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* unify C API naming * fix demo and move apis/c/* -> apis/c/mmdeploy/* * fix lint * fix C# project * fix Java API
164 lines
5.1 KiB
C++
164 lines
5.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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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/model.h"
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#include "mmdeploy/text_detector.h"
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#include "mmdeploy/text_recognizer.h"
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struct ctx_t {
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mmdeploy_mat_t* mat;
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mmdeploy_text_detection_t* dets{};
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int* det_count;
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};
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mmdeploy_value_t cont(mmdeploy_value_t det_output, void* context) {
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auto* ctx = static_cast<ctx_t*>(context);
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int ec = MMDEPLOY_SUCCESS;
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ec = mmdeploy_text_detector_get_result(det_output, &ctx->dets, &ctx->det_count);
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if (ec) {
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fprintf(stderr, "failed to get detection result, code = %d\n", ec);
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return nullptr;
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}
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mmdeploy_value_destroy(det_output);
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mmdeploy_value_t input{};
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ec = mmdeploy_text_recognizer_create_input(ctx->mat, 1, ctx->dets, ctx->det_count, &input);
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if (ec) {
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fprintf(stderr, "failed to create recognizer input, code = %d\n", ec);
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return nullptr;
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}
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return input;
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}
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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 ocr device_name det_model_path reg_model_path image_path\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 det_model_path = argv[2];
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auto reg_model_path = argv[3];
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auto image_path = argv[4];
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cv::Mat img = cv::imread(image_path);
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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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auto pool = mmdeploy_executor_system_pool();
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auto thread = mmdeploy_executor_create_thread();
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mmdeploy_exec_info prep_exec_info{{}, "Preprocess", pool};
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mmdeploy_exec_info dbnet_exec_info{&prep_exec_info, "dbnet", thread};
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mmdeploy_exec_info post_exec_info{&dbnet_exec_info, "postprocess", pool};
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mmdeploy_text_detector_t text_detector{};
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int status{};
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mmdeploy_model_t det_model{};
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status = mmdeploy_model_create_by_path(det_model_path, &det_model);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to create model %s\n", det_model_path);
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return 1;
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}
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mmdeploy_model_t reg_model{};
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status = mmdeploy_model_create_by_path(reg_model_path, ®_model);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to create model %s\n", det_model_path);
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return 1;
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}
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status =
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mmdeploy_text_detector_create_v2(det_model, device_name, 0, &post_exec_info, &text_detector);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to create text_detector, code: %d\n", (int)status);
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return 1;
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}
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mmdeploy_exec_info crnn_exec_info{&prep_exec_info, "crnnnet", thread};
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post_exec_info.next = &crnn_exec_info;
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mmdeploy_text_recognizer_t text_recognizer{};
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status = mmdeploy_text_recognizer_create_v2(reg_model, device_name, 0, &post_exec_info,
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&text_recognizer);
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if (status != MMDEPLOY_SUCCESS) {
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fprintf(stderr, "failed to create text_recognizer, code: %d\n", (int)status);
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return 1;
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}
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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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mmdeploy_value_t input{};
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if ((status = mmdeploy_text_detector_create_input(&mat, 1, &input)) != 0) {
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fprintf(stderr, "failed to create input for text detector, code = %d\n", status);
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return 1;
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}
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auto sender = mmdeploy_executor_just(input);
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assert(sender);
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if ((status = mmdeploy_text_detector_apply_async(text_detector, sender, &sender)) != 0) {
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fprintf(stderr, "failed to apply text detector asyncly, code = %d\n", status);
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return 1;
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}
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ctx_t context{&mat, {}, {}};
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sender = mmdeploy_executor_then(sender, cont, &context);
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assert(sender);
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if ((status = mmdeploy_text_recognizer_apply_async(text_recognizer, sender, &sender)) != 0) {
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fprintf(stderr, "failed to apply text recognizer asyncly, code = %d\n", status);
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return 1;
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}
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auto output = mmdeploy_executor_sync_wait(sender);
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if (!output) {
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fprintf(stderr, "failed to sync wait result\n");
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return 1;
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}
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mmdeploy_text_recognition_t* texts{};
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mmdeploy_text_recognizer_get_result(output, &texts);
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if (!texts) {
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fprintf(stderr, "failed to gettext recognizer result\n");
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return 1;
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}
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mmdeploy_value_destroy(output);
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// det results is available after sync_wait
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auto bboxes = context.dets;
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auto bbox_count = context.det_count;
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for (int i = 0; i < *bbox_count; ++i) {
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fprintf(stdout, "box[%d]: %s\n", i, texts[i].text);
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std::vector<cv::Point> poly_points;
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for (int j = 0; j < 4; ++j) {
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auto const& pt = bboxes[i].bbox[j];
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fprintf(stdout, "x: %.2f, y: %.2f, ", pt.x, pt.y);
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poly_points.push_back({(int)pt.x, (int)pt.y});
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}
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fprintf(stdout, "\n");
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cv::polylines(img, poly_points, true, cv::Scalar{0, 255, 0});
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}
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cv::imwrite("output_ocr.png", img);
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mmdeploy_text_recognizer_release_result(texts, *bbox_count);
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mmdeploy_text_recognizer_destroy(text_recognizer);
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mmdeploy_model_destroy(reg_model);
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mmdeploy_text_detector_release_result(context.dets, context.det_count, 1);
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mmdeploy_text_detector_destroy(text_detector);
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mmdeploy_model_destroy(det_model);
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mmdeploy_scheduler_destroy(pool);
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mmdeploy_scheduler_destroy(thread);
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return 0;
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}
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