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Improved Haar detector.
* Use a scanning factor proportional to the current scale. * Use the new integral moving window to allow two integral images (sum and sum squared) for fast mean, variance and standard deviation. * Higher FPS and more accurate detection.
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@ -3,7 +3,7 @@
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* Copyright (c) 2013/2014 Ibrahim Abdelkader <i.abdalkader@gmail.com>
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* This work is licensed under the MIT license, see the file LICENSE for details.
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*
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* Viola-Jones face detector implementation.
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* Viola-Jones object 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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@ -15,162 +15,142 @@
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// built-in cascades
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#include "cascade.h"
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static int imlib_std(image_t *image)
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static int eval_weak_classifier(cascade_t *cascade, point_t pt, int t_idx, int w_idx, int r_idx)
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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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int32_t sumw=0;
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mw_image_t *sum = cascade->sum;
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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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/* The node threshold is multiplied by the standard deviation of the sub window */
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int32_t t = cascade->tree_thresh_array[t_idx] * cascade->std;
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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*n-(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] * 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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int y = cascade->rectangles_array[r_index + (i<<2) + 1];
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int w = cascade->rectangles_array[r_index + (i<<2) + 2];
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int h = cascade->rectangles_array[r_index + (i<<2) + 3];
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int idx0=sum->w*y + x + p_offset;
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int idx1=sum->w*(y + h) + x + p_offset;
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sumw += ( sum->data[idx0]
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- sum->data[idx0 + w]
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- sum->data[idx1]
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+ sum->data[idx1 + w])
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* (cascade->weights_array[w_index + i]<<12);
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for (int i=0; i<cascade->num_rectangles_array[t_idx]; i++) {
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int x = cascade->rectangles_array[r_idx + (i<<2) + 0];
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int y = cascade->rectangles_array[r_idx + (i<<2) + 1];
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int w = cascade->rectangles_array[r_idx + (i<<2) + 2];
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int h = cascade->rectangles_array[r_idx + (i<<2) + 3];
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// Lookup the feature
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sumw += imlib_integral_mw_lookup(sum, pt.x+x, y, w, h) * (cascade->weights_array[w_idx + i]<<12);
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}
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if (sumw >= t) {
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return cascade->alpha2_array[tree_index];
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return cascade->alpha2_array[t_idx];
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}
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return cascade->alpha1_array[tree_index];
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return cascade->alpha1_array[t_idx];
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}
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static int runCascadeClassifier(cascade_t* cascade, struct point pt, int start_stage)
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static int run_cascade_classifier(cascade_t* cascade, point_t pt)
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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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int p_offset = pt.y * cascade->sum->w + pt.x;
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int win_w = cascade->window.w;
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int win_h = cascade->window.h;
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int32_t n = (win_w * win_h);
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int32_t i_s = imlib_integral_mw_lookup (cascade->sum, pt.x, 0, win_w, win_h);
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int32_t i_sq = imlib_integral_mw_lookup(cascade->ssq, pt.x, 0, win_w, win_h);
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int32_t v = i_sq*n-(i_s*i_s);
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cascade->std = fast_sqrtf(fast_fabsf(v));
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for (int i=start_stage; i<cascade->n_stages; i++) {
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for (int i=0, w_idx=0, r_idx=0, t_idx=0; 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, 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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for (int j=0; j<cascade->stages_array[i]; j++, t_idx++) {
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// Send the shifted window to a haar filter
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stage_sum += eval_weak_classifier(cascade, pt, t_idx, w_idx, r_idx);
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w_idx += cascade->num_rectangles_array[t_idx];
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r_idx += cascade->num_rectangles_array[t_idx] * 4;
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}
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/* If the sum is below the stage threshold, no faces are detected */
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if (stage_sum < cascade->threshold*cascade->stages_thresh_array[i]) {
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// If the sum is below the stage threshold, no objects were detected
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if (stage_sum < (cascade->threshold * cascade->stages_thresh_array[i])) {
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return -i;
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}
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}
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return 1;
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}
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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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struct point p;
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/* When filter window shifts to image boarder, some margin need to be kept */
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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 (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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result = runCascadeClassifier(cascade, p, 0);
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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), fast_roundf(y*factor), win_w, win_h));
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}
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}
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}
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}
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array_t *imlib_detect_objects(image_t *image, cascade_t *cascade)
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{
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/* allocate the detections array */
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// Integral images
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mw_image_t sum;
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mw_image_t ssq;
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// Detected objects array
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array_t *objects;
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// Allocate the objects array
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array_alloc(&objects, xfree);
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/* allocate integral image */
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i_image_t sum;
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imlib_integral_image_alloc(&sum, image->w, image->h);
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/* set cascade image pointer */
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// Set cascade image pointers
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cascade->img = image;
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/* sets cascade integral image */
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cascade->sum = ∑
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cascade->ssq = &ssq;
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/* set image standard deviation */
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cascade->std = imlib_std(image);
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// Set scanning step.
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// Viola and Jones achieved best results using a scaling factor
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// of 1.25 and a scanning factor proportional to the current scale.
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float scale_factor = cascade->scale_factor;
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cascade->step = (image->w*75)/1000; //7.5% of the image width
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// Start with a step of 5% of the image width and reduce at each scaling step
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cascade->step = (image->w*50)/1000;
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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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cascade->step = cascade->step/factor;
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cascade->step = (cascade->step == 0) ? 1:cascade->step;
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// Make sure step is less than feature height + 1
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if (cascade->step > cascade->window.w) {
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cascade->step = cascade->window.w;
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}
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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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// Allocate integral images
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imlib_integral_mw_alloc(&sum, image->w, cascade->window.h+1);
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imlib_integral_mw_alloc(&ssq, image->w, cascade->window.h+1);
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// Iterate over the image pyramid
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for(float factor=1.0f; ; factor *= cascade->scale_factor) {
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// Set the scaled width and height
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int szw = image->w/factor;
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int szh = image->h/factor;
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// Break if scaled image is smaller than feature size
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if (szw < cascade->window.w || szh < cascade->window.h) {
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break;
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}
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/* Compute a new scaled integral image */
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imlib_integral_image_scaled(image, &sum);
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// Set the integral images scale
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imlib_integral_mw_scale(image, &sum, szw, szh);
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imlib_integral_mw_scale(image, &ssq, szw, szh);
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/* Process the current scale */
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ScaleImageInvoker(cascade, factor, sum.h, sum.w, objects);
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// Compute new scaled integral images
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imlib_integral_mw_ss(image, &sum, &ssq);
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// Scale the scanning step
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cascade->step = cascade->step/factor;
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cascade->step = (cascade->step == 0) ? 1 : cascade->step;
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// Process image at the current scale
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// When filter window shifts to borders, some margin need to be kept
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int y2 = szh - cascade->window.h;
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int x2 = szw - cascade->window.w;
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// Shift the filter window over the image.
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for (int y=0; y<y2; y+=cascade->step) {
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for (int x=0; x<x2; x+=cascade->step) {
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point_t p = {x, y};
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// If an object is detected, record the coordinates of the filter window
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if (run_cascade_classifier(cascade, p) > 0) {
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array_push_back(objects, rectangle_alloc(fast_roundf(x*factor), fast_roundf(y*factor),
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fast_roundf(cascade->window.w*factor), fast_roundf(cascade->window.h*factor)));
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}
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}
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if (array_length(objects) >1) {
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// If not last line, shift integral images
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if ((y+cascade->step) < y2) {
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imlib_integral_mw_shift_ss(cascade->img, cascade->sum, cascade->ssq, cascade->step);
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}
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}
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}
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imlib_integral_mw_free(&ssq);
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imlib_integral_mw_free(&sum);
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if (array_length(objects) > 1) {
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// Merge objects detected at different scales
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objects = rectangle_merge(objects);
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}
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return objects;
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}
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