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