add self learn classifier support

sipeed · Jun 19, 2024 · 382fed4 · 382fed4
1 parent 69c61b0
commit 382fed4
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Showing 5 changed files with 622 additions and 2 deletions.
diff --git a/components/maixcam_lib/include/maix_nn_maixcam.hpp b/components/maixcam_lib/include/maix_nn_maixcam.hpp
@@ -4,6 +4,7 @@
 namespace maix::nn
 {
     err::Err maixcam_load_cvimodel(const std::string &model_path, MUD *mud_obj);
+    int maix_nn_self_learn_classifier_learn(std::vector<float *> &features, std::vector<float *> &features_samples, int feature_num);
 
     class NN_MaixCam : public NNBase
     {

diff --git a/components/maixcam_lib/lib/libmaixcam_lib.so b/components/maixcam_lib/lib/libmaixcam_lib.so
diff --git a/components/nn/include/maix_nn.hpp b/components/nn/include/maix_nn.hpp
@@ -98,6 +98,20 @@ namespace maix::nn
          */
         std::vector<int> shape;
 
+        /**
+         * Shape as one int type, multiply all dims of shape
+         * @maixpy maix.nn.LayerInfo.shape_int
+         */
+        int shape_int()
+        {
+            int n = shape.size() == 0 ? 0 : 1;
+            for(auto i : shape)
+            {
+                n *= i;
+            }
+            return n;
+        }
+
         /**
          * To string
          * @maixpy maix.nn.LayerInfo.to_str

diff --git a/components/nn/include/maix_nn_classifier.hpp b/components/nn/include/maix_nn_classifier.hpp
@@ -215,18 +215,19 @@ namespace maix::nn
          * Forward image to model, get result. Only for image input, use classify_raw for tensor input.
          * @param img image, format should match model input_type， or will raise err.Exception
          * @param softmax if true, will do softmax to result, or will return raw value
+         * @param fit image resize fit mode, default Fit.FIT_COVER, see image.Fit.
          * @throw If error occurred, will raise err::Exception, you can find reason in log, mostly caused by args error or hardware error.
          * @return result, a list of (label, score). In C++, you need to delete it after use.
          * @maixpy maix.nn.Classifier.classify
          */
-        std::vector<std::pair<int, float>> *classify(image::Image &img, bool softmax = true)
+        std::vector<std::pair<int, float>> *classify(image::Image &img, bool softmax = true, image::Fit fit = image::FIT_COVER)
         {
             if (img.format() != _input_img_fmt)
             {
                 throw err::Exception("image format not match, input_type: " + image::fmt_names[_input_img_fmt] + ", image format: " + image::fmt_names[img.format()]);
             }
             tensor::Tensors *outputs;
-            outputs = _model->forward_image(img, this->mean, this->scale, maix::image::FIT_COVER, false);
+            outputs = _model->forward_image(img, this->mean, this->scale, fit, false);
             if (!outputs)
             {
                 throw err::Exception("forward image failed");