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)