From ee5ff3258f30116521d69553fc0143851538432e Mon Sep 17 00:00:00 2001 From: Adrien-ANTON-LUDWIG Date: Wed, 12 Jul 2023 16:55:14 +0000 Subject: [PATCH] Use windowed read/write in median_smoothing See the issue description in this forum comment: https://community.opendronemap.org/t/post-processing-after-odm/16314/16?u=adrien-anton-ludwig TL;DR: Median smoothing used windowing to go through the array but read it entirely in RAM. Now the full potential of windowing is exploited to read/write by chunks. --- opendm/dem/commands.py | 39 +++++++++++++++++++++++++-------------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/opendm/dem/commands.py b/opendm/dem/commands.py index 56c65d9d..af1fee64 100755 --- a/opendm/dem/commands.py +++ b/opendm/dem/commands.py @@ -313,11 +313,11 @@ def median_smoothing(geotiff_path, output_path, smoothing_iterations=1, window_s log.ODM_INFO('Starting smoothing...') - with rasterio.open(geotiff_path) as img: + # imgout needs to be 'w+' (write/read) to work in place for all the iterations but the first + with rasterio.open(geotiff_path) as img, rasterio.open(output_path, 'w+', BIGTIFF="IF_SAFER", **img.profile) as imgout: nodata = img.nodatavals[0] dtype = img.dtypes[0] shape = img.shape - arr = img.read()[0] for i in range(smoothing_iterations): log.ODM_INFO("Smoothing iteration %s" % str(i + 1)) rows, cols = numpy.meshgrid(numpy.arange(0, shape[0], window_size), numpy.arange(0, shape[1], window_size)) @@ -330,40 +330,51 @@ def median_smoothing(geotiff_path, output_path, smoothing_iterations=1, window_s filter = functools.partial(ndimage.median_filter, size=9, output=dtype, mode='nearest') # threading backend and GIL released filter are important for memory efficiency and multi-core performance - window_arrays = Parallel(n_jobs=num_workers, backend='threading')(delayed(window_filter_2d)(arr, nodata , window, 9, filter) for window in windows) + Parallel(n_jobs=num_workers, backend='threading')(delayed(window_filter_2d)(img, imgout, nodata , window, 9, filter) for window in windows) + + # After the first iteration, modifications are done in place + if i == 0: + img = imgout - for window, win_arr in zip(windows, window_arrays): - arr[window[0]:window[2], window[1]:window[3]] = win_arr - log.ODM_INFO("Smoothing completed in %s" % str(datetime.now() - start)) - # write output - with rasterio.open(output_path, 'w', BIGTIFF="IF_SAFER", **img.profile) as imgout: - imgout.write(arr, 1) log.ODM_INFO('Completed smoothing to create %s in %s' % (output_path, datetime.now() - start)) + exit(42) return output_path -def window_filter_2d(arr, nodata, window, kernel_size, filter): +def window_filter_2d(img, imgout, nodata, window, kernel_size, filter): """ Apply a filter to dem within a window, expects to work with kernal based filters - :param geotiff_path: path to the geotiff to filter + :param img: path to the geotiff to filter + :param imgout: path to write the giltered geotiff to. It can be the same as img to do the modification in place. :param window: the window to apply the filter, should be a list contains row start, col_start, row_end, col_end :param kernel_size: the size of the kernel for the filter, works with odd numbers, need to test if it works with even numbers :param filter: the filter function which takes a 2d array as input and filter results as output. """ - shape = arr.shape[:2] + shape = img.shape[:2] if window[0] < 0 or window[1] < 0 or window[2] > shape[0] or window[3] > shape[1]: raise Exception('Window is out of bounds') 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) ] - win_arr = arr[expanded_window[0]:expanded_window[2], expanded_window[1]:expanded_window[3]] + + # Read input window + width = expanded_window[3] - expanded_window[1] + height = expanded_window[2] - expanded_window[0] + rasterio_window = rasterio.windows.Window(col_off=expanded_window[1], row_off=expanded_window[0], width=width, height=height) + win_arr = img.read(indexes=1, window=rasterio_window) + # Should have a better way to handle nodata, similar to the way the filter algorithms handle the border (reflection, nearest, interpolation, etc). # For now will follow the old approach to guarantee identical outputs nodata_locs = win_arr == nodata win_arr = filter(win_arr) win_arr[nodata_locs] = nodata win_arr = win_arr[window[0] - expanded_window[0] : window[2] - expanded_window[0], window[1] - expanded_window[1] : window[3] - expanded_window[1]] - return win_arr + + # Write output window + width = window[3] - window[1] + height = window[2] - window[0] + rasterio_window = rasterio.windows.Window(col_off=window[1], row_off=window[0], width=width, height=height) + imgout.write(win_arr, indexes=1, window=rasterio_window) def get_dem_radius_steps(stats_file, steps, resolution, multiplier = 1.0):