tell batch inference demos and single image inference demos apart (#986)
(cherry picked from commit 4c872a41c3)
pull/1276/head
lvhan028
parent
97a9182a65
commit
f311cfd437
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@ -30,7 +30,9 @@ function(add_example dep folder name)
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endfunction()
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add_example(classifier c image_classification)
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add_example(classifier c batch_image_classification)
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add_example(detector c object_detection)
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add_example(detector c batch_object_detection)
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add_example(segmentor c image_segmentation)
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add_example(restorer c image_restorer)
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add_example(text_detector c ocr)
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@ -0,0 +1,100 @@
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#include <fstream>
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#include <opencv2/imgcodecs/imgcodecs.hpp>
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#include <string>
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#include "mmdeploy/classifier.h"
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static int batch_inference(mmdeploy_classifier_t classifier,
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const std::vector<int>& image_ids,
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const std::vector<mmdeploy_mat_t>& mats);
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int main(int argc, char* argv[]) {
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if (argc < 5) {
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fprintf(stderr, "usage:\n image_classification device_name dump_model_directory "
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"imagelist.txt batch_size\n");
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return 1;
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}
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auto device_name = argv[1];
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auto model_path = argv[2];
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mmdeploy_classifier_t classifier{};
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int status{};
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status = mmdeploy_classifier_create_by_path(model_path, device_name, 0, &classifier);
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if (status != MMDEPLOY_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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// `file_path` is the path of an image list file
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std::string file_path = argv[3];
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const int batch = std::stoi(argv[argc-1]);
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// read image paths from the file
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std::ifstream ifs(file_path);
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std::string img_path;
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std::vector<std::string> img_paths;
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while (ifs >> img_path) {
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img_paths.emplace_back(std::move(img_path));
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}
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// read images and process batch inference
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std::vector<cv::Mat> images;
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std::vector<int> image_ids;
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std::vector<mmdeploy_mat_t> mats;
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for (int i = 0; i < (int)img_paths.size(); ++i) {
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auto img = cv::imread(img_paths[i]);
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if (!img.data) {
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fprintf(stderr, "failed to load image: %s\n", img_paths[i].c_str());
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continue;
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}
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images.push_back(img);
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image_ids.push_back(i);
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mmdeploy_mat_t mat{
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img.data, img.rows, img.cols, 3, MMDEPLOY_PIXEL_FORMAT_BGR, MMDEPLOY_DATA_TYPE_UINT8};
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mats.push_back(mat);
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// process batch inference
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if ((int)mats.size() == batch) {
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if (batch_inference(classifier, image_ids, mats) != 0) {
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continue;
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}
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// clear buffer for next batch
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mats.clear();
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image_ids.clear();
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images.clear();
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}
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}
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// process batch inference if there are still unhandled images
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if (!mats.empty()) {
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(void)batch_inference(classifier, image_ids, mats);
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}
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mmdeploy_classifier_destroy(classifier);
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return 0;
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}
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int batch_inference(mmdeploy_classifier_t classifier, const std::vector<int>& image_ids,
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const std::vector<mmdeploy_mat_t>& mats) {
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mmdeploy_classification_t* res{};
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int* res_count{};
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auto status = mmdeploy_classifier_apply(classifier, mats.data(), (int)mats.size(),
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&res, &res_count);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to apply classifier to batch images %d, code: %d\n",
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(int)mats.size(), (int)status);
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return 1;
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}
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// print the inference results
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auto res_ptr = res;
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for (int j = 0; j < (int)mats.size(); ++j) {
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fprintf(stderr, "results in the %d-th image:\n", image_ids[j]);
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for (int k = 0; k < res_count[j]; ++k, ++res_ptr) {
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fprintf(stderr, " label: %d, score: %.4f\n", res_ptr->label_id, res_ptr->score);
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}
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}
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// release results buffer
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mmdeploy_classifier_release_result(res, res_count, (int)mats.size());
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return 0;
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}
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@ -0,0 +1,147 @@
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#include <fstream>
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#include <opencv2/imgcodecs/imgcodecs.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include <string>
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#include "mmdeploy/detector.h"
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static int batch_inference(mmdeploy_detector_t detector, std::vector<cv::Mat>& images,
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const std::vector<int>& image_ids,
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const std::vector<mmdeploy_mat_t>& mats);
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static void visualize_detection(const std::string& output_name, cv::Mat& image,
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const mmdeploy_detection_t* bboxes_ptr, int bboxes_num);
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int main(int argc, char* argv[]) {
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if (argc < 5) {
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fprintf(stderr, "usage:\n object_detection device_name sdk_model_path "
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"file_path batch_size\n");
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return 1;
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}
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auto device_name = argv[1];
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auto model_path = argv[2];
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mmdeploy_detector_t detector{};
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int status{};
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status = mmdeploy_detector_create_by_path(model_path, device_name, 0, &detector);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to create detector, code: %d\n", (int)status);
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return 1;
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}
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// file_path is the path of an image list file
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std::string file_path = argv[3];
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const int batch = std::stoi(argv[argc-1]);
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// read image paths from the file
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std::ifstream ifs(file_path);
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std::string img_path;
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std::vector<std::string> img_paths;
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while (ifs >> img_path) {
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img_paths.emplace_back(std::move(img_path));
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}
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// read images and process batch inference
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std::vector<cv::Mat> images;
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std::vector<int> image_ids;
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std::vector<mmdeploy_mat_t> mats;
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for (int i = 0; i < (int)img_paths.size(); ++i) {
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auto img = cv::imread(img_paths[i]);
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if (!img.data) {
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fprintf(stderr, "failed to load image: %s\n", img_paths[i].c_str());
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continue;
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}
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images.push_back(img);
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image_ids.push_back(i);
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mmdeploy_mat_t mat{
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img.data, img.rows, img.cols, 3, MMDEPLOY_PIXEL_FORMAT_BGR, MMDEPLOY_DATA_TYPE_UINT8};
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mats.push_back(mat);
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// process batch inference
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if ((int)mats.size() == batch) {
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if (batch_inference(detector, images, image_ids, mats) != 0) {
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continue;
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}
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// clear buffer for next batch
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mats.clear();
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image_ids.clear();
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images.clear();
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}
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}
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// process batch inference if there are still unhandled images
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if (!mats.empty()) {
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(void)batch_inference(detector, images, image_ids, mats);
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}
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mmdeploy_detector_destroy(detector);
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return 0;
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}
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int batch_inference(mmdeploy_detector_t detector, std::vector<cv::Mat>& images,
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const std::vector<int>& image_ids,
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const std::vector<mmdeploy_mat_t>& mats) {
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mmdeploy_detection_t* bboxes{};
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int* res_count{};
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auto status = mmdeploy_detector_apply(detector, mats.data(), mats.size(), &bboxes, &res_count);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to apply detector, code: %d\n", (int)status);
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return 1;
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}
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mmdeploy_detection_t* bboxes_ptr = bboxes;
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for (int i = 0; i < (int)mats.size(); ++i) {
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fprintf(stdout, "results in the %d-th image:\n bbox_count=%d\n", image_ids[i], res_count[i]);
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const std::string output_name = "output_detection_" + std::to_string(image_ids[i]) + ".png";
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visualize_detection(output_name, images[i], bboxes_ptr, res_count[i]);
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bboxes_ptr = bboxes_ptr + res_count[i];
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}
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mmdeploy_detector_release_result(bboxes, res_count, mats.size());
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return 0;
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}
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void visualize_detection(const std::string& output_name, cv::Mat& image,
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const mmdeploy_detection_t* bboxes_ptr, int bbox_num) {
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for (int i = 0; i < bbox_num; ++i, ++bboxes_ptr) {
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const auto& box = bboxes_ptr->bbox;
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const auto& mask = bboxes_ptr->mask;
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fprintf(stdout,
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" box %d, left=%.2f, top=%.2f, right=%.2f, bottom=%.2f, "
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"label=%d, score=%.4f\n",
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i, box.left, box.top, box.right, box.bottom, bboxes_ptr->label_id, bboxes_ptr->score);
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// skip detections with invalid bbox size (bbox height or width < 1)
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if ((box.right - box.left) < 1 || (box.bottom - box.top) < 1) {
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continue;
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}
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// skip detections less than specified score threshold
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if (bboxes_ptr->score < 0.3) {
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continue;
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}
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// generate mask overlay if model exports masks
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if (mask != nullptr) {
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fprintf(stdout, "mask %d, height=%d, width=%d\n", i, mask->height, mask->width);
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cv::Mat imgMask(mask->height, mask->width, CV_8UC1, &mask->data[0]);
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auto x0 = std::max(std::floor(box.left) - 1, 0.f);
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auto y0 = std::max(std::floor(box.top) - 1, 0.f);
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cv::Rect roi((int)x0, (int)y0, mask->width, mask->height);
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// split the RGB channels, overlay mask to a specific color channel
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cv::Mat ch[3];
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split(image, ch);
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int col = 0;
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cv::bitwise_or(imgMask, ch[col](roi), ch[col](roi));
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merge(ch, 3, image);
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}
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cv::rectangle(image, cv::Point{(int)box.left, (int)box.top},
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cv::Point{(int)box.right, (int)box.bottom}, cv::Scalar{0, 255, 0});
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}
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cv::imwrite(output_name, image);
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}
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