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orthorectify.py
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orthorectify.py
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#!/usr/bin/env python3
# Author: Piero Toffanin
# License: AGPLv3
import os
import sys
sys.path.insert(0, os.path.join("..", "..", os.path.dirname(__file__)))
from math import sqrt
import rasterio
import numpy as np
import numpy.ma as ma
import multiprocessing
import argparse
import functools
from skimage.draw import line
opensfm_path = '/home/roboticslab/code/ODM_gpu/SuperBuild/install/bin/opensfm'
sys.path.insert(0, opensfm_path)
import opensfm
from opensfm import dataset
default_dem_path = "odm_orthophoto.tif"
default_outdir = "orthorectified"
default_image_list = "img_list.txt"
parser = argparse.ArgumentParser(description='Orthorectification Tool')
parser.add_argument('dataset',
type=str,
help='Path to ODM dataset')
parser.add_argument('--dem',
type=str,
default=default_dem_path,
help='Absolute path to DEM to use to orthorectify images. Default: %(default)s')
parser.add_argument('--no-alpha',
type=bool,
help="Don't output an alpha channel")
parser.add_argument('--interpolation',
type=str,
choices=('nearest', 'bilinear'),
default='bilinear',
help="Type of interpolation to use to sample pixel values.Default: %(default)s")
parser.add_argument('--outdir',
type=str,
default=default_outdir,
help="Output directory where to store results. Default: %(default)s")
parser.add_argument('--image-list',
type=str,
default=default_image_list,
help="Path to file that contains the list of image filenames to orthorectify. By default all images in a dataset are processed. Default: %(default)s")
parser.add_argument('--images',
type=str,
default="",
help="Comma-separated list of filenames to rectify. Use as an alternative to --image-list. Default: process all images.")
parser.add_argument('--threads',
type=int,
default=multiprocessing.cpu_count(),
help="Number of CPU processes to use. Default: %(default)s")
parser.add_argument('--skip-visibility-test',
type=bool,
help="Skip visibility testing (faster but leaves artifacts due to relief displacement)")
args = parser.parse_args()
dataset_path = args.dataset
dem_path = os.path.join(dataset_path, default_dem_path) if args.dem == default_dem_path else args.dem
interpolation = args.interpolation
with_alpha = not args.no_alpha
image_list = os.path.join(dataset_path, default_image_list) if args.image_list == default_image_list else args.image_list
cwd_path = os.path.join(dataset_path, default_outdir) if args.outdir == default_outdir else args.outdir
if not os.path.exists(cwd_path):
os.makedirs(cwd_path)
target_images = [] # all
if args.images:
target_images = list(map(str.strip, args.images.split(",")))
print("Processing %s images" % len(target_images))
elif args.image_list:
with open(image_list) as f:
target_images = list(filter(lambda filename: filename != '', map(str.strip, f.read().split("\n"))))
print("Processing %s images" % len(target_images))
if not os.path.exists(dem_path):
print("Whoops! %s does not exist. Provide a path to a valid DEM" % dem_path)
exit(1)
def bilinear_interpolate(im, x, y):
x = np.asarray(x)
y = np.asarray(y)
x0 = np.floor(x).astype(int)
x1 = x0 + 1
y0 = np.floor(y).astype(int)
y1 = y0 + 1
x0 = np.clip(x0, 0, im.shape[1]-1)
x1 = np.clip(x1, 0, im.shape[1]-1)
y0 = np.clip(y0, 0, im.shape[0]-1)
y1 = np.clip(y1, 0, im.shape[0]-1)
Ia = im[ y0, x0 ]
Ib = im[ y1, x0 ]
Ic = im[ y0, x1 ]
Id = im[ y1, x1 ]
wa = (x1-x) * (y1-y)
wb = (x1-x) * (y-y0)
wc = (x-x0) * (y1-y)
wd = (x-x0) * (y-y0)
return wa*Ia + wb*Ib + wc*Ic + wd*Id
# Read DEM
print("Reading DEM: %s" % dem_path)
with rasterio.open(dem_path) as dem_raster:
dem = dem_raster.read()[0]
dem_has_nodata = dem_raster.profile.get('nodata') is not None
if dem_has_nodata:
m = ma.array(dem, mask=dem==dem_raster.nodata)
dem_min_value = m.min()
dem_max_value = m.max()
else:
dem_min_value = dem.min()
dem_max_value = dem.max()
print("DEM Minimum: %s" % dem_min_value)
print("DEM Maximum: %s" % dem_max_value)
h, w = dem.shape
crs = dem_raster.profile.get('crs')
dem_offset_x, dem_offset_y = (0, 0)
if crs:
print("DEM has a CRS: %s" % str(crs))
# Read coords.txt
coords_file = os.path.join(dataset_path, "odm_georeferencing", "coords.txt")
if not os.path.exists(coords_file):
print("Whoops! Cannot find %s (we need that!)" % coords_file)
exit(1)
with open(coords_file) as f:
l = f.readline() # discard
# second line is a northing/easting offset
l = f.readline().rstrip()
dem_offset_x, dem_offset_y = map(float, l.split(" "))
print("DEM offset: (%s, %s)" % (dem_offset_x, dem_offset_y))
print("DEM dimensions: %sx%s pixels" % (w, h))
# Read reconstruction
udata = dataset.UndistortedDataSet(dataset.DataSet(os.path.join(dataset_path, "opensfm")), undistorted_data_path=os.path.join(dataset_path, "opensfm", "undistorted"))
reconstructions = udata.load_undistorted_reconstruction()
if len(reconstructions) == 0:
raise Exception("No reconstructions available")
max_workers = args.threads
print("Using %s threads" % max_workers)
reconstruction = reconstructions[0]
for shot in reconstruction.shots.values():
if len(target_images) == 0 or shot.id in target_images:
print("Processing %s..." % shot.id)
shot_image = udata.load_undistorted_image(shot.id)
r = shot.pose.get_rotation_matrix()
Xs, Ys, Zs = shot.pose.get_origin()
cam_grid_y, cam_grid_x = dem_raster.index(Xs + dem_offset_x, Ys + dem_offset_y)
a1 = r[0][0]
b1 = r[0][1]
c1 = r[0][2]
a2 = r[1][0]
b2 = r[1][1]
c2 = r[1][2]
a3 = r[2][0]
b3 = r[2][1]
c3 = r[2][2]
if not args.skip_visibility_test:
distance_map = np.full((h, w), np.nan)
for j in range(0, h):
for i in range(0, w):
distance_map[j][i] = sqrt((cam_grid_x - i) ** 2 + (cam_grid_y - j) ** 2)
distance_map[distance_map==0] = 1e-7
print("Camera pose: (%f, %f, %f)" % (Xs, Ys, Zs))
img_h, img_w, num_bands = shot_image.shape
half_img_w = (img_w - 1) / 2.0
half_img_h = (img_h - 1) / 2.0
print("Image dimensions: %sx%s pixels" % (img_w, img_h))
f = shot.camera.focal * max(img_h, img_w)
has_nodata = dem_raster.profile.get('nodata') is not None
def process_pixels(step):
imgout = np.full((num_bands, dem_bbox_h, dem_bbox_w), np.nan)
minx = dem_bbox_w
miny = dem_bbox_h
maxx = 0
maxy = 0
for j in range(dem_bbox_miny, dem_bbox_maxy + 1):
if j % max_workers == step:
im_j = j - dem_bbox_miny
for i in range(dem_bbox_minx, dem_bbox_maxx + 1):
im_i = i - dem_bbox_minx
# World coordinates
Za = dem[j][i]
# Skip nodata
if has_nodata and Za == dem_raster.nodata:
continue
Xa, Ya = dem_raster.xy(j, i)
# Remove offset (our cameras don't have the geographic offset)
Xa -= dem_offset_x
Ya -= dem_offset_y
# Colinearity function http://web.pdx.edu/~jduh/courses/geog493f14/Week03.pdf
dx = (Xa - Xs)
dy = (Ya - Ys)
dz = (Za - Zs)
den = a3 * dx + b3 * dy + c3 * dz
x = half_img_w - (f * (a1 * dx + b1 * dy + c1 * dz) / den)
y = half_img_h - (f * (a2 * dx + b2 * dy + c2 * dz) / den)
if x >= 0 and y >= 0 and x <= img_w - 1 and y <= img_h - 1:
# Visibility test
if not args.skip_visibility_test:
check_dem_points = np.column_stack(line(i, j, cam_grid_x, cam_grid_y))
check_dem_points = check_dem_points[np.all(np.logical_and(np.array([0, 0]) <= check_dem_points, check_dem_points < [w, h]), axis=1)]
visible = True
for p in check_dem_points:
ray_z = Zs + (distance_map[p[1]][p[0]] / distance_map[j][i]) * dz
if ray_z > dem_max_value:
break
if dem[p[1]][p[0]] > ray_z:
visible = False
break
if not visible:
continue
if interpolation == 'bilinear':
xi = img_w - 1 - x
yi = img_h - 1 - y
values = bilinear_interpolate(shot_image, xi, yi)
else:
# nearest
xi = img_w - 1 - int(round(x))
yi = img_h - 1 - int(round(y))
values = shot_image[yi][xi]
# We don't consider all zero values (pure black)
# to be valid sample values. This will sometimes miss
# valid sample values.
if not np.all(values == 0):
minx = min(minx, im_i)
miny = min(miny, im_j)
maxx = max(maxx, im_i)
maxy = max(maxy, im_j)
for b in range(num_bands):
imgout[b][im_j][im_i] = values[b]
# for b in range(num_bands):
# minx = min(minx, im_i)
# miny = min(miny, im_j)
# maxx = max(maxx, im_i)
# maxy = max(maxy, im_j)
# imgout[b][im_j][im_i] = 255
return (imgout, (minx, miny, maxx, maxy))
# Compute bounding box of image coverage
# assuming a flat plane at Z = min Z
# (Otherwise we have to scan the entire DEM)
# The Xa,Ya equations are just derived from the colinearity equations
# solving for Xa and Ya instead of x,y
def dem_coordinates(cpx, cpy):
"""
:param cpx principal point X (image coordinates)
:param cpy principal point Y (image coordinates)
"""
Za = dem_min_value
m = (a3*b1*cpy - a1*b3*cpy - (a3*b2 - a2*b3)*cpx - (a2*b1 - a1*b2)*f)
Xa = dem_offset_x + (m*Xs + (b3*c1*cpy - b1*c3*cpy - (b3*c2 - b2*c3)*cpx - (b2*c1 - b1*c2)*f)*Za - (b3*c1*cpy - b1*c3*cpy - (b3*c2 - b2*c3)*cpx - (b2*c1 - b1*c2)*f)*Zs)/m
Ya = dem_offset_y + (m*Ys - (a3*c1*cpy - a1*c3*cpy - (a3*c2 - a2*c3)*cpx - (a2*c1 - a1*c2)*f)*Za + (a3*c1*cpy - a1*c3*cpy - (a3*c2 - a2*c3)*cpx - (a2*c1 - a1*c2)*f)*Zs)/m
y, x = dem_raster.index(Xa, Ya)
return (x, y)
dem_ul = dem_coordinates(-(img_w - 1) / 2.0, -(img_h - 1) / 2.0)
dem_ur = dem_coordinates((img_w - 1) / 2.0, -(img_h - 1) / 2.0)
dem_lr = dem_coordinates((img_w - 1) / 2.0, (img_h - 1) / 2.0)
dem_ll = dem_coordinates(-(img_w - 1) / 2.0, (img_h - 1) / 2.0)
dem_bbox = [dem_ul, dem_ur, dem_lr, dem_ll]
dem_bbox_x = np.array(list(map(lambda xy: xy[0], dem_bbox)))
dem_bbox_y = np.array(list(map(lambda xy: xy[1], dem_bbox)))
dem_bbox_minx = min(w - 1, max(0, dem_bbox_x.min()))
dem_bbox_miny = min(h - 1, max(0, dem_bbox_y.min()))
dem_bbox_maxx = min(w - 1, max(0, dem_bbox_x.max()))
dem_bbox_maxy = min(h - 1, max(0, dem_bbox_y.max()))
dem_bbox_w = 1 + dem_bbox_maxx - dem_bbox_minx
dem_bbox_h = 1 + dem_bbox_maxy - dem_bbox_miny
print("Iterating over DEM box: [(%s, %s), (%s, %s)] (%sx%s pixels)" % (dem_bbox_minx, dem_bbox_miny, dem_bbox_maxx, dem_bbox_maxy, dem_bbox_w, dem_bbox_h))
if max_workers > 1:
with multiprocessing.Pool(max_workers) as p:
results = p.map(process_pixels, range(max_workers))
else:
results = [process_pixels(0)]
results = list(filter(lambda r: r[1][0] <= r[1][2] and r[1][1] <= r[1][3], results))
# Merge image
imgout, _ = results[0]
for j in range(dem_bbox_miny, dem_bbox_maxy + 1):
im_j = j - dem_bbox_miny
resimg, _ = results[j % max_workers]
for b in range(num_bands):
imgout[b][im_j] = resimg[b][im_j]
# Merge bounds
minx = dem_bbox_w
miny = dem_bbox_h
maxx = 0
maxy = 0
for _, bounds in results:
minx = min(bounds[0], minx)
miny = min(bounds[1], miny)
maxx = max(bounds[2], maxx)
maxy = max(bounds[3], maxy)
print("Output bounds: (%s, %s), (%s, %s) pixels" % (minx, miny, maxx, maxy))
if minx <= maxx and miny <= maxy:
imgout = imgout[:,miny:maxy+1,minx:maxx+1]
if with_alpha:
alpha = np.zeros((imgout.shape[1], imgout.shape[2]), dtype=np.uint8)
# Set all not-NaN indices to 255
alpha[~np.isnan(imgout[0])] = 255
# Cast
imgout = imgout.astype(shot_image.dtype)
dem_transform = dem_raster.profile['transform']
offset_x, offset_y = dem_raster.xy(dem_bbox_miny + miny, dem_bbox_minx + minx, offset='ul')
profile = {
'driver': 'GTiff',
'width': imgout.shape[2],
'height': imgout.shape[1],
'count': num_bands + 1 if with_alpha else num_bands,
'dtype': imgout.dtype.name,
'transform': rasterio.transform.Affine(dem_transform[0], dem_transform[1], offset_x,
dem_transform[3], dem_transform[4], offset_y),
'nodata': None,
'crs': crs
}
outfile = os.path.join(cwd_path, shot.id)
if not outfile.endswith(".tif"):
outfile = outfile + ".tif"
with rasterio.open(outfile, 'w', BIGTIFF="IF_SAFER", **profile) as wout:
for b in range(num_bands):
wout.write(imgout[b], b + 1)
if with_alpha:
wout.write(alpha, num_bands + 1)
print("Wrote %s" % outfile)
else:
print("Cannot orthorectify image (is the image inside the DEM bounds?)")