QRgb 学习

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  41. #ifndef QRGB_H
  42. #define QRGB_H

  43. #include <QtCore/qglobal.h>

  44. QT_BEGIN_HEADER

  45. QT_BEGIN_NAMESPACE

  46. QT_MODULE(Gui)

  47. typedef unsigned int QRgb; // RGB triplet

  48. const QRgb RGB_MASK = 0x00ffffff; // masks RGB values

  49. Q_GUI_EXPORT_INLINE int qRed(QRgb rgb) // get red part of RGB
  50. { return ((rgb >> 16) & 0xff); }

  51. Q_GUI_EXPORT_INLINE int qGreen(QRgb rgb) // get green part of RGB
  52. { return ((rgb >> 8) & 0xff); }

  53. Q_GUI_EXPORT_INLINE int qBlue(QRgb rgb) // get blue part of RGB
  54. { return (rgb & 0xff); }

  55. Q_GUI_EXPORT_INLINE int qAlpha(QRgb rgb) // get alpha part of RGBA
  56. { return rgb >> 24; }

  57. Q_GUI_EXPORT_INLINE QRgb qRgb(int r, int g, int b)// set RGB value
  58. { return (0xffu << 24) | ((& 0xff) << 16) | ((& 0xff) << 8) | (& 0xff); }

  59. Q_GUI_EXPORT_INLINE QRgb qRgba(int r, int g, int b, int a)// set RGBA value
  60. { return ((& 0xff) << 24) | ((& 0xff) << 16) | ((& 0xff) << 8) | (& 0xff); }

  61. Q_GUI_EXPORT_INLINE int qGray(int r, int g, int b)// convert R,G,to gray 0..255
  62. { return (r*11+g*16+b*5)/32; }

  63. Q_GUI_EXPORT_INLINE int qGray(QRgb rgb) // convert RGB to gray 0..255
  64. { return qGray(qRed(rgb), qGreen(rgb), qBlue(rgb)); }

  65. Q_GUI_EXPORT_INLINE bool qIsGray(QRgb rgb)
  66. { return qRed(rgb) == qGreen(rgb) && qRed(rgb) == qBlue(rgb); }

  67. QT_END_NAMESPACE

  68. QT_END_HEADER

  69. #endif // QRGB_H
以下是使用TensorFlow C++ API在Qt中调用遥感地图语义分割深度学习模型进行图片预测的代码示例: ```c++ #include <tensorflow/cc/client/client_session.h> #include <tensorflow/cc/ops/standard_ops.h> #include <tensorflow/core/framework/tensor.h> #include <tensorflow/core/framework/tensor_shape.h> #include <tensorflow/core/graph/default_device.h> #include <tensorflow/core/platform/env.h> #include <tensorflow/core/platform/logging.h> #include <tensorflow/core/public/session.h> #include <QImage> using namespace tensorflow; // 加载模型文件 Status LoadModel(Session* session, const std::string& filename) { GraphDef graph_def; Status status = ReadBinaryProto(Env::Default(), filename, &graph_def); if (!status.ok()) { return status; } // 将模型文件加载到session中 status = session->Create(graph_def); if (!status.ok()) { return status; } return Status::OK(); } // 图片预处理,将QImage转换为TensorFlow可用的数据格式 void PreprocessImage(const QImage& image, Tensor* input_tensor) { const int width = image.width(); const int height = image.height(); const int channels = 3; auto input_tensor_mapped = input_tensor->tensor<float, 4>(); for (int y = 0; y < height; ++y) { const QRgb* row = reinterpret_cast<const QRgb*>(image.scanLine(y)); for (int x = 0; x < width; ++x) { const QRgb pixel = row[x]; const float r = qRed(pixel) / 255.0f; const float g = qGreen(pixel) / 255.0f; const float b = qBlue(pixel) / 255.0f; input_tensor_mapped(0, y, x, 0) = r; input_tensor_mapped(0, y, x, 1) = g; input_tensor_mapped(0, y, x, 2) = b; } } } // 图片后处理,将模型输出的结果转换为QImage QImage PostprocessOutput(const Tensor& output_tensor) { const int width = output_tensor.shape().dim_size(2); const int height = output_tensor.shape().dim_size(1); QImage result(width, height, QImage::Format_RGB888); auto output_tensor_mapped = output_tensor.tensor<float, 4>(); for (int y = 0; y < height; ++y) { QRgb* row = reinterpret_cast<QRgb*>(result.scanLine(y)); for (int x = 0; x < width; ++x) { const float r = output_tensor_mapped(0, y, x, 0); const float g = output_tensor_mapped(0, y, x, 1); const float b = output_tensor_mapped(0, y, x, 2); // 将模型输出的RGB值转换为QColor,并写入QImage中 const QColor color(r * 255.0f, g * 255.0f, b * 255.0f); row[x] = color.rgb(); } } return result; } // 进行图片预测 QImage PredictImage(const QImage& input_image, Session* session) { Tensor input_tensor(DT_FLOAT, TensorShape({1, input_image.height(), input_image.width(), 3})); PreprocessImage(input_image, &input_tensor); std::vector<std::pair<std::string, Tensor>> inputs = {{"input_tensor:0", input_tensor}}; std::vector<tensorflow::Tensor> outputs; Status status = session->Run(inputs, {"output_tensor:0"}, {}, &outputs); if (!status.ok()) { LOG(ERROR) << "Failed to run model: " << status; return QImage(); } return PostprocessOutput(outputs[0]); } int main() { // 初始化TensorFlow tensorflow::Session* session; tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(), &session); if (!status.ok()) { LOG(ERROR) << "Failed to create TensorFlow session: " << status; return 1; } // 加载模型文件 status = LoadModel(session, "model.pb"); if (!status.ok()) { LOG(ERROR) << "Failed to load model: " << status; return 1; } // 加载图片并进行预测 QImage input_image("input.jpg"); QImage output_image = PredictImage(input_image, session); return 0; } ``` 需要根据实际情况修改模型文件的名字和路径,以及输入图片的名字和路径。同时,也可以根据实际情况修改图片预处理和后处理的代码,以满足模型的输入和输出要求。
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