def download_crop(outfile, asset, aoi, aoi_type):
"""
Download a crop defined in geographic coordinates using gdal or rasterio.
Args:
outfile (string): path to the output file
asset (dict): dictionary containing the image information
aoi (geojson.Polygon or 6-tuple): either a (lon, lat) polygon or a UTM
rectangle (ulx, uly, lrx, lry, utm_zone, lat_band), where
ulx, uly (floats): x/y UTM coordinates of the upper left (ul) corner
lrx, lry (floats): x/y UTM coordinates of the lower right (lr) corner
utm_zone (int): number between 1 and 60 indicating the UTM zone with
respect to which the UTM coordinates have to be interpreted.
lat_band (string): letter between C and X indicating the latitude band.
aoi_type (string): "lonlat_polygon" or "utm_rectangle"
"""
url = poll_activation(asset)
if url is not None:
if aoi_type == "utm_rectangle":
utils.crop_with_gdal_translate(outfile, url, *aoi)
elif aoi_type == "lonlat_polygon":
with rasterio.open(url, 'r') as src:
rpc_tags = src.tags(ns='RPC')
crop, x, y = utils.crop_aoi(url, aoi)
utils.rio_write(outfile,
crop,
tags={'CROP_OFFSET_XY': '{} {}'.format(x, y)},
namespace_tags={'RPC': rpc_tags})
def sift_roi(file1, file2, aoi, z):
"""
Args:
file1, file2 (str): paths or urls to two GeoTIFF images
aoi (geojson.Polygon): area of interest
z (float): base altitude for the aoi
Returns:
two numpy arrays with the coordinates of the matching points in the
original (full-size) image domains
"""
# image crops
crop1, x1, y1 = utils.crop_aoi(file1, aoi, z=z)
crop2, x2, y2 = utils.crop_aoi(file2, aoi, z=z)
# sift keypoint matches
p1, p2 = match_pair(crop1, crop2)
q1 = utils.points_apply_homography(utils.matrix_translation(x1, y1), p1)
q2 = utils.points_apply_homography(utils.matrix_translation(x2, y2), p2)
return q1, q2
def sift_roi(file1, file2, aoi, z):
"""
Args:
file1, file2: filename of two satellite images
aoi: area of interest
z: base height for the aoi
Returns:
q1, q2: numpy arrays with the coordinates of the matching points in the
original (full-size) image domains
"""
# image crops
crop1, x1, y1 = utils.crop_aoi(file1, aoi, z=z)
crop2, x2, y2 = utils.crop_aoi(file2, aoi, z=z)
# sift keypoint matches
p1, p2 = match_pair(crop1, crop2)
q1 = utils.points_apply_homography(matrix_translation(x1, y1), p1)
q2 = utils.points_apply_homography(matrix_translation(x2, y2), p2)
return q1, q2
def trace_epipolar_curve(image1, image2, aoi, x0, y0):
"""auxiliary function to display in image2 the epipolar curve
corresponding to the point x0,y0 in the cropped image1"""
import matplotlib.pyplot as plt
# get the altitude of the center of the AOI
lon, lat = aoi['center']
z = srtm4.srtm4(lon, lat)
# read the RPC coefficients of images i and j
rpc1 = utils.rpc_from_geotiff(image1)
rpc2 = utils.rpc_from_geotiff(image2)
# crop the two images
im1, x1, y1 = utils.crop_aoi(image1, aoi, z)
im2, x2, y2 = utils.crop_aoi(image2, aoi, z)
# translation matrices needed to compensate the crop offset
H1 = matrix_translation(x1, y1)
H2 = matrix_translation(x2, y2)
# select a point in the first image
#x0, y0 = 200, 200
# compensate the crop offset of the first image
x, y = np.dot(H1, [x0, y0, 1])[:2]
# compute the epipolar curve
epi = epipolar_curve(rpc1, rpc2, x, y)
# compensate for the crop offset of the second image
p = np.array([np.dot(np.linalg.inv(H2), [x, y, 1])[:2] for x, y in epi])
# plot the epipolar curve on the second image
f, ax = plt.subplots(1, 2, figsize=(13, 10))
ax[0].plot(x0, y0, 'r+')
ax[1].plot(p[:, 0], p[:, 1], 'r-')
ax[0].imshow(np.sqrt(im1.squeeze()), cmap='gray')
ax[1].imshow(np.sqrt(im2.squeeze()), cmap='gray')
def crop_rectangular(image, aoi, base_h=0):
a, _, _ = utils.crop_aoi(image, aoi, base_h)
return a
def crop_vertical(image, aoi, base_h=0):
rpc = utils.rpc_from_geotiff(image)
a, _, _ = utils.crop_aoi(image, aoi, base_h)
R = build_verticalizing_rotation(rpc, a.shape)
Ra = ndimage.affine_transform(a.T, np.linalg.inv(R)).T
return Ra