def setUp(self):
subdirs = ["spectral_libraries", "usgs_splib07", "ASCIIdata"]
self.usgs_dir = file_utils.get_path_from_subdirs(
demo_data_base_dir, subdirs)
self.test_spectrum_fname_part = "Cordierite-QtzMus_HS346.1B_ASDFRc_AREF.txt"
self.asd_ascii_subdirs = ["ASCIIdata_splib07b_cvASD"]
self.test_spectrum_dir = "ChapterS_SoilsAndMixtures"
def run_pca():
radiance_cube_fname = file_utils.get_path_from_subdirs(
demo_data_base_dir, [
"image_data", "hyperspectral", "cooke_city", "self_test", "HyMap",
"self_test_rad.img"
])
radiance_envi_image = ImageFactory.envi.from_gdal_file_and_point_calcs(
radiance_cube_fname)
radiance_image_data = radiance_envi_image.read_all_image_data_from_disk()
print("")
print("RUN PCA DEMO")
n_pca_bands = 10
pca_result = sip_2d.compute_image_cube_pca(radiance_image_data,
n_components=n_pca_bands,
whiten=True)
for band_number in range(n_pca_bands):
result = pca_result[:, :, band_number]
save_image_fname = os.path.join(
save_dir,
"cooke_city_pca_band_" + str(band_number).zfill(5) + ".png")
imageio.imwrite(save_image_fname, result)
print("saved envi cube to: " + save_image_fname)
def setUp(self):
print("")
self.png_fullpath = file_utils.get_path_from_subdirs(
demo_data_base_dir, [
"image_data", "overhead_vehicle_data", "Potsdam_ISPRS",
"top_potsdam_2_14_RGB.png"
])
self.png_image_data = imread(self.png_fullpath)
png_shape = self.png_image_data.shape
self.ny = png_shape[0]
self.nx = png_shape[1]
self.nbands = png_shape[2]
def setUp(self):
subdirs = ["spectral_libraries", "usgs_splib07", "ASCIIdata"]
self.usgs_dir = file_utils.get_path_from_subdirs(
demo_data_base_dir, subdirs)
self.test_spectrum_fname_part = "Cordierite-QtzMus_HS346.1B_ASDFRc_AREF.txt"
self.aviris_ascii_subdirs = [
"ASCIIdata_splib07b_cvAVIRISc1995",
"ASCIIdata_splib07b_cvAVIRISc1996",
"ASCIIdata_splib07b_cvAVIRISc1997",
"ASCIIdata_splib07b_cvAVIRISc1998",
"ASCIIdata_splib07b_cvAVIRISc1999",
"ASCIIdata_splib07b_cvAVIRISc2000",
"ASCIIdata_splib07b_cvAVIRISc2001",
"ASCIIdata_splib07b_cvAVIRISc2005",
"ASCIIdata_splib07b_cvAVIRISc2006",
"ASCIIdata_splib07b_cvAVIRISc2009",
"ASCIIdata_splib07b_cvAVIRISc2010",
"ASCIIdata_splib07b_cvAVIRISc2011",
"ASCIIdata_splib07b_cvAVIRISc2012",
"ASCIIdata_splib07b_cvAVIRISc2013",
"ASCIIdata_splib07b_cvAVIRISc2014"
]
self.test_spectrum_dir = "ChapterS_SoilsAndMixtures"
from resippy.utils import file_utils as file_utils
from resippy.utils import envi_utils
from resippy.image_objects.image_factory import ImageFactory
from resippy.spectral.spectrum_factories.spectrum_factory import SpectrumFactory
import resippy.spectral.spectral_image_processing_2d as sip_2d
import resippy.utils.image_utils.image_utils as image_utils
import numpy as np
import imageio
import seaborn
import os
import matplotlib.pyplot as plt
reflectance_cube_fname = file_utils.get_path_from_subdirs(
demo_data_base_dir, [
"image_data", "hyperspectral", "cooke_city", "self_test", "HyMap",
"self_test_refl.img"
])
reflectance_envi_image = ImageFactory.envi.from_gdal_file_and_point_calcs(
reflectance_cube_fname)
reflectance_image_data = reflectance_envi_image.read_all_image_data_from_disk()
reflectance_image_data = reflectance_image_data.astype(float) * 0.01
ny, nx, nbands = reflectance_image_data.shape
seaborn_color_palette = seaborn.color_palette("RdBu_r")
test_spectrum_fname = file_utils.get_path_from_subdirs(demo_data_base_dir, [
"image_data", "hyperspectral", "cooke_city", "self_test", "SPL", "F1",
"F1_l.txt"
])
import os
# choose a gsd
gsd_m = 0.5
# constants calculated from https://msi.nga.mil/MSISiteContent/StaticFiles/Calculators/degree.html
# based on approx latitude of center of images
gsd_lon = gsd_m / 81417.0
gsd_lat = gsd_m / 111095.0
# set up filename bundle for a frame, note that it is assumed that the band number is the last item in the filename
micasense_dir = file_utils.get_path_from_subdirs(demo_data_base_dir, ['image_data',
'multispectral',
'micasense',
'20181019_hana',
'1703',
'micasense',
'processed'
])
band_fname_dict = {}
for cam_num in range(1, 6):
band_num = str(cam_num)
band_fname_dict['band' + band_num] = \
file_utils.get_path_from_subdirs(micasense_dir, ['merged', 'L0_IMG_0404_' + band_num + '.tif'])
# set up path to pix4d parameters used to build point calculator etc
params_dir = file_utils.get_path_from_subdirs(micasense_dir, ['pix4d_1703_mica_imgs85_1607',
'1_initial',
'params'])
import resippy.utils.image_utils.image_utils as image_utils
import resippy.utils.file_utils as file_utils
from resippy.test_runner import demo_data_base_dir
from resippy.test_runner import demo_data_save_dir
import imageio
import os
import numpy as np
save_dir = os.path.join(demo_data_save_dir, "colormap_demos")
file_utils.make_dir_if_not_exists(save_dir)
png_fullpath = file_utils.get_path_from_subdirs(demo_data_base_dir, [
"image_data", "overhead_vehicle_data", "Potsdam_ISPRS",
"top_potsdam_2_14_RGB.png"
])
def main():
image_data = imageio.imread(png_fullpath)
image_data = image_data[:, :, 0:3]
grayscale_image = image_data[:, :, 0]
colormapped_image = image_utils.apply_colormap_to_grayscale_image(
grayscale_image)
imageio.imwrite(os.path.join(save_dir, "colormapped_image.png"),
colormapped_image)
blended_image = np.asarray(image_utils.blend_images(
image_data, colormapped_image),
dtype=np.uint8)
imageio.imwrite(os.path.join(save_dir, "blended_image.png"), blended_image)
import resippy.photogrammetry.ortho_tools as ortho_tools
from resippy.image_objects.image_factory import ImageFactory
from tests.demo_tests import demo_data_base_dir, demo_data_save_dir
from resippy.utils import file_utils as file_utils
import os
import logging
dg_dir = file_utils.get_path_from_subdirs(demo_data_base_dir, [
"image_data", "digital_globe",
"Tripoli_View-Ready_Stereo_8_Band_Bundle_30cm", "056082094030"
])
multispectral_image_fname_1 = file_utils.get_path_from_subdirs(
dg_dir, [
"056082094030_01_P001_MUL",
"15AUG31100732-M2AS-056082094030_01_P001.TIF"
])
save_dir = os.path.join(demo_data_save_dir, "digital_globe_demos")
file_utils.make_dir_if_not_exists(save_dir)
# make the image object object
digital_globe_image = ImageFactory.digital_globe.view_ready_stereo.from_file(
multispectral_image_fname_1)
def ortho_digital_globe_image():
print("STARTING A DIGITAL GLOBE DEMO")
nx = 1000
import os
from resippy.image_objects.image_factory import ImageFactory
from resippy.test_runner import demo_data_base_dir
from resippy.test_runner import demo_data_save_dir
from resippy.utils import file_utils as file_utils
micasense_dir = file_utils.get_path_from_subdirs(demo_data_base_dir, ['image_data',
'multispectral',
'micasense',
'20181019_hana',
'1703',
'micasense',
'processed'
])
gtiff_full_path = file_utils.get_path_from_subdirs(micasense_dir,
['pix4d_1703_mica_imgs85_1607',
'3_dsm_ortho',
'pix4d_1704_mica_85_1607_dsm.tif'])
save_dir = os.path.join(demo_data_save_dir, "gtiff_demos")
file_utils.make_dir_if_not_exists(save_dir)
def create_maptiles():
print("")
print("CREATE MAPTILES DEMO")
file_utils.make_dir_if_not_exists(save_dir)
gtiff_image_object = ImageFactory.geotiff.from_file(gtiff_full_path)
gtiff_image_object.create_map_tiles("/tmp/")
from resippy.utils import file_utils
import resippy.utils.image_utils.image_chipper as image_chipper
from resippy.utils.image_utils import image_utils
import numpy as np
from numpy import ndarray
import os
import logging
save_dir = os.path.join(demo_data_save_dir, "mmi_demos")
file_utils.make_dir_if_not_exists(save_dir)
micasense_dir = file_utils.get_path_from_subdirs(demo_data_base_dir, ['image_data',
'multispectral',
'micasense',
'20181019_hana',
'1703',
'micasense',
'processed'
])
band_fname_dict = {}
for cam_num in range(1, 6):
band_num = str(cam_num)
band_fname_dict['band' + band_num] = \
file_utils.get_path_from_subdirs(micasense_dir, ['merged', 'L0_IMG_0404_' + band_num + '.tif'])
def mmi_window_demo():
print("")
print("STARTING A SLIDING WINDOW MUTUAL INFORMATION DEMO, THIS MIGHT TAKE A LITTLE BIT...")
max_pixel_shift = 50
n_steps = 10
import os
from resippy.test_runner import demo_data_base_dir
from resippy.test_runner import demo_data_save_dir
from resippy.utils import file_utils as file_utils
from resippy.utils.image_utils import image_mask_utils
from resippy.utils.image_utils import image_chipper
from resippy.utils import numpy_and_array_utils
from imageio import imread
png_fullpath = file_utils.get_path_from_subdirs(demo_data_base_dir, [
"image_data", "overhead_vehicle_data", "Potsdam_ISPRS",
"top_potsdam_2_14_RGB.png"
])
png_positive_mask_fullpath = file_utils.get_path_from_subdirs(
demo_data_base_dir, [
"image_data", "overhead_vehicle_data", "Potsdam_ISPRS",
"top_potsdam_2_14_RGB_Annotated_Cars.png"
])
png_negative_mask_fullpath = file_utils.get_path_from_subdirs(
demo_data_base_dir, [
"image_data", "overhead_vehicle_data", "Potsdam_ISPRS",
"top_potsdam_2_14_RGB_Annotated_Negatives.png"
])
save_dir_base = file_utils.get_path_from_subdirs(
demo_data_save_dir, ["overhead_vehicles", "training_data"])
file_utils.make_dir_if_not_exists(save_dir_base)