Face detection demo from Emza Visual Sense
Signed-off-by: Michael Levit michaell@emza-vs.com
Change-Id: I7958b05b5dbe9a785e0f8a241b716c17a9ca976f
diff --git a/source/use_case/object_detection/src/DetectorPostProcessing.cc b/source/use_case/object_detection/src/DetectorPostProcessing.cc
new file mode 100755
index 0000000..e781b62
--- /dev/null
+++ b/source/use_case/object_detection/src/DetectorPostProcessing.cc
@@ -0,0 +1,447 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "DetectorPostProcessing.hpp"
+#include <algorithm>
+#include <cmath>
+#include <stdint.h>
+#include <forward_list>
+
+
+typedef struct boxabs {
+ float left, right, top, bot;
+} boxabs;
+
+
+typedef struct branch {
+ int resolution;
+ int num_box;
+ float *anchor;
+ int8_t *tf_output;
+ float scale;
+ int zero_point;
+ size_t size;
+ float scale_x_y;
+} branch;
+
+typedef struct network {
+ int input_w;
+ int input_h;
+ int num_classes;
+ int num_branch;
+ branch *branchs;
+ int topN;
+} network;
+
+
+typedef struct box {
+ float x, y, w, h;
+} box;
+
+typedef struct detection{
+ box bbox;
+ float *prob;
+ float objectness;
+} detection;
+
+
+
+static int sort_class;
+
+static void free_dets(std::forward_list<detection> &dets){
+ std::forward_list<detection>::iterator it;
+ for ( it = dets.begin(); it != dets.end(); ++it ){
+ free(it->prob);
+ }
+}
+
+float sigmoid(float x)
+{
+ return 1.f/(1.f + exp(-x));
+}
+
+static bool det_objectness_comparator(detection &pa, detection &pb)
+{
+ return pa.objectness < pb.objectness;
+}
+
+static void insert_topN_det(std::forward_list<detection> &dets, detection det)
+{
+ std::forward_list<detection>::iterator it;
+ std::forward_list<detection>::iterator last_it;
+ for ( it = dets.begin(); it != dets.end(); ++it ){
+ if(it->objectness > det.objectness)
+ break;
+ last_it = it;
+ }
+ if(it != dets.begin()){
+ dets.emplace_after(last_it, det);
+ free(dets.begin()->prob);
+ dets.pop_front();
+ }
+ else{
+ free(det.prob);
+ }
+}
+
+static std::forward_list<detection> get_network_boxes(network *net, int image_w, int image_h, float thresh, int *num)
+{
+ std::forward_list<detection> dets;
+ int i;
+ int num_classes = net->num_classes;
+ *num = 0;
+
+ for (i = 0; i < net->num_branch; ++i) {
+ int height = net->branchs[i].resolution;
+ int width = net->branchs[i].resolution;
+ int channel = net->branchs[i].num_box*(5+num_classes);
+
+ for (int h = 0; h < net->branchs[i].resolution; h++) {
+ for (int w = 0; w < net->branchs[i].resolution; w++) {
+ for (int anc = 0; anc < net->branchs[i].num_box; anc++) {
+
+ // objectness score
+ int bbox_obj_offset = h * width * channel + w * channel + anc * (num_classes + 5) + 4;
+ float objectness = sigmoid(((float)net->branchs[i].tf_output[bbox_obj_offset] - net->branchs[i].zero_point) * net->branchs[i].scale);
+
+ if(objectness > thresh){
+ detection det;
+ det.prob = (float*)calloc(num_classes, sizeof(float));
+ det.objectness = objectness;
+ //get bbox prediction data for each anchor, each feature point
+ int bbox_x_offset = bbox_obj_offset -4;
+ int bbox_y_offset = bbox_x_offset + 1;
+ int bbox_w_offset = bbox_x_offset + 2;
+ int bbox_h_offset = bbox_x_offset + 3;
+ int bbox_scores_offset = bbox_x_offset + 5;
+ //int bbox_scores_step = 1;
+ det.bbox.x = ((float)net->branchs[i].tf_output[bbox_x_offset] - net->branchs[i].zero_point) * net->branchs[i].scale;
+ det.bbox.y = ((float)net->branchs[i].tf_output[bbox_y_offset] - net->branchs[i].zero_point) * net->branchs[i].scale;
+ det.bbox.w = ((float)net->branchs[i].tf_output[bbox_w_offset] - net->branchs[i].zero_point) * net->branchs[i].scale;
+ det.bbox.h = ((float)net->branchs[i].tf_output[bbox_h_offset] - net->branchs[i].zero_point) * net->branchs[i].scale;
+
+
+ float bbox_x, bbox_y;
+
+ // Eliminate grid sensitivity trick involved in YOLOv4
+ bbox_x = sigmoid(det.bbox.x); //* net->branchs[i].scale_x_y - (net->branchs[i].scale_x_y - 1) / 2;
+ bbox_y = sigmoid(det.bbox.y); //* net->branchs[i].scale_x_y - (net->branchs[i].scale_x_y - 1) / 2;
+ det.bbox.x = (bbox_x + w) / width;
+ det.bbox.y = (bbox_y + h) / height;
+
+ det.bbox.w = exp(det.bbox.w) * net->branchs[i].anchor[anc*2] / net->input_w;
+ det.bbox.h = exp(det.bbox.h) * net->branchs[i].anchor[anc*2+1] / net->input_h;
+
+ for (int s = 0; s < num_classes; s++) {
+ det.prob[s] = sigmoid(((float)net->branchs[i].tf_output[bbox_scores_offset + s] - net->branchs[i].zero_point) * net->branchs[i].scale)*objectness;
+ det.prob[s] = (det.prob[s] > thresh) ? det.prob[s] : 0;
+ }
+
+ //correct_yolo_boxes
+ det.bbox.x *= image_w;
+ det.bbox.w *= image_w;
+ det.bbox.y *= image_h;
+ det.bbox.h *= image_h;
+
+ if (*num < net->topN || net->topN <=0){
+ dets.emplace_front(det);
+ *num += 1;
+ }
+ else if(*num == net->topN){
+ dets.sort(det_objectness_comparator);
+ insert_topN_det(dets,det);
+ *num += 1;
+ }else{
+ insert_topN_det(dets,det);
+ }
+ }
+ }
+ }
+ }
+ }
+ if(*num > net->topN)
+ *num -=1;
+ return dets;
+}
+
+// init part
+
+static branch create_brach(int resolution, int num_box, float *anchor, int8_t *tf_output, size_t size, float scale, int zero_point)
+{
+ branch b;
+ b.resolution = resolution;
+ b.num_box = num_box;
+ b.anchor = anchor;
+ b.tf_output = tf_output;
+ b.size = size;
+ b.scale = scale;
+ b.zero_point = zero_point;
+ return b;
+}
+
+static network creat_network(int input_w, int input_h, int num_classes, int num_branch, branch* branchs, int topN)
+{
+ network net;
+ net.input_w = input_w;
+ net.input_h = input_h;
+ net.num_classes = num_classes;
+ net.num_branch = num_branch;
+ net.branchs = branchs;
+ net.topN = topN;
+ return net;
+}
+
+// NMS part
+
+static float Calc1DOverlap(float x1_center, float width1, float x2_center, float width2)
+{
+ float left_1 = x1_center - width1/2;
+ float left_2 = x2_center - width2/2;
+ float leftest;
+ if (left_1 > left_2) {
+ leftest = left_1;
+ } else {
+ leftest = left_2;
+ }
+
+ float right_1 = x1_center + width1/2;
+ float right_2 = x2_center + width2/2;
+ float rightest;
+ if (right_1 < right_2) {
+ rightest = right_1;
+ } else {
+ rightest = right_2;
+ }
+
+ return rightest - leftest;
+}
+
+
+static float CalcBoxIntersect(box box1, box box2)
+{
+ float width = Calc1DOverlap(box1.x, box1.w, box2.x, box2.w);
+ if (width < 0) return 0;
+ float height = Calc1DOverlap(box1.y, box1.h, box2.y, box2.h);
+ if (height < 0) return 0;
+
+ float total_area = width*height;
+ return total_area;
+}
+
+
+static float CalcBoxUnion(box box1, box box2)
+{
+ float boxes_intersection = CalcBoxIntersect(box1, box2);
+ float boxes_union = box1.w*box1.h + box2.w*box2.h - boxes_intersection;
+ return boxes_union;
+}
+
+
+static float CalcBoxIOU(box box1, box box2)
+{
+ float boxes_intersection = CalcBoxIntersect(box1, box2);
+
+ if (boxes_intersection == 0) return 0;
+
+ float boxes_union = CalcBoxUnion(box1, box2);
+
+ if (boxes_union == 0) return 0;
+
+ return boxes_intersection / boxes_union;
+}
+
+
+static bool CompareProbs(detection &prob1, detection &prob2)
+{
+ return prob1.prob[sort_class] > prob2.prob[sort_class];
+}
+
+
+static void CalcNMS(std::forward_list<detection> &detections, int classes, float iou_threshold)
+{
+ int k;
+
+ for (k = 0; k < classes; ++k) {
+ sort_class = k;
+ detections.sort(CompareProbs);
+
+ for (std::forward_list<detection>::iterator it=detections.begin(); it != detections.end(); ++it){
+ if (it->prob[k] == 0) continue;
+ for (std::forward_list<detection>::iterator itc=std::next(it, 1); itc != detections.end(); ++itc){
+ if (itc->prob[k] == 0) continue;
+ if (CalcBoxIOU(it->bbox, itc->bbox) > iou_threshold) {
+ itc->prob[k] = 0;
+ }
+ }
+ }
+ }
+}
+
+
+static void inline check_and_fix_offset(int im_w,int im_h,int *offset)
+{
+
+ if (!offset) return;
+
+ if ( (*offset) >= im_w*im_h*FORMAT_MULTIPLY_FACTOR)
+ (*offset) = im_w*im_h*FORMAT_MULTIPLY_FACTOR -1;
+ else if ( (*offset) < 0)
+ *offset =0;
+
+}
+
+
+static void DrawBoxOnImage(uint8_t *img_in,int im_w,int im_h,int bx,int by,int bw,int bh)
+{
+
+ if (!img_in) {
+ return;
+ }
+
+ int offset=0;
+ for (int i=0; i < bw; i++) {
+ /*draw two lines */
+ for (int line=0; line < 2; line++) {
+ /*top*/
+ offset =(i + (by + line)*im_w + bx)*FORMAT_MULTIPLY_FACTOR;
+ check_and_fix_offset(im_w,im_h,&offset);
+ img_in[offset] = 0xFF; /* FORMAT_MULTIPLY_FACTOR for rgb or grayscale*/
+ /*bottom*/
+ offset = (i + (by + bh - line)*im_w + bx)*FORMAT_MULTIPLY_FACTOR;
+ check_and_fix_offset(im_w,im_h,&offset);
+ img_in[offset] = 0xFF;
+ }
+ }
+
+ for (int i=0; i < bh; i++) {
+ /*draw two lines */
+ for (int line=0; line < 2; line++) {
+ /*left*/
+ offset = ((i + by)*im_w + bx + line)*FORMAT_MULTIPLY_FACTOR;
+ check_and_fix_offset(im_w,im_h,&offset);
+ img_in[offset] = 0xFF;
+ /*right*/
+ offset = ((i + by)*im_w + bx + bw - line)*FORMAT_MULTIPLY_FACTOR;
+ check_and_fix_offset(im_w,im_h,&offset);
+ img_in[offset] = 0xFF;
+ }
+ }
+
+}
+
+
+void arm::app::RunPostProcessing(uint8_t *img_in,TfLiteTensor* model_output[2],std::vector<arm::app::DetectionResult> & results_out)
+{
+
+ TfLiteTensor* output[2] = {nullptr,nullptr};
+ int input_w = INPUT_IMAGE_WIDTH;
+ int input_h = INPUT_IMAGE_HEIGHT;
+
+ for(int anchor=0;anchor<2;anchor++)
+ {
+ output[anchor] = model_output[anchor];
+ }
+
+ /* init postprocessing */
+ int num_classes = 1;
+ int num_branch = 2;
+ int topN = 0;
+
+ branch* branchs = (branch*)calloc(num_branch, sizeof(branch));
+
+ /*NOTE: anchors are different for any given input model size, estimated during training phase */
+ float anchor1[] = {38, 77, 47, 97, 61, 126};
+ float anchor2[] = {14, 26, 19, 37, 28, 55 };
+
+
+ branchs[0] = create_brach(INPUT_IMAGE_WIDTH/32, 3, anchor1, output[0]->data.int8, output[0]->bytes, ((TfLiteAffineQuantization*)(output[0]->quantization.params))->scale->data[0], ((TfLiteAffineQuantization*)(output[0]->quantization.params))->zero_point->data[0]);
+
+ branchs[1] = create_brach(INPUT_IMAGE_WIDTH/16, 3, anchor2, output[1]->data.int8, output[1]->bytes, ((TfLiteAffineQuantization*)(output[1]->quantization.params))->scale->data[0],((TfLiteAffineQuantization*)(output[1]->quantization.params))->zero_point->data[0]);
+
+ network net = creat_network(input_w, input_h, num_classes, num_branch, branchs,topN);
+ /* end init */
+
+ /* start postprocessing */
+ int nboxes=0;
+ float thresh = .5;//50%
+ float nms = .45;
+ int orig_image_width = ORIGINAL_IMAGE_WIDTH;
+ int orig_image_height = ORIGINAL_IMAGE_HEIGHT;
+ std::forward_list<detection> dets = get_network_boxes(&net, orig_image_width, orig_image_height, thresh, &nboxes);
+ /* do nms */
+ CalcNMS(dets, net.num_classes, nms);
+ uint8_t temp_unsuppressed_counter = 0;
+ int j;
+ for (std::forward_list<detection>::iterator it=dets.begin(); it != dets.end(); ++it){
+ float xmin = it->bbox.x - it->bbox.w / 2.0f;
+ float xmax = it->bbox.x + it->bbox.w / 2.0f;
+ float ymin = it->bbox.y - it->bbox.h / 2.0f;
+ float ymax = it->bbox.y + it->bbox.h / 2.0f;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > orig_image_width) xmax = orig_image_width;
+ if (ymax > orig_image_height) ymax = orig_image_height;
+
+ float bx = xmin;
+ float by = ymin;
+ float bw = xmax - xmin;
+ float bh = ymax - ymin;
+
+ for (j = 0; j < net.num_classes; ++j) {
+ if (it->prob[j] > 0) {
+
+ arm::app::DetectionResult tmp_result = {};
+
+ tmp_result.m_normalisedVal = it->prob[j];
+ tmp_result.m_x0=bx;
+ tmp_result.m_y0=by;
+ tmp_result.m_w=bw;
+ tmp_result.m_h=bh;
+
+ results_out.push_back(tmp_result);
+
+ DrawBoxOnImage(img_in,orig_image_width,orig_image_height,bx,by,bw,bh);
+
+ temp_unsuppressed_counter++;
+ }
+ }
+ }
+
+ free_dets(dets);
+ free(branchs);
+
+}
+
+void arm::app::RgbToGrayscale(const uint8_t *rgb,uint8_t *gray, int im_w,int im_h)
+{
+ float R=0.299;
+ float G=0.587;
+ float B=0.114;
+ for (int i=0; i< im_w*im_h; i++ ) {
+
+ uint32_t int_gray = rgb[i*3 + 0]*R + rgb[i*3 + 1]*G+ rgb[i*3 + 2]*B;
+ /*clip if need */
+ if (int_gray <= UINT8_MAX) {
+ gray[i] = int_gray;
+ } else {
+ gray[i] = UINT8_MAX;
+ }
+
+ }
+
+}
+