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* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
45 lines
1.2 KiB
C++
45 lines
1.2 KiB
C++
#include "classifier.h"
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#include <fstream>
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#include <string>
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#include <opencv2/highgui/highgui.hpp>
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int main(int argc, char *argv[]) {
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if (argc != 3) {
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fprintf(stderr, "usage:\n image_classification model_path image_path\n");
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return 1;
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}
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auto model_path = argv[1];
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auto image_path = argv[2];
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cv::Mat img = cv::imread(argv[2]);
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if (!img.data) {
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fprintf(stderr, "failed to load image: %s\n", image_path);
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return 1;
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}
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mm_handle_t classifier{};
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int status{};
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status = mmdeploy_classifier_create_by_path(model_path, "cpu", 0, &classifier);
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if (status != MM_SUCCESS) {
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fprintf(stderr, "failed to create classifier, code: %d\n", (int) status);
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return 1;
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}
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mm_mat_t mat{img.data, img.rows, img.cols, 3, MM_BGR, MM_INT8};
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mm_class_t *res{};
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int *res_count{};
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status = mmdeploy_classifier_apply(classifier, &mat, 1, &res, &res_count);
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if (status != MM_SUCCESS) {
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fprintf(stderr, "failed to apply classifier, code: %d\n", (int) status);
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return 1;
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}
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fprintf(stderr, "label: %d, score: %.4f\n", res->label_id, res->score);
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mmdeploy_classifier_release_result(res, res_count, 1);
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mmdeploy_classifier_destroy(classifier);
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return 0;
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}
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