Last active
February 16, 2019 00:59
-
-
Save symisc/9f9235dc3e590b773488b85da5f72ec5 to your computer and use it in GitHub Desktop.
Real-Time multi-scale face detection using SOD CNN - https://sod.pixlab.io
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
/* | |
* Programming introduction with the SOD Embedded Convolutional/Recurrent Neural Networks (CNN/RNN) API. | |
* 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 cnn_face_detection.c -lm -Ofast -march=native -Wall -std=c99 -o sod_cnn_intro | |
* | |
* 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 <stdio.h> | |
#include "sod.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 *zInput = argc > 1 ? argv[1] : "./cnn_faces.png"; | |
/* Draw detection boxes (i.e. rectangles) on this output image which | |
* is a copy of the input plus the boxes. | |
*/ | |
const char *zOut = argc > 2 ? argv[2] : "./out.png"; | |
/* | |
* The CNN handle that should perform the detection process */ | |
sod_cnn *pNet; | |
/* Load the input image */ | |
sod_img imgIn = sod_img_load_from_file(zInput,SOD_IMG_COLOR/* Full colors*/); | |
if (imgIn.data == 0) { | |
/* Invalid path, unsupported format, memory failure, etc. */ | |
puts("Cannot load input image..exiting"); | |
return 0; | |
} | |
/* Make a copy so we can draw anything we want. */ | |
sod_img imgOut = sod_copy_image(imgIn); | |
int rc; | |
const char *zErr; /* Error log if any */ | |
/* | |
* Create our CNN handle using the built-in `face` | |
* architecture trained to detect frontal, partial, | |
* tiny & large faces at Real-time. | |
*/ | |
rc = sod_cnn_create(&pNet, ":face", "./face_cnn.sod", &zErr); | |
/* | |
* ":face" is the magic word for the built-in face (single class) | |
* architecture. The list of built-in Magic words (pre-ready to use | |
* configurations and their associated models) are documented here: | |
* https://sod.pixlab.io/c_api/sod_cnn_create.html. | |
* | |
* "face.sod" is the pre-trained model associated with the ":face" architecture | |
* and is available to download from https://pixlab.io/downloads | |
*/ | |
if (rc != SOD_OK) { | |
/* Display the error message and exit */ | |
puts(zErr); | |
return 0; | |
} | |
/* | |
* A sod_box instance always store the coordinates for each detected object | |
* returned by the CNN via sod_cnn_predict() as we'll see later. | |
*/ | |
sod_box *box; | |
int i, nbox; | |
/* Prepare our input image for the detection process which | |
* is resized to the network dimension (This op is always very fast) | |
*/ | |
float * blob = sod_cnn_prepare_image(pNet, imgIn); | |
if (!blob) { | |
/* Very unlikely this happen: Invalid architecture, out-of-memory */ | |
puts("Something went wrong while preparing image.."); | |
return 0; | |
} | |
puts("Starting CNN face detection"); | |
/* Detect.. */ | |
sod_cnn_predict(pNet, blob, &box, &nbox); | |
/* Report the detection result. */ | |
printf("%d face(s) were detected..\n",nbox); | |
for (i = 0; i < nbox; i++) { | |
/* Report the coordinates and score of the current detected face */ | |
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); | |
if( box[i].score < 0.3) continue; /* Discard low score detection, remove if you want to report all objects */ | |
/* | |
* Draw a rose (RGB: 255,0,255) circle of width 3 on the object coordinates. */ | |
sod_image_draw_circle_thickness(imgOut, box[i].x + (box[i].w / 2), box[i].y + (box[i].h / 2), box[i].w, 5, 255., 0, 225.); | |
/* Of course, one could draw a box via sod_image_draw_bbox_width() or | |
* crop the entire region via sod_crop_image() instead of drawing a circle. */ | |
} | |
/* Finally save our output image with the boxes drawn on it */ | |
sod_img_save_as_png(imgOut, zOut); | |
/* Cleanup */ | |
sod_free_image(imgIn); | |
sod_free_image(imgOut); | |
/* Release all resources allocated to the CNN handle */ | |
sod_cnn_destroy(pNet); | |
return 0; | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
SOD Embedded Homepage: https://sod.pixlab.io
SOD C/C++ API documentation: https://sod.pixlab.io/api.html
Getting Started with SOD Embedded: https://sod.pixlab.io/intro.html