718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
122 lines
3.2 KiB
C++
122 lines
3.2 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
|
|
//
|
|
// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
|
|
//
|
|
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
|
|
// in compliance with the License. You may obtain a copy of the License at
|
|
//
|
|
// https://opensource.org/licenses/BSD-3-Clause
|
|
//
|
|
// 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 "c_api.h"
|
|
|
|
#include <algorithm>
|
|
#if defined(USE_NCNN_SIMPLEOCV)
|
|
#include "simpleocv.h"
|
|
#else
|
|
#include <opencv2/core/core.hpp>
|
|
#include <opencv2/highgui/highgui.hpp>
|
|
#endif
|
|
#include <stdio.h>
|
|
#include <vector>
|
|
|
|
static int detect_squeezenet(const cv::Mat& bgr, std::vector<float>& cls_scores)
|
|
{
|
|
ncnn_net_t squeezenet = ncnn_net_create();
|
|
|
|
ncnn_option_t opt = ncnn_option_create();
|
|
ncnn_option_set_use_vulkan_compute(opt, 1);
|
|
|
|
ncnn_net_set_option(squeezenet, opt);
|
|
|
|
// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
|
|
ncnn_net_load_param(squeezenet, "squeezenet_v1.1.param");
|
|
ncnn_net_load_model(squeezenet, "squeezenet_v1.1.bin");
|
|
|
|
ncnn_mat_t in = ncnn_mat_from_pixels_resize(bgr.data, NCNN_MAT_PIXEL_BGR, bgr.cols, bgr.rows, bgr.cols * 3, 227, 227, NULL);
|
|
|
|
const float mean_vals[3] = {104.f, 117.f, 123.f};
|
|
ncnn_mat_substract_mean_normalize(in, mean_vals, 0);
|
|
|
|
ncnn_extractor_t ex = ncnn_extractor_create(squeezenet);
|
|
|
|
ncnn_extractor_input(ex, "data", in);
|
|
|
|
ncnn_mat_t out;
|
|
ncnn_extractor_extract(ex, "prob", &out);
|
|
|
|
const int out_w = ncnn_mat_get_w(out);
|
|
const float* out_data = (const float*)ncnn_mat_get_data(out);
|
|
|
|
cls_scores.resize(out_w);
|
|
for (int j = 0; j < out_w; j++)
|
|
{
|
|
cls_scores[j] = out_data[j];
|
|
}
|
|
|
|
ncnn_mat_destroy(in);
|
|
ncnn_mat_destroy(out);
|
|
|
|
ncnn_extractor_destroy(ex);
|
|
|
|
ncnn_option_destroy(opt);
|
|
|
|
ncnn_net_destroy(squeezenet);
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int print_topk(const std::vector<float>& cls_scores, int topk)
|
|
{
|
|
// partial sort topk with index
|
|
int size = cls_scores.size();
|
|
std::vector<std::pair<float, int> > vec;
|
|
vec.resize(size);
|
|
for (int i = 0; i < size; i++)
|
|
{
|
|
vec[i] = std::make_pair(cls_scores[i], i);
|
|
}
|
|
|
|
std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
|
|
std::greater<std::pair<float, int> >());
|
|
|
|
// print topk and score
|
|
for (int i = 0; i < topk; i++)
|
|
{
|
|
float score = vec[i].first;
|
|
int index = vec[i].second;
|
|
fprintf(stderr, "%d = %f\n", index, score);
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
if (argc != 2)
|
|
{
|
|
fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
|
|
return -1;
|
|
}
|
|
|
|
const char* imagepath = argv[1];
|
|
|
|
cv::Mat m = cv::imread(imagepath, 1);
|
|
if (m.empty())
|
|
{
|
|
fprintf(stderr, "cv::imread %s failed\n", imagepath);
|
|
return -1;
|
|
}
|
|
|
|
std::vector<float> cls_scores;
|
|
detect_squeezenet(m, cls_scores);
|
|
|
|
print_topk(cls_scores, 3);
|
|
|
|
return 0;
|
|
}
|