feat: added some post processing for retinaface

src/facedet.rs

@@ -1,13 +1,14 @@
 use crate::errors::*;
+use bounding_box::Aabb2;
 use error_stack::ResultExt;
 use mnn_bridge::ndarray::*;
-use nalgebra::Point2;
+use nalgebra::{Point2, Vector2};
 use ndarray_resize::NdFir;
 use std::path::Path;
 
 pub struct FaceDetectionConfig {
-    min_sizes: Vec<Point2<u32>>,
-    steps: Vec<u32>,
+    min_sizes: Vec<Vector2<usize>>,
+    steps: Vec<usize>,
     variance: Vec<f32>,
 }
 pub struct FaceDetection {
@@ -20,6 +21,37 @@ pub struct FaceDetectionModelOutput {
     pub landmark: ndarray::Array3<f32>,
 }
 
+impl FaceDetectionModelOutput {
+    pub fn postprocess(self, config: FaceDetectionConfig) -> Result<Vec<Aabb2<f32>>> {
+        // for k, step in enumerate(cfg['steps']):
+        //     feature_size = 640 // step
+        //     for i in range(feature_size):
+        //         for j in range(feature_size):
+        //             for min_size in cfg['min_sizes'][k]:
+        //                 cx = (j + 0.5) * step / 640
+        //                 cy = (i + 0.5) * step / 640
+        //                 s_kx = s_ky = min_size / 640
+        //                 anchors.append([cx, cy, s_kx, s_ky])
+        let mut anchors = Vec::new();
+        config.steps.iter().enumerate().for_each(|(k, step)| {
+            let feature_size = 640 / step;
+            for i in 0..feature_size {
+                for j in 0..feature_size {
+                    for min_size in &config.min_sizes[k] {
+                        let cx = (j as f32 + 0.5) * *step as f32 / 640.0;
+                        let cy = (i as f32 + 0.5) * *step as f32 / 640.0;
+                        let s_kx = *min_size as f32 / 640.0;
+                        let s_ky = *min_size as f32 / 640.0;
+                        anchors.push([cx, cy, s_kx, s_ky]);
+                    }
+                }
+            }
+        });
+
+        Ok(Vec::new())
+    }
+}
+
 impl FaceDetectionModelOutput {
     pub fn print(&self, limit: usize) {
         tracing::info!("Detected {} faces", self.bbox.shape()[1]);