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examples/yoloworld/cpp/src/main.cpp

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+/*
+ * 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 <chrono>
+#include <tuple>
+#include <opencv2/opencv.hpp>
+#include "postprocess.h"
+#include "model_loader.h"
+
+const std::string DEFAULT_OUTPUT_PATH = "./result.jpg";
+const int MODEL_INPUT_WIDTH = 640;
+const int MODEL_INPUT_HEIGHT = 480;
+const float SCORE_THRESHOLD = 0.4f;
+const float NMS_THRESHOLD = 0.45f;
+
+const std::vector<std::string> CLASS_NAMES = {
+    "handbag", "backpack", "wallet",
+    "watch", "necklace", "bracelet", "earrings", "finger ring", "sunglass", "hat", "shoes", "belt",
+    "makeup palette", "lipstick tube",
+    "car", "truck", "bicycle", "motorcycle",
+    "phone", "laptop", "camera", "wine bottle", "stuffed toy"
+};
+
+int main(int argc, char** argv) {
+    std::string model_path;
+    std::string image_path;
+    if (argc != 3)
+    {
+        printf("%s <model_path> <image_path>\n", argv[0]);
+        return -1;
+    }
+
+    if (argc > 1) model_path = argv[1];
+    if (argc > 2) image_path = argv[2];
+
+    std::cout << "YOLOWorld Native Demo" << std::endl;
+    std::cout << "Model: " << model_path << std::endl;
+    std::cout << "Image: " << image_path << std::endl;
+    std::cout << "Output: " << DEFAULT_OUTPUT_PATH << std::endl;
+
+    // 1. Load Image
+    cv::Mat img = cv::imread(image_path);
+    if (img.empty()) {
+        std::cerr << "Failed to load image from " << image_path << std::endl;
+        return -1;
+    }
+
+    // 2. Initialize Network
+    void* context = init_network(model_path.c_str());
+    if (!context) {
+        std::cerr << "Failed to initialize network." << std::endl;
+        return -1;
+    }
+
+    // 3. Preprocess
+    auto start_time = std::chrono::high_resolution_clock::now();
+    
+    std::tuple<cv::Mat, float, std::tuple<int, int>> input_tuple = 
+        preprocess(img, std::make_tuple(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH));
+    
+    // 4. Run Network
+    void* output_ptr = run_network(context, {input_tuple});
+    if (!output_ptr) {
+        std::cerr << "Failed to run network." << std::endl;
+        uninit_network(context);
+        return -1;
+    }
+    nn_output* outdata = (nn_output*)output_ptr;
+
+    // 5. Postprocess
+    float* outbuf0 = (float*)outdata->out[0].buf;
+    float* outbuf1 = (float*)outdata->out[1].buf;
+    float* outbuf2 = (float*)outdata->out[2].buf;
+    
+    int num_classes = CLASS_NAMES.size();
+    int channels = 87; 
+    
+    // Using standard stride logic assuming standard YOLOv8/World export
+    std::vector<Detection> detections = postprocess(
+        std::make_tuple(outbuf0, std::make_tuple(MODEL_INPUT_HEIGHT / 8, MODEL_INPUT_WIDTH / 8, channels), 8),
+        std::make_tuple(outbuf1, std::make_tuple(MODEL_INPUT_HEIGHT / 16, MODEL_INPUT_WIDTH / 16, channels), 16),
+        std::make_tuple(outbuf2, std::make_tuple(MODEL_INPUT_HEIGHT / 32, MODEL_INPUT_WIDTH / 32, channels), 32),
+        input_tuple,
+        SCORE_THRESHOLD,
+        NMS_THRESHOLD,
+        num_classes,
+        1 // reverse=1 for YOLOWorld format
+    );
+
+    auto end_time = std::chrono::high_resolution_clock::now();
+    std::chrono::duration<double, std::milli> inference_time = end_time - start_time;
+    std::cout << "Inference + Postprocess time: " << inference_time.count() << " ms" << std::endl;
+    std::cout << "Detections found: " << detections.size() << std::endl;
+
+    // 6. Draw and Save
+    cv::Mat result_img = draw_detections(img, detections, CLASS_NAMES);
+    cv::imwrite(DEFAULT_OUTPUT_PATH, result_img);
+    std::cout << "Result saved to " << DEFAULT_OUTPUT_PATH << std::endl;
+
+    // 7. Cleanup
+    uninit_network(context);
+    
+    return 0;
+}