chisqr = np.asarray(outBands[bands]).ravel()
ncp = 1 - stats.chi2.cdf(chisqr, [bands - 1])
idx = np.where(ncp > ncpThresh)
aa = []
bb = []
j = 1
bands = len(pos)
# Cáculo ortoregresión con imagen referencia e imagen objetivo obteniendo
# valores de pendiente, intercepción y correlación entre las mismas.
outBand = []
for k in pos:
x = np.asarray(rasterBands2[k - 1]).ravel()
y = np.asarray(rasterBands1[k - 1]).ravel()
b, a, R = orthoregress(y[idx], x[idx])
print('band: {} slope: {} intercept: {} correlation: {}'.format(
k, b, a, R))
my = max(y[idx])
aa.append(a)
bb.append(b)
outBand.append(np.zeros([rows, cols]))
# Imagen normalizada resultante
outBand[k - 1] = np.resize(a + b * y, (rows, cols))
# outBand.FlushCache()
j += 1
print('outband_f')
print(outBand)
ncoords = []
def main(img_imad, ncpThresh=0.95, pos=None, dims=None, img_target=None, graphics=False):
if img_target is not None:
path = os.path.dirname(img_target)
basename = os.path.basename(img_target)
root, ext = os.path.splitext(basename)
fsoutfn = path + '/' + root + '_norm_all' + ext
path = os.path.dirname(img_imad)
basename = os.path.basename(img_imad)
root, ext = os.path.splitext(basename)
b = root.find('(')
err = root.find(')')
referenceroot, targetbasename = root[b + 1:err].split('&')
referencefn = path + '/' + referenceroot + ext
targetfn = path + '/' + targetbasename
targetroot, targetext = os.path.splitext(targetbasename)
outfn = path + '/' + targetroot + '_norm' + targetext
imadDataset = gdal.Open(img_imad, GA_ReadOnly)
try:
imadbands = imadDataset.RasterCount
cols = imadDataset.RasterXSize
rows = imadDataset.RasterYSize
except Exception as err:
print('Error: {} --Image could not be read'.format(err))
sys.exit(1)
referenceDataset = gdal.Open(referencefn, GA_ReadOnly)
targetDataset = gdal.Open(targetfn, GA_ReadOnly)
if pos is None:
pos = list(range(1, referenceDataset.RasterCount + 1))
if dims is None:
x0 = 0
y0 = 0
else:
x0, y0, cols, rows = dims
chisqr = imadDataset.GetRasterBand(imadbands).ReadAsArray(0, 0, cols, rows).ravel()
ncp = 1 - stats.chi2.cdf(chisqr, [imadbands - 1])
idx = where(ncp > ncpThresh)
print(time.asctime())
print('reference: ' + referencefn)
print('target : ' + targetfn)
print('no-change probability threshold: ' + str(ncpThresh))
print('no-change pixels: ' + str(len(idx[0])))
start = time.time()
driver = targetDataset.GetDriver()
outDataset = driver.Create(outfn, cols, rows, len(pos), GDT_UInt16)
projection = imadDataset.GetProjection()
geotransform = imadDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
aa = []
bb = []
if graphics:
plt.figure(1, (9, 6))
j = 1
bands = len(pos)
for k in pos:
x = referenceDataset.GetRasterBand(k).ReadAsArray(x0, y0, cols, rows).astype(float).ravel()
y = targetDataset.GetRasterBand(k).ReadAsArray(x0, y0, cols, rows).astype(float).ravel()
b, a, R = orthoregress(y[idx], x[idx])
print('band: {} slope: {} intercept: {} correlation: {}'.format(k, b, a, R))
my = max(y[idx])
if (j < 7) and graphics:
plt.subplot(2, 3, j)
plt.plot(y[idx], x[idx], '.')
plt.plot([0, my], [a, a + b * my])
plt.title('Band {}'.format(k))
if ((j < 4) and (bands < 4)) or j > 3:
plt.xlabel('Target')
if (j == 1) or (j == 4):
plt.ylabel('Reference')
aa.append(a)
bb.append(b)
outBand = outDataset.GetRasterBand(j)
outBand.WriteArray(resize(a + b * y, (rows, cols)), 0, 0)
outBand.FlushCache()
j += 1
if graphics:
plt.show()
plt.close()
referenceDataset = None
targetDataset = None
outDataset = None
print('result written to: ' + outfn)
if img_target is not None:
print('normalizing ' + img_target + '...')
fsDataset = gdal.Open(img_target, GA_ReadOnly)
try:
cols = fsDataset.RasterXSize
rows = fsDataset.RasterYSize
except Exception as err:
print('Error %s -- Image could not be read in')
sys.exit(1)
driver = fsDataset.GetDriver()
outDataset = driver.Create(fsoutfn, cols, rows, len(pos), GDT_UInt16)
projection = fsDataset.GetProjection()
geotransform = fsDataset.GetGeoTransform()
if geotransform is not None:
outDataset.SetGeoTransform(geotransform)
if projection is not None:
outDataset.SetProjection(projection)
j = 1
for k in pos:
inBand = fsDataset.GetRasterBand(k)
outBand = outDataset.GetRasterBand(j)
for i in range(rows):
y = inBand.ReadAsArray(0, i, cols, 1)
outBand.WriteArray(aa[j - 1] + bb[j - 1] * y, 0, i)
outBand.FlushCache()
j += 1
outDataset = None
fsDataset = None
print('full result written to: ' + fsoutfn)
return fsoutfn
print('elapsed time: {}'.format(time.time() - start))
return outfn