Awesome

ThinSection-Semantic-Image-Segmentation-Keras

This repository was inspired from Divam Gupta's GitHub repository on Image Segmentation Keras:

https://github.com/divamgupta/image-segmentation-keras

This is a brilliant repository that has served as the foundation for numerous image segmentation applications that are available on the web (GitHub) today.

Objectives

The primary goal for this repository is to discriminate 5 different image objects observed in typical clastic rock petrographic Thin Sections. This is still work in progress. Our next objectives will be to classify Petrophysical Rock Types (PRT) and Petrophysical properties based on image segmentation alone, where our estimates will be based totally on Thin Section photomicrograph of the rock.

Typical Thin Section

The following image is an example of a typical Sandstone Thin Section similar to what we used for our training data:

Data used for Training

Our primary training data structure was setup similar to what Mr. Gupta had done for the repository that is mentioned above. Our only exception is that we have used Thin Section images for both training and testing using the following data structure:

dataset1
  images_prepped_train
  images_prepped_test
  annotations_prepped_train
  annotations_prepped_test

For our training data we have used 40 Thin section images for the initial training with 40 matching annotation images that were created using the methodology explained in the following GitHub repository:

https://github.com/Philliec459/Create-Thin-Section-Image-Labels-for-Image-Segmentation-Training

Annotated Images

We have created our own annotated images. Each annotated image has 5 labeled segments ranging from 1 to 5. This labeling represents the 5 distinguishable features observed in Thin Section. We first create a gray-level image on the Thin section and then partition the gray-level image data into different bins which become our labeled images:

label = np.zeros(gradient.shape )

label[gradient < 0.25] = 1 #black grains 
label[gradient > 0.25] = 2 #darker grains
label[gradient > 0.4]  = 3 #blue-dye epoxy or visual porosity  
label[gradient > 0.6]  = 4 #darker grains 
label[gradient > 0.75] = 5 #bright quartz grains   

Training Code

For the training portion of the project we used 40 training images and 20 validation images in datset1. Due to confidentiality these data are not being provided in this repository. The following is our training code:

from keras_segmentation.models.unet import vgg_unet
from keras_segmentation.predict import model_from_checkpoint_path


model = vgg_unet(
        n_classes=51 ,  
        input_height=416, 
        input_width=608 
)


model=model_from_checkpoint_path("weights/vgg_unet_1")

model.train(
    train_images =  "dataset1/images_prepped_train/",
    train_annotations = "dataset1/annotations_prepped_train/",
    val_images="dataset1/images_prepped_test/" ,
    val_annotations="dataset1/annotations_prepped_test/",
    verify_dataset=True,
    #load_weights="weights/vgg_unet_1.0" ,
    optimizer_name='adadelta' , do_augment=True , augmentation_name="aug_all",    
    checkpoints_path = "weights/vgg_unet_1" , epochs=5
)


#Display the model's architecture
model.summary()

#Save the entire model to a HDF5 file.
#The '.h5' extension indicates that the model should be saved to HDF5.
model.save('vgg_unet_1.h5') 

#predict an image from the training data
out = model.predict_segmentation(
    checkpoints_path="weights/vgg_unet_1" , 
    inp="dataset1/images_prepped_test/43.jpg",
    out_fname="newout.png"
)

from keras_segmentation.predict import predict_multiple

predict_multiple( 
  checkpoints_path="weights/vgg_unet_1" , 
  inp_dir="dataset1/images_prepped_test/" , 
  out_dir="weights/out/" 
)

Test Data

For the test image Thin Section data that is provided in this repository, we are providing 12 "dataset1/images_prepped_test/" images with 12 matching "dataset1/annotations_prepped_test/" image files. We ae using "review_images_Create_Labels_out_gray.py" to create the labeled, annotated images from the test images. We are writing out the annotated images to "dataset1/annotations_prepped_test/". The labeled images are scaled from 1 to 5 representing the n_classes. However, the annotated images are not being used in the predictive process.

Predicted Results

We are using the same code as shown above for testing except that we have commented out the training portion of the code (model.train) for our image segmentation predictions.

The "predict_multiple" predicted Thin Sections for these test data were written to the "weights/out/" subdirectory. The following figure shows the process flow from the original Thin Section image, to the annotated image and finally to the predicted image shown in the upper right of this figure.

The predicted image shwon in the upper right corner is an RGB image. The histogram from the predicted image appears to illustrate the image segmentation, but there are numerous peaks found in this histogram. This segmented histogram is in sharp contrast to the rather Gaussian distribution observed from the original Thin Section image on the left. The following image shows the frequency histograms for all RGB values, and this does illustrate the RGB peaks for each of our segments:

We are predicting 5 primary image segments or objects from our test Thin Sections using the python program "main_load_your_own_model_from_existing_checkpoint.py" where the checkpoint weights being used are stored int the "weights/" subdirectory. We are not furnishing the original training data images and annotations due to the proprietary nature of these data. However; our test images were downloaded from the web and are similar in Clastic sandstone Rock Types to the training data.

(At this time I am unable to supply the vgg_unet_1 type of weights file due to a size restriction in GitHub???)

Way Forward

The figure below shows a comparison of the of the original vs. the predicted, segmented Thin Section image. This figure demonstrates that the image segmentation process is working well in discriminating various features observed in the Thin Section images. In the future we will use image segmentation to classify the Thin Sections into Petrophysical Rock Types (PRT) based on the results of this process. Each PRT should also have similar Petrophysical properties (Porosity, Permeability) range within each rock type. Since we have the Petrophysical property data available from our training data set, we could easily create regressions algorithms for these estimations. This is actually the more traditional approach to Petrophysics, however; for this application we plan on performing this estimation directly from image segmentation results correlated to the Routine Core Analysis Petrophysial data.

For the above image on the left we have used the "interactive_plot.py" driven from a command line xterm to observe the RGB values representing each image segment.

This method appears to provide a objective approach to image segmentation. In another reposity under Philliec459 we discuss using kMean clusters to segregate these image features. Unfortunately, at this point without PRT classification the mKean technique will not work. The kMean method requires user input for the number of clusters. The number of clusters will be dynamic depending on rock type. In a poor quality rock we might only have 2 clusters for segmentation where in a better quality rock we could have 5 clusters for segments. For kMean we need to assign the number of clusters needed for each PRT before employing this technique. By using the image segmentation techniques as proposed from Divam Gupta, our annotated images inherently contained the correct number of segments for each Thin Section so that this was not an issue.

For this repository we have been working on a Ubuntu workstation, and each python program has been driven from an xterm command line.