470 lines
17 KiB
C++
470 lines
17 KiB
C++
|
// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
|
||
|
|
//
|
||
|
|
// 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 <stdio.h>
|
||
|
|
#include <stdlib.h>
|
||
|
|
#include <math.h>
|
||
|
|
#include <string.h>
|
||
|
|
#include <sys/time.h>
|
||
|
|
#include <vector>
|
||
|
|
#include "yolov5_detect_postprocess.h"
|
||
|
|
#include <stdint.h>
|
||
|
|
|
||
|
|
|
||
|
|
static char labels[YOLOV5_CLASS_NUM][30] = {"0", "1"};
|
||
|
|
|
||
|
|
const int anchor0[6] = {10, 13, 16, 30, 33, 23};
|
||
|
|
const int anchor1[6] = {30, 61, 62, 45, 59, 119};
|
||
|
|
const int anchor2[6] = {116, 90, 156, 198, 373, 326};
|
||
|
|
|
||
|
|
inline static int clamp(float val, int min, int max)
|
||
|
|
{
|
||
|
|
return val > min ? (val < max ? val : max) : min;
|
||
|
|
}
|
||
|
|
|
||
|
|
static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1, float ymax1)
|
||
|
|
{
|
||
|
|
float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0);
|
||
|
|
float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0);
|
||
|
|
float i = w * h;
|
||
|
|
float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i;
|
||
|
|
return u <= 0.f ? 0.f : (i / u);
|
||
|
|
}
|
||
|
|
|
||
|
|
static int nms(int validCount, std::vector<float> &outputLocations, std::vector<int> &order, float threshold)
|
||
|
|
{
|
||
|
|
for (int i = 0; i < validCount; ++i)
|
||
|
|
{
|
||
|
|
if (order[i] == -1)
|
||
|
|
{
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
int n = order[i];
|
||
|
|
for (int j = i + 1; j < validCount; ++j)
|
||
|
|
{
|
||
|
|
int m = order[j];
|
||
|
|
if (m == -1)
|
||
|
|
{
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
float xmin0 = outputLocations[n * 4 + 0];
|
||
|
|
float ymin0 = outputLocations[n * 4 + 1];
|
||
|
|
float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2];
|
||
|
|
float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3];
|
||
|
|
|
||
|
|
float xmin1 = outputLocations[m * 4 + 0];
|
||
|
|
float ymin1 = outputLocations[m * 4 + 1];
|
||
|
|
float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2];
|
||
|
|
float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3];
|
||
|
|
|
||
|
|
float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1);
|
||
|
|
|
||
|
|
if (iou > threshold)
|
||
|
|
{
|
||
|
|
order[j] = -1;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
static int quick_sort_indice_inverse(
|
||
|
|
std::vector<float> &input,
|
||
|
|
int left,
|
||
|
|
int right,
|
||
|
|
std::vector<int> &indices)
|
||
|
|
{
|
||
|
|
float key;
|
||
|
|
int key_index;
|
||
|
|
int low = left;
|
||
|
|
int high = right;
|
||
|
|
if (left < right)
|
||
|
|
{
|
||
|
|
key_index = indices[left];
|
||
|
|
key = input[left];
|
||
|
|
while (low < high)
|
||
|
|
{
|
||
|
|
while (low < high && input[high] <= key)
|
||
|
|
{
|
||
|
|
high--;
|
||
|
|
}
|
||
|
|
input[low] = input[high];
|
||
|
|
indices[low] = indices[high];
|
||
|
|
while (low < high && input[low] >= key)
|
||
|
|
{
|
||
|
|
low++;
|
||
|
|
}
|
||
|
|
input[high] = input[low];
|
||
|
|
indices[high] = indices[low];
|
||
|
|
}
|
||
|
|
input[low] = key;
|
||
|
|
indices[low] = key_index;
|
||
|
|
quick_sort_indice_inverse(input, left, low - 1, indices);
|
||
|
|
quick_sort_indice_inverse(input, low + 1, right, indices);
|
||
|
|
}
|
||
|
|
return low;
|
||
|
|
}
|
||
|
|
|
||
|
|
static float sigmoid(float x)
|
||
|
|
{
|
||
|
|
return 1.0 / (1.0 + expf(-x));
|
||
|
|
}
|
||
|
|
|
||
|
|
static float unsigmoid(float y)
|
||
|
|
{
|
||
|
|
return -1.0 * logf((1.0 / y) - 1.0);
|
||
|
|
}
|
||
|
|
|
||
|
|
inline static int32_t __clip(float val, float min, float max)
|
||
|
|
{
|
||
|
|
float f = val <= min ? min : (val >= max ? max : val);
|
||
|
|
return f;
|
||
|
|
}
|
||
|
|
|
||
|
|
static uint8_t qnt_f32_to_affine(float f32, uint8_t zp, float scale)
|
||
|
|
{
|
||
|
|
float dst_val = (f32 / scale) + zp;
|
||
|
|
uint8_t res = (uint8_t)__clip(dst_val, 0, 255);
|
||
|
|
return res;
|
||
|
|
}
|
||
|
|
|
||
|
|
static float deqnt_affine_to_f32(uint8_t qnt, uint8_t zp, float scale)
|
||
|
|
{
|
||
|
|
return ((float)qnt - (float)zp) * scale;
|
||
|
|
}
|
||
|
|
|
||
|
|
static int process_u8(uint8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
|
||
|
|
std::vector<float> &boxes, std::vector<float> &boxScores, std::vector<int> &classId,
|
||
|
|
float threshold, uint8_t zp, float scale)
|
||
|
|
{
|
||
|
|
|
||
|
|
int validCount = 0;
|
||
|
|
int grid_len = grid_h * grid_w;
|
||
|
|
float thres = unsigmoid(threshold);
|
||
|
|
uint8_t thres_u8 = qnt_f32_to_affine(thres, zp, scale);
|
||
|
|
for (int a = 0; a < 3; a++)
|
||
|
|
{
|
||
|
|
for (int i = 0; i < grid_h; i++)
|
||
|
|
{
|
||
|
|
for (int j = 0; j < grid_w; j++)
|
||
|
|
{
|
||
|
|
uint8_t box_confidence = input[(YOLOV5_PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
|
||
|
|
if (box_confidence >= thres_u8)
|
||
|
|
{
|
||
|
|
int offset = (YOLOV5_PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
|
||
|
|
uint8_t *in_ptr = input + offset;
|
||
|
|
float box_x = sigmoid(deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5;
|
||
|
|
float box_y = sigmoid(deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5;
|
||
|
|
float box_w = sigmoid(deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0;
|
||
|
|
float box_h = sigmoid(deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0;
|
||
|
|
box_x = (box_x + j) * (float)stride;
|
||
|
|
box_y = (box_y + i) * (float)stride;
|
||
|
|
box_w = box_w * box_w * (float)anchor[a * 2];
|
||
|
|
box_h = box_h * box_h * (float)anchor[a * 2 + 1];
|
||
|
|
box_x -= (box_w / 2.0);
|
||
|
|
box_y -= (box_h / 2.0);
|
||
|
|
boxes.push_back(box_x);
|
||
|
|
boxes.push_back(box_y);
|
||
|
|
boxes.push_back(box_w);
|
||
|
|
boxes.push_back(box_h);
|
||
|
|
|
||
|
|
uint8_t maxClassProbs = in_ptr[5 * grid_len];
|
||
|
|
int maxClassId = 0;
|
||
|
|
for (int k = 1; k < YOLOV5_CLASS_NUM; ++k)
|
||
|
|
{
|
||
|
|
uint8_t prob = in_ptr[(5 + k) * grid_len];
|
||
|
|
if (prob > maxClassProbs)
|
||
|
|
{
|
||
|
|
maxClassId = k;
|
||
|
|
maxClassProbs = prob;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
float box_conf_f32 = sigmoid(deqnt_affine_to_f32(box_confidence, zp, scale));
|
||
|
|
float class_prob_f32 = sigmoid(deqnt_affine_to_f32(maxClassProbs, zp, scale));
|
||
|
|
boxScores.push_back(box_conf_f32* class_prob_f32);
|
||
|
|
classId.push_back(maxClassId);
|
||
|
|
validCount++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return validCount;
|
||
|
|
}
|
||
|
|
|
||
|
|
static int process_fp(float *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
|
||
|
|
std::vector<float> &boxes, std::vector<float> &boxScores, std::vector<int> &classId,
|
||
|
|
float threshold)
|
||
|
|
{
|
||
|
|
|
||
|
|
int validCount = 0;
|
||
|
|
int grid_len = grid_h * grid_w;
|
||
|
|
float thres_sigmoid = unsigmoid(threshold);
|
||
|
|
for (int a = 0; a < 3; a++)
|
||
|
|
{
|
||
|
|
for (int i = 0; i < grid_h; i++)
|
||
|
|
{
|
||
|
|
for (int j = 0; j < grid_w; j++)
|
||
|
|
{
|
||
|
|
float box_confidence = input[(YOLOV5_PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
|
||
|
|
if (box_confidence >= thres_sigmoid)
|
||
|
|
{
|
||
|
|
int offset = (YOLOV5_PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
|
||
|
|
float *in_ptr = input + offset;
|
||
|
|
float box_x = sigmoid(*in_ptr) * 2.0 - 0.5;
|
||
|
|
float box_y = sigmoid(in_ptr[grid_len]) * 2.0 - 0.5;
|
||
|
|
float box_w = sigmoid(in_ptr[2 * grid_len]) * 2.0;
|
||
|
|
float box_h = sigmoid(in_ptr[3 * grid_len]) * 2.0;
|
||
|
|
box_x = (box_x + j) * (float)stride;
|
||
|
|
box_y = (box_y + i) * (float)stride;
|
||
|
|
box_w = box_w * box_w * (float)anchor[a * 2];
|
||
|
|
box_h = box_h * box_h * (float)anchor[a * 2 + 1];
|
||
|
|
box_x -= (box_w / 2.0);
|
||
|
|
box_y -= (box_h / 2.0);
|
||
|
|
boxes.push_back(box_x);
|
||
|
|
boxes.push_back(box_y);
|
||
|
|
boxes.push_back(box_w);
|
||
|
|
boxes.push_back(box_h);
|
||
|
|
|
||
|
|
float maxClassProbs = in_ptr[5 * grid_len];
|
||
|
|
int maxClassId = 0;
|
||
|
|
for (int k = 1; k < YOLOV5_CLASS_NUM; ++k)
|
||
|
|
{
|
||
|
|
float prob = in_ptr[(5 + k) * grid_len];
|
||
|
|
if (prob > maxClassProbs)
|
||
|
|
{
|
||
|
|
maxClassId = k;
|
||
|
|
maxClassProbs = prob;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
float box_conf_f32 = sigmoid(box_confidence);
|
||
|
|
float class_prob_f32 = sigmoid(maxClassProbs);
|
||
|
|
boxScores.push_back(box_conf_f32* class_prob_f32);
|
||
|
|
classId.push_back(maxClassId);
|
||
|
|
validCount++;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return validCount;
|
||
|
|
}
|
||
|
|
|
||
|
|
int yolov5_post_process_u8(uint8_t *input0, uint8_t *input1, uint8_t *input2, int model_in_h, int model_in_w,
|
||
|
|
float conf_threshold, float nms_threshold,
|
||
|
|
std::vector<uint8_t> &qnt_zps, std::vector<float> &qnt_scales,
|
||
|
|
yolov5_detect_result_group_t *group)
|
||
|
|
{
|
||
|
|
static int init = -1;
|
||
|
|
if (init == -1)
|
||
|
|
{
|
||
|
|
/*
|
||
|
|
int ret = 0;
|
||
|
|
ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
|
||
|
|
if (ret < 0)
|
||
|
|
{
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
*/
|
||
|
|
init = 0;
|
||
|
|
}
|
||
|
|
memset(group, 0, sizeof(yolov5_detect_result_group_t));
|
||
|
|
|
||
|
|
std::vector<float> filterBoxes;
|
||
|
|
std::vector<float> boxesScore;
|
||
|
|
std::vector<int> classId;
|
||
|
|
int stride0 = 8;
|
||
|
|
int grid_h0 = model_in_h / stride0;
|
||
|
|
int grid_w0 = model_in_w / stride0;
|
||
|
|
int validCount0 = 0;
|
||
|
|
validCount0 = process_u8(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w,
|
||
|
|
stride0, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[0], qnt_scales[0]);
|
||
|
|
|
||
|
|
int stride1 = 16;
|
||
|
|
int grid_h1 = model_in_h / stride1;
|
||
|
|
int grid_w1 = model_in_w / stride1;
|
||
|
|
int validCount1 = 0;
|
||
|
|
validCount1 = process_u8(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w,
|
||
|
|
stride1, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[1], qnt_scales[1]);
|
||
|
|
|
||
|
|
int stride2 = 32;
|
||
|
|
int grid_h2 = model_in_h / stride2;
|
||
|
|
int grid_w2 = model_in_w / stride2;
|
||
|
|
int validCount2 = 0;
|
||
|
|
validCount2 = process_u8(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w,
|
||
|
|
stride2, filterBoxes, boxesScore, classId, conf_threshold, qnt_zps[2], qnt_scales[2]);
|
||
|
|
|
||
|
|
int validCount = validCount0 + validCount1 + validCount2;
|
||
|
|
// no object detect
|
||
|
|
if (validCount <= 0)
|
||
|
|
{
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<int> indexArray;
|
||
|
|
for (int i = 0; i < validCount; ++i)
|
||
|
|
{
|
||
|
|
indexArray.push_back(i);
|
||
|
|
}
|
||
|
|
|
||
|
|
quick_sort_indice_inverse(boxesScore, 0, validCount - 1, indexArray);
|
||
|
|
|
||
|
|
nms(validCount, filterBoxes, indexArray, nms_threshold);
|
||
|
|
|
||
|
|
int last_count = 0;
|
||
|
|
group->count = 0;
|
||
|
|
/* box valid detect target */
|
||
|
|
for (int i = 0; i < validCount; ++i)
|
||
|
|
{
|
||
|
|
|
||
|
|
if (indexArray[i] == -1 || boxesScore[i] < conf_threshold || last_count >= YOLOV5_NUMB_MAX_SIZE)
|
||
|
|
{
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
int n = indexArray[i];
|
||
|
|
|
||
|
|
float x1 = filterBoxes[n * 4 + 0];
|
||
|
|
float y1 = filterBoxes[n * 4 + 1];
|
||
|
|
float x2 = x1 + filterBoxes[n * 4 + 2];
|
||
|
|
float y2 = y1 + filterBoxes[n * 4 + 3];
|
||
|
|
int id = classId[n];
|
||
|
|
|
||
|
|
/*
|
||
|
|
group->results[last_count].box.left = (int)((clamp(x1, 0, model_in_w) - w_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.top = (int)((clamp(y1, 0, model_in_h) - h_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.right = (int)((clamp(x2, 0, model_in_w) - w_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.bottom = (int)((clamp(y2, 0, model_in_h) - h_offset) / resize_scale);
|
||
|
|
*/
|
||
|
|
group->results[last_count].box.left = (int) clamp(x1, 0, model_in_w);
|
||
|
|
group->results[last_count].box.top = (int) clamp(y1, 0, model_in_h);
|
||
|
|
group->results[last_count].box.right = (int) clamp(x2, 0, model_in_w);
|
||
|
|
group->results[last_count].box.bottom = (int) clamp(y2, 0, model_in_h);
|
||
|
|
|
||
|
|
group->results[last_count].prop = boxesScore[i];
|
||
|
|
group->results[last_count].class_index = id;
|
||
|
|
char *label = labels[id];
|
||
|
|
strncpy(group->results[last_count].name, label, YOLOV5_NAME_MAX_SIZE);
|
||
|
|
|
||
|
|
// printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, group->results[last_count].box.top,
|
||
|
|
// group->results[last_count].box.right, group->results[last_count].box.bottom, label);
|
||
|
|
last_count++;
|
||
|
|
}
|
||
|
|
group->count = last_count;
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
int yolov5_post_process_fp(float *input0, float *input1, float *input2, int model_in_h, int model_in_w,
|
||
|
|
float conf_threshold, float nms_threshold,
|
||
|
|
yolov5_detect_result_group_t *group)
|
||
|
|
{
|
||
|
|
static int init = -1;
|
||
|
|
if (init == -1)
|
||
|
|
{
|
||
|
|
/*
|
||
|
|
int ret = 0;
|
||
|
|
ret = loadLabelName(LABEL_NALE_TXT_PATH, labels);
|
||
|
|
if (ret < 0)
|
||
|
|
{
|
||
|
|
return -1;
|
||
|
|
}
|
||
|
|
*/
|
||
|
|
|
||
|
|
init = 0;
|
||
|
|
}
|
||
|
|
memset(group, 0, sizeof(yolov5_detect_result_group_t));
|
||
|
|
|
||
|
|
std::vector<float> filterBoxes;
|
||
|
|
std::vector<float> boxesScore;
|
||
|
|
std::vector<int> classId;
|
||
|
|
int stride0 = 8;
|
||
|
|
int grid_h0 = model_in_h / stride0;
|
||
|
|
int grid_w0 = model_in_w / stride0;
|
||
|
|
int validCount0 = 0;
|
||
|
|
validCount0 = process_fp(input0, (int *)anchor0, grid_h0, grid_w0, model_in_h, model_in_w,
|
||
|
|
stride0, filterBoxes, boxesScore, classId, conf_threshold);
|
||
|
|
|
||
|
|
int stride1 = 16;
|
||
|
|
int grid_h1 = model_in_h / stride1;
|
||
|
|
int grid_w1 = model_in_w / stride1;
|
||
|
|
int validCount1 = 0;
|
||
|
|
validCount1 = process_fp(input1, (int *)anchor1, grid_h1, grid_w1, model_in_h, model_in_w,
|
||
|
|
stride1, filterBoxes, boxesScore, classId, conf_threshold);
|
||
|
|
|
||
|
|
int stride2 = 32;
|
||
|
|
int grid_h2 = model_in_h / stride2;
|
||
|
|
int grid_w2 = model_in_w / stride2;
|
||
|
|
int validCount2 = 0;
|
||
|
|
validCount2 = process_fp(input2, (int *)anchor2, grid_h2, grid_w2, model_in_h, model_in_w,
|
||
|
|
stride2, filterBoxes, boxesScore, classId, conf_threshold);
|
||
|
|
|
||
|
|
int validCount = validCount0 + validCount1 + validCount2;
|
||
|
|
// no object detect
|
||
|
|
if (validCount <= 0)
|
||
|
|
{
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<int> indexArray;
|
||
|
|
for (int i = 0; i < validCount; ++i)
|
||
|
|
{
|
||
|
|
indexArray.push_back(i);
|
||
|
|
}
|
||
|
|
|
||
|
|
quick_sort_indice_inverse(boxesScore, 0, validCount - 1, indexArray);
|
||
|
|
|
||
|
|
nms(validCount, filterBoxes, indexArray, nms_threshold);
|
||
|
|
|
||
|
|
int last_count = 0;
|
||
|
|
group->count = 0;
|
||
|
|
/* box valid detect target */
|
||
|
|
for (int i = 0; i < validCount; ++i)
|
||
|
|
{
|
||
|
|
|
||
|
|
if (indexArray[i] == -1 || boxesScore[i] < conf_threshold || last_count >= YOLOV5_NUMB_MAX_SIZE)
|
||
|
|
{
|
||
|
|
continue;
|
||
|
|
}
|
||
|
|
int n = indexArray[i];
|
||
|
|
|
||
|
|
float x1 = filterBoxes[n * 4 + 0];
|
||
|
|
float y1 = filterBoxes[n * 4 + 1];
|
||
|
|
float x2 = x1 + filterBoxes[n * 4 + 2];
|
||
|
|
float y2 = y1 + filterBoxes[n * 4 + 3];
|
||
|
|
int id = classId[n];
|
||
|
|
|
||
|
|
/*
|
||
|
|
group->results[last_count].box.left = (int)((clamp(x1, 0, model_in_w) - w_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.top = (int)((clamp(y1, 0, model_in_h) - h_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.right = (int)((clamp(x2, 0, model_in_w) - w_offset) / resize_scale);
|
||
|
|
group->results[last_count].box.bottom = (int)((clamp(y2, 0, model_in_h) - h_offset) / resize_scale);
|
||
|
|
*/
|
||
|
|
group->results[last_count].box.left = (int) clamp(x1, 0, model_in_w);
|
||
|
|
group->results[last_count].box.top = (int) clamp(y1, 0, model_in_h);
|
||
|
|
group->results[last_count].box.right = (int) clamp(x2, 0, model_in_w);
|
||
|
|
group->results[last_count].box.bottom = (int) clamp(y2, 0, model_in_h);
|
||
|
|
|
||
|
|
group->results[last_count].prop = boxesScore[i];
|
||
|
|
group->results[last_count].class_index = id;
|
||
|
|
char *label = labels[id];
|
||
|
|
strncpy(group->results[last_count].name, label, YOLOV5_NAME_MAX_SIZE);
|
||
|
|
|
||
|
|
// printf("result %2d: (%4d, %4d, %4d, %4d), %s\n", i, group->results[last_count].box.left, group->results[last_count].box.top,
|
||
|
|
// group->results[last_count].box.right, group->results[last_count].box.bottom, label);
|
||
|
|
last_count++;
|
||
|
|
}
|
||
|
|
group->count = last_count;
|
||
|
|
|
||
|
|
return 0;
|
||
|
|
}
|