kopia lustrzana https://github.com/OpenDroneMap/ODM
Piero Toffanin
GitHub
commit
32d933027e
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@ -251,6 +251,15 @@ externalproject_add(odm_orthophoto
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${WIN32_CMAKE_ARGS} ${WIN32_GDAL_ARGS}
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)
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externalproject_add(fastrasterfilter
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GIT_REPOSITORY https://github.com/OpenDroneMap/FastRasterFilter.git
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GIT_TAG main
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PREFIX ${SB_BINARY_DIR}/fastrasterfilter
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SOURCE_DIR ${SB_SOURCE_DIR}/fastrasterfilter
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CMAKE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${SB_INSTALL_DIR}
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${WIN32_CMAKE_ARGS} ${WIN32_GDAL_ARGS}
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)
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externalproject_add(lastools
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GIT_REPOSITORY https://github.com/OpenDroneMap/LAStools.git
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GIT_TAG 250
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@ -5,8 +5,6 @@ import numpy
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import math
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import time
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import shutil
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import functools
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import threading
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import glob
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import re
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from joblib import delayed, Parallel
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@ -15,11 +13,9 @@ from opendm import point_cloud
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from opendm import io
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from opendm import system
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from opendm.concurrency import get_max_memory, parallel_map, get_total_memory
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from scipy import ndimage
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from datetime import datetime
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from opendm.vendor.gdal_fillnodata import main as gdal_fillnodata
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from opendm import log
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import threading
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from .ground_rectification.rectify import run_rectification
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from . import pdal
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@ -237,106 +233,31 @@ def compute_euclidean_map(geotiff_path, output_path, overwrite=False):
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return output_path
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def median_smoothing(geotiff_path, output_path, smoothing_iterations=1, window_size=512, num_workers=1):
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def median_smoothing(geotiff_path, output_path, window_size=512, num_workers=1, radius=4):
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""" Apply median smoothing """
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start = datetime.now()
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if not os.path.exists(geotiff_path):
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raise Exception('File %s does not exist!' % geotiff_path)
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# Prepare temporary files
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folder_path, output_filename = os.path.split(output_path)
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basename, ext = os.path.splitext(output_filename)
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output_dirty_in = os.path.join(folder_path, "{}.dirty_1{}".format(basename, ext))
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output_dirty_out = os.path.join(folder_path, "{}.dirty_2{}".format(basename, ext))
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log.ODM_INFO('Starting smoothing...')
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with rasterio.open(geotiff_path, num_threads=num_workers) as img, rasterio.open(output_dirty_in, "w+", BIGTIFF="IF_SAFER", num_threads=num_workers, **img.profile) as imgout, rasterio.open(output_dirty_out, "w+", BIGTIFF="IF_SAFER", num_threads=num_workers, **img.profile) as imgout2:
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nodata = img.nodatavals[0]
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dtype = img.dtypes[0]
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shape = img.shape
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for i in range(smoothing_iterations):
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log.ODM_INFO("Smoothing iteration %s" % str(i + 1))
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rows, cols = numpy.meshgrid(numpy.arange(0, shape[0], window_size), numpy.arange(0, shape[1], window_size))
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rows = rows.flatten()
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cols = cols.flatten()
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rows_end = numpy.minimum(rows + window_size, shape[0])
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cols_end= numpy.minimum(cols + window_size, shape[1])
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windows = numpy.dstack((rows, cols, rows_end, cols_end)).reshape(-1, 4)
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filt = functools.partial(ndimage.median_filter, size=9, output=dtype, mode='nearest')
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# We cannot read/write to the same file from multiple threads without causing race conditions.
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# To safely read/write from multiple threads, we use a lock to protect the DatasetReader/Writer.
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read_lock = threading.Lock()
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write_lock = threading.Lock()
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# threading backend and GIL released filter are important for memory efficiency and multi-core performance
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Parallel(n_jobs=num_workers, backend='threading')(delayed(window_filter_2d)(img, imgout, nodata , window, 9, filt, read_lock, write_lock) for window in windows)
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# Between each iteration we swap the input and output temporary files
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#img_in, img_out = img_out, img_in
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if (i == 0):
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img = imgout
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imgout = imgout2
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else:
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img, imgout = imgout, img
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# If the number of iterations was even, we need to swap temporary files
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if (smoothing_iterations % 2 != 0):
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output_dirty_in, output_dirty_out = output_dirty_out, output_dirty_in
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# Cleaning temporary files
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if os.path.exists(output_dirty_out):
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os.replace(output_dirty_out, output_path)
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if os.path.exists(output_dirty_in):
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os.remove(output_dirty_in)
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kwargs = {
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'input': geotiff_path,
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'output': output_path,
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'window': window_size,
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'radius': radius,
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}
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system.run('fastrasterfilter "{input}" '
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'--output "{output}" '
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'--window-size {window} '
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'--radius {radius} '
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'--co TILED=YES '
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'--co BIGTIFF=IF_SAFER '
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'--co COMPRESS=DEFLATE '.format(**kwargs), env_vars={'OMP_NUM_THREADS': num_workers})
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log.ODM_INFO('Completed smoothing to create %s in %s' % (output_path, datetime.now() - start))
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return output_path
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def window_filter_2d(img, imgout, nodata, window, kernel_size, filter, read_lock, write_lock):
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"""
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Apply a filter to dem within a window, expects to work with kernal based filters
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:param img: path to the geotiff to filter
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:param imgout: path to write the giltered geotiff to. It can be the same as img to do the modification in place.
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:param window: the window to apply the filter, should be a list contains row start, col_start, row_end, col_end
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:param kernel_size: the size of the kernel for the filter, works with odd numbers, need to test if it works with even numbers
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:param filter: the filter function which takes a 2d array as input and filter results as output.
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:param read_lock: threading lock for the read operation
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:param write_lock: threading lock for the write operation
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"""
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shape = img.shape[:2]
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if window[0] < 0 or window[1] < 0 or window[2] > shape[0] or window[3] > shape[1]:
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raise Exception('Window is out of bounds')
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expanded_window = [ max(0, window[0] - kernel_size // 2), max(0, window[1] - kernel_size // 2), min(shape[0], window[2] + kernel_size // 2), min(shape[1], window[3] + kernel_size // 2) ]
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# Read input window
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width = expanded_window[3] - expanded_window[1]
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height = expanded_window[2] - expanded_window[0]
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rasterio_window = rasterio.windows.Window(col_off=expanded_window[1], row_off=expanded_window[0], width=width, height=height)
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with read_lock:
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win_arr = img.read(indexes=1, window=rasterio_window)
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# Should have a better way to handle nodata, similar to the way the filter algorithms handle the border (reflection, nearest, interpolation, etc).
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# For now will follow the old approach to guarantee identical outputs
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nodata_locs = win_arr == nodata
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win_arr = filter(win_arr)
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win_arr[nodata_locs] = nodata
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win_arr = win_arr[window[0] - expanded_window[0] : window[2] - expanded_window[0], window[1] - expanded_window[1] : window[3] - expanded_window[1]]
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# Write output window
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width = window[3] - window[1]
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height = window[2] - window[0]
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rasterio_window = rasterio.windows.Window(col_off=window[1], row_off=window[0], width=width, height=height)
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with write_lock:
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imgout.write(win_arr, indexes=1, window=rasterio_window)
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def get_dem_radius_steps(stats_file, steps, resolution, multiplier = 1.0):
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radius_steps = [point_cloud.get_spacing(stats_file, resolution) * multiplier]
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for _ in range(steps - 1):
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