#
#------------------------------------------------------------------------------
# Copyright (c) 2013-2014, Christian Therien
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#------------------------------------------------------------------------------
#
# detect_int.py - This file is part of the PySptools package.
#
"""
MatchedFilter, ACE, CEM, GLRT, OSP classes
"""
import numpy as np
from . import detect
from .inval import *
from .docstring import *
def _plot_target_map(path, tmap, map_type, whiteOnBlack, suffix=None):
""" Plot a target map using matplotlib """
import matplotlib.pyplot as plt
import os.path as osp
if path != None:
plt.ioff()
img = plt.imshow(tmap)
if whiteOnBlack == True:
img.set_cmap('Greys_r')
elif whiteOnBlack == False:
img.set_cmap('Greys')
else:
# throw an error?
img.set_cmap('Blues')
if path != None:
if suffix == None:
fout = osp.join(path, 'tmap_{0}.png'.format(map_type))
else:
fout = osp.join(path, 'tmap_{0}_{1}.png'.format(map_type, suffix))
try:
plt.savefig(fout)
except IOError:
raise IOError('in detection._plot_target_map, no such file or directory: {0}'.format(path))
else:
if suffix == None:
plt.title('{0} Target Map'.format(map_type))
else:
plt.title('{0} Target Map - {1}'.format(map_type, suffix))
plt.show()
plt.clf()
def _document(cls):
import sys
if sys.version_info[0] == 2:
cls.plot.__func__.__doc__ = plot_docstring
cls.display.__func__.__doc__ = display_docstring
if sys.version_info[0] == 3:
cls.plot.__doc__ = plot_docstring
cls.display.__doc__ = display_docstring
[docs]class MatchedFilter(object):
"""
Performs the matched filter algorithm for target detection.
"""
def __init__(self):
self.target_map = None
@DetectInputValidation1('MatchedFilter')
def detect(self, M, t, threshold=None):
"""
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
t: `numpy array`
A target pixel (p).
threshold: `float or None [default None]`
Apply a threshold to the detection result.
Usefull to isolate the result.
Returns: `numpy array`
Vector of detector output (m x n x 1).
References:
Qian Du, Hsuan Ren, and Chein-I Cheng. A Comparative Study of
Orthogonal Subspace Projection and Constrained Energy Minimization.
IEEE TGRS. Volume 41. Number 6. June 2003.
"""
h,w,numBands = M.shape
self.h, self.w, self.numBands = M.shape
Mr = np.reshape(M, (w*h, numBands))
target = detect.MatchedFilter(Mr, t)
self.target_map = np.reshape(target, (h, w))
if threshold != None:
self.target_map = self.target_map > threshold
return self.target_map
def __str__(self):
return 'pysptools.detection.detect_int.MatchedFilter object, hcube: {0}x{1}x{2}'.format(self.h, self.w, self.numBands)
@PlotInputValidation('MatchedFilter')
def plot(self, path, whiteOnBlack=True, suffix=None):
_plot_target_map(path, self.target_map, 'MatchedFilter', whiteOnBlack, suffix)
@DisplayInputValidation('MatchedFilter')
def display(self, whiteOnBlack=True, suffix=None):
_plot_target_map(None, self.target_map, 'MatchedFilter', whiteOnBlack, suffix)
_document(MatchedFilter)
[docs]class ACE(object):
"""
Performs the adaptive cosin/coherent estimator algorithm for target
detection.
"""
def __init__(self):
self.target_map = None
@DetectInputValidation1('ACE')
def detect(self, M, t, threshold=None):
"""
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
t: `numpy array`
A target pixel (p).
threshold: `float or None [default None]`
Apply a threshold to the detection result.
Usefull to isolate the result.
Returns: `numpy array`
Vector of detector output (m x n x 1).
References:
X Jin, S Paswater, H Cline. "A Comparative Study of Target Detection
Algorithms for Hyperspectral Imagery." SPIE Algorithms and Technologies
for Multispectral, Hyperspectral, and Ultraspectral Imagery XV. Vol
7334. 2009.
"""
h,w,numBands = M.shape
self.h, self.w, self.numBands = M.shape
Mr = np.reshape(M, (w*h, numBands))
target = detect.ACE(Mr, t)
self.target_map = np.reshape(target, (h, w))
if threshold != None:
self.target_map = self.target_map > threshold
return self.target_map
def __str__(self):
return 'pysptools.detection.detect_int.ACE object, hcube: {0}x{1}x{2}'.format(self.h, self.w, self.numBands)
@PlotInputValidation('ACE')
def plot(self, path, whiteOnBlack=True, suffix=None):
_plot_target_map(path, self.target_map, 'ACE', whiteOnBlack, suffix)
@DisplayInputValidation('ACE')
def display(self, whiteOnBlack=True, suffix=None):
_plot_target_map(None, self.target_map, 'ACE', whiteOnBlack, suffix)
_document(ACE)
[docs]class CEM(object):
"""
Performs the constrained energy minimization algorithm for target
detection.
"""
def __init__(self):
self.target_map = None
@DetectInputValidation1('CEM')
def detect(self, M, t, threshold=None):
"""
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
t: `numpy array`
A target pixel (p).
threshold: `float or None [default None]`
Apply a threshold to the detection result.
Usefull to isolate the result.
Returns: `numpy array`
Vector of detector output (m x n x 1).
References:
Qian Du, Hsuan Ren, and Chein-I Cheng. A Comparative Study of
Orthogonal Subspace Projection and Constrained Energy Minimization.
IEEE TGRS. Volume 41. Number 6. June 2003.
"""
h,w,numBands = M.shape
self.h, self.w, self.numBands = M.shape
Mr = np.reshape(M, (w*h, numBands))
target = detect.CEM(Mr, t)
self.target_map = np.reshape(target, (h, w))
if threshold != None:
self.target_map = self.target_map > threshold
return self.target_map
def __str__(self):
return 'pysptools.detection.detect_int.CEM object, hcube: {0}x{1}x{2}'.format(self.h, self.w, self.numBands)
@PlotInputValidation('CEM')
def plot(self, path, whiteOnBlack=True, suffix=None):
_plot_target_map(path, self.target_map, 'CEM', whiteOnBlack, suffix)
@DisplayInputValidation('CEM')
def display(self, whiteOnBlack=True, suffix=None):
_plot_target_map(None, self.target_map, 'CEM', whiteOnBlack, suffix)
_document(CEM)
[docs]class GLRT(object):
"""
Performs the generalized likelihood test ratio algorithm for target
detection.
"""
def __init__(self):
self.target_map = None
@DetectInputValidation1('GLRT')
def detect(self, M, t, threshold=None):
"""
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
t: `numpy array`
A target pixel (p).
threshold: `float or None [default None]`
Apply a threshold to the detection result.
Usefull to isolate the result.
Returns: `numpy array`
Vector of detector output (m x n x 1).
References
T. F. AyouB, "Modified GLRT Signal Detection Algorithm," IEEE
Transactions on Aerospace and Electronic Systems, Vol 36, No 3, July
2000.
"""
h,w,numBands = M.shape
self.h, self.w, self.numBands = M.shape
Mr = np.reshape(M, (w*h, numBands))
target = detect.GLRT(Mr, t)
self.target_map = np.reshape(target, (h, w))
if threshold != None:
self.target_map = self.target_map > threshold
return self.target_map
def __str__(self):
return 'pysptools.detection.detect_int.GLRT object, hcube: {0}x{1}x{2}'.format(self.h, self.w, self.numBands)
@PlotInputValidation('GLRT')
def plot(self, path, whiteOnBlack=True, suffix=None):
_plot_target_map(path, self.target_map, 'GLRT', whiteOnBlack, suffix)
@DisplayInputValidation('GLRT')
def display(self, whiteOnBlack=True, suffix=None):
_plot_target_map(None, self.target_map, 'GLRT', whiteOnBlack, suffix)
_document(GLRT)
[docs]class OSP(object):
"""
Performs the othogonal subspace projection algorithm for target
detection.
"""
def __init__(self):
self.target_map = None
@DetectInputValidation2('OSP')
def detect(self, M, E, t, threshold=None):
"""
Parameters:
M: `numpy array`
A HSI cube (m x n x p).
E: `numpy array`
Background pixels (n x p).
t: `numpy array`
A target pixel (p).
threshold: `float or None [default None]`
Apply a threshold to the detection result.
Usefull to isolate the result.
Returns: `numpy array`
Vector of detector output (m x n x 1).
References:
Qian Du, Hsuan Ren, and Chein-I Cheng. "A Comparative Study of
Orthogonal Subspace Projection and Constrained Energy Minimization."
IEEE TGRS. Volume 41. Number 6. June 2003.
"""
h,w,numBands = M.shape
self.h, self.w, self.numBands = M.shape
Mr = np.reshape(M, (w*h, numBands))
target = detect.OSP(Mr, E, t)
self.target_map = np.reshape(target, (h, w))
if threshold != None:
self.target_map = self.target_map > threshold
return self.target_map
def __str__(self):
return 'pysptools.detection.detect_int.OSP object, hcube: {0}x{1}x{2}'.format(self.h, self.w, self.numBands)
@PlotInputValidation('OSP')
def plot(self, path, whiteOnBlack=True, suffix=None):
_plot_target_map(path, self.target_map, 'OSP', whiteOnBlack, suffix)
@DisplayInputValidation('OSP')
def display(self, whiteOnBlack=True, suffix=None):
_plot_target_map(None, self.target_map, 'OSP', whiteOnBlack, suffix)
_document(OSP)