Remove embedding overlay and facenet references

RidgeRun · Mar 30, 2021 · 50c6ba5 · 50c6ba5
1 parent 6233f97
commit 50c6ba5
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Showing 9 changed files with 2 additions and 89 deletions.
diff --git a/.gitignore b/.gitignore
@@ -38,4 +38,3 @@ cscope.out
 # Examples
 tests/examples/classification/classification
 tests/examples/detection/detection
-tests/examples/embedding/embedding
diff --git a/README.md b/README.md
@@ -10,7 +10,7 @@ GstInference is an open-source project from Ridgerun Engineering that provides a
 
 This repo uses **[R²Inference](https://github.com/RidgeRun/r2inference)**, an abstraction layer in C/C++ for a variety of machine learning frameworks. With R²Inference a single C/C++ application may work with models on different frameworks. This is useful to execute inference taking advantage of different hardware resources such as CPU, GPU, or  AI optimized acelerators.
 
-GstInference provides several example elements for common applications, such as [`Inception v4`](ext/r2inference/gstinceptionv4.c) for image classification, [`TinyYOLO v2`](ext/r2inference/gsttinyyolov2.c) for object detection, and [`FaceNet`](ext/r2inference/gstfacenetv1.c) for face recognition. Examples are provided for performing inference on any GStreamer video stream.
+GstInference provides several example elements for common applications, such as [`Inception v4`](ext/r2inference/gstinceptionv4.c) for image classification and  [`TinyYOLO v2`](ext/r2inference/gsttinyyolov2.c) for object detection. Examples are provided for performing inference on any GStreamer video stream.
 
 <img src="https://developer.ridgerun.com/wiki/images/thumb/4/4f/GstInference-examples.jpeg/800px-GstInference-examples.jpeg" width="800">
 
@@ -27,8 +27,6 @@ Follow the steps to get GstInference running on your platform:
 
 We provide GStreamer [example pipelines](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_pipelines) for all our suported platforms,architectures and backends. 
 
-We also provide [example applications](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_Applications) for classification, detection and face recognition.
-
-Our [smart lock](tests/examples/face_detection/README.md) example can get you started with a real security camera application.
+We also provide [example applications](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Example_Applications) for classification and detection.
 
 We also provide example trained models on our [model zoo](https://developer.ridgerun.com/wiki/index.php?title=GstInference/Model_Zoo)
diff --git a/ext/opencv/gstplugin.cc b/ext/opencv/gstplugin.cc
@@ -23,20 +23,13 @@
 #include "config.h"
 #endif
 
-#include "gstembeddingoverlay.h"
 #include "gstinferenceoverlay.h"
 
 static gboolean
 plugin_init (GstPlugin * plugin)
 {
   gboolean ret = TRUE;
 
-  ret =
-      gst_element_register (plugin, "embeddingoverlay", GST_RANK_NONE,
-      GST_TYPE_EMBEDDING_OVERLAY);
-  if (!ret) {
-    goto out;
-  }
   ret =
       gst_element_register (plugin, "inferenceoverlay", GST_RANK_NONE,
       GST_TYPE_INFERENCE_OVERLAY);

diff --git a/ext/opencv/meson.build b/ext/opencv/meson.build
@@ -1,7 +1,6 @@
 gstinference_sources = [
   'gstplugin.cc',
   'gstinferenceoverlay.cc',
-  'gstembeddingoverlay.cc'
 ]
 
 if opencv_dep.found()

diff --git a/ext/r2inference/gstinference.c b/ext/r2inference/gstinference.c
@@ -29,7 +29,6 @@
 #include "gstinceptionv4.h"
 #include "gsttinyyolov2.h"
 #include "gsttinyyolov3.h"
-#include "gstfacenetv1.h"
 #include "gstresnet50v1.h"
 #include "gstmobilenetv2.h"
 #include "gstmobilenetv2ssd.h"
@@ -90,12 +89,6 @@ plugin_init (GstPlugin * plugin)
     goto out;
   }
 
-  ret = gst_element_register (plugin, "facenetv1", GST_RANK_NONE,
-      GST_TYPE_FACENETV1);
-  if (!ret) {
-    goto out;
-  }
-
   ret = gst_element_register (plugin, "mobilenetv2ssd", GST_RANK_NONE,
       GST_TYPE_MOBILENETV2SSD);
   if (!ret) {

diff --git a/ext/r2inference/meson.build b/ext/r2inference/meson.build
@@ -1,5 +1,4 @@
 gstinference_sources = [
-	'gstfacenetv1.c',
 	'gstinceptionv1.c',
 	'gstinceptionv2.c',
 	'gstinceptionv3.c',

diff --git a/gst-libs/gst/r2inference/gstinferencepreprocess.c b/gst-libs/gst/r2inference/gstinferencepreprocess.c
@@ -132,63 +132,6 @@ gst_normalize (GstVideoFrame * inframe, GstVideoFrame * outframe, gdouble mean,
   return TRUE;
 }
 
-gboolean
-gst_normalize_face (GstVideoFrame * inframe, GstVideoFrame * outframe,
-    gint model_channels)
-{
-  gint i, j, pixel_stride, width, height, channels;
-  gdouble mean, std, variance, sum, normalized, R, G, B;
-  gint first_index = 0, last_index = 0, offset = 0;
-
-  channels = GST_VIDEO_FRAME_N_COMPONENTS (inframe);
-  pixel_stride = GST_VIDEO_FRAME_COMP_STRIDE (inframe, 0) / channels;
-  width = GST_VIDEO_FRAME_WIDTH (inframe);
-  height = GST_VIDEO_FRAME_HEIGHT (inframe);
-
-  sum = 0;
-  normalized = 0;
-
-  g_return_val_if_fail (inframe != NULL, FALSE);
-  g_return_val_if_fail (outframe != NULL, FALSE);
-
-  for (i = 0; i < height; ++i) {
-    for (j = 0; j < width; ++j) {
-      sum =
-          sum + (((guchar *) inframe->data[0])[(i * pixel_stride +
-                  j) * channels + 0]);
-      sum =
-          sum + (((guchar *) inframe->data[0])[(i * pixel_stride +
-                  j) * channels + 1]);
-      sum =
-          sum + (((guchar *) inframe->data[0])[(i * pixel_stride +
-                  j) * channels + 2]);
-    }
-  }
-
-  mean = sum / (float) (width * height * channels);
-
-  for (i = 0; i < height; ++i) {
-    for (j = 0; j < width; ++j) {
-      R = (gfloat) ((((guchar *) inframe->data[0])[(i * pixel_stride +
-                      j) * channels + 0])) - mean;
-      G = (gfloat) ((((guchar *) inframe->data[0])[(i * pixel_stride +
-                      j) * channels + 1])) - mean;
-      B = (gfloat) ((((guchar *) inframe->data[0])[(i * pixel_stride +
-                      j) * channels + 2])) - mean;
-      normalized = normalized + pow (R, 2);
-      normalized = normalized + pow (G, 2);
-      normalized = normalized + pow (B, 2);
-    }
-  }
-
-  variance = normalized / (float) (width * height * channels);
-  std = 1 / sqrt (variance);
-
-  gst_apply_means_std (inframe, outframe, first_index, last_index, offset,
-      channels, mean, mean, mean, std, std, std, model_channels);
-  return TRUE;
-}
-
 gboolean
 gst_subtract_mean (GstVideoFrame * inframe, GstVideoFrame * outframe,
     gdouble mean_red, gdouble mean_green, gdouble mean_blue,

diff --git a/gst-libs/gst/r2inference/gstinferencepreprocess.h b/gst-libs/gst/r2inference/gstinferencepreprocess.h
@@ -38,16 +38,6 @@ G_BEGIN_DECLS
 
 gboolean gst_normalize(GstVideoFrame * inframe, GstVideoFrame * outframe, gdouble mean, gdouble std, gint model_channels);
 
-/**
- * \brief Especial normalization used for facenet
- *
- * \param inframe The input frame
- * \param outframe The output frame after preprocess
- * \param model_channels The number of channels of the model
- */
-
-gboolean gst_normalize_face(GstVideoFrame * inframe, GstVideoFrame * outframe, gint model_channels);
-
 /**
  * \brief Substract the mean value to every pixel
  *

diff --git a/tests/examples/meson.build b/tests/examples/meson.build
@@ -1,3 +1,2 @@
 subdir('classification')
 subdir('detection')
-subdir('embedding')