HashMap;
+#[derive(Debug, Clone, Serialize, Deserialize)]
+enum FeatureResult {
+    /// A boolean. Just a boolean
+    Bool(bool),
+    /// Signed 32-bit integer
+    I32(i32),
+    /// 32-bit single precision floating point
+    /// can be used for aspect ratio or luminance
+    F32(f32),
+    /// Vector for nested multidimensional 
+    Vec(Vec<FeatureResult>),
+    /// Standard RGBA color 
+    RGBA(f32, f32, f32, f32),
+    /// Indices intended for the usage in historgrams
+    Indices(Vec<u64>)
+}
 
-fn extract_color_distribution(image_data: &[u8]) -> HashMap<u32, f32> {
-    let mut color_distribution: HashMap<u32, f32> = HashMap::new();
-    let total_pixels = image_data.len() as f32 / 4.0;//für 4 Werte
+impl Default for FeatureResult {
+    fn default() -> Self {
+        FeatureResult::Bool(false)
+    }
+}
 
-    for pixel in image_data.chunks_exact(4) {
-        let r = pixel[0] as u32;
-        let g = pixel[1] as u32;
-        let b = pixel[2] as u32;
-        let a = pixel[3] as u32;
-        let rgba = (r << 24) | (g << 16) | (b << 8) | a;
+/// For some feature return type we want to implement a custom compare function
+/// for example: historgrams are compared with cosine similarity
+impl PartialEq for FeatureResult {
+    fn eq(&self, other: &Self) -> bool {
+        match (self, other) {
+            (Self::Bool(l0), Self::Bool(r0)) => l0 == r0,
+            (Self::I32(l0), Self::I32(r0)) => l0 == r0,
+            (Self::F32(l0), Self::F32(r0)) => l0 == r0,
+            (Self::Vec(l0), Self::Vec(r0)) => l0 == r0,
+            (Self::RGBA(l0, l1, l2, l3), Self::RGBA(r0, r1, r2, r3)) => l0 == r0 && l1 == r1 && l2 == r2 && l3 == r3,
+            (Self::Indices(_), Self::Indices(_)) => todo!("implement cosine similarity"),
+            _ => false,
+        }
+    }
+}
 
-        *color_distribution.entry(rgba).or_insert(0.0) += 1.0;
+type FeatureGenerator = Box<dyn Fn(crate::Image<f32>) -> (String, FeatureResult)>;
+
+#[derive(Serialize, Deserialize, Default)]
+struct Database {
+    images: HashMap<String, HashMap<String, FeatureResult>>,
+
+    /// keep feature generator for the case when we add a new image
+    /// this field is not serialized and needs to be wrapped in an option
+    #[serde(skip)]
+    generators: Option<Vec<FeatureGenerator>>
+}
+
+impl Database {
+    
+    pub fn add_feature(&mut self, feature: FeatureGenerator) {
+        for (path, features) in self.images.iter_mut() {
+            // compute feature for every image
+            todo!("run this as a closure parallel with a thread pool");
+            let (name, res) = feature(todo!("load image from disk"));
+            features.insert(name, res);
+        }
+
+        if let Some(generators) = self.generators.as_mut() {
+            generators.push(feature);
+        } else {
+            self.generators = Some(vec![feature])
+        }
     }
 
-    for (_, count) in &mut color_distribution {
-        *count /= total_pixels;
+    pub fn add_image(&mut self, path: String) {
+        let image = todo!("load image from disk");
+        let mut features = HashMap::new();
+        if let Some(generators) = self.generators {
+            for generator in generators.iter() {
+                let (name, res) = generator(image);
+                features.insert(name, res);
+            }
+        }
+        self.images.insert(path, features);
     }
-
-    color_distribution
 }
 
-fn extract_average_brightness(image: &[u8]) -> u8 {
-    let mut sum: u32 = 0;
-    let mut count: u32 = 0;
-
-    for i in (0..image.len()).step_by(4) {
-        let r = image[i] as u32;
-        let g = image[i + 1] as u32;
-        let b = image[i + 2] as u32;
-
-        // (0.299 * R) + (0.587 * G) + (0.114 * B)
-        let brightness = ((0.299 * r as f32) + (0.587 * g as f32) + (0.114 * b as f32)).round() as u32;
-
-        sum += brightness;
-        count += 1;
-    }
-    let average_brightness = (sum / count) as u8;
-        average_brightness
+/// example feature implementation
+fn average_luminance(image: Image<f32>) -> (String, FeatureResult) {
+    (String::from("average-brightness"), FeatureResult::F32(0.0))
 }
 
+#[test]
+fn test() {
+    let mut data = Database::default();
 
-fn main() {
-
-test2();
+    data.add_feature(Box::new(average_luminance));
 
-}
-
-fn test2(){
-
-    let image_data: Vec<(u8, u8, u8, u8)> = vec![
-        (255, 0, 0, 255),   // Red
-        (0, 255, 0, 255),   // Green
-        (0, 0, 255, 255),   // Blue
-    ];
-    //convert image data to useable &u8 slice
-    let byte_slice: &[u8] = unsafe {
-        std::slice::from_raw_parts(
-            image_data.as_ptr() as *const u8,
-            image_data.len() * 4,
-        )
-    };
-    let color_distribution = extract_color_distribution(&byte_slice);
-    let color_distribution_vec: Vec<f32> = color_distribution.values().cloned().collect();
-    let average_brightness = extract_average_brightness(&byte_slice);
-    println!("{:?}", average_brightness);
-    println!("{:?}", color_distribution_vec);
+    let _as_json = serde_json::to_string(&data);
 }