from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
from pandas_ml import ConfusionMatrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
# Input data
print("------------------------")
print("Input data")
print("------------------------")
input = Pavia_Input.Pavia_Input()
print("Training pixels", np.count_nonzero(input.train_data))
print("Test pixels", np.count_nonzero(input.test_data))
print("------------------------")
# Configurable parameters
config = {}
patch_size = 5
feature_selection = False
apply_filter = False
classifiers = ["RF", "SVM", "1NN", "3NN", "5NN"]
classifier = classifiers[0]
seed = None
folder = 'Pavia/'
rotation_oversampling = True
from SanFrancisco import SanFrancisco_Input
from Salinas import Salinas_Input
import time
import numpy as np
from collections import Counter
from spectral import imshow, save_rgb
import CV_Decoder, CV_Postprocessing
import os
import pandas as pd
import CNNTrain_2D
# Input data
images = ["IndianPines", "Pavia", "Flevoland", "SanFrancisco", "Salinas"]
images_inputs = {
"IndianPines": IndianPines_Input.IndianPines_Input(),
"Pavia": Pavia_Input.Pavia_Input(),
"Flevoland": Flevoland_Input.Flevoland_Input(),
"SanFrancisco": SanFrancisco_Input.SanFrancisco_Input(),
"Salinas": Salinas_Input.Salinas_Input()
}
# Select image to test
selected_img = images[3]
input = images_inputs[selected_img]
print("Image:" + selected_img)
for patch_size in [5]:
config = {}
config['patch_size'] = patch_size