def writer(self, array, *args, **kwargs):
if self.scale == 'SAR' and np.iscomplexobj(array):
array = np.abs(array)
array = np.squeeze(array)
out = np.zeros_like(array, dtype='uint8')
if array.ndim == 3 and self.chscl == True:
for k in range(array.shape[0]):
array[k, ...] = array[k, ...] / np.mean(array[k, ...])
if self.scale == 'SAR':
out = sarscale(array)
elif self.scale == 'phase':
out = phascale(array)
elif self.scale == 'coherence':
out = cohscale(array)
else:
logging.error("Scaling method unknown")
return False
if array.ndim == 3:
out = out[self.order, ...]
try:
misc.imsave(self.filename, out, format=self.key)
return True
except IOError as err:
logging.error("ERROR:"+str(err))
return False
else:
logging.error("UNKNOWN ERROR")
return False
def writer(self, array, *args, **kwargs):
if isinstance(self.file, tuple): # remove file type if present
self.file = self.file[0]
if (self.method == 'amplitude'
or self.method == 'intensity') and np.iscomplexobj(array):
array = np.abs(array)
array = np.squeeze(array)
if array.ndim == 4:
nchannels = np.prod(array.shape[0:array.ndim - 2])
array = np.reshape(array,
(nchannels, ) + array.shape[array.ndim - 2:])
if array.ndim == 3 and self.chscl == True:
for k in range(array.shape[0]):
array[k, ...] = array[k, ...] / np.mean(array[k, ...])
if self.method == 'intensity' or self.method == 'amplitude':
if self.method == 'amplitude':
array **= 0.7
if self.method == 'intensity':
array **= 0.35
out = sarscale(array, factor=self.scaling)
elif self.method == 'phase':
out = phascale(array)
elif self.method == 'coherence':
out = cohscale(array)
elif self.method == 'minmax':
start = array.min()
end = array.max()
out = np.uint8(
np.clip((array - start) / (end - start) * 255, 0, 255))
else:
logging.error("Scaling method unknown")
return False
if array.ndim == 3:
if out.shape[0] < 3:
oshp = out.shape
oshp[0] = 3
out = np.resize(out, oshp)
if out.shape[0] > 3:
out = out[0:3, ...]
out = np.rollaxis(np.rollaxis(out[self.order, ...], 2), 2)
else:
out = colortables(self.palette)[1][out]
try:
pilimg = Image.fromarray(out)
pilimg.save(self.file, format=self.key)
logging.info("FINISHED SAVING IMAGE")
return True
except IOError as err:
logging.error("ERROR:" + str(err))
return False
else:
logging.error("UNKNOWN ERROR")
return False
def filter(self, array, *args, **kwargs):
shp = array.shape
array = array.reshape((np.prod(shp[0:-2]),) + shp[-2:]) # reshape to (nch, ny, nx)
selem = np.ones(self.win)
for k, amp in enumerate(np.abs(array)): # loop over channels
array[k, ...] = skentropy(sarscale(amp), selem)
array = array.reshape(shp) # back to original shape
return array
def filter(self, array, *args, **kwargs):
shp = array.shape
array = array.reshape(
(np.prod(shp[0:-2]), ) + shp[-2:]) # reshape to (nch, ny, nx)
selem = np.ones(self.win)
for k, amp in enumerate(np.abs(array)): # loop over channels
array[k, ...] = skentropy(sarscale(amp), selem)
array = array.reshape(shp) # back to original shape
return array
def data2img(self, cut_box, scale=0):
if self.config['colour'] is False:
channel = [int(self.config['bwlayer'].split('D')[1])]
else:
channel = [
int(foo.split('D')[1]) for foo in self.config['rgblayer']
]
method = self.config['scaling']
img = self.data[scale][..., cut_box[2]:cut_box[3],
cut_box[0]:cut_box[1]][channel, ...].copy()
if img.dtype == 'uint8':
img = img.copy()
elif method == 'amplitude' or method == 'intensity' or method == None:
if method == 'intensity':
img = np.abs(img)**0.35
else:
img = np.abs(img)**0.7
if self.config['colour'] is True:
for k in range(img.shape[0]):
img[k, ...] /= np.mean(img[k, ...])
img = sarscale(img, factor=self.sarscale)
elif method == 'phase':
img = np.uint8(np.clip(img / np.pi * 128 + 127, 0.0, 255.0))
elif method == '0.0->1.0':
img = np.uint8(np.clip(img * 255.0, 0.0, 255.0))
elif method == 'min->max':
img -= np.min(img)
img = np.uint8(img / np.max(img) * 255.0)
elif method == 'labels':
img -= np.min(self.data[0])
img = np.uint8(img / np.max(self.data[0]) * 255.0)
else:
img = np.uint8(int)
# img = img[..., 0:img.shape[-2] // 4 * 4, 0:img.shape[-1] // 4 * 4] # QT Limitation!!
img = np.rollaxis(np.rollaxis(img, axis=2), axis=2)
if self.config['palette'] != 0:
self.img = colortables(self.config['palette'])[1][img]
else:
self.img = img
# img = np.rot90(np.rollaxis(np.rollaxis(img, axis=2), axis=2))
if self.config['colour'] is True:
return QtGui.QImage(img.tostring(), img.shape[1], img.shape[0],
QtGui.QImage.Format_RGB888)
else:
return QtGui.QImage(img.tostring(), img.shape[1], img.shape[0],
QtGui.QImage.Format_Indexed8)
def writer(self, array, *args, **kwargs):
if isinstance(self.file, tuple): # remove file type if present
self.file = self.file[0]
if (self.method == 'amplitude' or self.method == 'intensity') and np.iscomplexobj(array):
array = np.abs(array)
array = np.squeeze(array)
if array.ndim == 4:
nchannels = np.prod(array.shape[0:array.ndim-2])
array = np.reshape(array, (nchannels, )+array.shape[array.ndim-2:])
if array.ndim == 3 and self.chscl == True:
for k in range(array.shape[0]):
array[k, ...] = array[k, ...] / np.mean(array[k, ...])
if self.method == 'intensity' or self.method == 'amplitude':
if self.method == 'amplitude':
array **= 0.7
if self.method == 'intensity':
array **= 0.35
out = sarscale(array, factor=self.scaling)
elif self.method == 'phase':
out = phascale(array)
elif self.method == 'coherence':
out = cohscale(array)
elif self.method == 'minmax':
start = array.min()
end = array.max()
out = np.uint8(np.clip((array - start) / (end - start) * 255, 0, 255))
else:
logging.error("Scaling method unknown")
return False
if array.ndim == 3:
out = np.rollaxis(np.rollaxis(out[self.order, ...], 2), 2)
else:
out = colortables(self.palette)[1][out]
try:
pilimg = Image.fromarray(out)
pilimg.save(self.file, format=self.key)
logging.info("FINISHED SAVING IMAGE")
return True
except IOError as err:
logging.error("ERROR:" + str(err))
return False
else:
logging.error("UNKNOWN ERROR")
return False
def writer(self, array, *args, **kwargs):
if isinstance(self.file, tuple): # remove file type if present
self.file = self.file[0]
if (self.method == 'amplitude'
or self.method == 'intensity') and np.iscomplexobj(array):
array = np.abs(array)
array = np.squeeze(array)
if array.ndim == 4:
nchannels = np.prod(array.shape[0:array.ndim - 2])
array = np.reshape(array,
(nchannels, ) + array.shape[array.ndim - 2:])
if array.ndim == 3 and self.chscl == True:
for k in range(array.shape[0]):
array[k, ...] = array[k, ...] / np.mean(array[k, ...])
if self.method == 'intensity' or self.method == 'amplitude':
if self.method == 'amplitude':
array **= 0.7
if self.method == 'intensity':
array **= 0.35
out = sarscale(array, factor=self.scaling)
elif self.method == 'phase':
out = phascale(array)
elif self.method == 'coherence':
out = cohscale(array)
else:
logging.error("Scaling method unknown")
return False
if array.ndim == 3:
out = np.rollaxis(np.rollaxis(out[self.order, ...], 2), 2)
else:
out = colortables(self.palette)[1][out]
try:
misc.imsave(self.file, out, format=self.key)
logging.info("FINISHED SAVING IMAGE")
return True
except IOError as err:
logging.error("ERROR:" + str(err))
return False
else:
logging.error("UNKNOWN ERROR")
return False
def writer(self, array, *args, **kwargs):
if isinstance(self.file, tuple): # remove file type if present
self.file = self.file[0]
if self.method == 'amplitude' or self.method == 'intensity' and np.iscomplexobj(
array):
array = np.abs(array)
array = np.squeeze(array)
out = np.zeros_like(array, dtype='uint8')
if array.ndim == 3 and self.chscl == True:
for k in range(array.shape[0]):
array[k, ...] = array[k, ...] / np.mean(array[k, ...])
if self.method == 'intensity' or self.method == 'amplitude':
if self.method == 'amplitude':
array **= 0.7
if self.method == 'intensity':
array **= 0.35
out = sarscale(array, factor=self.scaling)
elif self.method == 'phase':
out = phascale(array)
elif self.method == 'coherence':
out = cohscale(array)
else:
logging.error("Scaling method unknown")
return False
if array.ndim == 3:
out = out[self.order, ...]
else:
out = colortables(self.palette)[1][out]
try:
misc.imsave(self.file, out, format=self.key)
return True
except IOError as err:
logging.error("ERROR:" + str(err))
return False
else:
logging.error("UNKNOWN ERROR")
return False
def data2img(self, cut_box, scale=0):
if self.config['colour'] is False:
channel = [int(self.config['bwlayer'].split('D')[1])]
else:
channel = [int(foo.split('D')[1]) for foo in self.config['rgblayer']]
method = self.config['scaling']
img = self.data[scale][..., cut_box[2]:cut_box[3], cut_box[0]:cut_box[1]][channel, ...].copy()
if img.dtype == 'uint8':
img = img.copy()
elif method == 'amplitude' or method == 'intensity':
if method == 'intensity':
img **= 0.35
else:
img **= 0.7
if self.config['colour'] is True:
for k in range(img.shape[0]):
img[k, ...] /= np.mean(img[k, ...])
img = sarscale(img, factor=self.sarscale)
elif method == 'phase':
img = np.uint8(np.clip(img / np.pi * 128 + 127, 0.0, 255.0))
elif method == '0.0->1.0':
img = np.uint8(np.clip(img * 255.0, 0.0, 255.0))
elif method == 'min->max' or method == 'lables':
img += np.min(img)
img = np.uint8(img / np.max(img) * 255.0)
else:
img = np.uint8(int)
# img = img[..., 0:img.shape[-2] // 4 * 4, 0:img.shape[-1] // 4 * 4] # QT Limitation!!
img = np.rollaxis(np.rollaxis(img, axis=2), axis=2)
# img = np.rot90(np.rollaxis(np.rollaxis(img, axis=2), axis=2))
if self.config['colour'] is True:
return QtGui.QImage(img.tostring(), img.shape[1], img.shape[0], QtGui.QImage.Format_RGB888)
else:
return QtGui.QImage(img.tostring(), img.shape[1], img.shape[0], QtGui.QImage.Format_Indexed8)
def pre(self, *args, **kwargs):
amp = pyrat.data.getData()
amp = np.uint8(np.float32(sarscale(amp)) / (256.0 / self.levels))
newlayer = pyrat.data.addLayer(amp)
pyrat.data.activateLayer(newlayer)
def pre(self, *args, **kwargs):
amp = pyrat.data.getData()
amp = np.uint8(np.float32(sarscale(amp)) / (256.0 / self.levels))
newlayer = pyrat.data.addLayer(amp)
pyrat.data.activateLayer(newlayer)
