xlearn.segmentation

Module containing model_choose, seg_train and seg_predict routines

Functions:

seg_train(img_x, img_y[, patch_size, …]) Function description.
seg_predict(img, wpath, spath[, patch_size, …]) Function description
xlearn.segmentation.model_choose(ih, iw, nb_conv, size_conv, nb_down, nb_gpu)[source]
xlearn.segmentation.seg_train(img_x, img_y, patch_size=32, patch_step=1, nb_conv=32, size_conv=3, batch_size=1000, nb_epoch=20, nb_down=2, nb_gpu=1)[source]

Function description.

Parameters:
  • img_x (array, 2D or 3D) – Training input of the model. It is the raw image for the segmentation.
  • img_y (array, 2D or 3D) – Training output of the model. It is the corresponding segmentation of the training input.
  • patch_size (int) – The size of the small patches extracted from the input images. This size should be big enough to cover the features of the segmentation object.
  • patch_step (int) – The pixel steps between neighbour patches. Larger steps leads faster speed, but less quality. I recommend 1 unless you need quick test of the algorithm.
  • nb_conv (int) – Number of the covolutional kernals for the first layer. This number doubles after each downsampling layer.
  • size_conv (int) – Size of the convolutional kernals.
  • batch_size (int) – Batch size for the training. Bigger size leads faster speed. However, it is restricted by the memory size of the GPU. If the user got the memory error, please decrease the batch size.
  • nb_epoch (int) – Number of the epoches for the training. It can be understand as the number of iterations during the training. Please define this number as the actual convergence for different data.
  • nb_down (int) – Number of the downsampling for the images in the model.
  • nb_gpu (int) – Number of GPUs you want to use for the training.
Returns:

mdl – The trained CNN model for segmenation. The model can be saved for future segmentations.
xlearn.segmentation.seg_predict(img, wpath, spath, patch_size=32, patch_step=1, nb_conv=32, size_conv=3, batch_size=1000, nb_down=2, nb_gpu=1)[source]

Function description

Parameters:
  • img (array) – The images need to be segmented.
  • wpath (string) – The path where the trained weights of the model can be read.
  • spath (string) – The path to save the segmented images.
  • patch_size (int) – The size of the small patches extracted from the input images. This size should be big enough to cover the features of the segmentation object.
  • patch_step (int) – The pixel steps between neighbour patches. Larger steps leads faster speed, but less quality. I recommend 1 unless you need quick test of the algorithm.
  • nb_conv (int) – Number of the covolutional kernals for the first layer. This number doubles after each downsampling layer.
  • size_conv (int) – Size of the convolutional kernals.
  • batch_size (int) – Batch size for the training. Bigger size leads faster speed. However, it is restricted by the memory size of the GPU. If the user got the memory error, please decrease the batch size.
  • nb_epoch (int) – Number of the epoches for the training. It can be understand as the number of iterations during the training. Please define this number as the actual convergence for different data.
  • nb_down (int) – Number of the downsampling for the images in the model.
  • nb_gpu (int) – Number of GPUs you want to use for the training.
Returns:

save the segmented images to the spath.