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Originally meant to abstract gc_collect but we could just use m_alloc and friends. Also was meant to provide functions like alloc0, alloc_maybe etc.. which are all available in MP anyway. Signed-off-by: iabdalkader <i.abdalkader@gmail.com>
302 lines
12 KiB
C
302 lines
12 KiB
C
/*
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* SPDX-License-Identifier: MIT
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*
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* Copyright (C) 2013-2024 OpenMV, LLC.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*
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* Viola-Jones object detector implementation.
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* Based on the work of Francesco Comaschi (f.comaschi@tue.nl)
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*/
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#include <stdio.h>
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#include "py/obj.h"
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#include "py/runtime.h"
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#if MICROPY_VFS
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#include "py/stream.h"
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#include "extmod/vfs.h"
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#endif
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#include "imlib.h"
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#ifdef IMLIB_ENABLE_FEATURES
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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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int32_t sumw = 0;
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mw_image_t *sum = cascade->sum;
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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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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[t_idx];
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}
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return cascade->alpha1_array[t_idx];
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}
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static int run_cascade_classifier(cascade_t *cascade, point_t pt) {
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int win_w = cascade->window.w;
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int win_h = cascade->window.h;
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uint32_t n = (win_w * win_h);
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uint32_t i_s = imlib_integral_mw_lookup(cascade->sum, pt.x, 0, win_w, win_h);
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uint32_t i_sq = imlib_integral_mw_lookup(cascade->ssq, pt.x, 0, win_w, win_h);
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uint32_t m = i_s / n;
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uint32_t v = i_sq / n - (m * m);
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// Skip homogeneous regions.
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if (v < (50 * 50)) {
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return 0;
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}
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cascade->std = fast_sqrtf(i_sq * n - (i_s * i_s));
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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++, 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 objects were detected
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if (stage_sum < (cascade->threshold * cascade->stages_thresh_array[i])) {
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return 0;
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}
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}
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return 1;
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}
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array_t *imlib_detect_objects(image_t *image, cascade_t *cascade, rectangle_t *roi) {
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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, m_free);
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// Set cascade image pointers
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cascade->img = image;
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cascade->sum = ∑
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cascade->ssq = &ssq;
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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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// Start with a step of 5% of the image width and reduce at each scaling step
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cascade->step = (roi->w * 50) / 1000;
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// Make sure step is less than window height + 1
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if (cascade->step > cascade->window.h) {
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cascade->step = cascade->window.h;
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}
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// Allocate integral images
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imlib_integral_mw_alloc(&sum, roi->w, cascade->window.h + 1);
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imlib_integral_mw_alloc(&ssq, roi->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 = roi->w / factor;
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int szh = roi->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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// Set the integral images scale
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imlib_integral_mw_scale(roi, &sum, szw, szh);
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imlib_integral_mw_scale(roi, &ssq, szw, szh);
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// Compute new scaled integral images
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imlib_integral_mw_ss(image, &sum, &ssq, roi);
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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,
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rectangle_alloc(fast_roundf(x * factor) + roi->x, fast_roundf(y * factor) + roi->y,
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fast_roundf(cascade->window.w * factor),
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fast_roundf(cascade->window.h * factor)));
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}
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}
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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(image, &sum, &ssq, roi, 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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#if MICROPY_VFS
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static void *cascade_buffer_read(uint8_t **buf, size_t size) {
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uint8_t *buf8 = *buf;
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*buf += size;
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return buf8;
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}
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int imlib_load_cascade_from_file(cascade_t *cascade, const char *path) {
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int error = 0;
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mp_obj_t args[2] = {
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mp_obj_new_str_from_cstr(path),
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MP_OBJ_NEW_QSTR(MP_QSTR_rb),
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};
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memset(cascade, 0, sizeof(cascade_t));
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mp_buffer_info_t bufinfo;
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mp_obj_t file = mp_vfs_open(MP_ARRAY_SIZE(args), args, (mp_map_t *) &mp_const_empty_map);
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if (mp_get_buffer(file, &bufinfo, MP_BUFFER_READ)) {
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uint8_t *buf = (uint8_t *) bufinfo.buf + 12;
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// Set detection window size and the number of stages.
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cascade->window.w = ((uint32_t *) bufinfo.buf)[0];
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cascade->window.h = ((uint32_t *) bufinfo.buf)[1];
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cascade->n_stages = ((uint32_t *) bufinfo.buf)[2];
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// Set the number features in each stages
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cascade->stages_array = cascade_buffer_read(&buf, cascade->n_stages);
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// Skip alignment
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if ((uint32_t) buf % 4) {
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buf += 4 - ((uint32_t) buf % 4);
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}
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// Sum the number of features in each stages
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for (size_t i = 0; i < cascade->n_stages; i++) {
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cascade->n_features += cascade->stages_array[i];
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}
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// Set features thresh array, alpha1, alpha 2,rects weights and rects
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cascade->stages_thresh_array = cascade_buffer_read(&buf, sizeof(int16_t) * cascade->n_stages);
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cascade->tree_thresh_array = cascade_buffer_read(&buf, sizeof(int16_t) * cascade->n_features);
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cascade->alpha1_array = cascade_buffer_read(&buf, sizeof(int16_t) * cascade->n_features);
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cascade->alpha2_array = cascade_buffer_read(&buf, sizeof(int16_t) * cascade->n_features);
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cascade->num_rectangles_array = cascade_buffer_read(&buf, sizeof(int8_t) * cascade->n_features);
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// Sum the number of rectangles in all features
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for (size_t i = 0; i < cascade->n_features; i++) {
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cascade->n_rectangles += cascade->num_rectangles_array[i];
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}
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// Set rectangles weights and rectangles (number of rectangles * 4 points)
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cascade->weights_array = cascade_buffer_read(&buf, cascade->n_rectangles);
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cascade->rectangles_array = cascade_buffer_read(&buf, cascade->n_rectangles * 4);
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} else {
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// Read detection window size.
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mp_stream_read_exactly(file, &cascade->window, sizeof(cascade->window), &error);
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// Read the number of stages.
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mp_stream_read_exactly(file, &cascade->n_stages, sizeof(cascade->n_stages), &error);
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// Allocate stages array.
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cascade->stages_array = m_malloc(sizeof(int8_t) * cascade->n_stages);
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// Read number of features in each stages
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mp_stream_read_exactly(file, cascade->stages_array, cascade->n_stages, &error);
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// Skip alignment
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uint8_t padding[4];
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if (cascade->n_stages % 4) {
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mp_stream_read_exactly(file, padding, 4 - (cascade->n_stages % 4), &error);
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}
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// Sum the number of features in each stages
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for (size_t i = 0; i < cascade->n_stages; i++) {
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cascade->n_features += cascade->stages_array[i];
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}
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// Alloc features thresh array, alpha1, alpha 2,rects weights and rects
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cascade->stages_thresh_array = m_malloc(sizeof(int16_t) * cascade->n_stages);
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cascade->tree_thresh_array = m_malloc(sizeof(int16_t) * cascade->n_features);
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cascade->alpha1_array = m_malloc(sizeof(int16_t) * cascade->n_features);
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cascade->alpha2_array = m_malloc(sizeof(int16_t) * cascade->n_features);
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cascade->num_rectangles_array = m_malloc(sizeof(int8_t) * cascade->n_features);
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// Read features thresh array, alpha1, alpha 2,rects weights and rects
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mp_stream_read_exactly(file, cascade->stages_thresh_array, sizeof(int16_t) * cascade->n_stages, &error);
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mp_stream_read_exactly(file, cascade->tree_thresh_array, sizeof(int16_t) * cascade->n_features, &error);
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mp_stream_read_exactly(file, cascade->alpha1_array, sizeof(int16_t) * cascade->n_features, &error);
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mp_stream_read_exactly(file, cascade->alpha2_array, sizeof(int16_t) * cascade->n_features, &error);
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mp_stream_read_exactly(file, cascade->num_rectangles_array, cascade->n_features, &error);
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// Sum the number of rectangles per feature
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for (size_t i = 0; i < cascade->n_features; i++) {
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cascade->n_rectangles += cascade->num_rectangles_array[i];
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}
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// Allocate weights and rectangles arrays.
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cascade->weights_array = m_malloc(cascade->n_rectangles);
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cascade->rectangles_array = m_malloc(cascade->n_rectangles * 4);
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// Read rectangles weights and rectangles (number of rectangles * 4 points)
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mp_stream_read_exactly(file, cascade->weights_array, sizeof(int8_t) * cascade->n_rectangles, &error);
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mp_stream_read_exactly(file, cascade->rectangles_array, sizeof(int8_t) * cascade->n_rectangles * 4, &error);
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}
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if (error != 0) {
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mp_raise_OSError(error);
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}
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mp_stream_close(file);
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return 0;
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}
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#endif //(IMLIB_ENABLE_IMAGE_FILE_IO)
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int imlib_load_cascade(cascade_t *cascade, const char *path) {
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#if MICROPY_VFS
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// xml cascade
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return imlib_load_cascade_from_file(cascade, path);
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#else
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return -1;
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#endif
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}
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#endif // IMLIB_ENABLE_FEATURES
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