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</pre><pre class="rust"><code><span class="comment">// This file is part of the open-source port of SeetaFace engine, which originally includes three modules:
// SeetaFace Detection, SeetaFace Alignment, and SeetaFace Identification.
//
// This file is part of the SeetaFace Detection module, containing codes implementing the face detection method described in the following paper:
//
// Funnel-structured cascade for multi-view face detection with alignment awareness,
// Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen.
// In Neurocomputing (under review)
//
// Copyright (C) 2016, Visual Information Processing and Learning (VIPL) group,
// Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
//
// As an open-source face recognition engine: you can redistribute SeetaFace source codes
// and/or modify it under the terms of the BSD 2-Clause License.
//
// You should have received a copy of the BSD 2-Clause License along with the software.
// If not, see &lt; https://opensource.org/licenses/BSD-2-Clause&gt;.
</span><span class="kw">use </span><span class="kw">super</span>::Score;
<span class="kw">use </span><span class="kw">crate</span>::feat::LabBoostedFeatureMap;
<span class="kw">use </span><span class="kw">crate</span>::Rectangle;
<span class="attribute">#[derive(Clone)]
</span><span class="kw">pub struct </span>LabBoostedClassifier {
features: Vec&lt;(i32, i32)&gt;,
base_classifiers: Vec&lt;BaseClassifier&gt;,
}
<span class="attribute">#[derive(Clone)]
</span><span class="kw">struct </span>BaseClassifier {
weights: Vec&lt;f32&gt;,
thresh: f32,
}
<span class="kw">impl </span>LabBoostedClassifier {
<span class="attribute">#[inline]
</span><span class="kw">pub fn </span>new() -&gt; <span class="self">Self </span>{
LabBoostedClassifier {
features: Vec::new(),
base_classifiers: Vec::new(),
}
}
<span class="attribute">#[inline]
</span><span class="kw">pub fn </span>add_feature(<span class="kw-2">&amp;mut </span><span class="self">self</span>, x: i32, y: i32) {
<span class="self">self</span>.features.push((x, y));
}
<span class="attribute">#[inline]
</span><span class="kw">pub fn </span>add_base_classifier(<span class="kw-2">&amp;mut </span><span class="self">self</span>, weights: Vec&lt;f32&gt;, thresh: f32) {
<span class="self">self</span>.base_classifiers
.push(BaseClassifier { weights, thresh })
}
}
<span class="kw">const </span>K_FEAT_GROUP_SIZE: usize = <span class="number">10</span>;
<span class="kw">const </span>K_STDDEV_THRESH: f64 = <span class="number">10.0</span>;
<span class="kw">impl </span>LabBoostedClassifier {
<span class="kw">pub fn </span>classify(<span class="kw-2">&amp;</span><span class="self">self</span>, feature_map: <span class="kw-2">&amp;</span>LabBoostedFeatureMap, roi: Rectangle) -&gt; Score {
<span class="kw">let </span><span class="kw-2">mut </span>positive = <span class="bool-val">true</span>;
<span class="kw">let </span><span class="kw-2">mut </span>score = <span class="number">0.0</span>;
<span class="kw">let </span><span class="kw-2">mut </span>i = <span class="number">0</span>;
<span class="kw">while </span>positive &amp;&amp; i &lt; <span class="self">self</span>.base_classifiers.len() {
<span class="kw">for _ in </span><span class="number">0</span>..K_FEAT_GROUP_SIZE {
<span class="kw">let </span>(offset_x, offset_y) = <span class="self">self</span>.features[i];
<span class="kw">let </span>feature_val = feature_map.get_feature_val(offset_x, offset_y, roi);
score += <span class="self">self</span>.base_classifiers[i].weights[feature_val <span class="kw">as </span>usize];
i += <span class="number">1</span>;
}
positive = score &gt;= <span class="self">self</span>.base_classifiers[i - <span class="number">1</span>].thresh;
}
positive = positive &amp;&amp; (feature_map.get_std_dev(roi) &gt; K_STDDEV_THRESH);
Score { positive, score }
}
}
</code></pre></div>
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