Bug fixes and optimization

This commit is contained in:
iabdalkader 2014-09-12 18:59:49 +02:00
parent c30adb6ea1
commit 9fa248f44b

View File

@ -3,16 +3,47 @@
#include "xalloc.h" #include "xalloc.h"
#include "imlib.h" #include "imlib.h"
#include <arm_math.h> #include <arm_math.h>
/*
/* Viola-Jones face detector implementation * This file is part of the OpenMV project.
* Original Author: Francesco Comaschi (f.comaschi@tue.nl) * Copyright (c) 2013 Ibrahim Abd Elkader <i.abdalkader@gmail.com>
* This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 License.
* To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/
*
* Viola-Jones face detector implementation.
* Original Author: Francesco Comaschi (f.comaschi@tue.nl)
*
*/ */
static int evalWeakClassifier(cascade_t *cascade, int std, int p_offset, int tree_index, int w_index, int r_index) static int imlib_std(image_t *image)
{
int w=image->w;
int h=image->h;
int n = w*h;
uint8_t *data = image->pixels;
uint32_t s=0, sq=0;
for (int i=0; i<n; i+=2) {
s += data[i+0] + data[i+1];
uint32_t v0 = __PKHBT(data[i+0],
data[i+1], 16);
sq = __SMLAD(v0, v0, sq);
}
/* mean */
int m = s/n;
/* variance */
uint32_t v = sq*24*24-(m*m);
/* std */
return fast_sqrtf(v);
}
static int evalWeakClassifier(cascade_t *cascade, int p_offset, int tree_index, int w_index, int r_index)
{ {
int sumw=0; int sumw=0;
i_image_t *sum = cascade->sum; i_image_t *sum = cascade->sum;
/* the node threshold is multiplied by the standard deviation of the image */ /* the node threshold is multiplied by the standard deviation of the image */
int t = cascade->tree_thresh_array[tree_index] * std; int t = cascade->tree_thresh_array[tree_index] * cascade->std;
for (int i=0; i<cascade->num_rectangles_array[tree_index]; i++) { for (int i=0; i<cascade->num_rectangles_array[tree_index]; i++) {
int x = cascade->rectangles_array[r_index + (i<<2) + 0]; int x = cascade->rectangles_array[r_index + (i<<2) + 0];
@ -36,48 +67,18 @@ static int evalWeakClassifier(cascade_t *cascade, int std, int p_offset, int tre
return cascade->alpha1_array[tree_index]; return cascade->alpha1_array[tree_index];
} }
static int runCascadeClassifier(cascade_t* cascade, struct point pt, int start_stage) static int runCascadeClassifier(cascade_t* cascade, struct point pt, int start_stage)
{ {
int w_index = 0; int w_index = 0;
int r_index = 0; int r_index = 0;
int tree_index = 0; int tree_index = 0;
int p_offset = pt.y * cascade->sum->w + pt.x;
uint32_t sumsq = 0;
int32_t std, mean;
int x_ratio = cascade->x_ratio;
int y_ratio = cascade->y_ratio;
image_t *image = cascade->img;
for (int y=pt.y; y<cascade->window.h; y++) {
int sy = ((y*y_ratio)>>16)*image->w;
for (int x=pt.x; x<cascade->window.w; x+=2) {
int sx = sy+((x*x_ratio)>>16);
uint32_t v0 = __PKHBT(image->pixels[sx+0],
image->pixels[sx+1], 16);
sumsq = __SMLAD(v0, v0, sumsq);
}
}
/* Image normalization */
i_image_t *sum = cascade->sum;
int win_w = cascade->window.w - 1;
int win_h = cascade->window.h - 1;
int p_offset = pt.y * (sum->w) + pt.x;
mean = sum->data[p_offset]
- sum->data[win_w + p_offset]
- sum->data[sum->w * win_h + p_offset]
+ sum->data[sum->w * win_h + win_w + p_offset];
std = fast_sqrtf(sumsq * cascade->window.w * cascade->window.h - mean * mean);
for (int i=start_stage; i<cascade->n_stages; i++) { for (int i=start_stage; i<cascade->n_stages; i++) {
int stage_sum = 0; int stage_sum = 0;
for (int j=0; j<cascade->stages_array[i]; j++, tree_index++) { for (int j=0; j<cascade->stages_array[i]; j++, tree_index++) {
/* send the shifted window to a haar filter */ /* send the shifted window to a haar filter */
stage_sum += evalWeakClassifier(cascade, std, p_offset, tree_index, w_index, r_index); stage_sum += evalWeakClassifier(cascade, p_offset, tree_index, w_index, r_index);
w_index+=cascade->num_rectangles_array[tree_index]; w_index+=cascade->num_rectangles_array[tree_index];
r_index+=4*cascade->num_rectangles_array[tree_index]; r_index+=4*cascade->num_rectangles_array[tree_index];
} }
@ -94,21 +95,18 @@ static int runCascadeClassifier(cascade_t* cascade, struct point pt, int start_s
static void ScaleImageInvoker(cascade_t *cascade, float factor, int sum_row, int sum_col, array_t *vec) static void ScaleImageInvoker(cascade_t *cascade, float factor, int sum_row, int sum_col, array_t *vec)
{ {
int result; int result;
int x, y, x2, y2;
struct point p; struct point p;
struct size win_size;
win_size.w = fast_roundf(cascade->window.w*factor);
win_size.h = fast_roundf(cascade->window.h*factor);
/* When filter window shifts to image boarder, some margin need to be kept */ /* When filter window shifts to image boarder, some margin need to be kept */
y2 = sum_row - win_size.h; int y2 = sum_row - cascade->window.w;
x2 = sum_col - win_size.w; int x2 = sum_col - cascade->window.h;
int win_w = fast_roundf(cascade->window.w*factor);
int win_h = fast_roundf(cascade->window.h*factor);
/* Shift the filter window over the image. */ /* Shift the filter window over the image. */
for (x=0; x<=x2; x+=cascade->step) { for (int x=0; x<=x2; x+=cascade->step) {
for (y=0; y<=y2; y+=cascade->step) { for (int y=0; y<=y2; y+=cascade->step) {
p.x = x; p.x = x;
p.y = y; p.y = y;
@ -116,8 +114,7 @@ static void ScaleImageInvoker(cascade_t *cascade, float factor, int sum_row, int
/* If a face is detected, record the coordinates of the filter window */ /* If a face is detected, record the coordinates of the filter window */
if (result > 0) { if (result > 0) {
array_push_back(vec, rectangle_alloc(fast_roundf(x*factor), array_push_back(vec, rectangle_alloc(fast_roundf(x*factor), fast_roundf(y*factor), win_w, win_h));
fast_roundf(y*factor), win_size.w, win_size.h));
} }
} }
} }
@ -125,10 +122,8 @@ static void ScaleImageInvoker(cascade_t *cascade, float factor, int sum_row, int
array_t *imlib_detect_objects(image_t *image, cascade_t *cascade) array_t *imlib_detect_objects(image_t *image, cascade_t *cascade)
{ {
float factor = 1.0f;
array_t *objects;
/* allocate the detections array */ /* allocate the detections array */
array_t *objects;
array_alloc(&objects, xfree); array_alloc(&objects, xfree);
/* allocate integral image */ /* allocate integral image */
@ -141,28 +136,30 @@ array_t *imlib_detect_objects(image_t *image, cascade_t *cascade)
/* sets cascade integral image */ /* sets cascade integral image */
cascade->sum = &sum; cascade->sum = &sum;
/* iterate over the image pyramid */ /* set image standard deviation */
while (true) { cascade->std = imlib_std(image);
/* Set the width and height of the images */
sum.w = (image->w*factor);
sum.h = (image->h*factor);
/* if scaled image is smaller than the original detection window, break */ //float scale_factor = (1.0f-cascade->scale_factor)+1.0f;
if (sum.w <= cascade->window.w ||
sum.h <= cascade->window.h) { float scale_factor = cascade->scale_factor;
/* iterate over the image pyramid */
for(float factor=1.0f; ; factor*=scale_factor) {
/* Set the width and height of the images */
sum.w = (image->w/factor);
sum.h = (image->h/factor);
/* Check if scaled image is smaller
than the original detection window */
if (sum.w < cascade->window.w ||
sum.h < cascade->window.h) {
break; break;
} }
cascade->x_ratio = (int)((image->w<<16)/sum.w) +1; /* Compute a new scaled integral image */
cascade->y_ratio = (int)((image->h<<16)/sum.h) +1;
/* compute a new scaled integral image */
imlib_integral_image_scaled(image, &sum); imlib_integral_image_scaled(image, &sum);
/* process the current scale with the cascaded fitler. */ /* Process the current scale */
ScaleImageInvoker(cascade, factor, sum.h, sum.w, objects); ScaleImageInvoker(cascade, factor, sum.h, sum.w, objects);
factor *= cascade->scale_factor;
} }
if (array_length(objects) >1) { if (array_length(objects) >1) {