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109 lines
4.2 KiB
109 lines
4.2 KiB
/*
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* Programming introduction with the SOD Embedded Convolutional/Recurrent Neural Networks (CNN/RNN) API.
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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 cnn_coco.c -lm -Ofast -march=native -Wall -std=c99 -o sod_cnn_intro
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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 *zInput = argc > 1 ? argv[1] : "./test.png";
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/* Draw detection boxes (i.e. rectangles) on this output image which
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* is a copy of the input plus the boxes.
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*/
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const char *zOut = argc > 2 ? argv[2] : "./out.png";
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/*
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* The CNN handle that should perform the detection process */
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sod_cnn *pNet;
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/* Load the input image */
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sod_img imgIn = sod_img_load_from_file(zInput,SOD_IMG_COLOR/* Full colors*/);
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if (imgIn.data == 0) {
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/* Invalid path, unsupported format, memory failure, etc. */
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puts("Cannot load input image..exiting");
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return 0;
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}
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/* Make a copy so we can draw anything we want. */
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sod_img imgOut = sod_copy_image(imgIn);
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int rc;
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const char *zErr; /* Error log if any */
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/*
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* Create our CNN handle using the built-in fast
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* architecture trained on the MS COCO dataset
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* and is able to detect 80 classes of objects at
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* real-time on a modern CPU.
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*/
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rc = sod_cnn_create(&pNet, ":coco", "./tiny80.sod", &zErr);
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/*
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* ":coco" is the magic word for the built-in MS COCO (80 classes)
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* fast architecture. The list of built-in Magic words (pre-ready to use
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* configurations and their associated models) are documented here:
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* https://sod.pixlab.io/c_api/sod_cnn_create.html.
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*
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* "tiny80.sod" is the pre-trained model associated with the ":coco" architecture
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* and is available to download from https://pixlab.io/downloads
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*/
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if (rc != SOD_OK) {
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/* Display the error message and exit */
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puts(zErr);
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return 0;
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}
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/*
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* A sod_box instance always store the coordinates for each detected object
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* returned by the CNN via sod_cnn_predict() as we'll see later.
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*/
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sod_box *box;
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int i, nbox;
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/* Prepare our input image for the detection process which
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* is resized to the network dimension (This op is always very fast)
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*/
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float * blob = sod_cnn_prepare_image(pNet, imgIn);
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if (!blob) {
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/* Very unlikely this happen: Invalid architecture, out-of-memory */
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puts("Something went wrong while preparing image..");
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return 0;
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}
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puts("Starting CNN object detection");
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/* Detect.. */
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sod_cnn_predict(pNet, blob, &box, &nbox);
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/* Report the detection result. */
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printf("%d object(s) were detected..\n",nbox);
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for (i = 0; i < nbox; i++) {
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/* Report the coordinates, name and score of the current detected object */
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printf("(%s) X:%d Y:%d Width:%d Height:%d score:%f%%\n", box[i].zName, box[i].x, box[i].y, box[i].w, box[i].h, box[i].score * 100);
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if( box[i].score < 0.3) continue; /* Discard low score detection, remove if you want to report all objects */
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/*
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* Draw a rose (RGB: 255,0,255) rectangle of width 3 on the object coordinates. */
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sod_image_draw_bbox_width(imgOut, box[i], 3, 255., 0, 225.);
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/* Of course, one could draw a circle via sod_image_draw_circle() or
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* crop the entire region via sod_crop_image() instead of drawing a rectangle. */
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}
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/* Finally save our output image with the boxes drawn on it */
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sod_img_save_as_png(imgOut, zOut);
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/* Cleanup */
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sod_free_image(imgIn);
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sod_free_image(imgOut);
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/* Release all resources allocated to the CNN handle */
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sod_cnn_destroy(pNet);
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return 0;
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} |