def tests():
import pysptools.util as util
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
axes = parse_ENVI_header(sample, header)
U = NFINDR(data, axes, result_path)
r = ROI(data, result_path)
test_SID(data, U, r, result_path)
test_SAM(data, U, r, result_path)
test_NormXCorr(data, U, r, result_path)
test_SID_single(data, U, result_path)
test_SAM_single(data, U, result_path)
test_NormXCorr_single(data, U, result_path)
test_AbundanceClassification(data, result_path)
test_one_spectrum(data, U, r, result_path)
def tests():
import pysptools.util as util
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
axes = parse_ENVI_header(sample, header)
U = NFINDR(data, axes, result_path)
r = ROI(data, result_path)
test_SID(data, U, r, result_path)
test_SAM(data, U, r, result_path)
test_NormXCorr(data, U, r, result_path)
test_SID_single(data, U, result_path)
test_SAM_single(data, U, result_path)
test_NormXCorr_single(data, U, result_path)
test_AbundanceClassification(data, result_path)
test_one_spectrum(data, U, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
# load the cube
sample = 'samson_part.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
# load the spectrum to detect
to_detect_hdr_name = 'white_roof.hdr'
y = load_signal_to_detect(data_path, to_detect_hdr_name)
# load some background pixels needed by OSP
background = 'bground1.hdr'
lib_file = osp.join(data_path, background)
U, info = util.load_ENVI_spec_lib(lib_file)
test_MatchedFilter(data, y, result_path)
test_ACE(data, y, result_path)
test_CEM(data, y, result_path)
test_GLRT(data, y, result_path)
test_OSP(data, U, y, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
# load the cube
sample = 'samson_part.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
# load the spectrum to detect
to_detect_hdr_name = 'white_roof.hdr'
y = load_signal_to_detect(data_path, to_detect_hdr_name)
# load some background pixels needed by OSP
background = 'bground1.hdr'
lib_file = osp.join(data_path, background)
U, info = util.load_ENVI_spec_lib(lib_file)
test_MatchedFilter(data, y, result_path)
test_ACE(data, y, result_path)
test_CEM(data, y, result_path)
test_GLRT(data, y, result_path)
test_OSP(data, U, y, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
data = remove_bands(data)
test_SVC(data, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
data = remove_bands(data)
test_SVC(data, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
test_hysime(data)
test_HfcVd(data)
test_synthetic_hypercube('ENVI')
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
#data = np.fliplr(data)
km = cls.KMeans()
km.predict(data, 5)
km.plot(result_path, colorMap='jet')
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
#data = np.fliplr(data)
km = cls.KMeans()
km.predict(data, 5)
km.plot(result_path, colorMap='jet')
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
data = remove_bands(data)
rois = class_labels(result_path, data)
test_HyperSVC(result_path, data, rois)
test_HyperGaussianNB(result_path, data, rois)
test_HyperKNeighborsClassifier(result_path, data, rois)
test_HyperLogisticRegression(result_path, data, rois)
test_HyperRandomForestClassifier(result_path, data, rois)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
data = remove_bands(data)
rois = class_labels(result_path, data)
test_HyperSVC(result_path, data, rois)
test_HyperGaussianNB(result_path, data, rois)
test_HyperKNeighborsClassifier(result_path, data, rois)
test_HyperLogisticRegression(result_path, data, rois)
test_HyperRandomForestClassifier(result_path, data, rois)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
axes = parse_ENVI_header(sample, header)
roi = ROI(data, result_path)
m = roi.get_mask()
test_PPI(data, axes, m, result_path)
test_ATGP(data, axes, m, result_path)
test_FIPPI(data, axes, m, result_path)
test_NFINDR(data, axes, m, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
axes = parse_ENVI_header(sample, header)
roi = ROI(data, result_path)
m = roi.get_mask()
test_PPI(data, axes, m, result_path)
test_ATGP(data, axes, m, result_path)
test_FIPPI(data, axes, m, result_path)
test_NFINDR(data, axes, m, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
n_clusters = 5
km = cls.KMeans()
km.predict(data, n_clusters)
km.plot(result_path, colorMap='jet', suffix='data')
n_components = 40
test_MNF(n_clusters, n_components, data, result_path)
test_whiten(n_clusters, data, result_path)
test_SavitzkyGolay(n_clusters, data, result_path)
def tests():
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
n_clusters = 5
km = skl.KMeans()
km.predict(data, n_clusters)
km.plot(result_path, colorMap='jet', suffix='data')
n_components = 40
test_MNF(n_clusters, n_components, data, result_path)
test_whiten(n_clusters, data, result_path)
test_SavitzkyGolay(n_clusters, data, result_path)
plt.colorbar()
fout = osp.join(path, 'plot_{0}.png'.format(desc))
plt.savefig(fout)
plt.clf()
if __name__ == '__main__':
# Load the cube
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = os.path.join(home, 'results')
sample = 'hematite.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
if osp.exists(result_path) == False:
os.makedirs(result_path)
axes = parse_ENVI_header(header)
# Telops cubes are flipped left-right
# Flipping them again restore the orientation
data = np.fliplr(data)
U = get_endmembers(data, axes, 4, result_path)
amaps = gen_abundance_maps(data, U, result_path)
# EM4 == quartz
quartz = amaps[:,:,3]
plt.colorbar()
fout = osp.join(path, '{0}.png'.format(desc))
plt.savefig(fout)
plt.clf()
if __name__ == '__main__':
plt.ioff()
# Load the cube
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
sample = 'Smokestack1.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
result_path = osp.join(home, 'results')
if osp.exists(result_path) == False:
os.makedirs(result_path)
axes = parse_ENVI_header(header)
# Telops cubes are flipped left-right
# Flipping them again restore the orientation
data = np.fliplr(data)
U_full_cube, mask = get_full_cube_em_set(data, axes, result_path)
U_masked = get_masked_em_set(data, axes, result_path, mask)
classification_analysis(data, result_path, U_masked)
img[i, j] = data[i, j, R], data[i, j, G], data[i, j, B]
d_R = np.max(img[:, :, 0]) - np.min(img[:, :, 0])
min_R = np.min(img[:, :, 0])
d_G = np.max(img[:, :, 1]) - np.min(img[:, :, 1])
min_G = np.min(img[:, :, 1])
d_B = np.max(img[:, :, 2]) - np.min(img[:, :, 2])
min_B = np.min(img[:, :, 2])
img1 = np.zeros((data.shape[0], data.shape[1], 3), dtype=np.int8)
for i in range(data.shape[0]):
for j in range(data.shape[1]):
img1[i, j] = (1 - ((img[i, j, 0] - min_R) / d_R)) * 255, (1 - (
(img[i, j, 1] - min_G) / d_G)) * 255, (1 - (
(img[i, j, 2] - min_B) / d_B)) * 255
return img1
if __name__ == '__main__':
import pysptools.util as util
data_path = '../data1'
project_path = '../'
result_path = os.join(project_path, 'results')
sample = '92AV3C.hdr'
data_file = os.join(data_path, sample)
data, info = util.load_ENVI_file(data_file)
plot_linear_stretch(data, result_path, 102, 85, 18, '1')
plot_linear_stretch(data, result_path, 98, 86, 22, '2')
plot_linear_stretch(data, result_path, 75, 34, 0, '3')
plot_linear_stretch(data, result_path, 74, 46, 18, '4')
for i in range(data.shape[0]):
for j in range(data.shape[1]):
img[i,j] = data[i,j,R], data[i,j,G], data[i,j,B]
d_R = np.max(img[:,:,0])-np.min(img[:,:,0])
min_R = np.min(img[:,:,0])
d_G = np.max(img[:,:,1])-np.min(img[:,:,1])
min_G = np.min(img[:,:,1])
d_B = np.max(img[:,:,2])-np.min(img[:,:,2])
min_B = np.min(img[:,:,2])
img1 = np.zeros((data.shape[0],data.shape[1],3), dtype=np.int8)
for i in range(data.shape[0]):
for j in range(data.shape[1]):
img1[i,j] = (1-((img[i,j,0]-min_R)/d_R))*255, (1-((img[i,j,1]-min_G)/d_G))*255, (1-((img[i,j,2]-min_B)/d_B))*255
return img1
if __name__ == '__main__':
import os.path as osp
import pysptools.util as util
data_path = '../data1'
project_path = '../'
result_path = osp.join(project_path, 'results')
sample = '92AV3C.hdr'
data_file = osp.join(data_path, sample)
data, info = util.load_ENVI_file(data_file)
plot_linear_stretch(data, result_path, 102, 85, 18, '1')
plot_linear_stretch(data, result_path, 98, 86, 22, '2')
plot_linear_stretch(data, result_path, 75, 34, 0, '3')
plot_linear_stretch(data, result_path, 74, 46, 18, '4')