halcon19 深度学习--分类（by shany shang）

硬件环境：

   Think_pad T480
   GPU：英伟达 MX150
   CPU: Intel i5 8th 

软件环境：

   windows 10 X64
   halcon 19.05
   halcon deepleaening 19.05
   CUDA10
   CUDNN7.3

源代码：

• This example shows how to train a deep learning fruit classifier, along with
• a short overview of the necessary steps.
• Initialization.
  dev_update_off ()
  dev_close_window ()
  WindowWidth := 800
  WindowHeight := 600
  dev_open_window_fit_size (0, 0, WindowWidth, WindowHeight, -1, -1, WindowHandle)
  set_display_font (WindowHandle, 16, ‘mono’, ‘true’, ‘false’)
• Some procedures use a random number generator. Set the seed for reproducibility.
  set_system (‘seed_rand’, 42)
• Introduction text.
  dev_disp_introduction_text ()
  stop ()
  dev_close_window ()
  dev_resize_window_fit_size (0, 0, WindowWidth, WindowHeight, -1, -1)
  dev_clear_window ()
• ** TRAINING **
• Read one of the pretrained networks.
  read_dl_classifier (‘pretrained_dl_classifier_compact.hdl’, DLClassifierHandle)
• Path to directory with images.
  *RawDataFolder := ‘food/’ + [‘apple_braeburn’,‘apple_golden_delicious’,‘apple_topaz’,‘peach’,‘pear’]
  RawDataFolder := ‘E:/Deeplearning/shape/’ + [‘circle’,‘line’,‘square’,‘star’,‘tangle’]
• Get the raw data set with labels.
  *使用正则化表达式提取所在文件夹的名称作为标签使用
  read_dl_classifier_data_set (RawDataFolder, ‘last_folder’, RawImageFiles, Labels, LabelIndices, Classes)
• Path of output directory for preprocessed data set.
  PreprocessedFolder := ‘shape_preprocessed’
• Set to true to overwrite existing images.
  OverwritePreprocessingFolder := false
• By default, we will remove the folder with the preprocessed data.
• In a real application you might want to keep this data (if the
• preprocessing does not change to save time).
  RemovePreprocessingAfterExample := false
• If the preprocessed has been generated already,
• we skip this part.
  file_exists (PreprocessedFolder, FileExists)
  if (not FileExists or OverwritePreprocessingFolder)
  • Preprocessing of the raw data.
    if (FileExists)
    remove_dir_recursively (PreprocessedFolder)
    endif
  • Create output directories.
    make_dir (PreprocessedFolder)
    for I := 0 to |Classes| - 1 by 1
    make_dir (PreprocessedFolder + ‘/’ + Classes[I])
    endfor
  • Define output file names.
    parse_filename (RawImageFiles, BaseNames, Extensions, Directories)
    ObjectFilesOut := PreprocessedFolder + ‘/’ + Labels + ‘/’ + BaseNames + ‘.hobj’
  • Check if output file names
  • overlap in the preprocessed folder.
  • This is just a sanity check.
    check_output_file_names_for_duplicates (RawImageFiles, ObjectFilesOut)
  • Preprocess images and save them as hobj files.
    for I := 0 to |RawImageFiles| - 1 by 1
    read_image (Image, RawImageFiles[I])
    • Preprocess the image with a custom procedure
    • in order to remove the background.
      preprocess_dl_fruit_example (Image, ImagePreprocessed, DLClassifierHandle)
    • Write preprocessed image to hobj file.
      write_object (ImagePreprocessed, ObjectFilesOut[I])
      dev_disp_preprocessing_progress (I, RawImageFiles, PreprocessedFolder, WindowHandle)
      endfor
      dev_clear_window ()
      dev_disp_text (‘Preprocessing done.’, ‘window’, ‘top’, ‘left’, ‘black’, [], [])
      endif
• 2. Split data into training, validation, and test set.
• Read the data, i.e., the paths of the images and their respective ground truth labels.
  read_dl_classifier_data_set (PreprocessedFolder, ‘last_folder’, ImageFiles, Labels, LabelsIndices, Classes)
• Split the data into three subsets,
• for training 70%, validation 15%, and testing 15%.
  TrainingPercent := 70
  ValidationPercent := 15
  split_dl_classifier_data_set (ImageFiles, Labels, TrainingPercent, ValidationPercent, TrainingImages, TrainingLabels, ValidationImages, ValidationLabels, TestImages, TestLabels)
• Set training hyper-parameters.
• In order to retrain the neural network, we have to specify
• the class names of our classification problem.
  *设置所有类型
  set_dl_classifier_param (DLClassifierHandle, ‘classes’, Classes)
  *设置训练bacth大小
• Set the batch size.
  BatchSize := 16
  set_dl_classifier_param (DLClassifierHandle, ‘batch_size’, BatchSize)
• Try to initialize the runtime environment.
  try
  set_dl_classifier_param (DLClassifierHandle, ‘runtime_init’, ‘immediately’)
  catch (Exception)
  dev_disp_error_text (Exception)
  if (RemovePreprocessingAfterExample and Exception[0] != 4104)
  remove_dir_recursively (PreprocessedFolder)
  dev_disp_text (‘Preprocessed data in folder "’ + PreprocessedFolder + ‘" have been deleted.’, ‘window’, ‘bottom’, ‘left’, ‘black’, [], [])
  endif
  stop ()
  endtry
• For this data set, an initial learning rate of 0.001
• has proven to yield good results.
  InitialLearningRate := 0.001
  set_dl_classifier_param (DLClassifierHandle, ‘learning_rate’, InitialLearningRate)
• In this example, we reduce the learning rate
• by a factor of 1/10 every 4th epoch.
  LearningRateStepEveryNthEpoch := 30
  LearningRateStepRatio := 0.1
• We iterate 100 times over the full training set.
  NumEpochs := 100
• Train the classifier.

dev_clear_window ()
dev_disp_text (‘Training has started…’, ‘window’, ‘top’, ‘left’, ‘black’, [], [])
*
PlotEveryNthEpoch := 4
FileName := ‘classifier_fruit.hdl’
train_fruit_classifier (DLClassifierHandle, FileName, NumEpochs, TrainingImages, TrainingLabels, ValidationImages, ValidationLabels, LearningRateStepEveryNthEpoch, LearningRateStepRatio, PlotEveryNthEpoch, WindowHandle)
dev_disp_text (‘Press Run (F5) to continue’, ‘window’, ‘bottom’, ‘right’, ‘black’, [], [])
stop ()
read_dl_classifier (FileName, DLClassifierHandle)
*

• Compute the confusion matrix for the validation data set.
  get_predicted_classes (ValidationImages, DLClassifierHandle, PredictedClassesValidation)
  gen_confusion_matrix (ValidationLabels, PredictedClassesValidation, [], [], WindowHandle, ConfusionMatrix)
  dev_disp_text (‘Validation data’, ‘window’, ‘top’, ‘left’, ‘gray’, ‘box’, ‘false’)
  dev_disp_text (‘Press Run (F5) to continue’, ‘window’, ‘bottom’, ‘right’, ‘black’, [], [])
  stop ()
  dev_clear_window ()
• ** INFERENCE **
• This part shows a typical inference scenario.
• Read the classifier.
  read_dl_classifier (FileName, DLClassifierHandle)
• If it is not possible to accumulate more than one image
• at a time the batch size should be set to 1.
  set_dl_classifier_param (DLClassifierHandle, ‘batch_size’, 1)
• Set the runtime to ‘cpu’ to perform the inference on the
• CPU, if this is possible on the current hardware.
• (see the system requirements in the Installation Guide)
  try
  set_dl_classifier_param (DLClassifierHandle, ‘runtime’, ‘cpu’)
  Runtime := ‘cpu’
  catch (Exception)
  • Keep the ‘gpu’ runtime if switching to ‘cpu’ failed.
    Runtime := ‘gpu’
    endtry
• This initializes the runtime environment immediately.
  set_dl_classifier_param (DLClassifierHandle, ‘runtime_init’, ‘immediately’)

dev_resize_window_fit_size (0, 0, WindowWidth, WindowHeight, -1, -1)
dev_disp_inference_text (Runtime)
stop ()

• Read / acquire images in a loop and classify them.
  for Index := 0 to 20 by 1
  ImageFile := RawImageFiles[floor(rand(1) * |RawImageFiles|)]
  read_image (Image, ImageFile)
  dev_resize_window_fit_image (Image, 0, 0, -1, -1)
  preprocess_dl_fruit_example (Image, ImagePreprocessed, DLClassifierHandle)
  apply_dl_classifier (ImagePreprocessed, DLClassifierHandle, DLClassifierResultHandle)
  get_dl_classifier_result (DLClassifierResultHandle, ‘all’, ‘predicted_classes’, PredictedClass)
  *
  dev_display (Image)
  Text := ‘Predicted class: ’ + PredictedClass
  dev_disp_text (Text, ‘window’, ‘top’, ‘left’, ‘white’, ‘box’, ‘false’)
  dev_disp_text (‘Press Run (F5) to continue’, ‘window’, ‘bottom’, ‘right’, ‘black’, [], [])
  stop ()
  endfor
  stop ()
  if (RemovePreprocessingAfterExample)
  remove_dir_recursively (PreprocessedFolder)
  dev_disp_text (‘End of program.\nPreprocessed data have been deleted.’, ‘window’, ‘bottom’, ‘right’, ‘black’, [], [])
  else
  dev_disp_text (’ End of program ', ‘window’, ‘bottom’, ‘right’, ‘black’, [], [])
  endif