broken: Remove the FaceDetectionConfig
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@@ -6,50 +6,13 @@ use nalgebra::{Point2, Vector2};
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use ndarray_resize::NdFir;
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use std::path::Path;
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pub struct FaceDetectionConfig {
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anchor_sizes: Vec<Vector2<usize>>,
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steps: Vec<usize>,
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variance: Vec<f32>,
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threshold: f32,
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nms_threshold: f32,
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}
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pub struct FaceDetectionConfig {}
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impl FaceDetectionConfig {
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pub fn with_min_sizes(mut self, min_sizes: Vec<Vector2<usize>>) -> Self {
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self.anchor_sizes = min_sizes;
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self
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}
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pub fn with_steps(mut self, steps: Vec<usize>) -> Self {
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self.steps = steps;
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self
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}
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pub fn with_variance(mut self, variance: Vec<f32>) -> Self {
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self.variance = variance;
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self
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}
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pub fn with_threshold(mut self, threshold: f32) -> Self {
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self.threshold = threshold;
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self
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}
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pub fn with_nms_threshold(mut self, nms_threshold: f32) -> Self {
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self.nms_threshold = nms_threshold;
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self
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}
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}
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impl FaceDetectionConfig {}
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impl Default for FaceDetectionConfig {
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fn default() -> Self {
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FaceDetectionConfig {
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anchor_sizes: vec![
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Vector2::new(16, 32),
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Vector2::new(64, 128),
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Vector2::new(256, 512),
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],
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steps: vec![8, 16, 32],
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variance: vec![0.1, 0.2],
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threshold: 0.8,
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nms_threshold: 0.4,
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}
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FaceDetectionConfig {}
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}
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}
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pub struct FaceDetection {
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@@ -89,79 +52,6 @@ pub struct FaceDetectionOutput {
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impl FaceDetectionModelOutput {
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pub fn postprocess(self, config: &FaceDetectionConfig) -> Result<FaceDetectionProcessedOutput> {
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let mut anchors = Vec::new();
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for (k, &step) in config.steps.iter().enumerate() {
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let feature_size = 1024 / step;
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let min_sizes = config.anchor_sizes[k];
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let sizes = [min_sizes.x, min_sizes.y];
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for i in 0..feature_size {
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for j in 0..feature_size {
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for &size in &sizes {
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let cx = (j as f32 + 0.5) * step as f32 / 1024.0;
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let cy = (i as f32 + 0.5) * step as f32 / 1024.0;
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let s_k = size as f32 / 1024.0;
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anchors.push((cx, cy, s_k, s_k));
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}
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}
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}
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}
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let mut boxes = Vec::new();
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let mut scores = Vec::new();
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let mut landmarks = Vec::new();
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let var0 = config.variance[0];
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let var1 = config.variance[1];
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let bbox_data = self.bbox;
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let conf_data = self.confidence;
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let landmark_data = self.landmark;
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let num_priors = bbox_data.shape()[1];
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for idx in 0..num_priors {
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let dx = bbox_data[[0, idx, 0]];
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let dy = bbox_data[[0, idx, 1]];
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let dw = bbox_data[[0, idx, 2]];
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let dh = bbox_data[[0, idx, 3]];
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let (anchor_cx, anchor_cy, anchor_w, anchor_h) = anchors[idx];
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let pred_cx = anchor_cx + dx * var0 * anchor_w;
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let pred_cy = anchor_cy + dy * var0 * anchor_h;
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let pred_w = anchor_w * (dw * var1).exp();
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let pred_h = anchor_h * (dh * var1).exp();
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let x_min = pred_cx - pred_w / 2.0;
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let y_min = pred_cy - pred_h / 2.0;
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let x_max = pred_cx + pred_w / 2.0;
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let y_max = pred_cy + pred_h / 2.0;
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let score = conf_data[[0, idx, 1]];
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if score > config.threshold {
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boxes.push(Aabb2::from_x1y1x2y2(x_min, y_min, x_max, y_max));
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scores.push(score);
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let left_eye_x = landmark_data[[0, idx, 0]] * anchor_w * var0 + anchor_cx;
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let left_eye_y = landmark_data[[0, idx, 1]] * anchor_h * var0 + anchor_cy;
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let right_eye_x = landmark_data[[0, idx, 2]] * anchor_w * var0 + anchor_cx;
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let right_eye_y = landmark_data[[0, idx, 3]] * anchor_h * var0 + anchor_cy;
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let nose_x = landmark_data[[0, idx, 4]] * anchor_w * var0 + anchor_cx;
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let nose_y = landmark_data[[0, idx, 5]] * anchor_h * var0 + anchor_cy;
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let left_mouth_x = landmark_data[[0, idx, 6]] * anchor_w * var0 + anchor_cx;
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let left_mouth_y = landmark_data[[0, idx, 7]] * anchor_h * var0 + anchor_cy;
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let right_mouth_x = landmark_data[[0, idx, 8]] * anchor_w * var0 + anchor_cx;
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let right_mouth_y = landmark_data[[0, idx, 9]] * anchor_h * var0 + anchor_cy;
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landmarks.push(FaceLandmarks {
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left_eye: Point2::new(left_eye_x, left_eye_y),
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right_eye: Point2::new(right_eye_x, right_eye_y),
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nose: Point2::new(nose_x, nose_y),
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left_mouth: Point2::new(left_mouth_x, left_mouth_y),
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right_mouth: Point2::new(right_mouth_x, right_mouth_y),
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});
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}
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}
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Ok(FaceDetectionProcessedOutput {
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bbox: boxes,
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confidence: scores,
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landmarks,
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})
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}
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}
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