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tang_feat.py
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"""WST-svm
"""
from collections import namedtuple
import itertools
import time
import os
import h5py
import hdf5storage
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import scipy.io as sio
from sklearn.metrics import confusion_matrix
from sklearn.svm import SVC
from lib.libsvm.python.svmutil import *
import windows as win
import rgb_pixelNN as pxnn
import pdb
DATA_PATH = os.environ['DATA_PATH']
DATASET_PATH = os.environ['DATASET_PATH']
layerO = namedtuple('layerO', ['strides', 'padding'])
def scat3d(x, win_params, layer_params):
"""
Args:
x is input with dim (batch, depth, height, width, channels)
win_params.filters is complex with dim (depth, height, width, channels)
"""
real1 = tf.layers.conv3d(
x,
win_params.nfilt,
win_params.kernel_size,
strides=layer_params.strides,
padding=layer_params.padding,
dilation_rate=(1, 1, 1),
activation=None,
use_bias=False,
kernel_initializer=tf.constant_initializer(win_params.filters.real, dtype=tf.float32),
trainable=False,
name=None
)
imag1 = tf.layers.conv3d(
x,
win_params.nfilt,
win_params.kernel_size,
strides=layer_params.strides,
padding=layer_params.padding,
dilation_rate=(1, 1, 1),
activation=None,
use_bias=False,
kernel_initializer=tf.constant_initializer(win_params.filters.imag, dtype=tf.float32),
trainable=False,
name=None
)
return tf.abs(tf.complex(real1, imag1))
def tang_net(x, reuse=tf.AUTO_REUSE):
"""Computes tang features for a specific pixel.
Args:
x: image in (height, width, bands) format, should be (19,19,nbands+18)
Output:
center pixel feature vector
Example Usage:
x = tf.placeholder(tf.float32, shape=(19,19,nbands+18))
feat = tang_net(x)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
...
subimg = padded_data[pixel_x:(pixel_x+19), pixel_y:(pixel_y+19), :]
feed_dict = {x: subimg}
labelled_pix_feat[pixel_i,:] = sess.run(feat, feed_dict)
"""
kernel_size = [7,7,7]
max_scale = 3
K = 3
psiO = win.tang_psi_factory(max_scale, K, kernel_size)
phiO = win.tang_phi_window_3D(max_scale, kernel_size)
with tf.variable_scope('TangNet', reuse=reuse):
x = tf.transpose(x, [2, 0, 1])
x = tf.expand_dims(x, 0)
x = tf.expand_dims(x, -1)
layer1_params = layerO((1,1,1), 'valid')
U1 = scat3d(x, psiO, layer1_params)
# swap channels with batch
U1 = tf.transpose(U1, [4, 1, 2, 3, 0])
# separate out different scales
U1 = tf.reshape(U1, (max_scale, U1.shape[0] / max_scale, U1.shape[1], U1.shape[2], U1.shape[3], U1.shape[4]))
layer2_params = layerO((1,1,1), 'valid')
# only continue scattering across increasing scale paths
U2j0 = scat3d(U1[0,:,:,:,:,:], win.tang_psi_factory(max_scale, K, kernel_size, 1), layer2_params)
U2j1 = scat3d(U1[1,:,:,:,:,:], win.tang_psi_factory(max_scale, K, kernel_size, 2), layer2_params)
def merge_channels_with_batch(Uz):
Uz = tf.transpose(Uz, [0, 4, 1, 2, 3])
Uz = tf.reshape(Uz, [Uz.shape[0]*Uz.shape[1], Uz.shape[2], Uz.shape[3], Uz.shape[4]])
return tf.expand_dims(Uz, -1)
U2j0 = merge_channels_with_batch(U2j0)
U2j1 = merge_channels_with_batch(U2j1)
# convolve with phis
layer2_lo_params = layerO((1,1,1), 'valid')
S2 = scat3d(tf.concat([U2j0, U2j1], 0), phiO, layer2_lo_params)
# merge the different scales
U1 = tf.reshape(U1, (U1.shape[0] * U1.shape[1], U1.shape[2], U1.shape[3], U1.shape[4], U1.shape[5]))
S1 = scat3d(U1[:,3:-3, 3:-3, 3:-3, :], phiO, layer2_lo_params)
S0 = scat3d(x[:,6:-6, 6:-6, 6:-6, :], phiO, layer2_lo_params)
# flatten everything
S2 = tf.reshape(S2, [S2.shape[0] * S2.shape[1]])
S1 = tf.reshape(S1, [S1.shape[0] * S1.shape[1]])
S0 = tf.reshape(S0, [S0.shape[1]])
return tf.concat([S0,S1,S2], 0)
def tang_save_features(data, labels, groundtruthfilename='100p'):
"""temp kludge
"""
[height, width, nbands] = data.shape
x = tf.placeholder(tf.float32, shape=(19,19,nbands+18))
feat = tang_net(x)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
padded_data = np.pad(data, ((9,9),(9,9),(9,9)), 'reflect')
all_pixels = np.array(list(itertools.product(range(width),range(height))))
labelled_pixels = all_pixels[:10]
print('requesting %d MB memory' % (labelled_pixels.shape[0] * 271*nbands * 4 / 1000000.0))
labelled_pix_feat = np.zeros((labelled_pixels.shape[0], 271*nbands), dtype=np.float32)
for pixel_i, pixel in enumerate(tqdm(labelled_pixels)):
# this iterates through columns first
[pixel_x, pixel_y] = pixel
subimg = padded_data[pixel_y:(pixel_y+19), pixel_x:(pixel_x+19), :]
feed_dict = {x: subimg}
labelled_pix_feat[pixel_i,:] = sess.run(feat, feed_dict)
pdb.set_trace()
flat_labels = labels.transpose().reshape(height*width)
trainY = flat_labels[flat_labels!=0]
print('starting training')
start = time.time()
clf = SVC(kernel='linear')
clf.fit(labelled_pix_feat, trainY)
end = time.time()
print(end - start)
# now start predicting the full image, 1 column at a time
col_feat = np.zeros((height, 271*nbands), dtype=np.float32)
pred_image = np.zeros((height,width), dtype=int)
test_flags = '-q'
for pixel_x in tqdm(range(width)):
# get feat
for pixel_y in range(height):
subimg = padded_data[pixel_y:(pixel_y+19), pixel_x:(pixel_x+19), :]
feed_dict = {x: subimg}
col_feat[pixel_y,:] = sess.run(feat, feed_dict)
# get pred for feat
# dontcare = [0] * height
p_label = clf.predict(col_feat);
pred_image[:,pixel_x] = np.array(p_label).astype(int)
imgmatfiledata = {}
imgmatfiledata[u'imgHat'] = pred_image
imgmatfiledata[u'groundtruthfilename'] = groundtruthfilename
hdf5storage.write(imgmatfiledata, filename=groundtruthfilename+'_100p_tang_fullimg.mat', matlab_compatible=True)
print('done making img, run hundredpercent_img_figures.m')
def tang_run_full_img(data, labels, groundtruthfilename='100p'):
"""
"""
[height, width, nbands] = data.shape
x = tf.placeholder(tf.float32, shape=(19,19,nbands+18))
feat = tang_net(x)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
padded_data = np.pad(data, ((9,9),(9,9),(9,9)), 'reflect')
all_pixels = np.array(list(itertools.product(range(width),range(height))))
labelled_pixels = np.array(filter(lambda (x,y): labels[y,x] != 0, all_pixels))
print('requesting %d MB memory' % (labelled_pixels.shape[0] * 271*nbands * 4 / 1000000.0))
labelled_pix_feat = np.zeros((labelled_pixels.shape[0], 271*nbands), dtype=np.float32)
for pixel_i, pixel in enumerate(tqdm(labelled_pixels)):
# this iterates through columns first
[pixel_x, pixel_y] = pixel
subimg = padded_data[pixel_y:(pixel_y+19), pixel_x:(pixel_x+19), :]
feed_dict = {x: subimg}
labelled_pix_feat[pixel_i,:] = sess.run(feat, feed_dict)
flat_labels = labels.transpose().reshape(height*width)
trainY = flat_labels[flat_labels!=0]
print('starting training')
start = time.time()
clf = SVC(kernel='linear')
clf.fit(labelled_pix_feat, trainY)
end = time.time()
print(end - start)
# now start predicting the full image, 1 column at a time
col_feat = np.zeros((height, 271*nbands), dtype=np.float32)
pred_image = np.zeros((height,width), dtype=int)
test_flags = '-q'
for pixel_x in tqdm(range(width)):
# get feat
for pixel_y in range(height):
subimg = padded_data[pixel_y:(pixel_y+19), pixel_x:(pixel_x+19), :]
feed_dict = {x: subimg}
col_feat[pixel_y,:] = sess.run(feat, feed_dict)
# get pred for feat
# dontcare = [0] * height
p_label = clf.predict(col_feat);
pred_image[:,pixel_x] = np.array(p_label).astype(int)
imgmatfiledata = {}
imgmatfiledata[u'imgHat'] = pred_image
imgmatfiledata[u'groundtruthfilename'] = groundtruthfilename
hdf5storage.write(imgmatfiledata, filename=groundtruthfilename+'_100p_tang_fullimg.mat', matlab_compatible=True)
print('done making img, run hundredpercent_img_figures.m')
def tang_run_acc(data, labels, traintestfilenames=None):
"""
"""
[height, width, nbands] = data.shape
all_pixels = np.array(list(itertools.product(range(width),range(height))))
labelled_pixels = np.array(filter(lambda (x,y): labels[y,x] != 0, all_pixels))
flat_labels = labels.transpose().reshape(height*width)
nlabels = len(set(flat_labels.tolist())) - 1
padded_data = np.pad(data, ((9,9),(9,9),(9,9)), 'wrap')
print('requesting %d MB memory' % (labelled_pixels.shape[0] * 271*nbands * 4 / 1000000.0))
labelled_pix_feat = np.zeros((labelled_pixels.shape[0], 271*nbands), dtype=np.float32)
def compute_features():
x = tf.placeholder(tf.float32, shape=(19,19,nbands+18))
feat = tang_net(x)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for pixel_i, pixel in enumerate(tqdm(labelled_pixels)):
# this iterates through columns first
[pixel_x, pixel_y] = pixel
subimg = padded_data[pixel_y:(pixel_y+19), pixel_x:(pixel_x+19), :]
feed_dict = {x: subimg}
labelled_pix_feat[pixel_i,:] = sess.run(feat, feed_dict)
compute_features()
for traintestfilename in traintestfilenames:
mat_contents = None
try:
mat_contents = sio.loadmat(os.path.join(DATA_PATH, traintestfilename))
except:
mat_contents = hdf5storage.loadmat(os.path.join(DATA_PATH, traintestfilename))
train_mask = mat_contents['train_mask'].astype(int).squeeze()
test_mask = mat_contents['test_mask'].astype(int).squeeze()
# resize train/test masks to labelled pixels
train_mask_skip_unlabelled = train_mask[flat_labels!=0]
test_mask_skip_unlabelled = test_mask[flat_labels!=0]
# get training set
trainY = flat_labels[train_mask==1]
trainX = labelled_pix_feat[train_mask_skip_unlabelled==1,:]
print('training now')
start = time.time()
clf = SVC(kernel='linear')
clf.fit(trainX, trainY)
end = time.time()
print(end - start)
# now test
test_chunk_size = 1000
testY = flat_labels[test_mask==1]
Yhat = np.zeros(testY.shape)
testX = labelled_pix_feat[test_mask_skip_unlabelled==1,:]
C = np.zeros((nlabels,nlabels))
print('testing now')
for i in tqdm(range(0,len(testY),test_chunk_size)):
p_label = clf.predict(testX[i:i+test_chunk_size,:]);
Yhat[i:i+test_chunk_size] = np.array(p_label).astype(int)
C += confusion_matrix(testY[i:i+test_chunk_size], p_label, labels=list(range(1,nlabels+1)))
pred_image = np.zeros(flat_labels.shape)
pred_image[test_mask==1] = Yhat
mat_outdata = {}
mat_outdata[u'metrics'] = {}
mat_outdata[u'metrics'][u'CM'] = C
mat_outdata[u'pred_image'] = pred_image.reshape((width, height)).transpose()
hdf5storage.write(mat_outdata, filename=os.path.join(DATA_PATH, traintestfilename+'_117_WST3D_expt.mat'), matlab_compatible=True)
def tang_run_all_full_imgs():
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Indian_pines_corrected.mat'))
# data = mat_contents['indian_pines_corrected'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Indian_pines_gt.mat'))
# labels = mat_contents['indian_pines_gt']
# tang_run_full_img(data, labels, groundtruthfilename='Indian_pines_gt')
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'PaviaU.mat'))
# data = mat_contents['paviaU'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'PaviaU_gt.mat'))
# labels = mat_contents['paviaU_gt']
# tang_run_full_img(data, labels, groundtruthfilename='PaviaU_gt')
mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Botswana.mat'))
data = mat_contents['Botswana'].astype(np.float32)
data /= np.max(np.abs(data))
mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Botswana_gt.mat'))
labels = mat_contents['Botswana_gt']
tang_save_features(data, labels, groundtruthfilename='Botswana_gt')
# # mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'KSC.mat'))
# # data = mat_contents['KSC'].astype(np.float32)
# # data /= np.max(np.abs(data))
# # mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'KSC_gt.mat'))
# # labels = mat_contents['KSC_gt']
# # tang_run_full_img(data, labels, groundtruthfilename='KSC_gt')
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Pavia_center_right'))
# data = mat_contents['Pavia_center_right'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Pavia_center_right_gt.mat'))
# labels = mat_contents['Pavia_center_right_gt']
# tang_run_full_img(data, labels, groundtruthfilename='Pavia_center_right_gt')
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Smith_117chan.mat'))
# data = mat_contents['Smith'].astype(np.float32)
# data = pxnn.normalize_channels(data)
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Smith_gt.mat'))
# labels = mat_contents['Smith_gt']
# tang_run_full_img(data, labels, groundtruthfilename='Smith_gt')
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Salinas_corrected.mat'))
# data = mat_contents['salinas_corrected'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Salinas_gt.mat'))
# labels = mat_contents['salinas_gt']
# tang_run_full_img(data, labels, groundtruthfilename='Salinas_gt')
# datasettrainingfiles = [ 'Indian_pines_gt_traintest_1_1abefb.mat', 'Indian_pines_gt_traintest_2_0bccd7.mat', 'Indian_pines_gt_traintest_3_7b4f69.mat', 'Indian_pines_gt_traintest_4_eeba08.mat', 'Indian_pines_gt_traintest_5_d75e59.mat', 'Indian_pines_gt_traintest_6_3a9ebd.mat', 'Indian_pines_gt_traintest_7_cad093.mat', 'Indian_pines_gt_traintest_8_97b27f.mat', 'Indian_pines_gt_traintest_9_1e4231.mat', 'Indian_pines_gt_traintest_10_6d71a1.mat' ];
# datasettrainingfiles = [ 'PaviaU_gt_traintest_1_334428.mat', 'PaviaU_gt_traintest_2_03ccd1.mat', 'PaviaU_gt_traintest_3_698d0c.mat', 'PaviaU_gt_traintest_4_7b2f96.mat', 'PaviaU_gt_traintest_5_8adc4a.mat', 'PaviaU_gt_traintest_6_b1ef2f.mat', 'PaviaU_gt_traintest_7_844918.mat', 'PaviaU_gt_traintest_8_16b8dc.mat', 'PaviaU_gt_traintest_9_e14191.mat', 'PaviaU_gt_traintest_10_c36f7c.mat' ];
# datasettrainingfiles = [ 'Botswana_gt_traintest_1_e24fae.mat', 'Botswana_gt_traintest_2_518c23.mat', 'Botswana_gt_traintest_3_7b7b6a.mat', 'Botswana_gt_traintest_4_588b5a.mat', 'Botswana_gt_traintest_5_60813e.mat', 'Botswana_gt_traintest_6_05a6b3.mat', 'Botswana_gt_traintest_7_fbba81.mat', 'Botswana_gt_traintest_8_a083a4.mat', 'Botswana_gt_traintest_9_8591e0.mat', 'Botswana_gt_traintest_10_996e67.mat' ];
# datasettrainingfiles = [ 'KSC_gt_traintest_1_6061b3.mat', 'KSC_gt_traintest_2_c4043d.mat', 'KSC_gt_traintest_3_db432b.mat', 'KSC_gt_traintest_4_95e0ef.mat', 'KSC_gt_traintest_5_3d7a8e.mat', 'KSC_gt_traintest_6_2a60db.mat', 'KSC_gt_traintest_7_ae63a4.mat', 'KSC_gt_traintest_8_b128c8.mat', 'KSC_gt_traintest_9_9ed856.mat', 'KSC_gt_traintest_10_548b31.mat' ];
# datasettrainingfiles{7} = { 'Salinas_gt_traintest_p05_1_4228ee.mat', 'Salinas_gt_traintest_p05_2_eb1804.mat', 'Salinas_gt_traintest_p05_3_fad367.mat', 'Salinas_gt_traintest_p05_4_8cb8a3.mat', 'Salinas_gt_traintest_p05_5_d2384b.mat', 'Salinas_gt_traintest_p05_6_e34195.mat', 'Salinas_gt_traintest_p05_7_249774.mat', 'Salinas_gt_traintest_p05_8_f772c1.mat', 'Salinas_gt_traintest_p05_9_371ee5.mat', 'Salinas_gt_traintest_p05_10_22b46b.mat' };
# datasettrainingfiles{8} = { 'Smith_gt_traintest_p05_1_dd77f9.mat', 'Smith_gt_traintest_p05_2_e75152.mat', 'Smith_gt_traintest_p05_3_c8e897.mat', 'Smith_gt_traintest_p05_4_e2bd4d.mat', 'Smith_gt_traintest_p05_5_59815b.mat', 'Smith_gt_traintest_p05_6_316c37.mat', 'Smith_gt_traintest_p05_7_6aef72.mat', 'Smith_gt_traintest_p05_8_c24907.mat', 'Smith_gt_traintest_p05_9_3c2737.mat', 'Smith_gt_traintest_p05_10_75deb4.mat' };
def tang_run_accs():
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Pavia_center_right.mat'))
# data = mat_contents['Pavia_center_right'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Pavia_center_right_gt.mat'))
# labels = mat_contents['Pavia_center_right_gt']
# datasettrainingfiles = [ 'Pavia_center_right_gt_traintest_p01_1_2ecf33.mat', 'Pavia_center_right_gt_traintest_p01_2_b162bf.mat', 'Pavia_center_right_gt_traintest_p01_3_b199a4.mat', 'Pavia_center_right_gt_traintest_p01_4_a182df.mat', 'Pavia_center_right_gt_traintest_p01_5_403e9e.mat', 'Pavia_center_right_gt_traintest_p01_6_b4cf2f.mat', 'Pavia_center_right_gt_traintest_p01_7_efa5c4.mat', 'Pavia_center_right_gt_traintest_p01_8_a6b7ec.mat', 'Pavia_center_right_gt_traintest_p01_9_725578.mat', 'Pavia_center_right_gt_traintest_p01_10_274170.mat' ];
# # datasettrainingfiles = [ 'Pavia_center_right_gt_traintest_1_c23379.mat', 'Pavia_center_right_gt_traintest_2_555d38.mat', 'Pavia_center_right_gt_traintest_3_436123.mat', 'Pavia_center_right_gt_traintest_4_392727.mat', 'Pavia_center_right_gt_traintest_5_da2b6f.mat', 'Pavia_center_right_gt_traintest_6_9848f9.mat', 'Pavia_center_right_gt_traintest_7_2e4963.mat', 'Pavia_center_right_gt_traintest_8_12c92f.mat', 'Pavia_center_right_gt_traintest_9_7593be.mat', 'Pavia_center_right_gt_traintest_10_30cc68.mat' ];
# # datasettrainingfiles = ['Pavia_center_right_gt_traintest_coarse_128px128p.mat','Pavia_center_right_gt_traintest_coarse_72px72p.mat','Pavia_center_right_gt_traintest_coarse_36px36p.mat']
# tang_run_acc(data, labels, traintestfilenames=datasettrainingfiles[:1])
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Indian_pines_corrected.mat'))
# data = mat_contents['indian_pines_corrected'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Indian_pines_gt.mat'))
# labels = mat_contents['indian_pines_gt']
# traintestfilenames = [ 'Indian_pines_gt_traintest_ma2015_1_9146f0.mat', 'Indian_pines_gt_traintest_ma2015_2_692f24.mat', 'Indian_pines_gt_traintest_ma2015_3_223f7e.mat', 'Indian_pines_gt_traintest_ma2015_4_447c47.mat', 'Indian_pines_gt_traintest_ma2015_5_82c5ad.mat', 'Indian_pines_gt_traintest_ma2015_6_a46a51.mat', 'Indian_pines_gt_traintest_ma2015_7_be4864.mat', 'Indian_pines_gt_traintest_ma2015_8_dacd43.mat', 'Indian_pines_gt_traintest_ma2015_9_962bab.mat', 'Indian_pines_gt_traintest_ma2015_10_f03ef8.mat']
# takesome = []
# traintestfilenames = [ 'Indian_pines_gt_traintest_p04_nozero_1_05cf41.mat', 'Indian_pines_gt_traintest_p04_nozero_2_ce4ce0.mat', 'Indian_pines_gt_traintest_p04_nozero_3_c2fb75.mat', 'Indian_pines_gt_traintest_p04_nozero_4_5d3141.mat', 'Indian_pines_gt_traintest_p04_nozero_5_0d824a.mat', 'Indian_pines_gt_traintest_p04_nozero_6_6e4725.mat', 'Indian_pines_gt_traintest_p04_nozero_7_3e6a00.mat', 'Indian_pines_gt_traintest_p04_nozero_8_957ed5.mat', 'Indian_pines_gt_traintest_p04_nozero_9_9eb6a2.mat', 'Indian_pines_gt_traintest_p04_nozero_10_76cc88.mat' ];
# takesome += traintestfilenames
# traintestfilenames = [ 'Indian_pines_gt_traintest_p03_nozero_1_c162cc.mat', 'Indian_pines_gt_traintest_p03_nozero_2_2db4c5.mat', 'Indian_pines_gt_traintest_p03_nozero_3_4a0c9f.mat', 'Indian_pines_gt_traintest_p03_nozero_4_b293fe.mat', 'Indian_pines_gt_traintest_p03_nozero_5_40d425.mat', 'Indian_pines_gt_traintest_p03_nozero_6_58f5f9.mat', 'Indian_pines_gt_traintest_p03_nozero_7_c677ec.mat', 'Indian_pines_gt_traintest_p03_nozero_8_f53e55.mat', 'Indian_pines_gt_traintest_p03_nozero_9_3bdfbf.mat', 'Indian_pines_gt_traintest_p03_nozero_10_ef5555.mat' ];
# takesome += traintestfilenames
# traintestfilenames = [ 'Indian_pines_gt_traintest_p02_nozero_1_93e12e.mat', 'Indian_pines_gt_traintest_p02_nozero_2_06eda5.mat', 'Indian_pines_gt_traintest_p02_nozero_3_e27f64.mat', 'Indian_pines_gt_traintest_p02_nozero_4_5268bc.mat', 'Indian_pines_gt_traintest_p02_nozero_5_9d0774.mat', 'Indian_pines_gt_traintest_p02_nozero_6_733c26.mat', 'Indian_pines_gt_traintest_p02_nozero_7_4696af.mat', 'Indian_pines_gt_traintest_p02_nozero_8_cc878b.mat', 'Indian_pines_gt_traintest_p02_nozero_9_351667.mat', 'Indian_pines_gt_traintest_p02_nozero_10_f7cbbe.mat' ];
# takesome += traintestfilenames
# traintestfilenames = [ 'Indian_pines_gt_traintest_p01_nozero_1_556ea4.mat', 'Indian_pines_gt_traintest_p01_nozero_2_6c358d.mat', 'Indian_pines_gt_traintest_p01_nozero_3_d5e750.mat', 'Indian_pines_gt_traintest_p01_nozero_4_2e12e8.mat', 'Indian_pines_gt_traintest_p01_nozero_5_d6b184.mat', 'Indian_pines_gt_traintest_p01_nozero_6_d9d30c.mat', 'Indian_pines_gt_traintest_p01_nozero_7_f3c39c.mat', 'Indian_pines_gt_traintest_p01_nozero_8_c16774.mat', 'Indian_pines_gt_traintest_p01_nozero_9_b6715b.mat', 'Indian_pines_gt_traintest_p01_nozero_10_8bc7e5.mat' ];
# takesome += traintestfilenames
# traintestfilenames = ['Indian_pines_gt_traintest_p05_1_f0b0f8.mat', 'Indian_pines_gt_traintest_p05_2_2c7710.mat', 'Indian_pines_gt_traintest_p05_3_dd1c2c.mat', 'Indian_pines_gt_traintest_p05_4_c44ed3.mat', 'Indian_pines_gt_traintest_p05_5_96acac.mat', 'Indian_pines_gt_traintest_p05_6_c99119.mat', 'Indian_pines_gt_traintest_p05_7_5c222a.mat', 'Indian_pines_gt_traintest_p05_8_a09f39.mat', 'Indian_pines_gt_traintest_p05_9_6e41d3.mat', 'Indian_pines_gt_traintest_p05_10_801219.mat' ]
# takesome += traintestfilenames
# datasettrainingfiles = ['Indian_pines_gt_traintest_coarse_14px14p.mat', 'Indian_pines_gt_traintest_coarse_6px6p.mat', 'Indian_pines_gt_traintest_coarse_10px10p.mat', 'Indian_pines_gt_traintest_coarse_12x12_add7s9.mat', 'Indian_pines_gt_traintest_coarse_12x12_skip7s9.mat']
# tang_run_acc(data, labels, traintestfilenames=takesome)
mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'PaviaU.mat'))
data = mat_contents['paviaU'].astype(np.float32)
data /= np.max(np.abs(data))
mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'PaviaU_gt.mat'))
labels = mat_contents['paviaU_gt']
# traintestfilenames = [ 'PaviaU_gt_traintest_1_334428.mat', 'PaviaU_gt_traintest_2_03ccd1.mat', 'PaviaU_gt_traintest_3_698d0c.mat', 'PaviaU_gt_traintest_4_7b2f96.mat', 'PaviaU_gt_traintest_5_8adc4a.mat', 'PaviaU_gt_traintest_6_b1ef2f.mat', 'PaviaU_gt_traintest_7_844918.mat', 'PaviaU_gt_traintest_8_16b8dc.mat', 'PaviaU_gt_traintest_9_e14191.mat', 'PaviaU_gt_traintest_10_c36f7c.mat' ];
# # traintestfilenames = [ 'PaviaU_gt_traintest_ma2015_1_0b3591.mat', 'PaviaU_gt_traintest_ma2015_2_88f4ce.mat', 'PaviaU_gt_traintest_ma2015_3_c51f99.mat', 'PaviaU_gt_traintest_ma2015_4_e3a361.mat', 'PaviaU_gt_traintest_ma2015_5_2922fa.mat', 'PaviaU_gt_traintest_ma2015_6_15194e.mat', 'PaviaU_gt_traintest_ma2015_7_df3db2.mat', 'PaviaU_gt_traintest_ma2015_8_ca5afe.mat', 'PaviaU_gt_traintest_ma2015_9_55492c.mat', 'PaviaU_gt_traintest_ma2015_10_a604d2.mat']
# # datasettrainingfiles = ['PaviaU_gt_traintest_coarse_16px16p.mat', 'PaviaU_gt_traintest_coarse_32px32p.mat', 'PaviaU_gt_traintest_coarse_64px64p.mat', 'PaviaU_gt_traintest_coarse_128px128p.mat']
traintestfilenames = ['PaviaU_gt_traintest_s200_1_591636.mat','PaviaU_gt_traintest_s200_2_2255d5.mat','PaviaU_gt_traintest_s200_3_628d0a.mat','PaviaU_gt_traintest_s200_4_26eddf.mat','PaviaU_gt_traintest_s200_5_25dd01.mat','PaviaU_gt_traintest_s200_6_2430e7.mat','PaviaU_gt_traintest_s200_7_409d67.mat','PaviaU_gt_traintest_s200_8_f79373.mat','PaviaU_gt_traintest_s200_9_dac1e4.mat','PaviaU_gt_traintest_s200_10_149f64.mat'];
tang_run_acc(data, labels, traintestfilenames=traintestfilenames)
# # mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'KSC.mat'))
# # data = pxnn.remove_intensity_gaps_in_chans(mat_contents['KSC'].astype(np.float32))
# # data = pxnn.normalize_channels(data)
# # mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'KSC_gt.mat'))
# # labels = mat_contents['KSC_gt']
# # datasettrainingfiles = [ 'KSC_gt_traintest_1_6061b3.mat', 'KSC_gt_traintest_2_c4043d.mat', 'KSC_gt_traintest_3_db432b.mat', 'KSC_gt_traintest_4_95e0ef.mat', 'KSC_gt_traintest_5_3d7a8e.mat', 'KSC_gt_traintest_6_2a60db.mat', 'KSC_gt_traintest_7_ae63a4.mat', 'KSC_gt_traintest_8_b128c8.mat', 'KSC_gt_traintest_9_9ed856.mat', 'KSC_gt_traintest_10_548b31.mat' ];
# # tang_run_acc(data, labels, traintestfilenames=datasettrainingfiles[:1])
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Smith_117chan.mat'))
# data = mat_contents['Smith'].astype(np.float32)
# data = pxnn.normalize_channels(data)
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Smith_gt.mat'))
# labels = mat_contents['Smith_gt']
# traintestfilenames = [ 'Smith_gt_traintest_p05_1_256610.mat', 'Smith_gt_traintest_p05_2_40467b.mat', 'Smith_gt_traintest_p05_3_34ac0b.mat', 'Smith_gt_traintest_p05_4_975f46.mat', 'Smith_gt_traintest_p05_5_7ad5ce.mat', 'Smith_gt_traintest_p05_6_588ff3.mat', 'Smith_gt_traintest_p05_7_be5a75.mat', 'Smith_gt_traintest_p05_8_e931a6.mat', 'Smith_gt_traintest_p05_9_00c835.mat', 'Smith_gt_traintest_p05_10_d8c90f.mat' ];
# # datasettrainingfiles = [ 'Smith_gt_traintest_p05_1_dd77f9.mat', 'Smith_gt_traintest_p05_2_e75152.mat', 'Smith_gt_traintest_p05_3_c8e897.mat', 'Smith_gt_traintest_p05_4_e2bd4d.mat', 'Smith_gt_traintest_p05_5_59815b.mat', 'Smith_gt_traintest_p05_6_316c37.mat', 'Smith_gt_traintest_p05_7_6aef72.mat', 'Smith_gt_traintest_p05_8_c24907.mat', 'Smith_gt_traintest_p05_9_3c2737.mat', 'Smith_gt_traintest_p05_10_75deb4.mat' ];
# # # # datasettrainingfiles = ['Smith_gt_traintest_coarse_18px18p.mat', 'Smith_gt_traintest_coarse_12px12p.mat']
# tang_run_acc(data, labels, traintestfilenames=traintestfilenames)
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Salinas_corrected.mat'))
# data = mat_contents['salinas_corrected'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Salinas_gt.mat'))
# labels = mat_contents['salinas_gt']
# # datasettrainingfiles = [ 'Salinas_gt_traintest_p05_1_4228ee.mat', 'Salinas_gt_traintest_p05_2_eb1804.mat', 'Salinas_gt_traintest_p05_3_fad367.mat', 'Salinas_gt_traintest_p05_4_8cb8a3.mat', 'Salinas_gt_traintest_p05_5_d2384b.mat', 'Salinas_gt_traintest_p05_6_e34195.mat', 'Salinas_gt_traintest_p05_7_249774.mat', 'Salinas_gt_traintest_p05_8_f772c1.mat', 'Salinas_gt_traintest_p05_9_371ee5.mat', 'Salinas_gt_traintest_p05_10_22b46b.mat' ];
# datasettrainingfiles = ['Salinas_gt_traintest_coarse_40px40p.mat', 'Salinas_gt_traintest_coarse_30px30p.mat', 'Salinas_gt_traintest_coarse_20px20p.mat', 'Salinas_gt_traintest_coarse_16x16.mat']
# tang_run_acc(data, labels, traintestfilenames=datasettrainingfiles[1:3])
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Botswana.mat'))
# data = mat_contents['Botswana'].astype(np.float32)
# data /= np.max(np.abs(data))
# mat_contents = sio.loadmat(os.path.join(DATASET_PATH, 'Botswana_gt.mat'))
# labels = mat_contents['Botswana_gt']
# traintestfilenames = [ 'Botswana_gt_traintest_1_e24fae.mat', 'Botswana_gt_traintest_2_518c23.mat', 'Botswana_gt_traintest_3_7b7b6a.mat', 'Botswana_gt_traintest_4_588b5a.mat', 'Botswana_gt_traintest_5_60813e.mat', 'Botswana_gt_traintest_6_05a6b3.mat', 'Botswana_gt_traintest_7_fbba81.mat', 'Botswana_gt_traintest_8_a083a4.mat', 'Botswana_gt_traintest_9_8591e0.mat', 'Botswana_gt_traintest_10_996e67.mat' ];
# traintestfilenames = ['Botswana_gt_traintest_coarse_36px36p.mat', 'Botswana_gt_traintest_coarse_12px12p.mat']
# tang_run_acc(data, labels, traintestfilenames=traintestfilenames[:1])
if __name__ == '__main__':
# tang_run_accs()
tang_run_all_full_imgs()
# now
# pdb.set_trace()
# [class_accs, oa, aa, kappa] = experiment_acc(struct('path', '/scratch0/ilya/locDoc/data/hyperspec'), 'Smith_gt_traintest_p05_1_dd77f9.mat_WST3D_expt.mat')