diff --git a/resource/onnx/skill_ready_cls.onnx b/resource/onnx/skill_ready_cls.onnx index 871b66abcf..2cc94a541c 100644 Binary files a/resource/onnx/skill_ready_cls.onnx and b/resource/onnx/skill_ready_cls.onnx differ diff --git a/src/MaaCore/Task/Interface/DebugTask.cpp b/src/MaaCore/Task/Interface/DebugTask.cpp index ad6ad2897b..0355df3125 100644 --- a/src/MaaCore/Task/Interface/DebugTask.cpp +++ b/src/MaaCore/Task/Interface/DebugTask.cpp @@ -47,25 +47,78 @@ void asst::DebugTask::test_skill_ready() { int total = 0; int correct = 0; + + // 测试 y 类别（预期为 ready，即 true） for (const auto& entry : std::filesystem::directory_iterator(R"(../../test/skill_ready/y)")) { cv::Mat image = imread(entry.path().string()); BattlefieldClassifier analyzer(image); analyzer.set_object_of_interest({ .skill_ready = true }); total++; - if (analyzer.analyze()->skill_ready.ready) { + auto result = analyzer.analyze()->skill_ready; + // 记录日志：文件、预期结果、实际预测、得分、概率信息 + Log.info( + __FUNCTION__, + "File: ", + entry.path().string(), + " | Expected: Y (ready: true)", + " | Predicted: ", + result.ready, + " | Score: ", + result.score, + " | Prob: ", + result.prob); + if (result.ready) { correct++; } } + + // 测试 n 类别（预期为 not ready，即 false） for (const auto& entry : std::filesystem::directory_iterator(R"(../../test/skill_ready/n)")) { cv::Mat image = imread(entry.path().string()); BattlefieldClassifier analyzer(image); analyzer.set_object_of_interest({ .skill_ready = true }); total++; - if (!analyzer.analyze()->skill_ready.ready) { + auto result = analyzer.analyze()->skill_ready; + Log.info( + __FUNCTION__, + "File: ", + entry.path().string(), + " | Expected: N (ready: false)", + " | Predicted: ", + result.ready, + " | Score: ", + result.score, + " | Prob: ", + result.prob); + if (!result.ready) { correct++; } } - Log.info(__FUNCTION__, correct, "/", total, ",", double(correct) / total); + + // 测试 c 类别（同样预期为 not ready） + for (const auto& entry : std::filesystem::directory_iterator(R"(../../test/skill_ready/c)")) { + cv::Mat image = imread(entry.path().string()); + BattlefieldClassifier analyzer(image); + analyzer.set_object_of_interest({ .skill_ready = true }); + total++; + auto result = analyzer.analyze()->skill_ready; + Log.info( + __FUNCTION__, + "File: ", + entry.path().string(), + " | Expected: C (ready: false)", + " | Predicted: ", + result.ready, + " | Score: ", + result.score, + " | Prob: ", + result.prob); + if (!result.ready) { + correct++; + } + } + + Log.info(__FUNCTION__, "Final Accuracy: ", correct, "/", total, " (", double(correct) / total, ")"); } void asst::DebugTask::test_battle_image() diff --git a/src/MaaCore/Vision/Battle/BattlefieldClassifier.cpp b/src/MaaCore/Vision/Battle/BattlefieldClassifier.cpp index cb12304d63..4f37127da2 100644 --- a/src/MaaCore/Vision/Battle/BattlefieldClassifier.cpp +++ b/src/MaaCore/Vision/Battle/BattlefieldClassifier.cpp @@ -42,11 +42,35 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy Rect roi = Rect(m_base_point.x, m_base_point.y, 0, 0).move(skill_roi_move); cv::Mat image = make_roi(m_image, correct_rect(roi, m_image)); - std::vector input = image_to_tensor(image); + + // 1. 图像大小调整(推理慢可不做) + cv::Mat resized_image; + cv::resize(image, resized_image, cv::Size(72, 72)); + + // 2. 中心裁剪(推理慢可不做) + int crop_size = 64; + int x = (resized_image.cols - crop_size) / 2; + int y = (resized_image.rows - crop_size) / 2; + cv::Rect crop_roi(x, y, crop_size, crop_size); + cv::Mat cropped_image = resized_image(crop_roi); + + // 3. 图像转换为 tensor + std::vector input = image_to_tensor(cropped_image); + + // 4. 归一化 + float mean[] = { 0.485f, 0.456f, 0.406f }; + float std[] = { 0.229f, 0.224f, 0.225f }; + for (size_t i = 0; i < input.size(); i++) { + int channel = i % 3; + input[i] = (input[i] - mean[channel]) / std[channel]; + } auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); constexpr int64_t batch_size = 1; - std::array input_shape { batch_size, image.channels(), image.cols, image.rows }; + + auto& session = OnnxSessions::get_instance().get("skill_ready_cls"); + + std::array input_shape { batch_size, cropped_image.channels(), cropped_image.cols, cropped_image.rows }; Ort::Value input_tensor = Ort::Value::CreateTensor( memory_info, @@ -54,6 +78,7 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy input.size(), input_shape.data(), input_shape.size()); + SkillReadyResult::Raw raw_results; std::array output_shape { batch_size, SkillReadyResult::ClsSize }; Ort::Value output_tensor = Ort::Value::CreateTensor( @@ -63,7 +88,6 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy output_shape.data(), output_shape.size()); - auto& session = OnnxSessions::get_instance().get("skill_ready_cls"); // 这俩是hardcode在模型里的 constexpr const char* input_names[] = { "input" }; // session.GetInputName() constexpr const char* output_names[] = { "output" }; // session.GetOutputName() @@ -74,14 +98,23 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy SkillReadyResult::Prob prob = softmax(raw_results); Log.info(__FUNCTION__, "prob:", prob); - bool ready = prob[1] > prob[0]; - float score = std::max(prob[0], prob[1]); + // 类别顺序为 c, n, y + size_t class_id = ranges::max_element(prob) - prob.begin(); + bool ready = (class_id == 2); // 只有当class_id为2（代表y）时，才认为是ready + float score = prob[class_id]; #ifdef ASST_DEBUG - if (ready) { + // 在调试模式下，根据不同类别绘制不同颜色的标记 + if (class_id == 2) { + // y类别：橙色 rectangle(m_image_draw, make_rect(roi), cv::Scalar(0, 165, 255), 2); putText(m_image_draw, std::to_string(score), cv::Point(roi.x, roi.y - 10), 1, 1.2, cv::Scalar(0, 165, 255), 2); } + else if (class_id == 0) { // c类别的特殊处理 + // 使用蓝色（BGR：255,0,0）标记c类别 + rectangle(m_image_draw, make_rect(roi), cv::Scalar(255, 0, 0), 2); + putText(m_image_draw, std::to_string(score), cv::Point(roi.x, roi.y - 10), 1, 1.2, cv::Scalar(255, 0, 0), 2); + } #endif const auto result = SkillReadyResult { @@ -99,19 +132,19 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy // 为重新训练模型截图 static Point last_base_point = { -1, -1 }; + static int last_class = -1; // 记录上一次的分类结果 static auto last_save_time = std::chrono::steady_clock::now(); - static bool last_ready = false; const auto now = std::chrono::steady_clock::now(); const auto duration_since_last_save = std::chrono::duration_cast(now - last_save_time).count(); auto need_save = false; - // 如果相同点且结果不同，保存 - if (last_base_point == m_base_point && last_ready != ready) { + // 如果相同点且分类结果变化了，则保存 + if (last_base_point == m_base_point && last_class != static_cast(class_id)) { need_save = true; } - // 如果不同点且 ready，保存 - else if (last_base_point != m_base_point && ready) { + // 如果检测到新的基准点且结果为ready（y）或c类别，也保存 + else if (last_base_point != m_base_point && (class_id == 2 || class_id == 0)) { need_save = true; } // 来点随机截图 @@ -121,19 +154,29 @@ BattlefieldClassifier::SkillReadyResult BattlefieldClassifier::skill_ready_analy } if (need_save) { - std::filesystem::path relative_path; - if (ready) { - relative_path = utils::path("debug") / utils::path("skill_ready") / utils::path("y") / - (utils::get_time_filestem() + "_" + std::to_string(m_base_point.x) + "_" + - std::to_string(m_base_point.y) + ".png"); - } - else { - relative_path = utils::path("debug") / utils::path("skill_ready") / utils::path("n") / - (utils::get_time_filestem() + "_" + std::to_string(m_base_point.x) + "_" + - std::to_string(m_base_point.y) + ".png"); + std::string base_filename = utils::get_time_filestem() + "_" + std::to_string(m_base_point.x) + "_" + + std::to_string(m_base_point.y) + "(c" + std::to_string(prob[0]) + ")(n" + + std::to_string(prob[1]) + ")(y" + std::to_string(prob[2]) + ").png"; + std::string subfolder; + switch (class_id) { + case 2: + subfolder = "y"; + break; + case 1: + subfolder = "n"; + break; + case 0: + subfolder = "c"; + break; + default: + subfolder = "unknown"; + break; } + + std::filesystem::path relative_path = + utils::path("debug") / utils::path("skill_ready") / utils::path(subfolder) / base_filename; last_base_point = m_base_point; - last_ready = ready; + last_class = static_cast(class_id); Log.trace("Save image", relative_path); asst::imwrite(relative_path, image); } diff --git a/src/MaaCore/Vision/Battle/BattlefieldClassifier.h b/src/MaaCore/Vision/Battle/BattlefieldClassifier.h index b8c6474e33..037af09a19 100644 --- a/src/MaaCore/Vision/Battle/BattlefieldClassifier.h +++ b/src/MaaCore/Vision/Battle/BattlefieldClassifier.h @@ -18,7 +18,7 @@ public: public: struct SkillReadyResult { - static constexpr size_t ClsSize = 2; + static constexpr size_t ClsSize = 3; using Raw = std::array; using Prob = Raw;