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docs: Update README and compilation guides for clarity and consistency, including path corrections and improved formatting. Add copyright notices to source files and adjust file permissions for several scripts and directories.

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dian.yuan 2026-02-28 11:06:26 +08:00
parent f960c5030d
commit bd891a96dd
136 changed files with 14413 additions and 9399 deletions
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182
examples/ppocr-det/cpp/src/main.cpp
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@ -1,91 +1,91 @@
/*
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <iostream>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
#include <chrono>
#include "nn_sdk.h"
#include "model_loader.h"
#include "postprocess.h"
const std::string DEFAULT_OUTPUT_PATH = "result.png";
int main(int argc, char** argv) {
if (argc < 3) {
printf("Usage: %s <model_path> <image_path> [output_path]\n", argv[0]);
return -1;
}
std::string model_path = argv[1];
std::string image_path = argv[2];
std::string output_path = (argc > 3) ? argv[3] : DEFAULT_OUTPUT_PATH;
printf("Model: %s\n", model_path.c_str());
printf("Image: %s\n", image_path.c_str());
// 1. Initialize Network
void* ctx = init_network(model_path.c_str());
if (!ctx) {
fprintf(stderr, "Failed to initialize network\n");
return -1;
}
// 2. Load Image
cv::Mat img = cv::imread(image_path);
if (img.empty()) {
fprintf(stderr, "Failed to load image: %s\n", image_path.c_str());
uninit_network(ctx);
return -1;
}
// 3. Preprocess
auto start_time = std::chrono::high_resolution_clock::now();
cv::Mat pre_image;
float scale = 1.0f;
preprocess(img, pre_image, MODEL_INPUT_WIDTH, MODEL_INPUT_HEIGHT, scale);
printf("scale: %f\n", scale);
// 4. Inference
nn_output* outdata = (nn_output*)run_paddleocr_network(ctx, pre_image, MODEL_INPUT_WIDTH, MODEL_INPUT_HEIGHT, MODEL_INPUT_CHANNELS);
if (!outdata) {
fprintf(stderr, "Inference failed\n");
uninit_network(ctx);
return -1;
}
// 5. Postprocess
float* out0 = (float*)outdata->out[0].buf;
std::vector<Object> results;
postprocess(out0, img, BOX_SCORE_THRESH, BOX_THRESH, results, scale);
auto end_time = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> inference_time = end_time - start_time;
printf("Inference + Postprocess time: %.2f ms\n", inference_time.count());
printf("Results: %zu\n", results.size());
// 6. Draw and Save
cv::Mat res = draw_objects(img, results);
cv::imwrite(output_path, res);
printf("Saved result to %s\n", output_path.c_str());
// 7. Cleanup
uninit_network(ctx);
return 0;
}
/*
* Copyright (C) 2024–2025 Amlogic, Inc. 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 <iostream>
#include <string>
#include <vector>
#include <opencv2/opencv.hpp>
#include <chrono>
#include "nn_sdk.h"
#include "model_loader.h"
#include "postprocess.h"
const std::string DEFAULT_OUTPUT_PATH = "result.png";
int main(int argc, char** argv) {
if (argc < 3) {
printf("Usage: %s <model_path> <image_path> [output_path]\n", argv[0]);
return -1;
}
std::string model_path = argv[1];
std::string image_path = argv[2];
std::string output_path = (argc > 3) ? argv[3] : DEFAULT_OUTPUT_PATH;
printf("Model: %s\n", model_path.c_str());
printf("Image: %s\n", image_path.c_str());
// 1. Initialize Network
void* ctx = init_network(model_path.c_str());
if (!ctx) {
fprintf(stderr, "Failed to initialize network\n");
return -1;
}
// 2. Load Image
cv::Mat img = cv::imread(image_path);
if (img.empty()) {
fprintf(stderr, "Failed to load image: %s\n", image_path.c_str());
uninit_network(ctx);
return -1;
}
// 3. Preprocess
auto start_time = std::chrono::high_resolution_clock::now();
cv::Mat pre_image;
float scale = 1.0f;
preprocess(img, pre_image, MODEL_INPUT_WIDTH, MODEL_INPUT_HEIGHT, scale);
printf("scale: %f\n", scale);
// 4. Inference
nn_output* outdata = (nn_output*)run_paddleocr_network(ctx, pre_image, MODEL_INPUT_WIDTH, MODEL_INPUT_HEIGHT, MODEL_INPUT_CHANNELS);
if (!outdata) {
fprintf(stderr, "Inference failed\n");
uninit_network(ctx);
return -1;
}
// 5. Postprocess
float* out0 = (float*)outdata->out[0].buf;
std::vector<Object> results;
postprocess(out0, img, BOX_SCORE_THRESH, BOX_THRESH, results, scale);
auto end_time = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> inference_time = end_time - start_time;
printf("Inference + Postprocess time: %.2f ms\n", inference_time.count());
printf("Results: %zu\n", results.size());
// 6. Draw and Save
cv::Mat res = draw_objects(img, results);
cv::imwrite(output_path, res);
printf("Saved result to %s\n", output_path.c_str());
// 7. Cleanup
uninit_network(ctx);
return 0;
}