LATEX_IN_FIGURES = True
if LATEX_IN_FIGURES:
enable_latex_infigures()
# Enable parallel processing (or not)
ENABLE_MULTI = True
# These two variables serves to split the original image into sub-images to be treated in parallel
# In general the optimal parameters are obtained for
# number_of_threads_rows*number_of_threads_columns = number of cores on the machine
number_of_threads_rows = 4
number_of_threads_columns = 4
if os.cpu_count() != (number_of_threads_rows * number_of_threads_columns):
print('WARNING: not all cpu are used')
# data
image, ground_truth_original, X, Y = load_UAVSAR(PATH, DEBUG,
FULL_TIME_SERIES)
# Parameters
if DEBUG:
WINDOWS_SIZES = np.arange(start=5, stop=9, step=2)
RANKS_LIST = np.arange(start=1, stop=5, step=2)
else:
WINDOWS_SIZES = np.arange(start=5, stop=11, step=2)
RANKS_LIST = np.arange(start=1, stop=7, step=2)
print('WINDOWS_SIZES=', WINDOWS_SIZES)
print('RANKS_LIST=', RANKS_LIST)
PFA_THRESHOLD = 0.1
n_r, n_rc, p, T = image.shape
function_to_compute = compute_several_statistics
base_path = os.path.dirname(os.path.abspath(__file__))
base_path = os.path.join(base_path, 'intensities_maps')
path_intensities = os.path.join(base_path,
'scene_' + str(SCENE_NUMBER) + '.npy')
if not os.path.exists(path_intensities):
print('ERROR with path of detection map')
print('path=', path_intensities)
sys.exit(1)
# Load detection map
intensities = np.load(path_intensities)
# Load ground truth
_, ground_truth_original, X, Y = load_UAVSAR(PATH,
DEBUG,
False,
scene_number=SCENE_NUMBER,
verbose=False)
m_r = ground_truth_original.shape[0] - intensities.shape[0]
m_c = ground_truth_original.shape[1] - intensities.shape[1]
ground_truth = ground_truth_original[int(m_r / 2):-int(m_r / 2),
int(m_c / 2):-int(m_c / 2)]
# Compute threshold
NUMBER_OF_POINTS = 500
thresholds = np.zeros(len(STATISTIC_NAMES))
pfas = np.zeros(len(STATISTIC_NAMES))
pds = np.zeros(len(STATISTIC_NAMES))
detection_maps = np.zeros(intensities.shape)
for i_s, statistic in enumerate(STATISTIC_NAMES):
# In general the optimal parameters are obtained for
# number_of_threads_rows*number_of_threads_columns = number of cores on the machine
number_of_threads_rows = 4
number_of_threads_columns = 4
if os.cpu_count() != (number_of_threads_rows * number_of_threads_columns):
print('WARNING: not all cpu are used')
# data
# DEBUG mode for fast debugging (use a small patch of 200x200 pixels)
DEBUG = False
PATH = 'data/UAVSAR/'
FULL_TIME_SERIES = False # if true: use the full time series, else: use only the first and last images of the time series
SCENE_NUMBER = 1
image, _, X, Y = load_UAVSAR(PATH, DEBUG, FULL_TIME_SERIES, SCENE_NUMBER)
# Parameters
n_r, n_rc, p, T = image.shape
windows_mask = np.ones((5, 5))
m_r, m_c = windows_mask.shape
function_to_compute = compute_several_statistics
# AIC rule rank over: image 1, image 2, time series (image 1 and 2)
statistic_names = [r'EDC']
statistic_list = [
rank_estimation_reshape for i in range(len(statistic_names))
]
args_list = [(stat) for stat in statistic_names]
number_of_statistics = len(statistic_list)
function_args = [statistic_list, args_list]
import random
if __name__ == '__main__':
# Activate latex in figures (or not)
LATEX_IN_FIGURES = False
if LATEX_IN_FIGURES:
enable_latex_infigures()
# data
# DEBUG mode for fast debugging (use a small patch of 200x200 pixels)
DEBUG = False
PATH = 'data/UAVSAR/'
FULL_TIME_SERIES = False # if true: use the full time series, else: use only the first and last images of the time series
image, _, X, Y = load_UAVSAR(PATH, DEBUG, FULL_TIME_SERIES)
# Parameters
n_r, n_c, p, T = image.shape
windows_mask = np.ones((5, 5))
m_r, m_c = windows_mask.shape
N = m_r * m_c
METHODS = ['AIC', 'BIC', 'AICc', 'EDC']
while True:
i_r = random.choice(list(range(int(m_r / 2), n_r - int(m_r / 2))))
i_c = random.choice(list(range(int(m_c / 2), n_c - int(m_c / 2))))
# Obtaining data corresponding to the neighborhood defined by the mask
local_data = image[i_r - int(m_r / 2):i_r + int(m_r / 2) + 1,