def crop_rpc_and_image(out_dir, img, rpc, rpc_ref, x, y, w, h):
"""
Crops an image and its rpc. The ROI may be defined on another image.
Args:
out_dir: path to the output directory. The cropped image and rpc files
will be written there.
img: path to the input image
rpc: path to the input rpc
rpc_ref: path to the rpc file of the reference image
x, y, w, h: 4 integers defining a rectangular ROI in the reference
image
"""
r = rpc_model.RPCModel(rpc)
# recompute the roi if the input image is not the reference image
if rpc_ref is not rpc:
r_ref = rpc_model.RPCModel(rpc_ref)
x, y, w, h = rpc_utils.corresponding_roi(r_ref, r, x, y, w, h)
# output filenames
crop_rpc_and_image.counter += 1
s = "_%02d" % crop_rpc_and_image.counter
out_img_file = os.path.join(out_dir, "img%s.tif" % s)
out_rpc_file = os.path.join(out_dir, "rpc%s.xml" % s)
out_prv_file = os.path.join(out_dir, "prv%s.png" % s)
# do the crop
out_r = rpc_apply_crop_to_rpc_model(r, x, y, w, h)
out_r.write(out_rpc_file)
common.run('gdal_translate -srcwin %d %d %d %d "%s" "%s"' %
(x, y, w, h, img, out_img_file))
# do the preview: it has to fit a 1366x768 rectangle
w = float(w)
h = float(h)
if w > 1366 or h > 768:
if w / h > float(1366) / 768:
f = w / 1366
else:
f = h / 768
tmp = common.tmpfile('.tif')
common.image_zoom_gdal(out_img_file, f, tmp, w, h)
common.run('gdal_translate -of png -ot Byte -scale %s %s' %
(tmp, out_prv_file))
else:
common.run('gdal_translate -of png -ot Byte -scale %s %s' %
(out_img_file, out_prv_file))
common.run('rm %s.aux.xml' % out_prv_file)
def colorize(crop_panchro, im_color, x, y, zoom, out_colorized, rmin,rmax):
"""
Colorizes a Pleiades gray crop using low-resolution color information.
Args:
crop_panchro: path to the panchro (ie gray) crop
im_color: path to the full color image (tiff or jp2)
x, y: coordinates of the top-left corner of crop_panchro, in the full
Pleiade image frame.
zoom: subsampling zoom-factor that was used to generate crop_panchro
out_colorized: path to the output file
"""
# get a translated and zoomed crop from the color image. It has to be
# sampled on exactly the same grid as the panchro crop.
# To do that we compose the translation + zoom transformation with a 4x
# zoom (because color pleiades images have 4x lower resolution). There is
# also a small horizontal translation (4 pixels at the panchro resolution)
# The resulting transformation is the composition of:
# translation (-1 - x/4, -y/4)
# zoom 4/z
w, h = common.image_size_tiffinfo(crop_panchro)
xx = np.floor(x / 4.0)
yy = np.floor(y / 4.0)
ww = np.ceil((x + w * zoom) / 4.0) - xx
hh = np.ceil((y + h * zoom) / 4.0) - yy
crop_ms = common.image_crop_TIFF(im_color, xx, yy, ww, hh)
crop_ms = common.image_zoom_gdal(crop_ms, zoom/4.0)
# crop_ms = common.image_safe_zoom_fft(crop_ms, zoom/4.0)
# crop the crop_ms image to remove the extra-pixels due to the integer crop
# followed by zoom
x0 = max(0,x - 4*xx)
y0 = max(0,y - 4*yy)
crop_ms = common.image_crop_TIFF(crop_ms, x0, y0, w, h)
assert(common.image_size_tiffinfo(crop_panchro) ==
common.image_size_tiffinfo(crop_ms))
# convert rgbi to rgb
rgb = common.rgbi_to_rgb(crop_ms, out=None, tilewise=True)
# blend intensity and color to obtain the result
# each channel value r, g or b is multiplied by 3*y / (r+g+b), where y
# denotes the panchro intensity
tmp = common.tmpfile('.tif')
pcmd = "dup split + + / * 3 *"
os.environ['TMPDIR'] = os.path.join(cfg['temporary_dir'], 'meta/')
cmd = 'tiffu meta \"plambda ^ ^1 \\\"%s\\\" -o @\" %s %s -- %s' % (pcmd,
crop_panchro,
rgb, tmp)
common.run(cmd)
if w * h > 25e6: # image larger than 5000 x 5000 pixels
common.image_qauto_otb(out_colorized, tmp)
else:
#common.image_qauto(tmp, out_colorized)
common.image_rescaleintensities(tmp, out_colorized, rmin, rmax)
return
def colorize(crop_panchro, im_color, x, y, zoom, out_colorized, rmin, rmax):
"""
Colorizes a Pleiades gray crop using low-resolution color information.
Args:
crop_panchro: path to the panchro (ie gray) crop
im_color: path to the full color image (tiff or jp2)
x, y: coordinates of the top-left corner of crop_panchro, in the full
Pleiade image frame.
zoom: subsampling zoom-factor that was used to generate crop_panchro
out_colorized: path to the output file
"""
# get a translated and zoomed crop from the color image. It has to be
# sampled on exactly the same grid as the panchro crop.
# To do that we compose the translation + zoom transformation with a 4x
# zoom (because color pleiades images have 4x lower resolution). There is
# also a small horizontal translation (4 pixels at the panchro resolution)
# The resulting transformation is the composition of:
# translation (-1 - x/4, -y/4)
# zoom 4/z
w, h = common.image_size_tiffinfo(crop_panchro)
xx = np.floor(x / 4.0)
yy = np.floor(y / 4.0)
ww = np.ceil((x + w * zoom) / 4.0) - xx
hh = np.ceil((y + h * zoom) / 4.0) - yy
crop_ms = common.image_crop_tif(im_color, xx, yy, ww, hh)
crop_ms = common.image_zoom_gdal(crop_ms, zoom / 4.0)
# crop_ms = common.image_safe_zoom_fft(crop_ms, zoom/4.0)
# crop the crop_ms image to remove the extra-pixels due to the integer crop
# followed by zoom
x0 = max(0, x - 4 * xx)
y0 = max(0, y - 4 * yy)
crop_ms = common.image_crop_tif(crop_ms, x0, y0, w, h)
assert (common.image_size_tiffinfo(crop_panchro) ==
common.image_size_tiffinfo(crop_ms))
# convert rgbi to rgb
rgb = common.rgbi_to_rgb(crop_ms, out=None, tilewise=True)
# blend intensity and color to obtain the result
# each channel value r, g or b is multiplied by 3*y / (r+g+b), where y
# denotes the panchro intensity
tmp = common.tmpfile('.tif')
pcmd = "dup split + + / * 3 *"
os.environ['TMPDIR'] = os.path.join(cfg['temporary_dir'], 'meta/')
cmd = 'tiffu meta \"plambda ^ ^1 \\\"%s\\\" -o @\" %s %s -- %s' % (
pcmd, crop_panchro, rgb, tmp)
common.run(cmd)
if w * h > 25e6: # image larger than 5000 x 5000 pixels
common.image_qauto_otb(out_colorized, tmp)
else:
#common.image_qauto(tmp, out_colorized)
common.image_rescaleintensities(tmp, out_colorized, rmin, rmax)
return
def crop_corresponding_areas(out_dir, images, roi, zoom=1):
"""
Crops areas corresponding to the reference ROI in the secondary images.
Args:
out_dir:
images: sequence of dicts containing the paths to input data
roi: dictionary containing the ROI definition
zoom: integer zoom out factor
"""
rpc_ref = images[0]['rpc']
for i, image in enumerate(images[1:]):
x, y, w, h = corresponding_roi(rpc_ref, image['rpc'], roi['x'],
roi['y'], roi['w'], roi['h'])
if zoom == 1:
common.image_crop_tif(image['img'], x, y, w, h, '%s/roi_sec_%d.tif' % (out_dir, i))
else:
# gdal is used for the zoom because it handles BigTIFF files, and
# before the zoom out the image may be that big
tmp = common.image_crop_tif(image['img'], x, y, w, h)
common.image_zoom_gdal(tmp, zoom, '%s/roi_sec_%d.tif' % (out_dir, i), w, h)
def crop_corresponding_areas(out_dir, images, roi, zoom=1):
"""
Crops areas corresponding to the reference ROI in the secondary images.
Args:
out_dir:
images: sequence of dicts containing the paths to input data
roi: dictionary containing the ROI definition
zoom: integer zoom out factor
"""
rpc_ref = images[0]['rpc']
for i, image in enumerate(images[1:]):
x, y, w, h = corresponding_roi(rpc_ref, image['rpc'], roi['x'],
roi['y'], roi['w'], roi['h'])
if zoom == 1:
common.image_crop_tif(image['img'], x, y, w, h, '%s/roi_sec_%d.tif' % (out_dir, i))
else:
# gdal is used for the zoom because it handles BigTIFF files, and
# before the zoom out the image may be that big
tmp = common.image_crop_tif(image['img'], x, y, w, h)
common.image_zoom_gdal(tmp, zoom, '%s/roi_sec_%d.tif' % (out_dir, i), w, h)