def parse(self):
try:
import sarpy.io.complex as cf
except ImportError:
raise Exception(
'You need to install sarpy from NGA - https://github.com/ngageoint/sarpy to work with SICD data'
)
self._sicdmeta = cf.open(self.sicd).sicdmeta
self.populateMetadata()
def load_image(self, image_index):
img_filename = self.image_names[image_index]
if os.path.splitext(img_filename)[-1] == 'nitf':
cdata = cf.open(img_filename).read_chip()
img = remap.density(cdata)
else:
img = skimage.io.imread(img_filename)
if len(img.shape) == 2:
img = img[..., np.newaxis]
return img.astype(np.float32)/255.0
def init_from_fname_and_canvas_size(
self,
fname, # type: str
canvas_ny, # type: int
canvas_nx, # type: int
scale_to_fit_canvas=False, # type: bool
):
self.fname = fname
self.reader_object = sarpy_complex.open(fname)
self.full_image_nx = self.reader_object.sicd_meta.ImageData.FullImage.NumCols
self.full_image_ny = self.reader_object.sicd_meta.ImageData.FullImage.NumRows
self.canvas_nx = canvas_nx
self.canvas_ny = canvas_ny
self.scale_to_fit_canvas = scale_to_fit_canvas
self.update_canvas_display_image_from_full_image()
def run(self):
cl = self.cl # type: AlgorithmChain.ChainLedger
params = self.params # type: dict
# Add your algorithm code here
# Open file
fname = params['filename']
if fname.startswith('~'):
fname = os.path.expanduser(fname)
ro = cf.open(fname)
cl.add_to_metadata('sarpy_reader', ro)
# Do not edit below this line
return cl
def callback_select_file(self, event):
sicd_fname = self.image_info_panel.file_selector.event_select_file(event)
self.app_variables.sicd_fname = sicd_fname
self.app_variables.sicd_reader_object = sarpy_complex.open(sicd_fname)
meticon_helper = SicdMetaIconHelper(self.app_variables.sicd_reader_object.sicd_meta)
data_container = meticon_helper.data_container
self.metaicon.create_from_metaicon_data_container(data_container)
popup = tkinter.Toplevel(self.master)
selected_region_popup = SelectedRegionPanel(popup, self.app_variables)
self.app_variables.sicd_reader_object = ComplexImageReader(self.app_variables.sicd_fname)
selected_region_popup.image_canvas.canvas.set_image_reader(self.app_variables.sicd_reader_object)
self.master.wait_window(popup)
selected_region_complex_data = self.app_variables.selected_region_complex_data
fft_complex_data = self.get_fft_complex_data(self.app_variables.sicd_reader_object.base_reader, selected_region_complex_data)
self.app_variables.fft_complex_data = fft_complex_data
self.app_variables.fft_display_data = remap.density(fft_complex_data)
fft_reader = NumpyImageReader(self.app_variables.fft_display_data)
self.frequency_vs_degree_panel.canvas.set_image_reader(fft_reader)
self.frequency_vs_degree_panel.canvas.set_current_tool_to_edit_shape()
self.frequency_vs_degree_panel.canvas.variables.current_shape_id = self.frequency_vs_degree_panel.canvas.variables.select_rect_id
self.frequency_vs_degree_panel.canvas.modify_existing_shape_using_image_coords(self.frequency_vs_degree_panel.canvas.variables.select_rect_id, self.get_fft_image_bounds())
canvas_drawing_bounds = self.frequency_vs_degree_panel.canvas.image_coords_to_canvas_coords(self.frequency_vs_degree_panel.canvas.variables.select_rect_id)
self.frequency_vs_degree_panel.canvas.variables.shape_drag_xy_limits[str(self.frequency_vs_degree_panel.canvas.variables.select_rect_id)] = canvas_drawing_bounds
self.app_variables.fft_canvas_bounds = self.frequency_vs_degree_panel.canvas.get_shape_canvas_coords(self.frequency_vs_degree_panel.canvas.variables.select_rect_id)
self.frequency_vs_degree_panel.canvas.show_shape(self.frequency_vs_degree_panel.canvas.variables.select_rect_id)
filtered_numpy_reader = NumpyImageReader(self.get_filtered_image())
self.filtered_panel.canvas.set_image_reader(filtered_numpy_reader)
self.image_info_panel.chip_size_panel.nx.set_text(numpy.shape(selected_region_complex_data)[1])
self.image_info_panel.chip_size_panel.ny.set_text(numpy.shape(selected_region_complex_data)[0])
self.update_phase_history_selection()
self.metaviewer.create_w_sicd(self.app_variables.sicd_reader_object.base_reader.sicd_meta)
self.frequency_vs_degree_panel.update()
self.frequency_vs_degree_panel.update_x_axis(start_val=-10, stop_val=10, label="Polar Angle (degrees)")
self.frequency_vs_degree_panel.update_y_axis(start_val=7.409, stop_val=11.39, label="Frequency (GHz)")
def populateImage(self):
import sarpy.io.complex as cf
img = cf.open(self.sicd)
data = img.read_chip()
if self._sicdmeta.SCPCOA.SideOfTrack.startswith('R'):
viewarr = data
else:
viewarr = data[:, ::-1]
data.T.tofile(self.output)
rawImage = isceobj.createSlcImage()
rawImage.setByteOrder('l')
rawImage.setFilename(self.output)
rawImage.setAccessMode('read')
rawImage.setWidth(self.frame.getNumberOfSamples())
rawImage.setXmax(self.frame.getNumberOfSamples())
rawImage.setXmin(0)
self.getFrame().setImage(rawImage)
def set_reader_file(self, fname):
self.base_reader = sarpy_complex.open(fname)
self.full_image_nx = self.base_reader.sicd_meta.ImageData.FullImage.NumCols
self.full_image_ny = self.base_reader.sicd_meta.ImageData.FullImage.NumRows
def add_node(k, v):
for key, val in v.items():
new_key = k + "_" + key
if "RcvChannels" in new_key:
stop = 1
if isinstance(val, OrderedDict):
tree.insert(k, 1, new_key, text=key)
add_node(new_key, val)
else:
tree.insert(k, 1, new_key, text=key + ": " + str(val))
sicd_fname = askopenfilename(initialdir=os.path.expanduser("~"))
reader_object = sarpy_complex.open(sicd_fname)
sicd_meta_dict = reader_object.sicd_meta.to_dict()
def insertv2(k, v):
print("k: " + k)
for key, val in v.items():
print("k: " + k)
print("key: " + key)
new_key = k + "_" + key
print("new key: " + new_key)
if isinstance(val, dict):
insertv2(new_key, val)
"""
Just a quick demo to show the basics of how to use SarPy
"""
import matplotlib.pyplot as plt
import sarpy.io.complex as cf
import sarpy.visualization.remap as remap
# Open file
fname = 'C:/Temp/my_sicd.nitf'
ro = cf.open(fname)
# Access SICD metadata (even if file read is not in SICD format)
print(ro.sicd_meta
) # Displays metadata from file in SICD format in human-readable form
print(ro.sicd_meta.CollectionInfo.CollectorName
) # extracting fields from metadata
###############
# Read complex pixel data from file
# cdata = ro[:] # Reads all complex data from file
# Read every 10th pixel:
cdata = ro[::10, ::10]
fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))
axs[0].imshow(remap.density(cdata), cmap='gray') # Display subsampled image
# Reads every other row and column from the first thousand rows and columns:
cdata = ro[:1000:2, :1000:2]
axs[1].imshow(remap.density(cdata), cmap='gray') # Display subsampled image
def create_from_fname(self, fname):
self.fname = fname
reader_object = sarpy_complex.open(fname)
self.create_from_sicd(reader_object.sicd_meta)