kopia lustrzana https://github.com/OpenDroneMap/ODM
Merge pull request #1253 from pierotofy/ortho2
Limit indirect coordinate search to bounding box estimatepull/1260/head
Piero Toffanin
GitHub
commit
bae16634c7
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@ -8,8 +8,10 @@ sys.path.insert(0, os.path.join("..", "..", os.path.dirname(__file__)))
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import rasterio
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import numpy as np
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import numpy.ma as ma
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import multiprocessing
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import argparse
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import functools
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from opensfm import dataset
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default_dem_path = "odm_dem/dsm.tif"
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@ -44,6 +46,10 @@ parser.add_argument('--images',
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type=str,
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default="",
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help="Comma-separeted list of filenames to rectify. Use as an alternative to --image-list. Default: process all images.")
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parser.add_argument('--threads',
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type=int,
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default=multiprocessing.cpu_count(),
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help="Number of CPU processes to use. Default: %(default)s")
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args = parser.parse_args()
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@ -103,6 +109,14 @@ def bilinear_interpolate(im, x, y):
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print("Reading DEM: %s" % dem_path)
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with rasterio.open(dem_path) as dem_raster:
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dem = dem_raster.read()[0]
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dem_has_nodata = dem_raster.profile.get('nodata') is not None
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if dem_has_nodata:
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dem_min_value = ma.array(dem, mask=dem==dem_raster.nodata).min()
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else:
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dem_min_value = dem.min()
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print("DEM Minimum: %s" % dem_min_value)
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h, w = dem.shape
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crs = dem_raster.profile.get('crs')
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@ -134,7 +148,7 @@ with rasterio.open(dem_path) as dem_raster:
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if len(reconstructions) == 0:
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raise Exception("No reconstructions available")
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max_workers = multiprocessing.cpu_count()
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max_workers = args.threads
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print("Using %s threads" % max_workers)
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reconstruction = reconstructions[0]
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@ -146,6 +160,15 @@ with rasterio.open(dem_path) as dem_raster:
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r = shot.pose.get_rotation_matrix()
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Xs, Ys, Zs = shot.pose.get_origin()
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a1 = r[0][0]
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b1 = r[0][1]
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c1 = r[0][2]
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a2 = r[1][0]
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b2 = r[1][1]
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c2 = r[1][2]
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a3 = r[2][0]
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b3 = r[2][1]
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c3 = r[2][2]
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print("Camera pose: (%f, %f, %f)" % (Xs, Ys, Zs))
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@ -155,15 +178,19 @@ with rasterio.open(dem_path) as dem_raster:
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has_nodata = dem_raster.profile.get('nodata') is not None
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def process_pixels(step):
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imgout = np.full((num_bands, h, w), np.nan)
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minx = w
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miny = h
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imgout = np.full((num_bands, dem_bbox_h, dem_bbox_w), np.nan)
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minx = dem_bbox_w
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miny = dem_bbox_h
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maxx = 0
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maxy = 0
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for j in range(h):
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for j in range(dem_bbox_miny, dem_bbox_maxy + 1):
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if j % max_workers == step:
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for i in range(w):
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im_j = j - dem_bbox_miny
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for i in range(dem_bbox_minx, dem_bbox_maxx + 1):
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im_i = i - dem_bbox_minx
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# World coordinates
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Xa, Ya = dem_raster.xy(j, i)
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Za = dem[j][i]
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@ -181,9 +208,9 @@ with rasterio.open(dem_path) as dem_raster:
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dy = (Ya - Ys)
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dz = (Za - Zs)
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den = r[2][0] * dx + r[2][1] * dy + r[2][2] * dz
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x = (img_w - 1) / 2.0 - (f * (r[0][0] * dx + r[0][1] * dy + r[0][2] * dz) / den)
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y = (img_h - 1) / 2.0 - (f * (r[1][0] * dx + r[1][1] * dy + r[1][2] * dz) / den)
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den = a3 * dx + b3 * dy + c3 * dz
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x = (img_w - 1) / 2.0 - (f * (a1 * dx + b1 * dy + c1 * dz) / den)
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y = (img_h - 1) / 2.0 - (f * (a2 * dx + b2 * dy + c2 * dz) / den)
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if x >= 0 and y >= 0 and x <= img_w - 1 and y <= img_h - 1:
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@ -202,31 +229,77 @@ with rasterio.open(dem_path) as dem_raster:
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# valid sample values.
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if not np.all(values == 0):
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minx = min(minx, i)
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miny = min(miny, j)
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maxx = max(maxx, i)
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maxy = max(maxy, j)
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minx = min(minx, im_i)
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miny = min(miny, im_j)
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maxx = max(maxx, im_i)
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maxy = max(maxy, im_j)
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for b in range(num_bands):
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imgout[b][j][i] = values[b]
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imgout[b][im_j][im_i] = values[b]
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# for b in range(num_bands):
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# imgout[b][j][i] = 255
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# minx = min(minx, im_i)
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# miny = min(miny, im_j)
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# maxx = max(maxx, im_i)
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# maxy = max(maxy, im_j)
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# imgout[b][im_j][im_i] = 255
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return (imgout, (minx, miny, maxx, maxy))
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with multiprocessing.Pool(max_workers) as p:
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results = p.map(process_pixels, range(max_workers))
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# Compute bounding box of image coverage
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# assuming a flat plane at Z = min Z
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# (Otherwise we have to scan the entire DEM)
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# The Xa,Ya equations are just derived from the colinearity equations
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# solving for Xa and Ya instead of x,y
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def dem_coordinates(cpx, cpy):
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"""
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:param cpx principal point X (image coordinates)
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:param cpy principal point Y (image coordinates)
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"""
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Za = dem_min_value
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m = (a3*b1*cpy - a1*b3*cpy - (a3*b2 - a2*b3)*cpx - (a2*b1 - a1*b2)*f)
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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
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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
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y, x = dem_raster.index(Xa, Ya)
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return (x, y)
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dem_ul = dem_coordinates(-(img_w - 1) / 2.0, -(img_h - 1) / 2.0)
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dem_ur = dem_coordinates((img_w - 1) / 2.0, -(img_h - 1) / 2.0)
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dem_lr = dem_coordinates((img_w - 1) / 2.0, (img_h - 1) / 2.0)
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dem_ll = dem_coordinates(-(img_w - 1) / 2.0, (img_h - 1) / 2.0)
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dem_bbox = [dem_ul, dem_ur, dem_lr, dem_ll]
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dem_bbox_x = np.array(list(map(lambda xy: xy[0], dem_bbox)))
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dem_bbox_y = np.array(list(map(lambda xy: xy[1], dem_bbox)))
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dem_bbox_minx = min(w - 1, max(0, dem_bbox_x.min()))
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dem_bbox_miny = min(h - 1, max(0, dem_bbox_y.min()))
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dem_bbox_maxx = min(w - 1, max(0, dem_bbox_x.max()))
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dem_bbox_maxy = min(h - 1, max(0, dem_bbox_y.max()))
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dem_bbox_w = 1 + dem_bbox_maxx - dem_bbox_minx
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dem_bbox_h = 1 + dem_bbox_maxy - dem_bbox_miny
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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))
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if max_workers > 1:
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with multiprocessing.Pool(max_workers) as p:
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results = p.map(process_pixels, range(max_workers))
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else:
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results = [process_pixels(0)]
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results = list(filter(lambda r: r[1][0] <= r[1][2] and r[1][1] <= r[1][3], results))
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# Merge image
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imgout, _ = results[0]
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for j in range(h):
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for j in range(dem_bbox_miny, dem_bbox_maxy + 1):
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im_j = j - dem_bbox_miny
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resimg, _ = results[j % max_workers]
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for b in range(num_bands):
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imgout[b][j] = resimg[b][j]
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imgout[b][im_j] = resimg[b][im_j]
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# Merge bounds
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minx = w
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miny = h
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minx = dem_bbox_w
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miny = dem_bbox_h
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maxx = 0
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maxy = 0
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@ -250,7 +323,7 @@ with rasterio.open(dem_path) as dem_raster:
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imgout = imgout.astype(shot_image.dtype)
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dem_transform = dem_raster.profile['transform']
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offset_x, offset_y = dem_raster.xy(miny, minx, offset='ul')
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offset_x, offset_y = dem_raster.xy(dem_bbox_miny + miny, dem_bbox_minx + minx, offset='ul')
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profile = {
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'driver': 'GTiff',
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