From 35d3d9be5ef89fc55ba86ce52831ac341c12a8c5 Mon Sep 17 00:00:00 2001 From: Symisc Systems Date: Tue, 16 Apr 2019 03:46:10 +0200 Subject: [PATCH] Embedded real-time face detection via Realnets Real-time frontal face detection via SOD Realnets (~ 5 ms on HD video). This is the built-in realnet embedded face detection model. There is no need to bring an independent model in order to detect faces at real-time. All you need, is to register the model hex array defined in "sod_face_realnet.h" via `sod_realnet_load_model_from_mem()` and use the same C/C++ as for detecting faces via Realnets. This feature is available in the commercial version of the library. You can obtain your commercial license from https://pixlab.io/downloads. Advantages includes: * Multi-core CPU support for all platforms - Up to 3 ~ 10 times faster processing speed. * Built-in (C Code), high performance RealNets frontal face detector. * 75 days of integration & technical assistance. * Royalty-free commercial licenses without any GPL restrictions. * Application source code stays private. --- samples/realnet_face_detection_embedded.c | 114 ++++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 samples/realnet_face_detection_embedded.c diff --git a/samples/realnet_face_detection_embedded.c b/samples/realnet_face_detection_embedded.c new file mode 100644 index 0000000..8218b60 --- /dev/null +++ b/samples/realnet_face_detection_embedded.c @@ -0,0 +1,114 @@ +/* + * Programming introduction with the SOD Embedded RealNets API (Frontal Facial detection). + * Copyright (C) PixLab | Symisc Systems, https://sod.pixlab.io + */ +/* +* Compile this file together with the SOD embedded source code to generate +* the executable. For example: +* +* gcc sod.c realnet_face_detection_embedded.c -lm -Ofast -march=native -Wall -std=c99 -o sod_realnet_face_detect +* +* Under Microsoft Visual Studio (>= 2015), just drop `sod.c` and its accompanying +* header files on your source tree and you're done. If you have any trouble +* integrating SOD in your project, please submit a support request at: +* https://sod.pixlab.io/support.html +*/ +/* +* This simple program is a quick introduction on how to embed and start +* experimenting with SOD without having to do a lot of tedious +* reading and configuration. +* +* Make sure you have the latest release of SOD from: +* https://pixlab.io/downloads +* The SOD Embedded C/C++ documentation is available at: +* https://sod.pixlab.io/api.html +*/ +#include +#include "sod.h" +/* + * The following include is shipped with the built-in realnet embedded face detection model. + * There is no need to bring an independent model in order to detect faces at real-time. + * + * All you need, is to register the model hex array defined in "sod_face_realnet.h" via + * `sod_realnet_load_model_from_mem()` and use the same C/C++ as for detecting faces + * via Realnets as shown below in this sample. + * + * This feature is available in the commercial version of the library. + * You can obtain your commercial license from https://pixlab.io/downloads. + * + * Advantages includes: + * + * Multi-core CPU support for all platforms - Up to 3 ~ 10 times faster processing speed. + * Built-in (C Code), high performance RealNets frontal face detector. + * 75 days of integration & technical assistance. + * Royalty-free commercial licenses without any GPL restrictions. + * Application source code stays private. + */ +#include "sod_face_realnet.h" +int main(int argc, char *argv[]) +{ + /* Input image (pass a path or use the test image shipped with the samples ZIP archive) */ + const char *zFile = argc > 1 ? argv[1] : "./realnet_faces.jpg"; + /* + * By default, RealNets are designed to process video streams thanks + * to their very fast processing speed. However, for the sake of simplicity + * we'll stick with images for this programming intro to RealNets. + */ + sod_realnet *pNet; /* Realnet handle */ + int i,rc; + /* + * Allocate a new RealNet handle */ + rc = sod_realnet_create(&pNet); + if (rc != SOD_OK) return rc; + /* + * Load our RealNet model from memory. No need to bring an external one like the previous sample. + * + * You can train your own RealNet model on your CPU using the training interfaces [sod_realnet_train_start()] + * or download pre-trained models like this one from https://pixlab.io/downloads + */ + rc = sod_realnet_load_model_from_mem(pNet, face_model, face_model_length, 0); + if (rc != SOD_OK) return rc; + /* Load the target image in grayscale colorspace */ + sod_img img = sod_img_load_grayscale(zFile); + if (img.data == 0) { + puts("Cannot load image"); + return 0; + } + /* Load a full color copy of the target image so we draw rose boxes + * Note that drawing on grayscale images is also supported. + */ + sod_img color = sod_img_load_color(zFile); + /* + * convert the grayscale image to blob. + */ + unsigned char *zBlob = sod_image_to_blob(img); + /* + * Bounding boxes array + */ + sod_box *aBoxes; + int nbox; + /* + * Perform Real-Time detection on this blob + */ + rc = sod_realnet_detect(pNet, zBlob, img.w, img.h, &aBoxes, &nbox); + if (rc != SOD_OK) return rc; + /* Consume result */ + printf("%d potential face(s) were detected..\n", nbox); + for (i = 0; i < nbox; i++) { + /* Ignore low score detection */ + if (aBoxes[i].score < 5.0) continue; + /* Report current object */ + printf("(%s) x:%d y:%d w:%d h:%d prob:%f\n", aBoxes[i].zName, aBoxes[i].x, aBoxes[i].y, aBoxes[i].w, aBoxes[i].h, aBoxes[i].score); + /* Draw a rose box on the target coordinates */ + sod_image_draw_bbox_width(color, aBoxes[i], 3, 255., 0, 225.); + //sod_image_draw_circle(color, aBoxes[i].x + (aBoxes[i].w / 2), aBoxes[i].y + (aBoxes[i].h / 2), aBoxes[i].w, 255., 0, 225.); + } + /* Save the detection result */ + sod_img_save_as_png(color, argc > 2 ? argv[2] : "./out.png"); + /* cleanup */ + sod_free_image(img); + sod_free_image(color); + sod_image_free_blob(zBlob); + sod_realnet_destroy(pNet); + return 0; +}