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-rw-r--r--[-rwxr-xr-x]source/use_case/object_detection/src/DetectorPostProcessing.cc795
1 files changed, 348 insertions, 447 deletions
diff --git a/source/use_case/object_detection/src/DetectorPostProcessing.cc b/source/use_case/object_detection/src/DetectorPostProcessing.cc
index e781b62..edfb137 100755..100644
--- a/source/use_case/object_detection/src/DetectorPostProcessing.cc
+++ b/source/use_case/object_detection/src/DetectorPostProcessing.cc
@@ -1,447 +1,348 @@
-/*
- * 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;
- }
-
- }
-
-}
-
+/*
+ * 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>
+
+namespace arm {
+namespace app {
+namespace object_detection {
+
+DetectorPostprocessing::DetectorPostprocessing(
+ const float threshold,
+ const float nms,
+ int numClasses,
+ int topN)
+ : m_threshold(threshold),
+ m_nms(nms),
+ m_numClasses(numClasses),
+ m_topN(topN)
+{}
+
+void DetectorPostprocessing::RunPostProcessing(
+ uint8_t* imgIn,
+ uint32_t imgRows,
+ uint32_t imgCols,
+ TfLiteTensor* modelOutput0,
+ TfLiteTensor* modelOutput1,
+ std::vector<DetectionResult>& resultsOut)
+{
+ /* init postprocessing */
+ Network net {
+ .inputWidth = static_cast<int>(imgCols),
+ .inputHeight = static_cast<int>(imgRows),
+ .numClasses = m_numClasses,
+ .branches = {
+ Branch {
+ .resolution = static_cast<int>(imgCols/32),
+ .numBox = 3,
+ .anchor = anchor1,
+ .modelOutput = modelOutput0->data.int8,
+ .scale = ((TfLiteAffineQuantization*)(modelOutput0->quantization.params))->scale->data[0],
+ .zeroPoint = ((TfLiteAffineQuantization*)(modelOutput0->quantization.params))->zero_point->data[0],
+ .size = modelOutput0->bytes
+ },
+ Branch {
+ .resolution = static_cast<int>(imgCols/16),
+ .numBox = 3,
+ .anchor = anchor2,
+ .modelOutput = modelOutput1->data.int8,
+ .scale = ((TfLiteAffineQuantization*)(modelOutput1->quantization.params))->scale->data[0],
+ .zeroPoint = ((TfLiteAffineQuantization*)(modelOutput1->quantization.params))->zero_point->data[0],
+ .size = modelOutput1->bytes
+ }
+ },
+ .topN = m_topN
+ };
+ /* End init */
+
+ /* Start postprocessing */
+ int originalImageWidth = originalImageSize;
+ int originalImageHeight = originalImageSize;
+
+ std::forward_list<Detection> detections;
+ GetNetworkBoxes(net, originalImageWidth, originalImageHeight, m_threshold, detections);
+
+ /* Do nms */
+ CalculateNMS(detections, net.numClasses, m_nms);
+
+ for (auto& it: detections) {
+ 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 > originalImageWidth) {
+ xMax = originalImageWidth;
+ }
+ if (yMax > originalImageHeight) {
+ yMax = originalImageHeight;
+ }
+
+ float boxX = xMin;
+ float boxY = yMin;
+ float boxWidth = xMax - xMin;
+ float boxHeight = yMax - yMin;
+
+ for (int j = 0; j < net.numClasses; ++j) {
+ if (it.prob[j] > 0) {
+
+ DetectionResult tmpResult = {};
+ tmpResult.m_normalisedVal = it.prob[j];
+ tmpResult.m_x0 = boxX;
+ tmpResult.m_y0 = boxY;
+ tmpResult.m_w = boxWidth;
+ tmpResult.m_h = boxHeight;
+
+ resultsOut.push_back(tmpResult);
+
+ /* TODO: Instead of draw on the image, return the boxes and draw on the LCD */
+ DrawBoxOnImage(imgIn, originalImageWidth, originalImageHeight, boxX, boxY, boxWidth, boxHeight);;
+ }
+ }
+ }
+}
+
+float DetectorPostprocessing::Sigmoid(float x)
+{
+ return 1.f/(1.f + exp(-x));
+}
+
+void DetectorPostprocessing::InsertTopNDetections(std::forward_list<Detection>& detections, Detection& det)
+{
+ std::forward_list<Detection>::iterator it;
+ std::forward_list<Detection>::iterator last_it;
+ for ( it = detections.begin(); it != detections.end(); ++it ) {
+ if(it->objectness > det.objectness)
+ break;
+ last_it = it;
+ }
+ if(it != detections.begin()) {
+ detections.emplace_after(last_it, det);
+ detections.pop_front();
+ }
+}
+
+void DetectorPostprocessing::GetNetworkBoxes(Network& net, int imageWidth, int imageHeight, float threshold, std::forward_list<Detection>& detections)
+{
+ int numClasses = net.numClasses;
+ int num = 0;
+ auto det_objectness_comparator = [](Detection& pa, Detection& pb) {
+ return pa.objectness < pb.objectness;
+ };
+ for (size_t i = 0; i < net.branches.size(); ++i) {
+ int height = net.branches[i].resolution;
+ int width = net.branches[i].resolution;
+ int channel = net.branches[i].numBox*(5+numClasses);
+
+ for (int h = 0; h < net.branches[i].resolution; h++) {
+ for (int w = 0; w < net.branches[i].resolution; w++) {
+ for (int anc = 0; anc < net.branches[i].numBox; anc++) {
+
+ /* Objectness score */
+ int bbox_obj_offset = h * width * channel + w * channel + anc * (numClasses + 5) + 4;
+ float objectness = Sigmoid(((float)net.branches[i].modelOutput[bbox_obj_offset] - net.branches[i].zeroPoint) * net.branches[i].scale);
+
+ if(objectness > threshold) {
+ Detection det;
+ 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;
+
+ det.bbox.x = ((float)net.branches[i].modelOutput[bbox_x_offset] - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.y = ((float)net.branches[i].modelOutput[bbox_y_offset] - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.w = ((float)net.branches[i].modelOutput[bbox_w_offset] - net.branches[i].zeroPoint) * net.branches[i].scale;
+ det.bbox.h = ((float)net.branches[i].modelOutput[bbox_h_offset] - net.branches[i].zeroPoint) * net.branches[i].scale;
+
+
+ float bbox_x, bbox_y;
+
+ /* Eliminate grid sensitivity trick involved in YOLOv4 */
+ bbox_x = Sigmoid(det.bbox.x);
+ bbox_y = Sigmoid(det.bbox.y);
+ det.bbox.x = (bbox_x + w) / width;
+ det.bbox.y = (bbox_y + h) / height;
+
+ det.bbox.w = exp(det.bbox.w) * net.branches[i].anchor[anc*2] / net.inputWidth;
+ det.bbox.h = exp(det.bbox.h) * net.branches[i].anchor[anc*2+1] / net.inputHeight;
+
+ for (int s = 0; s < numClasses; s++) {
+ float sig = Sigmoid(((float)net.branches[i].modelOutput[bbox_scores_offset + s] - net.branches[i].zeroPoint) * net.branches[i].scale)*objectness;
+ det.prob.emplace_back((sig > threshold) ? sig : 0);
+ }
+
+ /* Correct_YOLO_boxes */
+ det.bbox.x *= imageWidth;
+ det.bbox.w *= imageWidth;
+ det.bbox.y *= imageHeight;
+ det.bbox.h *= imageHeight;
+
+ if (num < net.topN || net.topN <=0) {
+ detections.emplace_front(det);
+ num += 1;
+ } else if (num == net.topN) {
+ detections.sort(det_objectness_comparator);
+ InsertTopNDetections(detections,det);
+ num += 1;
+ } else {
+ InsertTopNDetections(detections,det);
+ }
+ }
+ }
+ }
+ }
+ }
+ if(num > net.topN)
+ num -=1;
+}
+
+float DetectorPostprocessing::Calculate1DOverlap(float x1Center, float width1, float x2Center, float width2)
+{
+ float left_1 = x1Center - width1/2;
+ float left_2 = x2Center - width2/2;
+ float leftest = left_1 > left_2 ? left_1 : left_2;
+
+ float right_1 = x1Center + width1/2;
+ float right_2 = x2Center + width2/2;
+ float rightest = right_1 < right_2 ? right_1 : right_2;
+
+ return rightest - leftest;
+}
+
+float DetectorPostprocessing::CalculateBoxIntersect(Box& box1, Box& box2)
+{
+ float width = Calculate1DOverlap(box1.x, box1.w, box2.x, box2.w);
+ if (width < 0) {
+ return 0;
+ }
+ float height = Calculate1DOverlap(box1.y, box1.h, box2.y, box2.h);
+ if (height < 0) {
+ return 0;
+ }
+
+ float total_area = width*height;
+ return total_area;
+}
+
+float DetectorPostprocessing::CalculateBoxUnion(Box& box1, Box& box2)
+{
+ float boxes_intersection = CalculateBoxIntersect(box1, box2);
+ float boxes_union = box1.w * box1.h + box2.w * box2.h - boxes_intersection;
+ return boxes_union;
+}
+
+
+float DetectorPostprocessing::CalculateBoxIOU(Box& box1, Box& box2)
+{
+ float boxes_intersection = CalculateBoxIntersect(box1, box2);
+ if (boxes_intersection == 0) {
+ return 0;
+ }
+
+ float boxes_union = CalculateBoxUnion(box1, box2);
+ if (boxes_union == 0) {
+ return 0;
+ }
+
+ return boxes_intersection / boxes_union;
+}
+
+void DetectorPostprocessing::CalculateNMS(std::forward_list<Detection>& detections, int classes, float iouThreshold)
+{
+ int idxClass{0};
+ auto CompareProbs = [idxClass](Detection& prob1, Detection& prob2) {
+ return prob1.prob[idxClass] > prob2.prob[idxClass];
+ };
+
+ for (idxClass = 0; idxClass < classes; ++idxClass) {
+ detections.sort(CompareProbs);
+
+ for (std::forward_list<Detection>::iterator it=detections.begin(); it != detections.end(); ++it) {
+ if (it->prob[idxClass] == 0) continue;
+ for (std::forward_list<Detection>::iterator itc=std::next(it, 1); itc != detections.end(); ++itc) {
+ if (itc->prob[idxClass] == 0) {
+ continue;
+ }
+ if (CalculateBoxIOU(it->bbox, itc->bbox) > iouThreshold) {
+ itc->prob[idxClass] = 0;
+ }
+ }
+ }
+ }
+}
+
+void DetectorPostprocessing::DrawBoxOnImage(uint8_t* imgIn, int imWidth, int imHeight, int boxX,int boxY, int boxWidth, int boxHeight)
+{
+ auto CheckAndFixOffset = [](int im_width,int im_height,int& offset) {
+ if ( (offset) >= im_width*im_height*channelsImageDisplayed) {
+ offset = im_width * im_height * channelsImageDisplayed -1;
+ }
+ else if ( (offset) < 0) {
+ offset = 0;
+ }
+ };
+
+ /* Consistency checks */
+ if (!imgIn) {
+ return;
+ }
+
+ int offset=0;
+ for (int i=0; i < boxWidth; i++) {
+ /* Draw two horizontal lines */
+ for (int line=0; line < 2; line++) {
+ /*top*/
+ offset =(i + (boxY + line)*imWidth + boxX) * channelsImageDisplayed; /* channelsImageDisplayed for rgb or grayscale*/
+ CheckAndFixOffset(imWidth,imHeight,offset);
+ imgIn[offset] = 0xFF;
+ /*bottom*/
+ offset = (i + (boxY + boxHeight - line)*imWidth + boxX) * channelsImageDisplayed;
+ CheckAndFixOffset(imWidth,imHeight,offset);
+ imgIn[offset] = 0xFF;
+ }
+ }
+
+ for (int i=0; i < boxHeight; i++) {
+ /* Draw two vertical lines */
+ for (int line=0; line < 2; line++) {
+ /*left*/
+ offset = ((i + boxY)*imWidth + boxX + line)*channelsImageDisplayed;
+ CheckAndFixOffset(imWidth,imHeight,offset);
+ imgIn[offset] = 0xFF;
+ /*right*/
+ offset = ((i + boxY)*imWidth + boxX + boxWidth - line)*channelsImageDisplayed;
+ CheckAndFixOffset(imWidth,imHeight, offset);
+ imgIn[offset] = 0xFF;
+ }
+ }
+
+}
+
+} /* namespace object_detection */
+} /* namespace app */
+} /* namespace arm */
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-12 13:17:53 +0000