data_proc.py

from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
# import acc
from operator import truediv

from plotly.offline import init_notebook_mode

import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import os
import spectral
from data_vis import *

def splitTrainTestSet(X, y, testRatio, randomState=345):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState,
                                                        stratify=y)
    return X_train, X_test, y_train, y_test

def applyPCA(X, numComponents=40):
    newX = np.reshape(X, (-1, X.shape[2]))
    pca = PCA(n_components=numComponents, whiten=True)
    newX = pca.fit_transform(newX)
    newX = np.reshape(newX, (X.shape[0],X.shape[1], numComponents))
    return newX, pca



def padWithZeros(X, margin=2):
    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))
    x_offset = margin
    y_offset = margin
    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X
    return newX

"""reference : S. K. Roy, G. Krishna, S. R. Dubey and B. B. Chaudhuri,
 "HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification," 
 in IEEE Geoscience and Remote Sensing Letters, 
 vol. 17, no. 2, pp. 277-281, Feb. 2020, doi: 10.1109/LGRS.2019.2918719."""

def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):
    margin = int((windowSize - 1) / 2)
    zeroPaddedX = padWithZeros(X, margin=margin)
    # split patches
    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))
    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))
    patchIndex = 0
    for r in range(margin, zeroPaddedX.shape[0] - margin):
        for c in range(margin, zeroPaddedX.shape[1] - margin):
            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   
            patchesData[patchIndex, :, :, :] = patch
            patchesLabels[patchIndex] = y[r-margin, c-margin]
            patchIndex = patchIndex + 1
    if removeZeroLabels:
        patchesData = patchesData[patchesLabels>0,:,:,:]
        patchesLabels = patchesLabels[patchesLabels>0]
        patchesLabels -= 1
    return patchesData, patchesLabels


"""change it accordingly"""

K = X_new.shape[2]
K = 10
X,pca = applyPCA(X_new,numComponents=K)


X, y  = createImageCubes(X,y,windowSize = windowSize)



Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, test_ratio)

Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape


Xtrain = Xtrain.reshape(-1, windowSize, windowSize, K, 1)
Xtrain.shape