Classification using a kmeans cluster model¶

The first function (pcv.predict_kmeans) takes a target image and uses a trained kmeans model produced by pcv.learn.train_kmeans to classify regions of the target image by the trained clusters. The second function (pcv.mask_kmeans) takes a list of clusters and produces the combined mask from clusters of interest. The target and training images may be in grayscale or RGB image format.

plantcv.predict_kmeans(img, model_path="./kmeansout.fit", patch_size=10)

outputs An image with regions colored and labeled according to cluster assignment

• Parameters:

  • img = Path to target image
  • model_path = Path to where the model fit (output from plantcv.learn.train_kmeans.py) is stored
  • patch_size = Size of the NxN neighborhood around each pixel, used for classification

• Context:

  • Used to classify cluster assignment of pixels in a target image using a trained kmeans clustering model.
  • Command line interface also available

• Example use below

plantcv.mask_kmeans(labeled_img, k, cat_list=None)

outputs Either a combined mask of the requestedlist of clusters or a dictionary of each cluster as a separate mask with keys corresponding to the cluster number

• Parameters:

  • labeled_img = The output from predict_kmeans, an image with pixels labeled according to their cluster assignment
  • k = The number of clusters in the trained model
  • cat_list = List of clusters to include in a combined mask. If None, output is a dictionary of separate masks for each cluster

• Context:

  • Used to create masks from kmeans cluster assignments on a target image.

• Example use:

Input image example

from plantcv import plantcv as pcv

#Labeling a target image
labeled_img = pcv.predict_kmeans(img='./leaf_example.png',
                                 model_path="./kmeansout_leaf.fit", patch_size=5)

#Choosing clusters for each category within the seed image
background = pcv.mask_kmeans(labeled_img=labeled_img, k=10 cat_list=[0, 2, 4, 6, 7])
sick = pcv.mask_kmeans(labeled_img=labeled_img, k=10, cat_list=[1, 3])
leaf = pcv.mask_kmeans(labeled_img=labeled_img, k=10, cat_list=[5, 8, 9])

Labeled image

Combined mask of background clusters

Combined mask of healthy clusters

Combined mask of sick clusters

Source Code: Here