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93 lines
3.4 KiB
93 lines
3.4 KiB
/*
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* Programming introduction with the SOD Embedded RealNets API (Frontal Facial detection).
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* Copyright (C) PixLab | Symisc Systems, https://sod.pixlab.io
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*/
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/*
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* Compile this file together with the SOD embedded source code to generate
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* the executable. For example:
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*
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* gcc sod.c realnet_face_detection.c -lm -Ofast -march=native -Wall -std=c99 -o sod_realnet_face_detect
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*
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* Under Microsoft Visual Studio (>= 2015), just drop `sod.c` and its accompanying
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* header files on your source tree and you're done. If you have any trouble
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* integrating SOD in your project, please submit a support request at:
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* https://sod.pixlab.io/support.html
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*/
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/*
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* This simple program is a quick introduction on how to embed and start
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* experimenting with SOD without having to do a lot of tedious
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* reading and configuration.
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*
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* Make sure you have the latest release of SOD from:
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* https://pixlab.io/downloads
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* The SOD Embedded C/C++ documentation is available at:
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* https://sod.pixlab.io/api.html
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*/
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#include <stdio.h>
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#include "sod.h"
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int main(int argc, char *argv[])
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{
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/* Input image (pass a path or use the test image shipped with the samples ZIP archive) */
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const char *zFile = argc > 1 ? argv[1] : "./realnet_faces.jpg";
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/*
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* By default, RealNets are designed to process video streams thanks
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* to their very fast processing speed. However, for the sake of simplicity
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* we'll stick with images for this programming intro to RealNets.
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*/
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sod_realnet *pNet; /* Realnet handle */
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int i,rc;
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/*
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* Allocate a new RealNet handle */
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rc = sod_realnet_create(&pNet);
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if (rc != SOD_OK) return rc;
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/*
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* Register and load a RealNet model.
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* You can train your own RealNet model on your CPU using the training interfaces [sod_realnet_train_start()]
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* or download pre-trained models like this one from https://pixlab.io/downloads
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*/
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rc = sod_realnet_load_model_from_disk(pNet, "./face.realnet.sod", 0);
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if (rc != SOD_OK) return rc;
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/* Load the target image in grayscale colorspace */
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sod_img img = sod_img_load_grayscale(zFile);
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if (img.data == 0) {
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puts("Cannot load image");
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return 0;
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}
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/* Load a full color copy of the target image so we draw rose boxes
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* Note that drawing on grayscale images is also supported.
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*/
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sod_img color = sod_img_load_color(zFile);
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/*
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* convert the grayscale image to blob.
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*/
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unsigned char *zBlob = sod_image_to_blob(img);
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/*
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* Bounding boxes array
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*/
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sod_box *aBoxes;
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int nbox;
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/*
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* Perform Real-Time detection on this blob
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*/
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rc = sod_realnet_detect(pNet, zBlob, img.w, img.h, &aBoxes, &nbox);
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if (rc != SOD_OK) return rc;
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/* Consume result */
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printf("%d potential face(s) were detected..\n", nbox);
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for (i = 0; i < nbox; i++) {
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/* Ignore low score detection */
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if (aBoxes[i].score < 5.0) continue;
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/* Report current object */
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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);
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/* Draw a rose box on the target coordinates */
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sod_image_draw_bbox_width(color, aBoxes[i], 3, 255., 0, 225.);
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//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.);
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}
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/* Save the detection result */
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sod_img_save_as_png(color, argc > 2 ? argv[2] : "./out.png");
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/* cleanup */
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sod_free_image(img);
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sod_free_image(color);
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sod_image_free_blob(zBlob);
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sod_realnet_destroy(pNet);
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return 0;
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} |